chore(deps): bump actions/setup-python from 5 to 6

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v5...v6)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

.github/workflows/bandit.yaml

@@ -21,7 +21,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: '3.12'
       - name: Install dependencies

.github/workflows/pytest.yml

@@ -10,7 +10,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies

README.md

@@ -3,11 +3,11 @@
 </h1>
 
 <p align="center">
-  <strong>Open-Source RAG Assistant</strong>
+  <strong>Private AI for agents, assistants and enterprise search</strong>
 </p>
 
 <p align="left">
-  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is an open-source genAI tool that helps users get reliable answers from any knowledge source, while avoiding hallucinations. It enables quick and reliable information retrieval, with tooling and agentic system capability built in.
+  <strong><a href="https://www.docsgpt.cloud/">DocsGPT</a></strong> is an open-source AI platform for building intelligent agents and assistants. Features Agent Builder, deep research tools, document analysis (PDF, Office, web content), Multi-model support (choose your provider or run locally), and rich API connectivity for agents with actionable tools and integrations. Deploy anywhere with complete privacy control.
 </p>
 
 <div align="center">
@@ -19,10 +19,10 @@
   <a href="https://discord.gg/n5BX8dh8rU">![link to discord](https://img.shields.io/discord/1070046503302877216)</a>
   <a href="https://twitter.com/docsgptai">![X (formerly Twitter) URL](https://img.shields.io/twitter/follow/docsgptai)</a>
 
-  <a href="https://docs.docsgpt.cloud/quickstart">⚡️ Quickstart</a> • <a href="https://app.docsgpt.cloud/">☁️ Cloud Version</a> • <a href="https://discord.gg/n5BX8dh8rU">💬 Discord</a>
-  <br>
-  <a href="https://docs.docsgpt.cloud/">📖 Documentation</a> • <a href="https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md">👫 Contribute</a> • <a href="https://blog.docsgpt.cloud/">🗞 Blog</a>
-  <br>
+<a href="https://docs.docsgpt.cloud/quickstart">⚡️ Quickstart</a> • <a href="https://app.docsgpt.cloud/">☁️ Cloud Version</a> • <a href="https://discord.gg/n5BX8dh8rU">💬 Discord</a>
+<br>
+<a href="https://docs.docsgpt.cloud/">📖 Documentation</a> • <a href="https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md">👫 Contribute</a> • <a href="https://blog.docsgpt.cloud/">🗞 Blog</a>
+<br>
 
 </div>
 <div align="center">
@@ -52,8 +52,12 @@
 - [x] Chatbots menu re-design to handle tools, agent types, and more (April 2025)
 - [x] New input box in the conversation menu (April 2025)
 - [x] Add triggerable actions / tools (webhook) (April 2025)
-- [ ] Anthropic Tool compatibility (May 2025)
-- [ ] Add OAuth 2.0 authentication for tools and sources
+- [x] Agent optimisations (May 2025)
+- [x] Filesystem sources update (July 2025)
+- [x] Json Responses (August 2025)
+- [ ] Sharepoint integration (August 2025)
+- [ ] MCP support (August 2025)
+- [ ] Add OAuth 2.0 authentication for tools and sources (August 2025)
 - [ ] Agent scheduling
 
 You can find our full roadmap [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
@@ -68,11 +72,10 @@ We're eager to provide personalized assistance when deploying your DocsGPT to a
 
 ## Join the Lighthouse Program 🌟
 
-Calling all developers and GenAI innovators! The **DocsGPT Lighthouse Program** connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.  
+Calling all developers and GenAI innovators! The **DocsGPT Lighthouse Program** connects technical leaders actively deploying or extending DocsGPT in real-world scenarios. Collaborate directly with our team to shape the roadmap, access priority support, and build enterprise-ready solutions with exclusive community insights.
 
 [Learn More & Apply →](https://docs.google.com/forms/d/1KAADiJinUJ8EMQyfTXUIGyFbqINNClNR3jBNWq7DgTE)
 
-
 ## QuickStart
 
 > [!Note]
@@ -103,7 +106,7 @@ A more detailed [Quickstart](https://docs.docsgpt.cloud/quickstart) is available
    PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
    ```
 
-Either script will guide you through setting up DocsGPT. Four options available: using the public API, running locally, connecting to a local inference engine, or using a cloud API provider.  Scripts will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
+Either script will guide you through setting up DocsGPT. Four options available: using the public API, running locally, connecting to a local inference engine, or using a cloud API provider. Scripts will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
 
 **Navigate to http://localhost:5173/**
 
@@ -112,6 +115,7 @@ To stop DocsGPT, open a terminal in the `DocsGPT` directory and run:
 ```bash
 docker compose -f deployment/docker-compose.yaml down
 ```
+
 (or use the specific `docker compose down` command shown after running the setup script).
 
 > [!Note]
@@ -139,7 +143,6 @@ Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information abou
 
 We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing.
 
-
 ## Many Thanks To Our Contributors⚡
 
 <a href="https://github.com/arc53/DocsGPT/graphs/contributors" alt="View Contributors">

application/agents/base.py

@@ -1,17 +1,20 @@
+import logging
 import uuid
 from abc import ABC, abstractmethod
 from typing import Dict, Generator, List, Optional
 
-from application.agents.llm_handler import get_llm_handler
+from bson.objectid import ObjectId
+
 from application.agents.tools.tool_action_parser import ToolActionParser
 from application.agents.tools.tool_manager import ToolManager
-
 from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.llm.handlers.handler_creator import LLMHandlerCreator
 from application.llm.llm_creator import LLMCreator
 from application.logging import build_stack_data, log_activity, LogContext
 from application.retriever.base import BaseRetriever
-from application.core.settings import settings
-from bson.objectid import ObjectId
+
+logger = logging.getLogger(__name__)
 
 
 class BaseAgent(ABC):
@@ -26,6 +29,7 @@ class BaseAgent(ABC):
         chat_history: Optional[List[Dict]] = None,
         decoded_token: Optional[Dict] = None,
         attachments: Optional[List[Dict]] = None,
+        json_schema: Optional[Dict] = None,
     ):
         self.endpoint = endpoint
         self.llm_name = llm_name
@@ -45,8 +49,11 @@ class BaseAgent(ABC):
             user_api_key=user_api_key,
             decoded_token=decoded_token,
         )
-        self.llm_handler = get_llm_handler(llm_name)
+        self.llm_handler = LLMHandlerCreator.create_handler(
+            llm_name if llm_name else "default"
+        )
         self.attachments = attachments or []
+        self.json_schema = json_schema
 
     @log_activity()
     def gen(
@@ -87,8 +94,8 @@ class BaseAgent(ABC):
         user_tools_collection = db["user_tools"]
         user_tools = user_tools_collection.find({"user": user, "status": True})
         user_tools = list(user_tools)
-        tools_by_id = {str(tool["_id"]): tool for tool in user_tools}
-        return tools_by_id
+
+        return {str(i): tool for i, tool in enumerate(user_tools)}
 
     def _build_tool_parameters(self, action):
         params = {"type": "object", "properties": {}, "required": []}
@@ -132,6 +139,49 @@ class BaseAgent(ABC):
         parser = ToolActionParser(self.llm.__class__.__name__)
         tool_id, action_name, call_args = parser.parse_args(call)
 
+        call_id = getattr(call, "id", None) or str(uuid.uuid4())
+        
+        # Check if parsing failed
+        if tool_id is None or action_name is None:
+            error_message = f"Error: Failed to parse LLM tool call. Tool name: {getattr(call, 'name', 'unknown')}"
+            logger.error(error_message)
+            
+            tool_call_data = {
+                "tool_name": "unknown",
+                "call_id": call_id,
+                "action_name": getattr(call, 'name', 'unknown'),
+                "arguments": call_args or {},
+                "result": f"Failed to parse tool call. Invalid tool name format: {getattr(call, 'name', 'unknown')}",
+            }
+            yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
+            self.tool_calls.append(tool_call_data)
+            return "Failed to parse tool call.", call_id
+        
+        # Check if tool_id exists in available tools
+        if tool_id not in tools_dict:
+            error_message = f"Error: Tool ID '{tool_id}' extracted from LLM call not found in available tools_dict. Available IDs: {list(tools_dict.keys())}"
+            logger.error(error_message)
+            
+            # Return error result
+            tool_call_data = {
+                "tool_name": "unknown",
+                "call_id": call_id,
+                "action_name": f"{action_name}_{tool_id}",
+                "arguments": call_args,
+                "result": f"Tool with ID {tool_id} not found. Available tools: {list(tools_dict.keys())}",
+            }
+            yield {"type": "tool_call", "data": {**tool_call_data, "status": "error"}}
+            self.tool_calls.append(tool_call_data)
+            return f"Tool with ID {tool_id} not found.", call_id
+        
+        tool_call_data = {
+            "tool_name": tools_dict[tool_id]["name"],
+            "call_id": call_id,
+            "action_name": f"{action_name}_{tool_id}",
+            "arguments": call_args,
+        }
+        yield {"type": "tool_call", "data": {**tool_call_data, "status": "pending"}}
+
         tool_data = tools_dict[tool_id]
         action_data = (
             tool_data["config"]["actions"][action_name]
@@ -184,19 +234,29 @@ class BaseAgent(ABC):
         else:
             print(f"Executing tool: {action_name} with args: {call_args}")
             result = tool.execute_action(action_name, **parameters)
-        call_id = getattr(call, "id", None)
+        tool_call_data["result"] = (
+            f"{str(result)[:50]}..." if len(str(result)) > 50 else result
+        )
 
-        tool_call_data = {
-            "tool_name": tool_data["name"],
-            "call_id": call_id if call_id is not None else "None",
-            "action_name": f"{action_name}_{tool_id}",
-            "arguments": call_args,
-            "result": result,
-        }
+        yield {"type": "tool_call", "data": {**tool_call_data, "status": "completed"}}
         self.tool_calls.append(tool_call_data)
 
         return result, call_id
 
+    def _get_truncated_tool_calls(self):
+        return [
+            {
+                **tool_call,
+                "result": (
+                    f"{str(tool_call['result'])[:50]}..."
+                    if len(str(tool_call["result"])) > 50
+                    else tool_call["result"]
+                ),
+                "status": "completed",
+            }
+            for tool_call in self.tool_calls
+        ]
+
     def _build_messages(
         self,
         system_prompt: str,
@@ -252,9 +312,31 @@ class BaseAgent(ABC):
         return retrieved_data
 
     def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
-        resp = self.llm.gen_stream(
-            model=self.gpt_model, messages=messages, tools=self.tools
-        )
+        gen_kwargs = {"model": self.gpt_model, "messages": messages}
+
+        if (
+            hasattr(self.llm, "_supports_tools")
+            and self.llm._supports_tools
+            and self.tools
+        ):
+            gen_kwargs["tools"] = self.tools
+
+        if (
+            self.json_schema
+            and hasattr(self.llm, "_supports_structured_output")
+            and self.llm._supports_structured_output()
+        ):
+            structured_format = self.llm.prepare_structured_output_format(
+                self.json_schema
+            )
+            if structured_format:
+                if self.llm_name == "openai":
+                    gen_kwargs["response_format"] = structured_format
+                elif self.llm_name == "google":
+                    gen_kwargs["response_schema"] = structured_format
+
+        resp = self.llm.gen_stream(**gen_kwargs)
+
         if log_context:
             data = build_stack_data(self.llm, exclude_attributes=["client"])
             log_context.stacks.append({"component": "llm", "data": data})
@@ -268,10 +350,51 @@ class BaseAgent(ABC):
         log_context: Optional[LogContext] = None,
         attachments: Optional[List[Dict]] = None,
     ):
-        resp = self.llm_handler.handle_response(
-            self, resp, tools_dict, messages, attachments
+        resp = self.llm_handler.process_message_flow(
+            self, resp, tools_dict, messages, attachments, True
         )
         if log_context:
             data = build_stack_data(self.llm_handler, exclude_attributes=["tool_calls"])
             log_context.stacks.append({"component": "llm_handler", "data": data})
         return resp
+
+    def _handle_response(self, response, tools_dict, messages, log_context):
+        is_structured_output = (
+            self.json_schema is not None
+            and hasattr(self.llm, "_supports_structured_output")
+            and self.llm._supports_structured_output()
+        )
+
+        if isinstance(response, str):
+            answer_data = {"answer": response}
+            if is_structured_output:
+                answer_data["structured"] = True
+                answer_data["schema"] = self.json_schema
+            yield answer_data
+            return
+        if hasattr(response, "message") and getattr(response.message, "content", None):
+            answer_data = {"answer": response.message.content}
+            if is_structured_output:
+                answer_data["structured"] = True
+                answer_data["schema"] = self.json_schema
+            yield answer_data
+            return
+        processed_response_gen = self._llm_handler(
+            response, tools_dict, messages, log_context, self.attachments
+        )
+
+        for event in processed_response_gen:
+            if isinstance(event, str):
+                answer_data = {"answer": event}
+                if is_structured_output:
+                    answer_data["structured"] = True
+                    answer_data["schema"] = self.json_schema
+                yield answer_data
+            elif hasattr(event, "message") and getattr(event.message, "content", None):
+                answer_data = {"answer": event.message.content}
+                if is_structured_output:
+                    answer_data["structured"] = True
+                    answer_data["schema"] = self.json_schema
+                yield answer_data
+            elif isinstance(event, dict) and "type" in event:
+                yield event

application/agents/classic_agent.py

@@ -1,8 +1,6 @@
 from typing import Dict, Generator
-
 from application.agents.base import BaseAgent
 from application.logging import LogContext
-
 from application.retriever.base import BaseRetriever
 import logging
 
@@ -10,55 +8,46 @@ logger = logging.getLogger(__name__)
 
 
 class ClassicAgent(BaseAgent):
+    """A simplified agent with clear execution flow.
+
+    Usage:
+    1. Processes a query through retrieval
+    2. Sets up available tools
+    3. Generates responses using LLM
+    4. Handles tool interactions if needed
+    5. Returns standardized outputs
+
+    Easy to extend by overriding specific steps.
+    """
+
     def _gen_inner(
         self, query: str, retriever: BaseRetriever, log_context: LogContext
     ) -> Generator[Dict, None, None]:
+        # Step 1: Retrieve relevant data
         retrieved_data = self._retriever_search(retriever, query, log_context)
-        if self.user_api_key:
-            tools_dict = self._get_tools(self.user_api_key)
-        else:
-            tools_dict = self._get_user_tools(self.user)
+
+        # Step 2: Prepare tools
+        tools_dict = (
+            self._get_user_tools(self.user)
+            if not self.user_api_key
+            else self._get_tools(self.user_api_key)
+        )
         self._prepare_tools(tools_dict)
 
+        # Step 3: Build and process messages
         messages = self._build_messages(self.prompt, query, retrieved_data)
+        llm_response = self._llm_gen(messages, log_context)
 
-        resp = self._llm_gen(messages, log_context)
+        # Step 4: Handle the response
+        yield from self._handle_response(
+            llm_response, tools_dict, messages, log_context
+        )
 
-        attachments = self.attachments
-
-        if isinstance(resp, str):
-            yield {"answer": resp}
-            return
-        if (
-            hasattr(resp, "message")
-            and hasattr(resp.message, "content")
-            and resp.message.content is not None
-        ):
-            yield {"answer": resp.message.content}
-            return
-
-        resp = self._llm_handler(resp, tools_dict, messages, log_context, attachments)
-
-        if isinstance(resp, str):
-            yield {"answer": resp}
-        elif (
-            hasattr(resp, "message")
-            and hasattr(resp.message, "content")
-            and resp.message.content is not None
-        ):
-            yield {"answer": resp.message.content}
-        else:
-            for line in resp:
-                if isinstance(line, str):
-                    yield {"answer": line}
+        # Step 5: Return metadata
+        yield {"sources": retrieved_data}
+        yield {"tool_calls": self._get_truncated_tool_calls()}
 
+        # Log tool calls for debugging
         log_context.stacks.append(
             {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
         )
-
-        yield {"sources": retrieved_data}
-        # clean tool_call_data only send first 50 characters of tool_call['result']
-        for tool_call in self.tool_calls:
-            if len(str(tool_call["result"])) > 50:
-                tool_call["result"] = str(tool_call["result"])[:50] + "..."
-        yield {"tool_calls": self.tool_calls.copy()}

application/agents/llm_handler.py

@@ -1,351 +0,0 @@
-import json
-import logging
-from abc import ABC, abstractmethod
-
-from application.logging import build_stack_data
-
-logger = logging.getLogger(__name__)
-
-
-class LLMHandler(ABC):
-    def __init__(self):
-        self.llm_calls = []
-        self.tool_calls = []
-
-    @abstractmethod
-    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, **kwargs):
-        pass
-
-    def prepare_messages_with_attachments(self, agent, messages, attachments=None):
-        """
-        Prepare messages with attachment content if available.
-
-        Args:
-            agent: The current agent instance.
-            messages (list): List of message dictionaries.
-            attachments (list): List of attachment dictionaries with content.
-
-        Returns:
-            list: Messages with attachment context added to the system prompt.
-        """
-        if not attachments:
-            return messages
-
-        logger.info(f"Preparing messages with {len(attachments)} attachments")
-
-        supported_types = agent.llm.get_supported_attachment_types()
-
-        supported_attachments = []
-        unsupported_attachments = []
-
-        for attachment in attachments:
-            mime_type = attachment.get('mime_type')
-            if mime_type in supported_types:
-                supported_attachments.append(attachment)
-            else:
-                unsupported_attachments.append(attachment)
-
-        # Process supported attachments with the LLM's custom method
-        prepared_messages = messages
-        if supported_attachments:
-            logger.info(f"Processing {len(supported_attachments)} supported attachments with {agent.llm.__class__.__name__}'s method")
-            prepared_messages = agent.llm.prepare_messages_with_attachments(messages, supported_attachments)
-
-        # Process unsupported attachments with the default method
-        if unsupported_attachments:
-            logger.info(f"Processing {len(unsupported_attachments)} unsupported attachments with default method")
-            prepared_messages = self._append_attachment_content_to_system(prepared_messages, unsupported_attachments)
-
-        return prepared_messages
-
-    def _append_attachment_content_to_system(self, messages, attachments):
-        """
-        Default method to append attachment content to the system prompt.
-
-        Args:
-            messages (list): List of message dictionaries.
-            attachments (list): List of attachment dictionaries with content.
-
-        Returns:
-            list: Messages with attachment context added to the system prompt.
-        """
-        prepared_messages = messages.copy()
-
-        attachment_texts = []
-        for attachment in attachments:
-            logger.info(f"Adding attachment {attachment.get('id')} to context")
-            if 'content' in attachment:
-                attachment_texts.append(f"Attached file content:\n\n{attachment['content']}")
-
-        if attachment_texts:
-            combined_attachment_text = "\n\n".join(attachment_texts)
-
-            system_found = False
-            for i in range(len(prepared_messages)):
-                if prepared_messages[i].get("role") == "system":
-                    prepared_messages[i]["content"] += f"\n\n{combined_attachment_text}"
-                    system_found = True
-                    break
-
-            if not system_found:
-                prepared_messages.insert(0, {"role": "system", "content": combined_attachment_text})
-
-        return prepared_messages
-
-class OpenAILLMHandler(LLMHandler):
-    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, stream: bool = True):
-
-        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
-        logger.info(f"Messages with attachments: {messages}")
-        if not stream:
-            while hasattr(resp, "finish_reason") and resp.finish_reason == "tool_calls":
-                message = json.loads(resp.model_dump_json())["message"]
-                keys_to_remove = {"audio", "function_call", "refusal"}
-                filtered_data = {
-                    k: v for k, v in message.items() if k not in keys_to_remove
-                }
-                messages.append(filtered_data)
-
-                tool_calls = resp.message.tool_calls
-                for call in tool_calls:
-                    try:
-                        self.tool_calls.append(call)
-                        tool_response, call_id = agent._execute_tool_action(
-                            tools_dict, call
-                        )
-                        function_call_dict = {
-                            "function_call": {
-                                "name": call.function.name,
-                                "args": call.function.arguments,
-                                "call_id": call_id,
-                            }
-                        }
-                        function_response_dict = {
-                            "function_response": {
-                                "name": call.function.name,
-                                "response": {"result": tool_response},
-                                "call_id": call_id,
-                            }
-                        }
-
-                        messages.append(
-                            {"role": "assistant", "content": [function_call_dict]}
-                        )
-                        messages.append(
-                            {"role": "tool", "content": [function_response_dict]}
-                        )
-
-                        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
-                    except Exception as e:
-                        logging.error(f"Error executing tool: {str(e)}", exc_info=True)
-                        messages.append(
-                            {
-                                "role": "tool",
-                                "content": f"Error executing tool: {str(e)}",
-                                "tool_call_id": call_id,
-                            }
-                        )
-                resp = agent.llm.gen_stream(
-                    model=agent.gpt_model, messages=messages, tools=agent.tools
-                )
-                self.llm_calls.append(build_stack_data(agent.llm))
-            return resp
-
-        else:
-            text_buffer = ""
-            while True:
-                tool_calls = {}
-                for chunk in resp:
-                    if isinstance(chunk, str) and len(chunk) > 0:
-                        yield chunk
-                        continue
-                    elif hasattr(chunk, "delta"):
-                        chunk_delta = chunk.delta
-
-                        if (
-                            hasattr(chunk_delta, "tool_calls")
-                            and chunk_delta.tool_calls is not None
-                        ):
-                            for tool_call in chunk_delta.tool_calls:
-                                index = tool_call.index
-                                if index not in tool_calls:
-                                    tool_calls[index] = {
-                                        "id": "",
-                                        "function": {"name": "", "arguments": ""},
-                                    }
-
-                                current = tool_calls[index]
-                                if tool_call.id:
-                                    current["id"] = tool_call.id
-                                if tool_call.function.name:
-                                    current["function"][
-                                        "name"
-                                    ] = tool_call.function.name
-                                if tool_call.function.arguments:
-                                    current["function"][
-                                        "arguments"
-                                    ] += tool_call.function.arguments
-                                tool_calls[index] = current
-
-                        if (
-                            hasattr(chunk, "finish_reason")
-                            and chunk.finish_reason == "tool_calls"
-                        ):
-                            for index in sorted(tool_calls.keys()):
-                                call = tool_calls[index]
-                                try:
-                                    self.tool_calls.append(call)
-                                    tool_response, call_id = agent._execute_tool_action(
-                                        tools_dict, call
-                                    )
-                                    if isinstance(call["function"]["arguments"], str):
-                                        call["function"]["arguments"] = json.loads(call["function"]["arguments"])
-
-                                    function_call_dict = {
-                                        "function_call": {
-                                            "name": call["function"]["name"],
-                                            "args": call["function"]["arguments"],
-                                            "call_id": call["id"],
-                                        }
-                                    }
-                                    function_response_dict = {
-                                        "function_response": {
-                                            "name": call["function"]["name"],
-                                            "response": {"result": tool_response},
-                                            "call_id": call["id"],
-                                        }
-                                    }
-
-                                    messages.append(
-                                        {
-                                            "role": "assistant",
-                                            "content": [function_call_dict],
-                                        }
-                                    )
-                                    messages.append(
-                                        {
-                                            "role": "tool",
-                                            "content": [function_response_dict],
-                                        }
-                                    )
-
-                                except Exception as e:
-                                    logging.error(f"Error executing tool: {str(e)}", exc_info=True)
-                                    messages.append(
-                                        {
-                                            "role": "assistant",
-                                            "content": f"Error executing tool: {str(e)}",
-                                        }
-                                    )
-                            tool_calls = {}
-                        if hasattr(chunk_delta, "content") and chunk_delta.content:
-                            # Add to buffer or yield immediately based on your preference
-                            text_buffer += chunk_delta.content
-                            yield text_buffer
-                            text_buffer = ""
-
-                        if (
-                            hasattr(chunk, "finish_reason")
-                            and chunk.finish_reason == "stop"
-                        ):
-                            return resp
-                    elif isinstance(chunk, str) and len(chunk) == 0:
-                            continue
-
-                logger.info(f"Regenerating with messages: {messages}")
-                resp = agent.llm.gen_stream(
-                    model=agent.gpt_model, messages=messages, tools=agent.tools
-                )
-                self.llm_calls.append(build_stack_data(agent.llm))
-
-
-class GoogleLLMHandler(LLMHandler):
-    def handle_response(self, agent, resp, tools_dict, messages, attachments=None, stream: bool = True):
-        from google.genai import types
-
-        messages = self.prepare_messages_with_attachments(agent, messages, attachments)
-
-        while True:
-            if not stream:
-                response = agent.llm.gen(
-                    model=agent.gpt_model, messages=messages, tools=agent.tools
-                )
-                self.llm_calls.append(build_stack_data(agent.llm))
-                if response.candidates and response.candidates[0].content.parts:
-                    tool_call_found = False
-                    for part in response.candidates[0].content.parts:
-                        if part.function_call:
-                            tool_call_found = True
-                            self.tool_calls.append(part.function_call)
-                            tool_response, call_id = agent._execute_tool_action(
-                                tools_dict, part.function_call
-                            )
-                            function_response_part = types.Part.from_function_response(
-                                name=part.function_call.name,
-                                response={"result": tool_response},
-                            )
-
-                            messages.append(
-                                {"role": "model", "content": [part.to_json_dict()]}
-                            )
-                            messages.append(
-                                {
-                                    "role": "tool",
-                                    "content": [function_response_part.to_json_dict()],
-                                }
-                            )
-
-                    if (
-                        not tool_call_found
-                        and response.candidates[0].content.parts
-                        and response.candidates[0].content.parts[0].text
-                    ):
-                        return response.candidates[0].content.parts[0].text
-                    elif not tool_call_found:
-                        return response.candidates[0].content.parts
-
-                else:
-                    return response
-
-            else:
-                response = agent.llm.gen_stream(
-                    model=agent.gpt_model, messages=messages, tools=agent.tools
-                )
-                self.llm_calls.append(build_stack_data(agent.llm))
-
-                tool_call_found = False
-                for result in response:
-                    if hasattr(result, "function_call"):
-                        tool_call_found = True
-                        self.tool_calls.append(result.function_call)
-                        tool_response, call_id = agent._execute_tool_action(
-                            tools_dict, result.function_call
-                        )
-                        function_response_part = types.Part.from_function_response(
-                            name=result.function_call.name,
-                            response={"result": tool_response},
-                        )
-
-                        messages.append(
-                            {"role": "model", "content": [result.to_json_dict()]}
-                        )
-                        messages.append(
-                            {
-                                "role": "tool",
-                                "content": [function_response_part.to_json_dict()],
-                            }
-                        )
-                    else:
-                        tool_call_found = False
-                        yield result
-
-                if not tool_call_found:
-                    return response
-
-
-def get_llm_handler(llm_type):
-    handlers = {
-        "openai": OpenAILLMHandler(),
-        "google": GoogleLLMHandler(),
-    }
-    return handlers.get(llm_type, OpenAILLMHandler())

application/agents/react_agent.py

@@ -1,33 +1,95 @@
 import os
-from typing import Dict, Generator, List
+from typing import Dict, Generator, List, Any
+import logging
 
 from application.agents.base import BaseAgent
 from application.logging import build_stack_data, LogContext
 from application.retriever.base import BaseRetriever
 
+logger = logging.getLogger(__name__)
+
 current_dir = os.path.dirname(
     os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 )
 with open(
     os.path.join(current_dir, "application/prompts", "react_planning_prompt.txt"), "r"
 ) as f:
-    planning_prompt = f.read()
+    planning_prompt_template = f.read()
 with open(
     os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
     "r",
 ) as f:
-    final_prompt = f.read()
-
+    final_prompt_template = f.read()
+    
+MAX_ITERATIONS_REASONING = 10
 
 class ReActAgent(BaseAgent):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.plan = ""
+        self.plan: str = ""
         self.observations: List[str] = []
 
+    def _extract_content_from_llm_response(self, resp: Any) -> str:
+        """
+        Helper to extract string content from various LLM response types.
+        Handles strings, message objects (OpenAI-like), and streams.
+        Adapt stream handling for your specific LLM client if not OpenAI.
+        """
+        collected_content = []
+        if isinstance(resp, str):
+            collected_content.append(resp)
+        elif ( # OpenAI non-streaming or Anthropic non-streaming (older SDK style)
+            hasattr(resp, "message")
+            and hasattr(resp.message, "content")
+            and resp.message.content is not None
+        ):
+            collected_content.append(resp.message.content)
+        elif ( # OpenAI non-streaming (Pydantic model), Anthropic new SDK non-streaming
+            hasattr(resp, "choices") and resp.choices and
+            hasattr(resp.choices[0], "message") and
+            hasattr(resp.choices[0].message, "content") and
+            resp.choices[0].message.content is not None
+        ):
+            collected_content.append(resp.choices[0].message.content) # OpenAI
+        elif ( # Anthropic new SDK non-streaming content block
+             hasattr(resp, "content") and isinstance(resp.content, list) and resp.content and
+             hasattr(resp.content[0], "text")
+        ):
+            collected_content.append(resp.content[0].text) # Anthropic
+        else:
+            # Assume resp is a stream if not a recognized object
+            try:
+                for chunk in resp: # This will fail if resp is not iterable (e.g. a non-streaming response object)
+                    content_piece = ""
+                    # OpenAI-like stream
+                    if hasattr(chunk, 'choices') and len(chunk.choices) > 0 and \
+                       hasattr(chunk.choices[0], 'delta') and \
+                       hasattr(chunk.choices[0].delta, 'content') and \
+                       chunk.choices[0].delta.content is not None:
+                        content_piece = chunk.choices[0].delta.content
+                    # Anthropic-like stream (ContentBlockDelta)
+                    elif hasattr(chunk, 'type') and chunk.type == 'content_block_delta' and \
+                         hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):
+                        content_piece = chunk.delta.text
+                    elif isinstance(chunk, str): # Simplest case: stream of strings
+                        content_piece = chunk
+
+                    if content_piece:
+                        collected_content.append(content_piece)
+            except TypeError: # If resp is not iterable (e.g. a final response object that wasn't caught above)
+                logger.debug(f"Response type {type(resp)} could not be iterated as a stream. It might be a non-streaming object not handled by specific checks.")
+            except Exception as e:
+                logger.error(f"Error processing potential stream chunk: {e}, chunk was: {getattr(chunk, '__dict__', chunk)}")
+
+
+        return "".join(collected_content)
+
     def _gen_inner(
         self, query: str, retriever: BaseRetriever, log_context: LogContext
     ) -> Generator[Dict, None, None]:
+        # Reset state for this generation call
+        self.plan = ""
+        self.observations = []
         retrieved_data = self._retriever_search(retriever, query, log_context)
 
         if self.user_api_key:
@@ -37,96 +99,131 @@ class ReActAgent(BaseAgent):
         self._prepare_tools(tools_dict)
 
         docs_together = "\n".join([doc["text"] for doc in retrieved_data])
-        plan = self._create_plan(query, docs_together, log_context)
-        for line in plan:
-            if isinstance(line, str):
-                self.plan += line
-                yield {"thought": line}
+        iterating_reasoning = 0
+        while iterating_reasoning < MAX_ITERATIONS_REASONING:
+            iterating_reasoning += 1
+            # 1. Create Plan
+            logger.info("ReActAgent: Creating plan...")
+            plan_stream = self._create_plan(query, docs_together, log_context)
+            current_plan_parts = []
+            yield {"thought": f"Reasoning... (iteration {iterating_reasoning})\n\n"}
+            for line_chunk in plan_stream:
+                current_plan_parts.append(line_chunk)
+                yield {"thought": line_chunk}
+            self.plan = "".join(current_plan_parts)
+            if self.plan:
+                self.observations.append(f"Plan: {self.plan} Iteration: {iterating_reasoning}")
 
-        prompt = self.prompt + f"\nFollow this plan: {self.plan}"
-        messages = self._build_messages(prompt, query, retrieved_data)
 
-        resp = self._llm_gen(messages, log_context)
-
-        if isinstance(resp, str):
-            self.observations.append(resp)
-        if (
-            hasattr(resp, "message")
-            and hasattr(resp.message, "content")
-            and resp.message.content is not None
-        ):
-            self.observations.append(resp.message.content)
-
-        resp = self._llm_handler(resp, tools_dict, messages, log_context)
-
-        for tool_call in self.tool_calls:
-            observation = (
-                f"Action '{tool_call['action_name']}' of tool '{tool_call['tool_name']}' "
-                f"with arguments '{tool_call['arguments']}' returned: '{tool_call['result']}'"
+            max_obs_len = 20000
+            obs_str = "\n".join(self.observations)
+            if len(obs_str) > max_obs_len:
+                obs_str = obs_str[:max_obs_len] + "\n...[observations truncated]"
+            execution_prompt_str = (
+                (self.prompt or "")
+                + f"\n\nFollow this plan:\n{self.plan}"
+                + f"\n\nObservations:\n{obs_str}"
+                + f"\n\nIf there is enough data to complete user query '{query}', Respond with 'SATISFIED' only. Otherwise, continue. Dont Menstion 'SATISFIED' in your response if you are not ready. "
             )
-            self.observations.append(observation)
+            
+            messages = self._build_messages(execution_prompt_str, query, retrieved_data)
 
-        if isinstance(resp, str):
-            self.observations.append(resp)
-        elif (
-            hasattr(resp, "message")
-            and hasattr(resp.message, "content")
-            and resp.message.content is not None
-        ):
-            self.observations.append(resp.message.content)
-        else:
-            completion = self.llm.gen_stream(
-                model=self.gpt_model, messages=messages, tools=self.tools
-            )
-            for line in completion:
-                if isinstance(line, str):
-                    self.observations.append(line)
+            resp_from_llm_gen = self._llm_gen(messages, log_context)
 
-        log_context.stacks.append(
-            {"component": "agent", "data": {"tool_calls": self.tool_calls.copy()}}
-        )
+            initial_llm_thought_content = self._extract_content_from_llm_response(resp_from_llm_gen)
+            if initial_llm_thought_content:
+                self.observations.append(f"Initial thought/response: {initial_llm_thought_content}")
+            else:
+                logger.info("ReActAgent: Initial LLM response (before handler) had no textual content (might be only tool calls).")
+            resp_after_handler = self._llm_handler(resp_from_llm_gen, tools_dict, messages, log_context)
+            
+            for tool_call_info in self.tool_calls: # Iterate over self.tool_calls populated by _llm_handler
+                observation_string = (
+                    f"Executed Action: Tool '{tool_call_info.get('tool_name', 'N/A')}' "
+                    f"with arguments '{tool_call_info.get('arguments', '{}')}'. Result: '{str(tool_call_info.get('result', ''))[:200]}...'"
+                )
+                self.observations.append(observation_string)
 
-        yield {"sources": retrieved_data}
-        # clean tool_call_data only send first 50 characters of tool_call['result']
-        for tool_call in self.tool_calls:
-            if len(str(tool_call["result"])) > 50:
-                tool_call["result"] = str(tool_call["result"])[:50] + "..."
-        yield {"tool_calls": self.tool_calls.copy()}
+            content_after_handler = self._extract_content_from_llm_response(resp_after_handler)
+            if content_after_handler:
+                self.observations.append(f"Response after tool execution: {content_after_handler}")
+            else:
+                logger.info("ReActAgent: LLM response after handler had no textual content.")
 
-        final_answer = self._create_final_answer(query, self.observations, log_context)
-        for line in final_answer:
-            if isinstance(line, str):
-                yield {"answer": line}
+            if log_context:
+                log_context.stacks.append(
+                    {"component": "agent_tool_calls", "data": {"tool_calls": self.tool_calls.copy()}}
+                )
+
+            yield {"sources": retrieved_data}
+
+            display_tool_calls = []
+            for tc in self.tool_calls:
+                cleaned_tc = tc.copy()
+                if len(str(cleaned_tc.get("result", ""))) > 50:
+                    cleaned_tc["result"] = str(cleaned_tc["result"])[:50] + "..."
+                display_tool_calls.append(cleaned_tc)
+            if display_tool_calls:
+                yield {"tool_calls": display_tool_calls}
+            
+            if "SATISFIED" in content_after_handler:
+                logger.info("ReActAgent: LLM satisfied with the plan and data. Stopping reasoning.")
+                break
+
+        # 3. Create Final Answer based on all observations
+        final_answer_stream = self._create_final_answer(query, self.observations, log_context)
+        for answer_chunk in final_answer_stream:
+            yield {"answer": answer_chunk}
+        logger.info("ReActAgent: Finished generating final answer.")
 
     def _create_plan(
         self, query: str, docs_data: str, log_context: LogContext = None
     ) -> Generator[str, None, None]:
-        plan_prompt = planning_prompt.replace("{query}", query)
-        if "{summaries}" in planning_prompt:
-            summaries = docs_data
-            plan_prompt = plan_prompt.replace("{summaries}", summaries)
+        plan_prompt_filled = planning_prompt_template.replace("{query}", query)
+        if "{summaries}" in plan_prompt_filled:
+            summaries = docs_data if docs_data else "No documents retrieved."
+            plan_prompt_filled = plan_prompt_filled.replace("{summaries}", summaries)
+        plan_prompt_filled = plan_prompt_filled.replace("{prompt}", self.prompt or "")
+        plan_prompt_filled = plan_prompt_filled.replace("{observations}", "\n".join(self.observations))
 
-        messages = [{"role": "user", "content": plan_prompt}]
-        print(self.tools)
-        plan = self.llm.gen_stream(
-            model=self.gpt_model, messages=messages, tools=self.tools
+        messages = [{"role": "user", "content": plan_prompt_filled}]
+
+        plan_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=getattr(self, 'tools', None) # Use self.tools
         )
         if log_context:
             data = build_stack_data(self.llm)
             log_context.stacks.append({"component": "planning_llm", "data": data})
-        return plan
+
+        for chunk in plan_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece
 
     def _create_final_answer(
         self, query: str, observations: List[str], log_context: LogContext = None
-    ) -> str:
+    ) -> Generator[str, None, None]:
         observation_string = "\n".join(observations)
-        final_answer_prompt = final_prompt.format(
+        max_obs_len = 10000
+        if len(observation_string) > max_obs_len:
+            observation_string = observation_string[:max_obs_len] + "\n...[observations truncated]"
+            logger.warning("ReActAgent: Truncated observations for final answer prompt due to length.")
+
+        final_answer_prompt_filled = final_prompt_template.format(
             query=query, observations=observation_string
         )
 
-        messages = [{"role": "user", "content": final_answer_prompt}]
-        final_answer = self.llm.gen_stream(model=self.gpt_model, messages=messages)
+        messages = [{"role": "user", "content": final_answer_prompt_filled}]
+
+        # Final answer should synthesize, not call tools.
+        final_answer_stream_from_llm = self.llm.gen_stream(
+            model=self.gpt_model, messages=messages, tools=None
+        )
         if log_context:
             data = build_stack_data(self.llm)
             log_context.stacks.append({"component": "final_answer_llm", "data": data})
-        return final_answer
+
+        for chunk in final_answer_stream_from_llm:
+            content_piece = self._extract_content_from_llm_response(chunk)
+            if content_piece:
+                yield content_piece

application/agents/tools/brave.py

@@ -25,27 +25,35 @@ class BraveSearchTool(Tool):
         else:
             raise ValueError(f"Unknown action: {action_name}")
 
-    def _web_search(self, query, country="ALL", search_lang="en", count=10, 
-                   offset=0, safesearch="off", freshness=None, 
-                   result_filter=None, extra_snippets=False, summary=False):
+    def _web_search(
+        self,
+        query,
+        country="ALL",
+        search_lang="en",
+        count=10,
+        offset=0,
+        safesearch="off",
+        freshness=None,
+        result_filter=None,
+        extra_snippets=False,
+        summary=False,
+    ):
         """
         Performs a web search using the Brave Search API.
         """
         print(f"Performing Brave web search for: {query}")
-        
+
         url = f"{self.base_url}/web/search"
-        
-        # Build query parameters
+
         params = {
             "q": query,
             "country": country,
             "search_lang": search_lang,
             "count": min(count, 20),
             "offset": min(offset, 9),
-            "safesearch": safesearch
+            "safesearch": safesearch,
         }
-        
-        # Add optional parameters only if they have values
+
         if freshness:
             params["freshness"] = freshness
         if result_filter:
@@ -54,68 +62,69 @@ class BraveSearchTool(Tool):
             params["extra_snippets"] = 1
         if summary:
             params["summary"] = 1
-        
-        # Set up headers
         headers = {
             "Accept": "application/json",
             "Accept-Encoding": "gzip",
-            "X-Subscription-Token": self.token
+            "X-Subscription-Token": self.token,
         }
-        
-        # Make the request
+
         response = requests.get(url, params=params, headers=headers)
-        
+
         if response.status_code == 200:
             return {
                 "status_code": response.status_code,
                 "results": response.json(),
-                "message": "Search completed successfully."
+                "message": "Search completed successfully.",
             }
         else:
             return {
                 "status_code": response.status_code,
-                "message": f"Search failed with status code: {response.status_code}."
+                "message": f"Search failed with status code: {response.status_code}.",
             }
-    
-    def _image_search(self, query, country="ALL", search_lang="en", count=5, 
-                     safesearch="off", spellcheck=False):
+
+    def _image_search(
+        self,
+        query,
+        country="ALL",
+        search_lang="en",
+        count=5,
+        safesearch="off",
+        spellcheck=False,
+    ):
         """
         Performs an image search using the Brave Search API.
         """
         print(f"Performing Brave image search for: {query}")
-        
+
         url = f"{self.base_url}/images/search"
-        
-        # Build query parameters
+
         params = {
             "q": query,
             "country": country,
             "search_lang": search_lang,
             "count": min(count, 100),  # API max is 100
             "safesearch": safesearch,
-            "spellcheck": 1 if spellcheck else 0
+            "spellcheck": 1 if spellcheck else 0,
         }
-        
-        # Set up headers
+
         headers = {
             "Accept": "application/json",
             "Accept-Encoding": "gzip",
-            "X-Subscription-Token": self.token
+            "X-Subscription-Token": self.token,
         }
-        
-        # Make the request
+
         response = requests.get(url, params=params, headers=headers)
-        
+
         if response.status_code == 200:
             return {
                 "status_code": response.status_code,
                 "results": response.json(),
-                "message": "Image search completed successfully."
+                "message": "Image search completed successfully.",
             }
         else:
             return {
                 "status_code": response.status_code,
-                "message": f"Image search failed with status code: {response.status_code}."
+                "message": f"Image search failed with status code: {response.status_code}.",
             }
 
     def get_actions_metadata(self):
@@ -130,42 +139,14 @@ class BraveSearchTool(Tool):
                             "type": "string",
                             "description": "The search query (max 400 characters, 50 words)",
                         },
-                        # "country": {
-                        #     "type": "string",
-                        #     "description": "The 2-character country code (default: US)",
-                        # },
                         "search_lang": {
                             "type": "string",
                             "description": "The search language preference (default: en)",
                         },
-                        # "count": {
-                        #     "type": "integer",
-                        #     "description": "Number of results to return (max 20, default: 10)",
-                        # },
-                        # "offset": {
-                        #     "type": "integer",
-                        #     "description": "Pagination offset (max 9, default: 0)",
-                        # },
-                        # "safesearch": {
-                        #     "type": "string",
-                        #     "description": "Filter level for adult content (off, moderate, strict)",
-                        # },
                         "freshness": {
                             "type": "string",
                             "description": "Time filter for results (pd: last 24h, pw: last week, pm: last month, py: last year)",
                         },
-                        # "result_filter": {
-                        #     "type": "string",
-                        #     "description": "Comma-delimited list of result types to include",
-                        # },
-                        # "extra_snippets": {
-                        #     "type": "boolean",
-                        #     "description": "Get additional excerpts from result pages",
-                        # },
-                        # "summary": {
-                        #     "type": "boolean",
-                        #     "description": "Enable summary generation in search results",
-                        # }
                     },
                     "required": ["query"],
                     "additionalProperties": False,
@@ -181,37 +162,21 @@ class BraveSearchTool(Tool):
                             "type": "string",
                             "description": "The search query (max 400 characters, 50 words)",
                         },
-                        # "country": {
-                        #     "type": "string",
-                        #     "description": "The 2-character country code (default: US)",
-                        # },
-                        # "search_lang": {
-                        #     "type": "string",
-                        #     "description": "The search language preference (default: en)",
-                        # },
                         "count": {
                             "type": "integer",
                             "description": "Number of results to return (max 100, default: 5)",
                         },
-                        # "safesearch": {
-                        #     "type": "string",
-                        #     "description": "Filter level for adult content (off, strict). Default: strict",
-                        # },
-                        # "spellcheck": {
-                        #     "type": "boolean",
-                        #     "description": "Whether to spellcheck provided query (default: true)",
-                        # }
                     },
                     "required": ["query"],
                     "additionalProperties": False,
                 },
-            }
+            },
         ]
 
     def get_config_requirements(self):
         return {
             "token": {
-                "type": "string", 
-                "description": "Brave Search API key for authentication"
+                "type": "string",
+                "description": "Brave Search API key for authentication",
             },
-        }
+        }

application/agents/tools/duckduckgo.py

@@ -0,0 +1,114 @@
+from application.agents.tools.base import Tool
+from duckduckgo_search import DDGS
+
+
+class DuckDuckGoSearchTool(Tool):
+    """
+    DuckDuckGo Search
+    A tool for performing web and image searches using DuckDuckGo.
+    """
+
+    def __init__(self, config):
+        self.config = config
+
+    def execute_action(self, action_name, **kwargs):
+        actions = {
+            "ddg_web_search": self._web_search,
+            "ddg_image_search": self._image_search,
+        }
+
+        if action_name in actions:
+            return actions[action_name](**kwargs)
+        else:
+            raise ValueError(f"Unknown action: {action_name}")
+
+    def _web_search(
+        self,
+        query,
+        max_results=5,
+    ):
+        print(f"Performing DuckDuckGo web search for: {query}")
+
+        try:
+            results = DDGS().text(
+                query,
+                max_results=max_results,
+            )
+
+            return {
+                "status_code": 200,
+                "results": results,
+                "message": "Web search completed successfully.",
+            }
+        except Exception as e:
+            return {
+                "status_code": 500,
+                "message": f"Web search failed: {str(e)}",
+            }
+
+    def _image_search(
+        self,
+        query,
+        max_results=5,
+    ):
+        print(f"Performing DuckDuckGo image search for: {query}")
+
+        try:
+            results = DDGS().images(
+                keywords=query,
+                max_results=max_results,
+            )
+
+            return {
+                "status_code": 200,
+                "results": results,
+                "message": "Image search completed successfully.",
+            }
+        except Exception as e:
+            return {
+                "status_code": 500,
+                "message": f"Image search failed: {str(e)}",
+            }
+
+    def get_actions_metadata(self):
+        return [
+            {
+                "name": "ddg_web_search",
+                "description": "Perform a web search using DuckDuckGo.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query",
+                        },
+                        "max_results": {
+                            "type": "integer",
+                            "description": "Number of results to return (default: 5)",
+                        },
+                    },
+                    "required": ["query"],
+                },
+            },
+            {
+                "name": "ddg_image_search",
+                "description": "Perform an image search using DuckDuckGo.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "Search query",
+                        },
+                        "max_results": {
+                            "type": "integer",
+                            "description": "Number of results to return (default: 5, max: 50)",
+                        },
+                    },
+                    "required": ["query"],
+                },
+            },
+        ]
+
+    def get_config_requirements(self):
+        return {}

application/agents/tools/tool_action_parser.py

@@ -17,26 +17,45 @@ class ToolActionParser:
         return parser(call)
 
     def _parse_openai_llm(self, call):
-        if isinstance(call, dict):
-            try:
-                call_args = json.loads(call["function"]["arguments"])
-                tool_id = call["function"]["name"].split("_")[-1]
-                action_name = call["function"]["name"].rsplit("_", 1)[0]
-            except (KeyError, TypeError) as e:
-                logger.error(f"Error parsing OpenAI LLM call: {e}")
-                return None, None, None
-        else:
-            try:
-                call_args = json.loads(call.function.arguments)
-                tool_id = call.function.name.split("_")[-1]
-                action_name = call.function.name.rsplit("_", 1)[0]
-            except (AttributeError, TypeError) as e:
-                logger.error(f"Error parsing OpenAI LLM call: {e}")
+        try:
+            call_args = json.loads(call.arguments)
+            tool_parts = call.name.split("_")
+            
+            # If the tool name doesn't contain an underscore, it's likely a hallucinated tool
+            if len(tool_parts) < 2:
+                logger.warning(f"Invalid tool name format: {call.name}. Expected format: action_name_tool_id")
                 return None, None, None
+                
+            tool_id = tool_parts[-1]
+            action_name = "_".join(tool_parts[:-1])
+            
+            # Validate that tool_id looks like a numerical ID
+            if not tool_id.isdigit():
+                logger.warning(f"Tool ID '{tool_id}' is not numerical. This might be a hallucinated tool call.")
+                
+        except (AttributeError, TypeError) as e:
+            logger.error(f"Error parsing OpenAI LLM call: {e}")
+            return None, None, None
         return tool_id, action_name, call_args
 
     def _parse_google_llm(self, call):
-        call_args = call.args
-        tool_id = call.name.split("_")[-1]
-        action_name = call.name.rsplit("_", 1)[0]
+        try:
+            call_args = call.arguments
+            tool_parts = call.name.split("_")
+            
+            # If the tool name doesn't contain an underscore, it's likely a hallucinated tool
+            if len(tool_parts) < 2:
+                logger.warning(f"Invalid tool name format: {call.name}. Expected format: action_name_tool_id")
+                return None, None, None
+                
+            tool_id = tool_parts[-1]
+            action_name = "_".join(tool_parts[:-1])
+            
+            # Validate that tool_id looks like a numerical ID
+            if not tool_id.isdigit():
+                logger.warning(f"Tool ID '{tool_id}' is not numerical. This might be a hallucinated tool call.")
+                
+        except (AttributeError, TypeError) as e:
+            logger.error(f"Error parsing Google LLM call: {e}")
+            return None, None, None
         return tool_id, action_name, call_args

application/api/__init__.py

@@ -0,0 +1,7 @@
+from flask_restx import Api
+
+api = Api(
+    version="1.0",
+    title="DocsGPT API",
+    description="API for DocsGPT",
+)

application/api/answer/__init__.py

@@ -0,0 +1,19 @@
+from flask import Blueprint
+
+from application.api import api
+from application.api.answer.routes.answer import AnswerResource
+from application.api.answer.routes.base import answer_ns
+from application.api.answer.routes.stream import StreamResource
+
+
+answer = Blueprint("answer", __name__)
+
+api.add_namespace(answer_ns)
+
+
+def init_answer_routes():
+    api.add_resource(StreamResource, "/stream")
+    api.add_resource(AnswerResource, "/api/answer")
+
+
+init_answer_routes()

application/api/answer/routes.py

@@ -1,915 +0,0 @@
-import asyncio
-import datetime
-import json
-import logging
-import os
-import traceback
-
-from bson.dbref import DBRef
-from bson.objectid import ObjectId
-from flask import Blueprint, make_response, request, Response
-from flask_restx import fields, Namespace, Resource
-
-from application.agents.agent_creator import AgentCreator
-
-from application.core.mongo_db import MongoDB
-from application.core.settings import settings
-from application.error import bad_request
-from application.extensions import api
-from application.llm.llm_creator import LLMCreator
-from application.retriever.retriever_creator import RetrieverCreator
-from application.utils import check_required_fields, limit_chat_history
-
-logger = logging.getLogger(__name__)
-
-mongo = MongoDB.get_client()
-db = mongo[settings.MONGO_DB_NAME]
-conversations_collection = db["conversations"]
-sources_collection = db["sources"]
-prompts_collection = db["prompts"]
-agents_collection = db["agents"]
-user_logs_collection = db["user_logs"]
-attachments_collection = db["attachments"]
-
-answer = Blueprint("answer", __name__)
-answer_ns = Namespace("answer", description="Answer related operations", path="/")
-api.add_namespace(answer_ns)
-
-gpt_model = ""
-# to have some kind of default behaviour
-if settings.LLM_NAME == "openai":
-    gpt_model = "gpt-4o-mini"
-elif settings.LLM_NAME == "anthropic":
-    gpt_model = "claude-2"
-elif settings.LLM_NAME == "groq":
-    gpt_model = "llama3-8b-8192"
-elif settings.LLM_NAME == "novita":
-    gpt_model = "deepseek/deepseek-r1"
-
-if settings.MODEL_NAME:  # in case there is particular model name configured
-    gpt_model = settings.MODEL_NAME
-
-# load the prompts
-current_dir = os.path.dirname(
-    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-)
-with open(os.path.join(current_dir, "prompts", "chat_combine_default.txt"), "r") as f:
-    chat_combine_template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_reduce_prompt.txt"), "r") as f:
-    chat_reduce_template = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_creative.txt"), "r") as f:
-    chat_combine_creative = f.read()
-
-with open(os.path.join(current_dir, "prompts", "chat_combine_strict.txt"), "r") as f:
-    chat_combine_strict = f.read()
-
-api_key_set = settings.API_KEY is not None
-embeddings_key_set = settings.EMBEDDINGS_KEY is not None
-
-
-async def async_generate(chain, question, chat_history):
-    result = await chain.arun({"question": question, "chat_history": chat_history})
-    return result
-
-
-def run_async_chain(chain, question, chat_history):
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    result = {}
-    try:
-        answer = loop.run_until_complete(async_generate(chain, question, chat_history))
-    finally:
-        loop.close()
-    result["answer"] = answer
-    return result
-
-
-def get_agent_key(agent_id, user_id):
-    if not agent_id:
-        return None, False, None
-
-    try:
-        agent = agents_collection.find_one({"_id": ObjectId(agent_id)})
-        if agent is None:
-            raise Exception("Agent not found", 404)
-
-        is_owner = agent.get("user") == user_id
-
-        if is_owner:
-            agents_collection.update_one(
-                {"_id": ObjectId(agent_id)},
-                {"$set": {"lastUsedAt": datetime.datetime.now(datetime.timezone.utc)}},
-            )
-            return str(agent["key"]), False, None
-
-        is_shared_with_user = agent.get(
-            "shared_publicly", False
-        ) or user_id in agent.get("shared_with", [])
-
-        if is_shared_with_user:
-            return str(agent["key"]), True, agent.get("shared_token")
-
-        raise Exception("Unauthorized access to the agent", 403)
-
-    except Exception as e:
-        logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
-        raise
-
-
-def get_data_from_api_key(api_key):
-    data = agents_collection.find_one({"key": api_key})
-    if not data:
-        raise Exception("Invalid API Key, please generate a new key", 401)
-
-    source = data.get("source")
-    if isinstance(source, DBRef):
-        source_doc = db.dereference(source)
-        data["source"] = str(source_doc["_id"])
-        data["retriever"] = source_doc.get("retriever", data.get("retriever"))
-    else:
-        data["source"] = {}
-
-    return data
-
-
-def get_retriever(source_id: str):
-    doc = sources_collection.find_one({"_id": ObjectId(source_id)})
-    if doc is None:
-        raise Exception("Source document does not exist", 404)
-    retriever_name = None if "retriever" not in doc else doc["retriever"]
-    return retriever_name
-
-
-def is_azure_configured():
-    return (
-        settings.OPENAI_API_BASE
-        and settings.OPENAI_API_VERSION
-        and settings.AZURE_DEPLOYMENT_NAME
-    )
-
-
-def save_conversation(
-    conversation_id,
-    question,
-    response,
-    thought,
-    source_log_docs,
-    tool_calls,
-    llm,
-    decoded_token,
-    index=None,
-    api_key=None,
-    agent_id=None,
-    is_shared_usage=False,
-    shared_token=None,
-):
-    current_time = datetime.datetime.now(datetime.timezone.utc)
-    if conversation_id is not None and index is not None:
-        conversations_collection.update_one(
-            {"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
-            {
-                "$set": {
-                    f"queries.{index}.prompt": question,
-                    f"queries.{index}.response": response,
-                    f"queries.{index}.thought": thought,
-                    f"queries.{index}.sources": source_log_docs,
-                    f"queries.{index}.tool_calls": tool_calls,
-                    f"queries.{index}.timestamp": current_time,
-                }
-            },
-        )
-        ##remove following queries from the array
-        conversations_collection.update_one(
-            {"_id": ObjectId(conversation_id), f"queries.{index}": {"$exists": True}},
-            {"$push": {"queries": {"$each": [], "$slice": index + 1}}},
-        )
-    elif conversation_id is not None and conversation_id != "None":
-        conversations_collection.update_one(
-            {"_id": ObjectId(conversation_id)},
-            {
-                "$push": {
-                    "queries": {
-                        "prompt": question,
-                        "response": response,
-                        "thought": thought,
-                        "sources": source_log_docs,
-                        "tool_calls": tool_calls,
-                        "timestamp": current_time,
-                    }
-                }
-            },
-        )
-
-    else:
-        # create new conversation
-        # generate summary
-        messages_summary = [
-            {
-                "role": "assistant",
-                "content": "Summarise following conversation in no more than 3 "
-                "words, respond ONLY with the summary, use the same "
-                "language as the system",
-            },
-            {
-                "role": "user",
-                "content": "Summarise following conversation in no more than 3 words, "
-                "respond ONLY with the summary, use the same language as the "
-                "system \n\nUser: " + question + "\n\n" + "AI: " + response,
-            },
-        ]
-
-        completion = llm.gen(model=gpt_model, messages=messages_summary, max_tokens=30)
-        conversation_data = {
-            "user": decoded_token.get("sub"),
-            "date": datetime.datetime.utcnow(),
-            "name": completion,
-            "queries": [
-                {
-                    "prompt": question,
-                    "response": response,
-                    "thought": thought,
-                    "sources": source_log_docs,
-                    "tool_calls": tool_calls,
-                    "timestamp": current_time,
-                }
-            ],
-        }
-        if api_key:
-            if agent_id:
-                conversation_data["agent_id"] = agent_id
-                if is_shared_usage:
-                    conversation_data["is_shared_usage"] = is_shared_usage
-                    conversation_data["shared_token"] = shared_token
-            api_key_doc = agents_collection.find_one({"key": api_key})
-            if api_key_doc:
-                conversation_data["api_key"] = api_key_doc["key"]
-        conversation_id = conversations_collection.insert_one(
-            conversation_data
-        ).inserted_id
-    return conversation_id
-
-
-def get_prompt(prompt_id):
-    if prompt_id == "default":
-        prompt = chat_combine_template
-    elif prompt_id == "creative":
-        prompt = chat_combine_creative
-    elif prompt_id == "strict":
-        prompt = chat_combine_strict
-    else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
-    return prompt
-
-
-def complete_stream(
-    question,
-    agent,
-    retriever,
-    conversation_id,
-    user_api_key,
-    decoded_token,
-    isNoneDoc=False,
-    index=None,
-    should_save_conversation=True,
-    attachments=None,
-    agent_id=None,
-    is_shared_usage=False,
-    shared_token=None,
-):
-    try:
-        response_full, thought, source_log_docs, tool_calls = "", "", [], []
-        attachment_ids = []
-
-        if attachments:
-            attachment_ids = [attachment["id"] for attachment in attachments]
-            logger.info(
-                f"Processing request with {len(attachments)} attachments: {attachment_ids}"
-            )
-
-        answer = agent.gen(query=question, retriever=retriever)
-
-        for line in answer:
-            if "answer" in line:
-                response_full += str(line["answer"])
-                data = json.dumps({"type": "answer", "answer": line["answer"]})
-                yield f"data: {data}\n\n"
-            elif "sources" in line:
-                truncated_sources = []
-                source_log_docs = line["sources"]
-                for source in line["sources"]:
-                    truncated_source = source.copy()
-                    if "text" in truncated_source:
-                        truncated_source["text"] = (
-                            truncated_source["text"][:100].strip() + "..."
-                        )
-                    truncated_sources.append(truncated_source)
-                if len(truncated_sources) > 0:
-                    data = json.dumps({"type": "source", "source": truncated_sources})
-                    yield f"data: {data}\n\n"
-            elif "tool_calls" in line:
-                tool_calls = line["tool_calls"]
-                data = json.dumps({"type": "tool_calls", "tool_calls": tool_calls})
-                yield f"data: {data}\n\n"
-            elif "thought" in line:
-                thought += line["thought"]
-                data = json.dumps({"type": "thought", "thought": line["thought"]})
-                yield f"data: {data}\n\n"
-
-        if isNoneDoc:
-            for doc in source_log_docs:
-                doc["source"] = "None"
-
-        llm = LLMCreator.create_llm(
-            settings.LLM_NAME,
-            api_key=settings.API_KEY,
-            user_api_key=user_api_key,
-            decoded_token=decoded_token,
-        )
-
-        if should_save_conversation:
-            conversation_id = save_conversation(
-                conversation_id,
-                question,
-                response_full,
-                thought,
-                source_log_docs,
-                tool_calls,
-                llm,
-                decoded_token,
-                index,
-                api_key=user_api_key,
-                agent_id=agent_id,
-                is_shared_usage=is_shared_usage,
-                shared_token=shared_token,
-            )
-        else:
-            conversation_id = None
-
-        # send data.type = "end" to indicate that the stream has ended as json
-        data = json.dumps({"type": "id", "id": str(conversation_id)})
-        yield f"data: {data}\n\n"
-
-        retriever_params = retriever.get_params()
-        user_logs_collection.insert_one(
-            {
-                "action": "stream_answer",
-                "level": "info",
-                "user": decoded_token.get("sub"),
-                "api_key": user_api_key,
-                "question": question,
-                "response": response_full,
-                "sources": source_log_docs,
-                "retriever_params": retriever_params,
-                "attachments": attachment_ids,
-                "timestamp": datetime.datetime.now(datetime.timezone.utc),
-            }
-        )
-        data = json.dumps({"type": "end"})
-        yield f"data: {data}\n\n"
-    except Exception as e:
-        logger.error(f"Error in stream: {str(e)}", exc_info=True)
-        data = json.dumps(
-            {
-                "type": "error",
-                "error": "Please try again later. We apologize for any inconvenience.",
-            }
-        )
-        yield f"data: {data}\n\n"
-        return
-
-
-@answer_ns.route("/stream")
-class Stream(Resource):
-    stream_model = api.model(
-        "StreamModel",
-        {
-            "question": fields.String(
-                required=True, description="Question to be asked"
-            ),
-            "history": fields.List(
-                fields.String, required=False, description="Chat history"
-            ),
-            "conversation_id": fields.String(
-                required=False, description="Conversation ID"
-            ),
-            "prompt_id": fields.String(
-                required=False, default="default", description="Prompt ID"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "token_limit": fields.Integer(required=False, description="Token limit"),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "api_key": fields.String(required=False, description="API key"),
-            "active_docs": fields.String(
-                required=False, description="Active documents"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-            "index": fields.Integer(
-                required=False, description="Index of the query to update"
-            ),
-            "save_conversation": fields.Boolean(
-                required=False,
-                default=True,
-                description="Whether to save the conversation",
-            ),
-            "attachments": fields.List(
-                fields.String, required=False, description="List of attachment IDs"
-            ),
-        },
-    )
-
-    @api.expect(stream_model)
-    @api.doc(description="Stream a response based on the question and retriever")
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]
-        if "index" in data:
-            required_fields = ["question", "conversation_id"]
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        save_conv = data.get("save_conversation", True)
-
-        try:
-            question = data["question"]
-            history = limit_chat_history(
-                json.loads(data.get("history", [])), gpt_model=gpt_model
-            )
-            conversation_id = data.get("conversation_id")
-            prompt_id = data.get("prompt_id", "default")
-            attachment_ids = data.get("attachments", [])
-
-            index = data.get("index", None)
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-            agent_id = data.get("agent_id", None)
-            agent_type = settings.AGENT_NAME
-            agent_key, is_shared_usage, shared_token = get_agent_key(
-                agent_id, request.decoded_token.get("sub")
-            )
-
-            if agent_key:
-                data.update({"api_key": agent_key})
-            else:
-                agent_id = None
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                prompt_id = data_key.get("prompt_id", "default")
-                source = {"active_docs": data_key.get("source")}
-                retriever_name = data_key.get("retriever", retriever_name)
-                user_api_key = data["api_key"]
-                agent_type = data_key.get("agent_type", agent_type)
-                if is_shared_usage:
-                    decoded_token = request.decoded_token
-                else:
-                    decoded_token = {"sub": data_key.get("user")}
-                    is_shared_usage = False
-
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                retriever_name = get_retriever(data["active_docs"]) or retriever_name
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            else:
-                source = {}
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            if not decoded_token:
-                return make_response({"error": "Unauthorized"}, 401)
-
-            attachments = get_attachments_content(
-                attachment_ids, decoded_token.get("sub")
-            )
-
-            logger.info(
-                f"/stream - request_data: {data}, source: {source}, attachments: {len(attachments)}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            prompt = get_prompt(prompt_id)
-            if "isNoneDoc" in data and data["isNoneDoc"] is True:
-                chunks = 0
-
-            agent = AgentCreator.create_agent(
-                agent_type,
-                endpoint="stream",
-                llm_name=settings.LLM_NAME,
-                gpt_model=gpt_model,
-                api_key=settings.API_KEY,
-                user_api_key=user_api_key,
-                prompt=prompt,
-                chat_history=history,
-                decoded_token=decoded_token,
-                attachments=attachments,
-            )
-
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                source=source,
-                chat_history=history,
-                prompt=prompt,
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-                decoded_token=decoded_token,
-            )
-            is_shared_usage_val = data.get("is_shared_usage", False)
-            is_shared_token_val = data.get("shared_token", None)
-            return Response(
-                complete_stream(
-                    question=question,
-                    agent=agent,
-                    retriever=retriever,
-                    conversation_id=conversation_id,
-                    user_api_key=user_api_key,
-                    decoded_token=decoded_token,
-                    isNoneDoc=data.get("isNoneDoc"),
-                    index=index,
-                    should_save_conversation=save_conv,
-                    agent_id=agent_id,
-                    is_shared_usage=is_shared_usage_val,
-                    shared_token=is_shared_token_val,
-                ),
-                mimetype="text/event-stream",
-            )
-
-        except ValueError:
-            message = "Malformed request body"
-            logger.error(f"/stream - error: {message}")
-            return Response(
-                error_stream_generate(message),
-                status=400,
-                mimetype="text/event-stream",
-            )
-        except Exception as e:
-            logger.error(
-                f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            status_code = 400
-            return Response(
-                error_stream_generate("Unknown error occurred"),
-                status=status_code,
-                mimetype="text/event-stream",
-            )
-
-
-def error_stream_generate(err_response):
-    data = json.dumps({"type": "error", "error": err_response})
-    yield f"data: {data}\n\n"
-
-
-@answer_ns.route("/api/answer")
-class Answer(Resource):
-    answer_model = api.model(
-        "AnswerModel",
-        {
-            "question": fields.String(
-                required=True, description="The question to answer"
-            ),
-            "history": fields.List(
-                fields.String, required=False, description="Conversation history"
-            ),
-            "conversation_id": fields.String(
-                required=False, description="Conversation ID"
-            ),
-            "prompt_id": fields.String(
-                required=False, default="default", description="Prompt ID"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "token_limit": fields.Integer(required=False, description="Token limit"),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "api_key": fields.String(required=False, description="API key"),
-            "active_docs": fields.String(
-                required=False, description="Active documents"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-        },
-    )
-
-    @api.expect(answer_model)
-    @api.doc(description="Provide an answer based on the question and retriever")
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        try:
-            question = data["question"]
-            history = limit_chat_history(
-                json.loads(data.get("history", [])), gpt_model=gpt_model
-            )
-            conversation_id = data.get("conversation_id")
-            prompt_id = data.get("prompt_id", "default")
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-            agent_type = settings.AGENT_NAME
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                prompt_id = data_key.get("prompt_id", "default")
-                source = {"active_docs": data_key.get("source")}
-                retriever_name = data_key.get("retriever", retriever_name)
-                user_api_key = data["api_key"]
-                agent_type = data_key.get("agent_type", agent_type)
-                decoded_token = {"sub": data_key.get("user")}
-
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                retriever_name = get_retriever(data["active_docs"]) or retriever_name
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            else:
-                source = {}
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            if not decoded_token:
-                return make_response({"error": "Unauthorized"}, 401)
-
-            prompt = get_prompt(prompt_id)
-
-            logger.info(
-                f"/api/answer - request_data: {data}, source: {source}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            agent = AgentCreator.create_agent(
-                agent_type,
-                endpoint="api/answer",
-                llm_name=settings.LLM_NAME,
-                gpt_model=gpt_model,
-                api_key=settings.API_KEY,
-                user_api_key=user_api_key,
-                prompt=prompt,
-                chat_history=history,
-                decoded_token=decoded_token,
-            )
-
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                source=source,
-                chat_history=history,
-                prompt=prompt,
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-                decoded_token=decoded_token,
-            )
-
-            response_full = ""
-            source_log_docs = []
-            tool_calls = []
-            stream_ended = False
-            thought = ""
-
-            for line in complete_stream(
-                question=question,
-                agent=agent,
-                retriever=retriever,
-                conversation_id=conversation_id,
-                user_api_key=user_api_key,
-                decoded_token=decoded_token,
-                isNoneDoc=data.get("isNoneDoc"),
-                index=None,
-                should_save_conversation=False,
-            ):
-                try:
-                    event_data = line.replace("data: ", "").strip()
-                    event = json.loads(event_data)
-
-                    if event["type"] == "answer":
-                        response_full += event["answer"]
-                    elif event["type"] == "source":
-                        source_log_docs = event["source"]
-                    elif event["type"] == "tool_calls":
-                        tool_calls = event["tool_calls"]
-                    elif event["type"] == "thought":
-                        thought = event["thought"]
-                    elif event["type"] == "error":
-                        logger.error(f"Error from stream: {event['error']}")
-                        return bad_request(500, event["error"])
-                    elif event["type"] == "end":
-                        stream_ended = True
-
-                except (json.JSONDecodeError, KeyError) as e:
-                    logger.warning(f"Error parsing stream event: {e}, line: {line}")
-                    continue
-
-            if not stream_ended:
-                logger.error("Stream ended unexpectedly without an 'end' event.")
-                return bad_request(500, "Stream ended unexpectedly.")
-
-            if data.get("isNoneDoc"):
-                for doc in source_log_docs:
-                    doc["source"] = "None"
-
-            llm = LLMCreator.create_llm(
-                settings.LLM_NAME,
-                api_key=settings.API_KEY,
-                user_api_key=user_api_key,
-                decoded_token=decoded_token,
-            )
-
-            result = {"answer": response_full, "sources": source_log_docs}
-            result["conversation_id"] = str(
-                save_conversation(
-                    conversation_id,
-                    question,
-                    response_full,
-                    thought,
-                    source_log_docs,
-                    tool_calls,
-                    llm,
-                    decoded_token,
-                    api_key=user_api_key,
-                )
-            )
-
-            retriever_params = retriever.get_params()
-            user_logs_collection.insert_one(
-                {
-                    "action": "api_answer",
-                    "level": "info",
-                    "user": decoded_token.get("sub"),
-                    "api_key": user_api_key,
-                    "question": question,
-                    "response": response_full,
-                    "sources": source_log_docs,
-                    "retriever_params": retriever_params,
-                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
-                }
-            )
-
-        except Exception as e:
-            logger.error(
-                f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            return bad_request(500, str(e))
-
-        return make_response(result, 200)
-
-
-@answer_ns.route("/api/search")
-class Search(Resource):
-    search_model = api.model(
-        "SearchModel",
-        {
-            "question": fields.String(
-                required=True, description="The question to search"
-            ),
-            "chunks": fields.Integer(
-                required=False, default=2, description="Number of chunks"
-            ),
-            "api_key": fields.String(
-                required=False, description="API key for authentication"
-            ),
-            "active_docs": fields.String(
-                required=False, description="Active documents for retrieval"
-            ),
-            "retriever": fields.String(required=False, description="Retriever type"),
-            "token_limit": fields.Integer(
-                required=False, description="Limit for tokens"
-            ),
-            "isNoneDoc": fields.Boolean(
-                required=False, description="Flag indicating if no document is used"
-            ),
-        },
-    )
-
-    @api.expect(search_model)
-    @api.doc(
-        description="Search for relevant documents based on the question and retriever"
-    )
-    def post(self):
-        data = request.get_json()
-        required_fields = ["question"]
-        missing_fields = check_required_fields(data, required_fields)
-        if missing_fields:
-            return missing_fields
-
-        try:
-            question = data["question"]
-            chunks = int(data.get("chunks", 2))
-            token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
-            retriever_name = data.get("retriever", "classic")
-
-            if "api_key" in data:
-                data_key = get_data_from_api_key(data["api_key"])
-                chunks = int(data_key.get("chunks", 2))
-                source = {"active_docs": data_key.get("source")}
-                user_api_key = data["api_key"]
-                decoded_token = {"sub": data_key.get("user")}
-
-            elif "active_docs" in data:
-                source = {"active_docs": data["active_docs"]}
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            else:
-                source = {}
-                user_api_key = None
-                decoded_token = request.decoded_token
-
-            if not decoded_token:
-                return make_response({"error": "Unauthorized"}, 401)
-
-            logger.info(
-                f"/api/answer - request_data: {data}, source: {source}",
-                extra={"data": json.dumps({"request_data": data, "source": source})},
-            )
-
-            retriever = RetrieverCreator.create_retriever(
-                retriever_name,
-                source=source,
-                chat_history=[],
-                prompt="default",
-                chunks=chunks,
-                token_limit=token_limit,
-                gpt_model=gpt_model,
-                user_api_key=user_api_key,
-                decoded_token=decoded_token,
-            )
-
-            docs = retriever.search(question)
-            retriever_params = retriever.get_params()
-
-            user_logs_collection.insert_one(
-                {
-                    "action": "api_search",
-                    "level": "info",
-                    "user": decoded_token.get("sub"),
-                    "api_key": user_api_key,
-                    "question": question,
-                    "sources": docs,
-                    "retriever_params": retriever_params,
-                    "timestamp": datetime.datetime.now(datetime.timezone.utc),
-                }
-            )
-
-            if data.get("isNoneDoc"):
-                for doc in docs:
-                    doc["source"] = "None"
-
-        except Exception as e:
-            logger.error(
-                f"/api/search - error: {str(e)} - traceback: {traceback.format_exc()}",
-                extra={"error": str(e), "traceback": traceback.format_exc()},
-            )
-            return bad_request(500, str(e))
-
-        return make_response(docs, 200)
-
-
-def get_attachments_content(attachment_ids, user):
-    """
-    Retrieve content from attachment documents based on their IDs.
-
-    Args:
-        attachment_ids (list): List of attachment document IDs
-        user (str): User identifier to verify ownership
-
-    Returns:
-        list: List of dictionaries containing attachment content and metadata
-    """
-    if not attachment_ids:
-        return []
-
-    attachments = []
-    for attachment_id in attachment_ids:
-        try:
-            attachment_doc = attachments_collection.find_one(
-                {"_id": ObjectId(attachment_id), "user": user}
-            )
-
-            if attachment_doc:
-                attachments.append(attachment_doc)
-        except Exception as e:
-            logger.error(
-                f"Error retrieving attachment {attachment_id}: {e}", exc_info=True
-            )
-
-    return attachments

application/api/answer/routes/answer.py

@@ -0,0 +1,122 @@
+import logging
+import traceback
+
+from flask import make_response, request
+from flask_restx import fields, Resource
+
+from application.api import api
+
+from application.api.answer.routes.base import answer_ns, BaseAnswerResource
+
+from application.api.answer.services.stream_processor import StreamProcessor
+
+logger = logging.getLogger(__name__)
+
+
+@answer_ns.route("/api/answer")
+class AnswerResource(Resource, BaseAnswerResource):
+    def __init__(self, *args, **kwargs):
+        Resource.__init__(self, *args, **kwargs)
+        BaseAnswerResource.__init__(self)
+
+    answer_model = answer_ns.model(
+        "AnswerModel",
+        {
+            "question": fields.String(
+                required=True, description="Question to be asked"
+            ),
+            "history": fields.List(
+                fields.String,
+                required=False,
+                description="Conversation history (only for new conversations)",
+            ),
+            "conversation_id": fields.String(
+                required=False,
+                description="Existing conversation ID (loads history)",
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+        },
+    )
+
+    @api.expect(answer_model)
+    @api.doc(description="Provide a response based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        if error := self.validate_request(data):
+            return error
+        decoded_token = getattr(request, "decoded_token", None)
+        processor = StreamProcessor(data, decoded_token)
+        try:
+            processor.initialize()
+            if not processor.decoded_token:
+                return make_response({"error": "Unauthorized"}, 401)
+            agent = processor.create_agent()
+            retriever = processor.create_retriever()
+
+            stream = self.complete_stream(
+                question=data["question"],
+                agent=agent,
+                retriever=retriever,
+                conversation_id=processor.conversation_id,
+                user_api_key=processor.agent_config.get("user_api_key"),
+                decoded_token=processor.decoded_token,
+                isNoneDoc=data.get("isNoneDoc"),
+                index=None,
+                should_save_conversation=data.get("save_conversation", True),
+            )
+            stream_result = self.process_response_stream(stream)
+
+            if len(stream_result) == 7:
+                (
+                    conversation_id,
+                    response,
+                    sources,
+                    tool_calls,
+                    thought,
+                    error,
+                    structured_info,
+                ) = stream_result
+            else:
+                conversation_id, response, sources, tool_calls, thought, error = (
+                    stream_result
+                )
+                structured_info = None
+
+            if error:
+                return make_response({"error": error}, 400)
+            result = {
+                "conversation_id": conversation_id,
+                "answer": response,
+                "sources": sources,
+                "tool_calls": tool_calls,
+                "thought": thought,
+            }
+
+            if structured_info:
+                result.update(structured_info)
+        except Exception as e:
+            logger.error(
+                f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return make_response({"error": str(e)}, 500)
+        return make_response(result, 200)

application/api/answer/routes/base.py

@@ -0,0 +1,263 @@
+import datetime
+import json
+import logging
+from typing import Any, Dict, Generator, List, Optional
+
+from flask import Response
+from flask_restx import Namespace
+
+from application.api.answer.services.conversation_service import ConversationService
+
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.llm.llm_creator import LLMCreator
+from application.utils import check_required_fields, get_gpt_model
+
+logger = logging.getLogger(__name__)
+
+
+answer_ns = Namespace("answer", description="Answer related operations", path="/")
+
+
+class BaseAnswerResource:
+    """Shared base class for answer endpoints"""
+
+    def __init__(self):
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        self.user_logs_collection = db["user_logs"]
+        self.gpt_model = get_gpt_model()
+        self.conversation_service = ConversationService()
+
+    def validate_request(
+        self, data: Dict[str, Any], require_conversation_id: bool = False
+    ) -> Optional[Response]:
+        """Common request validation"""
+        required_fields = ["question"]
+        if require_conversation_id:
+            required_fields.append("conversation_id")
+        if missing_fields := check_required_fields(data, required_fields):
+            return missing_fields
+        return None
+
+    def complete_stream(
+        self,
+        question: str,
+        agent: Any,
+        retriever: Any,
+        conversation_id: Optional[str],
+        user_api_key: Optional[str],
+        decoded_token: Dict[str, Any],
+        isNoneDoc: bool = False,
+        index: Optional[int] = None,
+        should_save_conversation: bool = True,
+        attachment_ids: Optional[List[str]] = None,
+        agent_id: Optional[str] = None,
+        is_shared_usage: bool = False,
+        shared_token: Optional[str] = None,
+    ) -> Generator[str, None, None]:
+        """
+        Generator function that streams the complete conversation response.
+
+        Args:
+            question: The user's question
+            agent: The agent instance
+            retriever: The retriever instance
+            conversation_id: Existing conversation ID
+            user_api_key: User's API key if any
+            decoded_token: Decoded JWT token
+            isNoneDoc: Flag for document-less responses
+            index: Index of message to update
+            should_save_conversation: Whether to persist the conversation
+            attachment_ids: List of attachment IDs
+            agent_id: ID of agent used
+            is_shared_usage: Flag for shared agent usage
+            shared_token: Token for shared agent
+
+        Yields:
+            Server-sent event strings
+        """
+        try:
+            response_full, thought, source_log_docs, tool_calls = "", "", [], []
+            is_structured = False
+            schema_info = None
+            structured_chunks = []
+
+            for line in agent.gen(query=question, retriever=retriever):
+                if "answer" in line:
+                    response_full += str(line["answer"])
+                    if line.get("structured"):
+                        is_structured = True
+                        schema_info = line.get("schema")
+                        structured_chunks.append(line["answer"])
+                    else:
+                        data = json.dumps({"type": "answer", "answer": line["answer"]})
+                        yield f"data: {data}\n\n"
+                elif "sources" in line:
+                    truncated_sources = []
+                    source_log_docs = line["sources"]
+                    for source in line["sources"]:
+                        truncated_source = source.copy()
+                        if "text" in truncated_source:
+                            truncated_source["text"] = (
+                                truncated_source["text"][:100].strip() + "..."
+                            )
+                        truncated_sources.append(truncated_source)
+                    if truncated_sources:
+                        data = json.dumps(
+                            {"type": "source", "source": truncated_sources}
+                        )
+                        yield f"data: {data}\n\n"
+                elif "tool_calls" in line:
+                    tool_calls = line["tool_calls"]
+                elif "thought" in line:
+                    thought += line["thought"]
+                    data = json.dumps({"type": "thought", "thought": line["thought"]})
+                    yield f"data: {data}\n\n"
+                elif "type" in line:
+                    data = json.dumps(line)
+                    yield f"data: {data}\n\n"
+
+            if is_structured and structured_chunks:
+                structured_data = {
+                    "type": "structured_answer",
+                    "answer": response_full,
+                    "structured": True,
+                    "schema": schema_info,
+                }
+                data = json.dumps(structured_data)
+                yield f"data: {data}\n\n"
+
+            if isNoneDoc:
+                for doc in source_log_docs:
+                    doc["source"] = "None"
+            llm = LLMCreator.create_llm(
+                settings.LLM_PROVIDER,
+                api_key=settings.API_KEY,
+                user_api_key=user_api_key,
+                decoded_token=decoded_token,
+            )
+
+            if should_save_conversation:
+                conversation_id = self.conversation_service.save_conversation(
+                    conversation_id,
+                    question,
+                    response_full,
+                    thought,
+                    source_log_docs,
+                    tool_calls,
+                    llm,
+                    self.gpt_model,
+                    decoded_token,
+                    index=index,
+                    api_key=user_api_key,
+                    agent_id=agent_id,
+                    is_shared_usage=is_shared_usage,
+                    shared_token=shared_token,
+                    attachment_ids=attachment_ids,
+                )
+            else:
+                conversation_id = None
+            id_data = {"type": "id", "id": str(conversation_id)}
+            data = json.dumps(id_data)
+            yield f"data: {data}\n\n"
+
+            retriever_params = retriever.get_params()
+            log_data = {
+                "action": "stream_answer",
+                "level": "info",
+                "user": decoded_token.get("sub"),
+                "api_key": user_api_key,
+                "question": question,
+                "response": response_full,
+                "sources": source_log_docs,
+                "retriever_params": retriever_params,
+                "attachments": attachment_ids,
+                "timestamp": datetime.datetime.now(datetime.timezone.utc),
+            }
+            if is_structured:
+                log_data["structured_output"] = True
+                if schema_info:
+                    log_data["schema"] = schema_info
+  
+            # clean up text fields to be no longer than 10000 characters
+            for key, value in log_data.items():
+                if isinstance(value, str) and len(value) > 10000:
+                    log_data[key] = value[:10000]
+            
+            self.user_logs_collection.insert_one(log_data)
+
+            # End of stream
+
+            data = json.dumps({"type": "end"})
+            yield f"data: {data}\n\n"
+        except Exception as e:
+            logger.error(f"Error in stream: {str(e)}", exc_info=True)
+            data = json.dumps(
+                {
+                    "type": "error",
+                    "error": "Please try again later. We apologize for any inconvenience.",
+                }
+            )
+            yield f"data: {data}\n\n"
+            return
+
+    def process_response_stream(self, stream):
+        """Process the stream response for non-streaming endpoint"""
+        conversation_id = ""
+        response_full = ""
+        source_log_docs = []
+        tool_calls = []
+        thought = ""
+        stream_ended = False
+        is_structured = False
+        schema_info = None
+
+        for line in stream:
+            try:
+                event_data = line.replace("data: ", "").strip()
+                event = json.loads(event_data)
+
+                if event["type"] == "id":
+                    conversation_id = event["id"]
+                elif event["type"] == "answer":
+                    response_full += event["answer"]
+                elif event["type"] == "structured_answer":
+                    response_full = event["answer"]
+                    is_structured = True
+                    schema_info = event.get("schema")
+                elif event["type"] == "source":
+                    source_log_docs = event["source"]
+                elif event["type"] == "tool_calls":
+                    tool_calls = event["tool_calls"]
+                elif event["type"] == "thought":
+                    thought = event["thought"]
+                elif event["type"] == "error":
+                    logger.error(f"Error from stream: {event['error']}")
+                    return None, None, None, None, event["error"]
+                elif event["type"] == "end":
+                    stream_ended = True
+            except (json.JSONDecodeError, KeyError) as e:
+                logger.warning(f"Error parsing stream event: {e}, line: {line}")
+                continue
+        if not stream_ended:
+            logger.error("Stream ended unexpectedly without an 'end' event.")
+            return None, None, None, None, "Stream ended unexpectedly"
+
+        result = (
+            conversation_id,
+            response_full,
+            source_log_docs,
+            tool_calls,
+            thought,
+            None,
+        )
+
+        if is_structured:
+            result = result + ({"structured": True, "schema": schema_info},)
+
+        return result
+
+    def error_stream_generate(self, err_response):
+        data = json.dumps({"type": "error", "error": err_response})
+        yield f"data: {data}\n\n"

application/api/answer/routes/stream.py

@@ -0,0 +1,117 @@
+import logging
+import traceback
+
+from flask import request, Response
+from flask_restx import fields, Resource
+
+from application.api import api
+
+from application.api.answer.routes.base import answer_ns, BaseAnswerResource
+
+from application.api.answer.services.stream_processor import StreamProcessor
+
+logger = logging.getLogger(__name__)
+
+
+@answer_ns.route("/stream")
+class StreamResource(Resource, BaseAnswerResource):
+    def __init__(self, *args, **kwargs):
+        Resource.__init__(self, *args, **kwargs)
+        BaseAnswerResource.__init__(self)
+
+    stream_model = answer_ns.model(
+        "StreamModel",
+        {
+            "question": fields.String(
+                required=True, description="Question to be asked"
+            ),
+            "history": fields.List(
+                fields.String,
+                required=False,
+                description="Conversation history (only for new conversations)",
+            ),
+            "conversation_id": fields.String(
+                required=False,
+                description="Existing conversation ID (loads history)",
+            ),
+            "prompt_id": fields.String(
+                required=False, default="default", description="Prompt ID"
+            ),
+            "chunks": fields.Integer(
+                required=False, default=2, description="Number of chunks"
+            ),
+            "token_limit": fields.Integer(required=False, description="Token limit"),
+            "retriever": fields.String(required=False, description="Retriever type"),
+            "api_key": fields.String(required=False, description="API key"),
+            "active_docs": fields.String(
+                required=False, description="Active documents"
+            ),
+            "isNoneDoc": fields.Boolean(
+                required=False, description="Flag indicating if no document is used"
+            ),
+            "index": fields.Integer(
+                required=False, description="Index of the query to update"
+            ),
+            "save_conversation": fields.Boolean(
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+            "attachments": fields.List(
+                fields.String, required=False, description="List of attachment IDs"
+            ),
+        },
+    )
+
+    @api.expect(stream_model)
+    @api.doc(description="Stream a response based on the question and retriever")
+    def post(self):
+        data = request.get_json()
+        if error := self.validate_request(data, "index" in data):
+            return error
+        decoded_token = getattr(request, "decoded_token", None)
+        processor = StreamProcessor(data, decoded_token)
+        try:
+            processor.initialize()
+            agent = processor.create_agent()
+            retriever = processor.create_retriever()
+
+            return Response(
+                self.complete_stream(
+                    question=data["question"],
+                    agent=agent,
+                    retriever=retriever,
+                    conversation_id=processor.conversation_id,
+                    user_api_key=processor.agent_config.get("user_api_key"),
+                    decoded_token=processor.decoded_token,
+                    isNoneDoc=data.get("isNoneDoc"),
+                    index=data.get("index"),
+                    should_save_conversation=data.get("save_conversation", True),
+                    attachment_ids=data.get("attachments", []),
+                    agent_id=data.get("agent_id"),
+                    is_shared_usage=processor.is_shared_usage,
+                    shared_token=processor.shared_token,
+                ),
+                mimetype="text/event-stream",
+            )
+        except ValueError as e:
+            message = "Malformed request body"
+            logger.error(
+                f"/stream - error: {message} - specific error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return Response(
+                self.error_stream_generate(message),
+                status=400,
+                mimetype="text/event-stream",
+            )
+        except Exception as e:
+            logger.error(
+                f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
+                extra={"error": str(e), "traceback": traceback.format_exc()},
+            )
+            return Response(
+                self.error_stream_generate("Unknown error occurred"),
+                status=400,
+                mimetype="text/event-stream",
+            )

application/api/answer/services/conversation_service.py

@@ -0,0 +1,180 @@
+import logging
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+
+from application.core.mongo_db import MongoDB
+
+from application.core.settings import settings
+from bson import ObjectId
+
+
+logger = logging.getLogger(__name__)
+
+
+class ConversationService:
+    def __init__(self):
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        self.conversations_collection = db["conversations"]
+        self.agents_collection = db["agents"]
+
+    def get_conversation(
+        self, conversation_id: str, user_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Retrieve a conversation with proper access control"""
+        if not conversation_id or not user_id:
+            return None
+        try:
+            conversation = self.conversations_collection.find_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "$or": [{"user": user_id}, {"shared_with": user_id}],
+                }
+            )
+
+            if not conversation:
+                logger.warning(
+                    f"Conversation not found or unauthorized - ID: {conversation_id}, User: {user_id}"
+                )
+                return None
+            conversation["_id"] = str(conversation["_id"])
+            return conversation
+        except Exception as e:
+            logger.error(f"Error fetching conversation: {str(e)}", exc_info=True)
+            return None
+
+    def save_conversation(
+        self,
+        conversation_id: Optional[str],
+        question: str,
+        response: str,
+        thought: str,
+        sources: List[Dict[str, Any]],
+        tool_calls: List[Dict[str, Any]],
+        llm: Any,
+        gpt_model: str,
+        decoded_token: Dict[str, Any],
+        index: Optional[int] = None,
+        api_key: Optional[str] = None,
+        agent_id: Optional[str] = None,
+        is_shared_usage: bool = False,
+        shared_token: Optional[str] = None,
+        attachment_ids: Optional[List[str]] = None,
+    ) -> str:
+        """Save or update a conversation in the database"""
+        user_id = decoded_token.get("sub")
+        if not user_id:
+            raise ValueError("User ID not found in token")
+        current_time = datetime.now(timezone.utc)
+        
+        # clean up in sources array such that we save max 1k characters for text part
+        for source in sources:
+            if "text" in source and isinstance(source["text"], str):
+                source["text"] = source["text"][:1000]
+
+        if conversation_id is not None and index is not None:
+            # Update existing conversation with new query
+
+            result = self.conversations_collection.update_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "user": user_id,
+                    f"queries.{index}": {"$exists": True},
+                },
+                {
+                    "$set": {
+                        f"queries.{index}.prompt": question,
+                        f"queries.{index}.response": response,
+                        f"queries.{index}.thought": thought,
+                        f"queries.{index}.sources": sources,
+                        f"queries.{index}.tool_calls": tool_calls,
+                        f"queries.{index}.timestamp": current_time,
+                        f"queries.{index}.attachments": attachment_ids,
+                    }
+                },
+            )
+
+            if result.matched_count == 0:
+                raise ValueError("Conversation not found or unauthorized")
+            self.conversations_collection.update_one(
+                {
+                    "_id": ObjectId(conversation_id),
+                    "user": user_id,
+                    f"queries.{index}": {"$exists": True},
+                },
+                {"$push": {"queries": {"$each": [], "$slice": index + 1}}},
+            )
+            return conversation_id
+        elif conversation_id:
+            # Append new message to existing conversation
+
+            result = self.conversations_collection.update_one(
+                {"_id": ObjectId(conversation_id), "user": user_id},
+                {
+                    "$push": {
+                        "queries": {
+                            "prompt": question,
+                            "response": response,
+                            "thought": thought,
+                            "sources": sources,
+                            "tool_calls": tool_calls,
+                            "timestamp": current_time,
+                            "attachments": attachment_ids,
+                        }
+                    }
+                },
+            )
+
+            if result.matched_count == 0:
+                raise ValueError("Conversation not found or unauthorized")
+            return conversation_id
+        else:
+            # Create new conversation
+
+            messages_summary = [
+                {
+                    "role": "assistant",
+                    "content": "Summarise following conversation in no more than 3 "
+                    "words, respond ONLY with the summary, use the same "
+                    "language as the user query",
+                },
+                {
+                    "role": "user",
+                    "content": "Summarise following conversation in no more than 3 words, "
+                    "respond ONLY with the summary, use the same language as the "
+                    "user query \n\nUser: " + question + "\n\n" + "AI: " + response,
+                },
+            ]
+
+            completion = llm.gen(
+                model=gpt_model, messages=messages_summary, max_tokens=30
+            )
+
+            conversation_data = {
+                "user": user_id,
+                "date": current_time,
+                "name": completion,
+                "queries": [
+                    {
+                        "prompt": question,
+                        "response": response,
+                        "thought": thought,
+                        "sources": sources,
+                        "tool_calls": tool_calls,
+                        "timestamp": current_time,
+                        "attachments": attachment_ids,
+                    }
+                ],
+            }
+
+            if api_key:
+                if agent_id:
+                    conversation_data["agent_id"] = agent_id
+                    if is_shared_usage:
+                        conversation_data["is_shared_usage"] = is_shared_usage
+                        conversation_data["shared_token"] = shared_token
+                agent = self.agents_collection.find_one({"key": api_key})
+                if agent:
+                    conversation_data["api_key"] = agent["key"]
+            result = self.conversations_collection.insert_one(conversation_data)
+            return str(result.inserted_id)

application/api/answer/services/stream_processor.py

@@ -0,0 +1,277 @@
+import datetime
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from bson.dbref import DBRef
+
+from bson.objectid import ObjectId
+
+from application.agents.agent_creator import AgentCreator
+from application.api.answer.services.conversation_service import ConversationService
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.retriever.retriever_creator import RetrieverCreator
+from application.utils import get_gpt_model, limit_chat_history
+
+logger = logging.getLogger(__name__)
+
+
+def get_prompt(prompt_id: str, prompts_collection=None) -> str:
+    """
+    Get a prompt by preset name or MongoDB ID
+    """
+    current_dir = Path(__file__).resolve().parents[3]
+    prompts_dir = current_dir / "prompts"
+
+    preset_mapping = {
+        "default": "chat_combine_default.txt",
+        "creative": "chat_combine_creative.txt",
+        "strict": "chat_combine_strict.txt",
+        "reduce": "chat_reduce_prompt.txt",
+    }
+
+    if prompt_id in preset_mapping:
+        file_path = os.path.join(prompts_dir, preset_mapping[prompt_id])
+        try:
+            with open(file_path, "r") as f:
+                return f.read()
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Prompt file not found: {file_path}")
+    try:
+        if prompts_collection is None:
+            mongo = MongoDB.get_client()
+            db = mongo[settings.MONGO_DB_NAME]
+            prompts_collection = db["prompts"]
+        prompt_doc = prompts_collection.find_one({"_id": ObjectId(prompt_id)})
+        if not prompt_doc:
+            raise ValueError(f"Prompt with ID {prompt_id} not found")
+        return prompt_doc["content"]
+    except Exception as e:
+        raise ValueError(f"Invalid prompt ID: {prompt_id}") from e
+
+
+class StreamProcessor:
+    def __init__(
+        self, request_data: Dict[str, Any], decoded_token: Optional[Dict[str, Any]]
+    ):
+        mongo = MongoDB.get_client()
+        self.db = mongo[settings.MONGO_DB_NAME]
+        self.agents_collection = self.db["agents"]
+        self.attachments_collection = self.db["attachments"]
+        self.prompts_collection = self.db["prompts"]
+
+        self.data = request_data
+        self.decoded_token = decoded_token
+        self.initial_user_id = (
+            self.decoded_token.get("sub") if self.decoded_token is not None else None
+        )
+        self.conversation_id = self.data.get("conversation_id")
+        self.source = (
+            {"active_docs": self.data["active_docs"]}
+            if "active_docs" in self.data
+            else {}
+        )
+        self.attachments = []
+        self.history = []
+        self.agent_config = {}
+        self.retriever_config = {}
+        self.is_shared_usage = False
+        self.shared_token = None
+        self.gpt_model = get_gpt_model()
+        self.conversation_service = ConversationService()
+
+    def initialize(self):
+        """Initialize all required components for processing"""
+        self._configure_retriever()
+        self._configure_agent()
+        self._load_conversation_history()
+        self._process_attachments()
+
+    def _load_conversation_history(self):
+        """Load conversation history either from DB or request"""
+        if self.conversation_id and self.initial_user_id:
+            conversation = self.conversation_service.get_conversation(
+                self.conversation_id, self.initial_user_id
+            )
+            if not conversation:
+                raise ValueError("Conversation not found or unauthorized")
+            self.history = [
+                {"prompt": query["prompt"], "response": query["response"]}
+                for query in conversation.get("queries", [])
+            ]
+        else:
+            self.history = limit_chat_history(
+                json.loads(self.data.get("history", "[]")), gpt_model=self.gpt_model
+            )
+
+    def _process_attachments(self):
+        """Process any attachments in the request"""
+        attachment_ids = self.data.get("attachments", [])
+        self.attachments = self._get_attachments_content(
+            attachment_ids, self.initial_user_id
+        )
+
+    def _get_attachments_content(self, attachment_ids, user_id):
+        """
+        Retrieve content from attachment documents based on their IDs.
+        """
+        if not attachment_ids:
+            return []
+        attachments = []
+        for attachment_id in attachment_ids:
+            try:
+                attachment_doc = self.attachments_collection.find_one(
+                    {"_id": ObjectId(attachment_id), "user": user_id}
+                )
+
+                if attachment_doc:
+                    attachments.append(attachment_doc)
+            except Exception as e:
+                logger.error(
+                    f"Error retrieving attachment {attachment_id}: {e}", exc_info=True
+                )
+        return attachments
+
+    def _get_agent_key(self, agent_id: Optional[str], user_id: Optional[str]) -> tuple:
+        """Get API key for agent with access control"""
+        if not agent_id:
+            return None, False, None
+        try:
+            agent = self.agents_collection.find_one({"_id": ObjectId(agent_id)})
+            if agent is None:
+                raise Exception("Agent not found")
+            is_owner = agent.get("user") == user_id
+            is_shared_with_user = agent.get(
+                "shared_publicly", False
+            ) or user_id in agent.get("shared_with", [])
+
+            if not (is_owner or is_shared_with_user):
+                raise Exception("Unauthorized access to the agent")
+            if is_owner:
+                self.agents_collection.update_one(
+                    {"_id": ObjectId(agent_id)},
+                    {
+                        "$set": {
+                            "lastUsedAt": datetime.datetime.now(datetime.timezone.utc)
+                        }
+                    },
+                )
+            return str(agent["key"]), not is_owner, agent.get("shared_token")
+        except Exception as e:
+            logger.error(f"Error in get_agent_key: {str(e)}", exc_info=True)
+            raise
+
+    def _get_data_from_api_key(self, api_key: str) -> Dict[str, Any]:
+        data = self.agents_collection.find_one({"key": api_key})
+        if not data:
+            raise Exception("Invalid API Key, please generate a new key", 401)
+        source = data.get("source")
+        if isinstance(source, DBRef):
+            source_doc = self.db.dereference(source)
+            data["source"] = str(source_doc["_id"])
+            data["retriever"] = source_doc.get("retriever", data.get("retriever"))
+            data["chunks"] = source_doc.get("chunks", data.get("chunks"))
+        else:
+            data["source"] = None
+        return data
+
+    def _configure_agent(self):
+        """Configure the agent based on request data"""
+        agent_id = self.data.get("agent_id")
+        self.agent_key, self.is_shared_usage, self.shared_token = self._get_agent_key(
+            agent_id, self.initial_user_id
+        )
+
+        api_key = self.data.get("api_key")
+        if api_key:
+            data_key = self._get_data_from_api_key(api_key)
+            self.agent_config.update(
+                {
+                    "prompt_id": data_key.get("prompt_id", "default"),
+                    "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
+                    "user_api_key": api_key,
+                    "json_schema": data_key.get("json_schema"),
+                }
+            )
+            self.initial_user_id = data_key.get("user")
+            self.decoded_token = {"sub": data_key.get("user")}
+            if data_key.get("source"):
+                self.source = {"active_docs": data_key["source"]}
+            if data_key.get("retriever"):
+                self.retriever_config["retriever_name"] = data_key["retriever"]
+            if data_key.get("chunks") is not None:
+                self.retriever_config["chunks"] = data_key["chunks"]
+        elif self.agent_key:
+            data_key = self._get_data_from_api_key(self.agent_key)
+            self.agent_config.update(
+                {
+                    "prompt_id": data_key.get("prompt_id", "default"),
+                    "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
+                    "user_api_key": self.agent_key,
+                    "json_schema": data_key.get("json_schema"),
+                }
+            )
+            self.decoded_token = (
+                self.decoded_token
+                if self.is_shared_usage
+                else {"sub": data_key.get("user")}
+            )
+            if data_key.get("source"):
+                self.source = {"active_docs": data_key["source"]}
+            if data_key.get("retriever"):
+                self.retriever_config["retriever_name"] = data_key["retriever"]
+            if data_key.get("chunks") is not None:
+                self.retriever_config["chunks"] = data_key["chunks"]
+        else:
+            self.agent_config.update(
+                {
+                    "prompt_id": self.data.get("prompt_id", "default"),
+                    "agent_type": settings.AGENT_NAME,
+                    "user_api_key": None,
+                    "json_schema": None,
+                }
+            )
+
+    def _configure_retriever(self):
+        """Configure the retriever based on request data"""
+        self.retriever_config = {
+            "retriever_name": self.data.get("retriever", "classic"),
+            "chunks": int(self.data.get("chunks", 2)),
+            "token_limit": self.data.get("token_limit", settings.DEFAULT_MAX_HISTORY),
+        }
+
+        if "isNoneDoc" in self.data and self.data["isNoneDoc"]:
+            self.retriever_config["chunks"] = 0
+
+    def create_agent(self):
+        """Create and return the configured agent"""
+        return AgentCreator.create_agent(
+            self.agent_config["agent_type"],
+            endpoint="stream",
+            llm_name=settings.LLM_PROVIDER,
+            gpt_model=self.gpt_model,
+            api_key=settings.API_KEY,
+            user_api_key=self.agent_config["user_api_key"],
+            prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
+            chat_history=self.history,
+            decoded_token=self.decoded_token,
+            attachments=self.attachments,
+            json_schema=self.agent_config.get("json_schema"),
+        )
+
+    def create_retriever(self):
+        """Create and return the configured retriever"""
+        return RetrieverCreator.create_retriever(
+            self.retriever_config["retriever_name"],
+            source=self.source,
+            chat_history=self.history,
+            prompt=get_prompt(self.agent_config["prompt_id"], self.prompts_collection),
+            chunks=self.retriever_config["chunks"],
+            token_limit=self.retriever_config["token_limit"],
+            gpt_model=self.gpt_model,
+            user_api_key=self.agent_config["user_api_key"],
+            decoded_token=self.decoded_token,
+        )

application/api/connector/routes.py

@@ -0,0 +1,627 @@
+import datetime
+import json
+import logging
+
+
+from bson.objectid import ObjectId
+from flask import (
+    Blueprint,
+    current_app,
+    jsonify,
+    make_response,
+    request
+)
+from flask_restx import fields, Namespace, Resource
+
+
+
+
+from application.api.user.tasks import (
+    ingest_connector_task,
+)
+from application.core.mongo_db import MongoDB
+from application.core.settings import settings
+from application.api import api
+
+
+from application.utils import (
+    check_required_fields
+)
+
+
+from application.parser.connectors.connector_creator import ConnectorCreator
+
+
+
+mongo = MongoDB.get_client()
+db = mongo[settings.MONGO_DB_NAME]
+sources_collection = db["sources"]
+sessions_collection = db["connector_sessions"]
+
+connector = Blueprint("connector", __name__)
+connectors_ns = Namespace("connectors", description="Connector operations", path="/")
+api.add_namespace(connectors_ns)
+
+
+
+@connectors_ns.route("/api/connectors/upload")
+class UploadConnector(Resource):
+    @api.expect(
+        api.model(
+            "ConnectorUploadModel",
+            {
+                "user": fields.String(required=True, description="User ID"),
+                "source": fields.String(
+                    required=True, description="Source type (google_drive, github, etc.)"
+                ),
+                "name": fields.String(required=True, description="Job name"),
+                "data": fields.String(required=True, description="Configuration data"),
+                "repo_url": fields.String(description="GitHub repository URL"),
+            },
+        )
+    )
+    @api.doc(
+        description="Uploads connector source for vectorization",
+    )
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        data = request.form
+        required_fields = ["user", "source", "name", "data"]
+        missing_fields = check_required_fields(data, required_fields)
+        if missing_fields:
+            return missing_fields
+        try:
+            config = json.loads(data["data"])
+            source_data = None
+            sync_frequency = config.get("sync_frequency", "never")
+
+            if data["source"] == "github":
+                source_data = config.get("repo_url")
+            elif data["source"] in ["crawler", "url"]:
+                source_data = config.get("url")
+            elif data["source"] == "reddit":
+                source_data = config
+            elif data["source"] in ConnectorCreator.get_supported_connectors():
+                session_token = config.get("session_token")
+                if not session_token:
+                    return make_response(jsonify({
+                        "success": False,
+                        "error": f"Missing session_token in {data['source']} configuration"
+                    }), 400)
+
+                file_ids = config.get("file_ids", [])
+                if isinstance(file_ids, str):
+                    file_ids = [id.strip() for id in file_ids.split(',') if id.strip()]
+                elif not isinstance(file_ids, list):
+                    file_ids = []
+
+                folder_ids = config.get("folder_ids", [])
+                if isinstance(folder_ids, str):
+                    folder_ids = [id.strip() for id in folder_ids.split(',') if id.strip()]
+                elif not isinstance(folder_ids, list):
+                    folder_ids = []
+
+                config["file_ids"] = file_ids
+                config["folder_ids"] = folder_ids
+
+                task = ingest_connector_task.delay(
+                    job_name=data["name"],
+                    user=decoded_token.get("sub"),
+                    source_type=data["source"],
+                    session_token=session_token,
+                    file_ids=file_ids,
+                    folder_ids=folder_ids,
+                    recursive=config.get("recursive", False),
+                    retriever=config.get("retriever", "classic"),
+                    sync_frequency=sync_frequency
+                )
+                return make_response(jsonify({"success": True, "task_id": task.id}), 200)
+            task = ingest_connector_task.delay(
+                source_data=source_data,
+                job_name=data["name"],
+                user=decoded_token.get("sub"),
+                loader=data["source"],
+                sync_frequency=sync_frequency
+            )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error uploading connector source: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True, "task_id": task.id}), 200)
+
+
+@connectors_ns.route("/api/connectors/task_status")
+class ConnectorTaskStatus(Resource):
+    task_status_model = api.model(
+        "ConnectorTaskStatusModel",
+        {"task_id": fields.String(required=True, description="Task ID")},
+    )
+
+    @api.expect(task_status_model)
+    @api.doc(description="Get connector task status")
+    def get(self):
+        task_id = request.args.get("task_id")
+        if not task_id:
+            return make_response(
+                jsonify({"success": False, "message": "Task ID is required"}), 400
+            )
+        try:
+            from application.celery_init import celery
+
+            task = celery.AsyncResult(task_id)
+            task_meta = task.info
+            print(f"Task status: {task.status}")
+            if not isinstance(
+                task_meta, (dict, list, str, int, float, bool, type(None))
+            ):
+                task_meta = str(task_meta)
+        except Exception as err:
+            current_app.logger.error(f"Error getting task status: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"status": task.status, "result": task_meta}), 200)
+
+
+@connectors_ns.route("/api/connectors/sources")
+class ConnectorSources(Resource):
+    @api.doc(description="Get connector sources")
+    def get(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        user = decoded_token.get("sub")
+        try:
+            sources = sources_collection.find({"user": user, "type": "connector"}).sort("date", -1)
+            connector_sources = []
+            for source in sources:
+                connector_sources.append({
+                    "id": str(source["_id"]),
+                    "name": source.get("name"),
+                    "date": source.get("date"),
+                    "type": source.get("type"),
+                    "source": source.get("source"),
+                    "tokens": source.get("tokens", ""),
+                    "retriever": source.get("retriever", "classic"),
+                    "syncFrequency": source.get("sync_frequency", ""),
+                })
+        except Exception as err:
+            current_app.logger.error(f"Error retrieving connector sources: {err}", exc_info=True)
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify(connector_sources), 200)
+
+
+@connectors_ns.route("/api/connectors/delete")
+class DeleteConnectorSource(Resource):
+    @api.doc(
+        description="Delete a connector source",
+        params={"source_id": "The source ID to delete"},
+    )
+    def delete(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+        source_id = request.args.get("source_id")
+        if not source_id:
+            return make_response(
+                jsonify({"success": False, "message": "source_id is required"}), 400
+            )
+        try:
+            result = sources_collection.delete_one(
+                {"_id": ObjectId(source_id), "user": decoded_token.get("sub")}
+            )
+            if result.deleted_count == 0:
+                return make_response(
+                    jsonify({"success": False, "message": "Source not found"}), 404
+                )
+        except Exception as err:
+            current_app.logger.error(
+                f"Error deleting connector source: {err}", exc_info=True
+            )
+            return make_response(jsonify({"success": False}), 400)
+        return make_response(jsonify({"success": True}), 200)
+
+
+@connectors_ns.route("/api/connectors/auth")
+class ConnectorAuth(Resource):
+    @api.doc(description="Get connector OAuth authorization URL", params={"provider": "Connector provider (e.g., google_drive)"})
+    def get(self):
+        try:
+            provider = request.args.get('provider') or request.args.get('source')
+            if not provider:
+                return make_response(jsonify({"success": False, "error": "Missing provider"}), 400)
+
+            if not ConnectorCreator.is_supported(provider):
+                return make_response(jsonify({"success": False, "error": f"Unsupported provider: {provider}"}), 400)
+
+            import uuid
+            state = str(uuid.uuid4())
+            auth = ConnectorCreator.create_auth(provider)
+            authorization_url = auth.get_authorization_url(state=state)
+            return make_response(jsonify({
+                "success": True,
+                "authorization_url": authorization_url,
+                "state": state
+            }), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error generating connector auth URL: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/callback")
+class ConnectorsCallback(Resource):
+    @api.doc(description="Handle OAuth callback for external connectors")
+    def get(self):
+        """Handle OAuth callback for external connectors"""
+        try:
+            from application.parser.connectors.connector_creator import ConnectorCreator
+            from flask import request, redirect
+            import uuid
+
+            provider = request.args.get('provider', 'google_drive')
+            authorization_code = request.args.get('code')
+            _ = request.args.get('state')
+            error = request.args.get('error')
+
+            if error:
+                return redirect(f"/api/connectors/callback-status?status=error&message=OAuth+error:+{error}.+Please+try+again+and+make+sure+to+grant+all+requested+permissions,+including+offline+access.&provider={provider}")
+
+            if not authorization_code:
+                return redirect(f"/api/connectors/callback-status?status=error&message=Authorization+code+not+provided.+Please+complete+the+authorization+process+and+make+sure+to+grant+offline+access.&provider={provider}")
+
+            try:
+                auth = ConnectorCreator.create_auth(provider)
+                token_info = auth.exchange_code_for_tokens(authorization_code)
+
+                session_token = str(uuid.uuid4())
+                
+
+                try:
+                    credentials = auth.create_credentials_from_token_info(token_info)
+                    service = auth.build_drive_service(credentials)
+                    user_info = service.about().get(fields="user").execute()
+                    user_email = user_info.get('user', {}).get('emailAddress', 'Connected User')
+                except Exception as e:
+                    current_app.logger.warning(f"Could not get user info: {e}")
+                    user_email = 'Connected User'
+
+                sanitized_token_info = {
+                    "access_token": token_info.get("access_token"),
+                    "refresh_token": token_info.get("refresh_token"),
+                    "token_uri": token_info.get("token_uri"),
+                    "expiry": token_info.get("expiry"),
+                    "scopes": token_info.get("scopes")
+                }
+
+                user_id = request.decoded_token.get("sub") if getattr(request, "decoded_token", None) else None
+                sessions_collection.insert_one({
+                    "session_token": session_token,
+                    "user": user_id,
+                    "token_info": sanitized_token_info,
+                    "created_at": datetime.datetime.now(datetime.timezone.utc),
+                    "user_email": user_email,
+                    "provider": provider
+                })
+
+                # Redirect to success page with session token and user email
+                return redirect(f"/api/connectors/callback-status?status=success&message=Authentication+successful&provider={provider}&session_token={session_token}&user_email={user_email}")
+
+            except Exception as e:
+                current_app.logger.error(f"Error exchanging code for tokens: {str(e)}", exc_info=True)
+                return redirect(f"/api/connectors/callback-status?status=error&message=Failed+to+exchange+authorization+code+for+tokens:+{str(e)}&provider={provider}")
+
+        except Exception as e:
+            current_app.logger.error(f"Error handling connector callback: {e}")
+            return redirect(f"/api/connectors/callback-status?status=error&message=Failed+to+complete+connector+authentication:+{str(e)}.+Please+try+again+and+make+sure+to+grant+all+requested+permissions,+including+offline+access.")
+
+
+@connectors_ns.route("/api/connectors/refresh")
+class ConnectorRefresh(Resource):
+    @api.expect(api.model("ConnectorRefreshModel", {"provider": fields.String(required=True), "refresh_token": fields.String(required=True)}))
+    @api.doc(description="Refresh connector access token")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            refresh_token = data.get('refresh_token')
+
+            if not provider or not refresh_token:
+                return make_response(jsonify({"success": False, "error": "provider and refresh_token are required"}), 400)
+
+            auth = ConnectorCreator.create_auth(provider)
+            token_info = auth.refresh_access_token(refresh_token)
+            return make_response(jsonify({"success": True, "token_info": token_info}), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error refreshing token for connector: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/files")
+class ConnectorFiles(Resource):
+    @api.expect(api.model("ConnectorFilesModel", {"provider": fields.String(required=True), "session_token": fields.String(required=True), "folder_id": fields.String(required=False), "limit": fields.Integer(required=False), "page_token": fields.String(required=False)}))
+    @api.doc(description="List files from a connector provider (supports pagination)")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            folder_id = data.get('folder_id')
+            limit = data.get('limit', 10)
+            page_token = data.get('page_token')
+            if not provider or not session_token:
+                return make_response(jsonify({"success": False, "error": "provider and session_token are required"}), 400)
+
+
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False, "error": "Unauthorized"}), 401)
+            user = decoded_token.get('sub')
+            session = sessions_collection.find_one({"session_token": session_token, "user": user})
+            if not session:
+                return make_response(jsonify({"success": False, "error": "Invalid or unauthorized session"}), 401)
+
+            loader = ConnectorCreator.create_connector(provider, session_token)
+            documents = loader.load_data({
+                'limit': limit,
+                'list_only': True,
+                'session_token': session_token,
+                'folder_id': folder_id,
+                'page_token': page_token
+            })
+
+            files = []
+            for doc in documents[:limit]:
+                metadata = doc.extra_info
+                modified_time = metadata.get('modified_time')
+                if modified_time:
+                    date_part = modified_time.split('T')[0]
+                    time_part = modified_time.split('T')[1].split('.')[0].split('Z')[0]
+                    formatted_time = f"{date_part} {time_part}"
+                else:
+                    formatted_time = None
+
+                files.append({
+                    'id': doc.doc_id,
+                    'name': metadata.get('file_name', 'Unknown File'),
+                    'type': metadata.get('mime_type', 'unknown'),
+                    'size': metadata.get('size', None),
+                    'modifiedTime': formatted_time
+                })
+
+            next_token = getattr(loader, 'next_page_token', None)
+            has_more = bool(next_token)
+
+            return make_response(jsonify({"success": True, "files": files, "total": len(files), "next_page_token": next_token, "has_more": has_more}), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error loading connector files: {e}")
+            return make_response(jsonify({"success": False, "error": f"Failed to load files: {str(e)}"}), 500)
+
+
+@connectors_ns.route("/api/connectors/validate-session")
+class ConnectorValidateSession(Resource):
+    @api.expect(api.model("ConnectorValidateSessionModel", {"provider": fields.String(required=True), "session_token": fields.String(required=True)}))
+    @api.doc(description="Validate connector session token and return user info")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            if not provider or not session_token:
+                return make_response(jsonify({"success": False, "error": "provider and session_token are required"}), 400)
+
+
+            decoded_token = request.decoded_token
+            if not decoded_token:
+                return make_response(jsonify({"success": False, "error": "Unauthorized"}), 401)
+            user = decoded_token.get('sub')
+
+            session = sessions_collection.find_one({"session_token": session_token, "user": user})
+            if not session or "token_info" not in session:
+                return make_response(jsonify({"success": False, "error": "Invalid or expired session"}), 401)
+
+            token_info = session["token_info"]
+            auth = ConnectorCreator.create_auth(provider)
+            is_expired = auth.is_token_expired(token_info)
+
+            return make_response(jsonify({
+                "success": True,
+                "expired": is_expired,
+                "user_email": session.get('user_email', 'Connected User')
+            }), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error validating connector session: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/disconnect")
+class ConnectorDisconnect(Resource):
+    @api.expect(api.model("ConnectorDisconnectModel", {"provider": fields.String(required=True), "session_token": fields.String(required=False)}))
+    @api.doc(description="Disconnect a connector session")
+    def post(self):
+        try:
+            data = request.get_json()
+            provider = data.get('provider')
+            session_token = data.get('session_token')
+            if not provider:
+                return make_response(jsonify({"success": False, "error": "provider is required"}), 400)
+
+
+            if session_token:
+                sessions_collection.delete_one({"session_token": session_token})
+            
+            return make_response(jsonify({"success": True}), 200)
+        except Exception as e:
+            current_app.logger.error(f"Error disconnecting connector session: {e}")
+            return make_response(jsonify({"success": False, "error": str(e)}), 500)
+
+
+@connectors_ns.route("/api/connectors/sync")
+class ConnectorSync(Resource):
+    @api.expect(
+        api.model(
+            "ConnectorSyncModel",
+            {
+                "source_id": fields.String(required=True, description="Source ID to sync"),
+                "session_token": fields.String(required=True, description="Authentication token")
+            },
+        )
+    )
+    @api.doc(description="Sync connector source to check for modifications")
+    def post(self):
+        decoded_token = request.decoded_token
+        if not decoded_token:
+            return make_response(jsonify({"success": False}), 401)
+
+        try:
+            data = request.get_json()
+            source_id = data.get('source_id')
+            session_token = data.get('session_token')
+
+            if not all([source_id, session_token]):
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "source_id and session_token are required"
+                    }), 
+                    400
+                )
+            source = sources_collection.find_one({"_id": ObjectId(source_id)})
+            if not source:
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Source not found"
+                    }), 
+                    404
+                )
+
+            if source.get('user') != decoded_token.get('sub'):
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Unauthorized access to source"
+                    }), 
+                    403
+                )
+
+            remote_data = {}
+            try:
+                if source.get('remote_data'):
+                    remote_data = json.loads(source.get('remote_data'))
+            except json.JSONDecodeError:
+                current_app.logger.error(f"Invalid remote_data format for source {source_id}")
+                remote_data = {}
+
+            source_type = remote_data.get('provider')
+            if not source_type:
+                return make_response(
+                    jsonify({
+                        "success": False,
+                        "error": "Source provider not found in remote_data"
+                    }), 
+                    400
+                )
+
+            # Extract configuration from remote_data
+            file_ids = remote_data.get('file_ids', [])
+            folder_ids = remote_data.get('folder_ids', [])
+            recursive = remote_data.get('recursive', True)
+
+            # Start the sync task
+            task = ingest_connector_task.delay(
+                job_name=source.get('name'),
+                user=decoded_token.get('sub'),
+                source_type=source_type,
+                session_token=session_token,
+                file_ids=file_ids,
+                folder_ids=folder_ids,
+                recursive=recursive,
+                retriever=source.get('retriever', 'classic'),
+                operation_mode="sync",
+                doc_id=source_id,
+                sync_frequency=source.get('sync_frequency', 'never')
+            )
+
+            return make_response(
+                jsonify({
+                    "success": True,
+                    "task_id": task.id
+                }), 
+                200
+            )
+
+        except Exception as err:
+            current_app.logger.error(
+                f"Error syncing connector source: {err}",
+                exc_info=True
+            )
+            return make_response(
+                jsonify({
+                    "success": False,
+                    "error": str(err)
+                }), 
+                400
+            )
+
+
+@connectors_ns.route("/api/connectors/callback-status")
+class ConnectorCallbackStatus(Resource):
+    @api.doc(description="Return HTML page with connector authentication status")
+    def get(self):
+        """Return HTML page with connector authentication status"""
+        try:
+            status = request.args.get('status', 'error')
+            message = request.args.get('message', '')
+            provider = request.args.get('provider', 'connector')
+            session_token = request.args.get('session_token', '')
+            user_email = request.args.get('user_email', '')
+            
+            html_content = f"""
+            <!DOCTYPE html>
+            <html>
+            <head>
+                <title>{provider.replace('_', ' ').title()} Authentication</title>
+                <style>
+                    body {{ font-family: Arial, sans-serif; text-align: center; padding: 40px; }}
+                    .container {{ max-width: 600px; margin: 0 auto; }}
+                    .success {{ color: #4CAF50; }}
+                    .error {{ color: #F44336; }}
+                </style>
+                <script>
+                    window.onload = function() {{
+                        const status = "{status}";
+                        const sessionToken = "{session_token}";
+                        const userEmail = "{user_email}";
+                        
+                        if (status === "success" && window.opener) {{
+                            window.opener.postMessage({{
+                                type: '{provider}_auth_success',
+                                session_token: sessionToken,
+                                user_email: userEmail
+                            }}, '*');
+                            
+                            setTimeout(() => window.close(), 3000);
+                        }}
+                    }};
+                </script>
+            </head>
+            <body>
+                <div class="container">
+                    <h2>{provider.replace('_', ' ').title()} Authentication</h2>
+                    <div class="{status}">
+                        <p>{message}</p>
+                        {f'<p>Connected as: {user_email}</p>' if status == 'success' else ''}
+                    </div>
+                    <p><small>You can close this window. {f"Your {provider.replace('_', ' ').title()} is now connected and ready to use." if status == 'success' else ''}</small></p>
+                </div>
+            </body>
+            </html>
+            """
+            
+            return make_response(html_content, 200, {'Content-Type': 'text/html'})
+        except Exception as e:
+            current_app.logger.error(f"Error rendering callback status page: {e}")
+            return make_response("Authentication error occurred", 500, {'Content-Type': 'text/html'})
+
+

application/api/internal/routes.py

@@ -1,5 +1,6 @@
 import os
 import datetime
+import json
 from flask import Blueprint, request, send_from_directory
 from werkzeug.utils import secure_filename
 from bson.objectid import ObjectId
@@ -37,16 +38,28 @@ def upload_index_files():
     """Upload two files(index.faiss, index.pkl) to the user's folder."""
     if "user" not in request.form:
         return {"status": "no user"}
-    user = secure_filename(request.form["user"])
+    user = request.form["user"] 
     if "name" not in request.form:
         return {"status": "no name"}
-    job_name = secure_filename(request.form["name"])
-    tokens = secure_filename(request.form["tokens"])
-    retriever = secure_filename(request.form["retriever"])
-    id = secure_filename(request.form["id"])
-    type = secure_filename(request.form["type"])
+    job_name = request.form["name"]
+    tokens = request.form["tokens"]
+    retriever = request.form["retriever"]
+    id = request.form["id"]
+    type = request.form["type"]
     remote_data = request.form["remote_data"] if "remote_data" in request.form else None
-    sync_frequency = secure_filename(request.form["sync_frequency"]) if "sync_frequency" in request.form else None
+    sync_frequency = request.form["sync_frequency"] if "sync_frequency" in request.form else None
+    
+    file_path = request.form.get("file_path")
+    directory_structure = request.form.get("directory_structure")
+    
+    if directory_structure:
+        try:
+            directory_structure = json.loads(directory_structure)
+        except Exception:
+            logger.error("Error parsing directory_structure")
+            directory_structure = {}
+    else:
+        directory_structure = {}
 
     storage = StorageCreator.get_storage()
     index_base_path = f"indexes/{id}"
@@ -64,10 +77,13 @@ def upload_index_files():
         file_pkl = request.files["file_pkl"]
         if file_pkl.filename == "":
             return {"status": "no file name"}
-        
+
         # Save index files to storage
-        storage.save_file(file_faiss, f"{index_base_path}/index.faiss")
-        storage.save_file(file_pkl, f"{index_base_path}/index.pkl")
+        faiss_storage_path = f"{index_base_path}/index.faiss"
+        pkl_storage_path = f"{index_base_path}/index.pkl"
+        storage.save_file(file_faiss, faiss_storage_path)
+        storage.save_file(file_pkl, pkl_storage_path)
+
 
     existing_entry = sources_collection.find_one({"_id": ObjectId(id)})
     if existing_entry:
@@ -85,6 +101,8 @@ def upload_index_files():
                     "retriever": retriever,
                     "remote_data": remote_data,
                     "sync_frequency": sync_frequency,
+                    "file_path": file_path,
+                    "directory_structure": directory_structure,
                 }
             },
         )
@@ -102,6 +120,8 @@ def upload_index_files():
                 "retriever": retriever,
                 "remote_data": remote_data,
                 "sync_frequency": sync_frequency,
+                "file_path": file_path,
+                "directory_structure": directory_structure,
             }
         )
     return {"status": "ok"}

application/api/user/tasks.py

@@ -11,8 +11,8 @@ from application.worker import (
 
 
 @celery.task(bind=True)
-def ingest(self, directory, formats, name_job, filename, user):
-    resp = ingest_worker(self, directory, formats, name_job, filename, user)
+def ingest(self, directory, formats, job_name, user, file_path, filename):
+    resp = ingest_worker(self, directory, formats, job_name, file_path, filename, user)
     return resp
 
 
@@ -22,6 +22,13 @@ def ingest_remote(self, source_data, job_name, user, loader):
     return resp
 
 
+@celery.task(bind=True)
+def reingest_source_task(self, source_id, user):
+    from application.worker import reingest_source_worker
+    resp = reingest_source_worker(self, source_id, user)
+    return resp
+
+
 @celery.task(bind=True)
 def schedule_syncs(self, frequency):
     resp = sync_worker(self, frequency)
@@ -40,6 +47,39 @@ def process_agent_webhook(self, agent_id, payload):
     return resp
 
 
+@celery.task(bind=True)
+def ingest_connector_task(
+    self,
+    job_name,
+    user,
+    source_type,
+    session_token=None,
+    file_ids=None,
+    folder_ids=None,
+    recursive=True,
+    retriever="classic",
+    operation_mode="upload",
+    doc_id=None,
+    sync_frequency="never"
+):
+    from application.worker import ingest_connector
+    resp = ingest_connector(
+        self,
+        job_name,
+        user,
+        source_type,
+        session_token=session_token,
+        file_ids=file_ids,
+        folder_ids=folder_ids,
+        recursive=recursive,
+        retriever=retriever,
+        operation_mode=operation_mode,
+        doc_id=doc_id,
+        sync_frequency=sync_frequency
+    )
+    return resp
+
+
 @celery.on_after_configure.connect
 def setup_periodic_tasks(sender, **kwargs):
     sender.add_periodic_task(

application/app.py

@@ -12,25 +12,26 @@ from application.core.logging_config import setup_logging
 
 setup_logging()
 
-from application.api.answer.routes import answer  # noqa: E402
+from application.api import api  # noqa: E402
+from application.api.answer import answer  # noqa: E402
 from application.api.internal.routes import internal  # noqa: E402
 from application.api.user.routes import user  # noqa: E402
+from application.api.connector.routes import connector  # noqa: E402
 from application.celery_init import celery  # noqa: E402
 from application.core.settings import settings  # noqa: E402
-from application.extensions import api  # noqa: E402
 
 
 if platform.system() == "Windows":
     import pathlib
 
     pathlib.PosixPath = pathlib.WindowsPath
-
 dotenv.load_dotenv()
 
 app = Flask(__name__)
 app.register_blueprint(user)
 app.register_blueprint(answer)
 app.register_blueprint(internal)
+app.register_blueprint(connector)
 app.config.update(
     UPLOAD_FOLDER="inputs",
     CELERY_BROKER_URL=settings.CELERY_BROKER_URL,
@@ -52,7 +53,6 @@ if settings.AUTH_TYPE in ("simple_jwt", "session_jwt") and not settings.JWT_SECR
         settings.JWT_SECRET_KEY = new_key
     except Exception as e:
         raise RuntimeError(f"Failed to setup JWT_SECRET_KEY: {e}")
-
 SIMPLE_JWT_TOKEN = None
 if settings.AUTH_TYPE == "simple_jwt":
     payload = {"sub": "local"}
@@ -92,7 +92,6 @@ def generate_token():
 def authenticate_request():
     if request.method == "OPTIONS":
         return "", 200
-
     decoded_token = handle_auth(request)
     if not decoded_token:
         request.decoded_token = None

application/core/settings.py

@@ -10,19 +10,19 @@ current_dir = os.path.dirname(
 
 
 class Settings(BaseSettings):
-    AUTH_TYPE: Optional[str] = None
-    LLM_NAME: str = "docsgpt"
-    MODEL_NAME: Optional[str] = (
-        None  # if LLM_NAME is openai, MODEL_NAME can be gpt-4 or gpt-3.5-turbo
+    AUTH_TYPE: Optional[str] = None  # simple_jwt, session_jwt, or None
+    LLM_PROVIDER: str = "docsgpt"
+    LLM_NAME: Optional[str] = (
+        None  # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo
     )
     EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
     CELERY_BROKER_URL: str = "redis://localhost:6379/0"
     CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
     MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
     MONGO_DB_NAME: str = "docsgpt"
-    MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
+    LLM_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
     DEFAULT_MAX_HISTORY: int = 150
-    MODEL_TOKEN_LIMITS: dict = {
+    LLM_TOKEN_LIMITS: dict = {
         "gpt-4o-mini": 128000,
         "gpt-3.5-turbo": 4096,
         "claude-2": 1e5,
@@ -30,11 +30,22 @@ class Settings(BaseSettings):
     }
     UPLOAD_FOLDER: str = "inputs"
     PARSE_PDF_AS_IMAGE: bool = False
+    PARSE_IMAGE_REMOTE: bool = False
     VECTOR_STORE: str = (
         "faiss"  #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
     )
-    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"]  # also brave_search
+    RETRIEVERS_ENABLED: list = ["classic_rag"]
     AGENT_NAME: str = "classic"
+    FALLBACK_LLM_PROVIDER: Optional[str] = None  # provider for fallback llm
+    FALLBACK_LLM_NAME: Optional[str] = None  # model name for fallback llm
+    FALLBACK_LLM_API_KEY: Optional[str] = None  # api key for fallback llm
+
+    # Google Drive integration
+    GOOGLE_CLIENT_ID: Optional[str] = None # Replace with your actual Google OAuth client ID
+    GOOGLE_CLIENT_SECRET: Optional[str] = None# Replace with your actual Google OAuth client secret
+    CONNECTOR_REDIRECT_BASE_URI: Optional[str] = "http://127.0.0.1:7091/api/connectors/callback" 
+    ##append ?provider={provider_name} in your Provider console like http://127.0.0.1:7091/api/connectors/callback?provider=google_drive
+
 
     # LLM Cache
     CACHE_REDIS_URL: str = "redis://localhost:6379/2"
@@ -85,7 +96,9 @@ class Settings(BaseSettings):
     QDRANT_HOST: Optional[str] = None
     QDRANT_PATH: Optional[str] = None
     QDRANT_DISTANCE_FUNC: str = "Cosine"
-
+    
+    # PGVector vectorstore config
+    PGVECTOR_CONNECTION_STRING: Optional[str] = None
     # Milvus vectorstore config
     MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
     MILVUS_URI: Optional[str] = "./milvus_local.db"  # milvus lite version as default
@@ -96,11 +109,10 @@ class Settings(BaseSettings):
     LANCEDB_TABLE_NAME: Optional[str] = (
         "docsgpts"  # Name of the table to use for storing vectors
     )
-    BRAVE_SEARCH_API_KEY: Optional[str] = None
 
     FLASK_DEBUG_MODE: bool = False
-    STORAGE_TYPE: str = "local"  # local or s3 
-
+    STORAGE_TYPE: str = "local"  # local or s3
+    URL_STRATEGY: str = "backend"  # backend or s3
 
     JWT_SECRET_KEY: str = ""
 

application/extensions.py

@@ -1,7 +0,0 @@
-from flask_restx import Api
-
-api = Api(
-    version="1.0",
-    title="DocsGPT API",
-    description="API for DocsGPT",
-)

application/llm/base.py

@@ -1,53 +1,117 @@
+import logging
 from abc import ABC, abstractmethod
 
 from application.cache import gen_cache, stream_cache
+
+from application.core.settings import settings
 from application.usage import gen_token_usage, stream_token_usage
 
+logger = logging.getLogger(__name__)
+
 
 class BaseLLM(ABC):
-    def __init__(self, decoded_token=None):
+    def __init__(
+        self,
+        decoded_token=None,
+    ):
         self.decoded_token = decoded_token
         self.token_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        self.fallback_provider = settings.FALLBACK_LLM_PROVIDER
+        self.fallback_model_name = settings.FALLBACK_LLM_NAME
+        self.fallback_llm_api_key = settings.FALLBACK_LLM_API_KEY
+        self._fallback_llm = None
 
-    def _apply_decorator(self, method, decorators, *args, **kwargs):
-        for decorator in decorators:
-            method = decorator(method)
-        return method(self, *args, **kwargs)
+    @property
+    def fallback_llm(self):
+        """Lazy-loaded fallback LLM instance."""
+        if (
+            self._fallback_llm is None
+            and self.fallback_provider
+            and self.fallback_model_name
+        ):
+            try:
+                from application.llm.llm_creator import LLMCreator
+
+                self._fallback_llm = LLMCreator.create_llm(
+                    self.fallback_provider,
+                    self.fallback_llm_api_key,
+                    None,
+                    self.decoded_token,
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to initialize fallback LLM: {str(e)}", exc_info=True
+                )
+        return self._fallback_llm
+
+    def _execute_with_fallback(
+        self, method_name: str, decorators: list, *args, **kwargs
+    ):
+        """
+        Unified method execution with fallback support.
+
+        Args:
+            method_name: Name of the raw method ('_raw_gen' or '_raw_gen_stream')
+            decorators: List of decorators to apply
+            *args: Positional arguments
+            **kwargs: Keyword arguments
+        """
+
+        def decorated_method():
+            method = getattr(self, method_name)
+            for decorator in decorators:
+                method = decorator(method)
+            return method(self, *args, **kwargs)
+
+        try:
+            return decorated_method()
+        except Exception as e:
+            if not self.fallback_llm:
+                logger.error(f"Primary LLM failed and no fallback available: {str(e)}")
+                raise
+            logger.warning(
+                f"Falling back to {self.fallback_provider}/{self.fallback_model_name}. Error: {str(e)}"
+            )
+
+            fallback_method = getattr(
+                self.fallback_llm, method_name.replace("_raw_", "")
+            )
+            return fallback_method(*args, **kwargs)
+
+    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
+        decorators = [gen_token_usage, gen_cache]
+        return self._execute_with_fallback(
+            "_raw_gen",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
+
+    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
+        decorators = [stream_cache, stream_token_usage]
+        return self._execute_with_fallback(
+            "_raw_gen_stream",
+            decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs,
+        )
 
     @abstractmethod
     def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
         pass
 
-    def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
-        decorators = [gen_token_usage, gen_cache]
-        return self._apply_decorator(
-            self._raw_gen,
-            decorators=decorators,
-            model=model,
-            messages=messages,
-            stream=stream,
-            tools=tools,
-            *args,
-            **kwargs
-        )
-
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
-    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
-        decorators = [stream_cache, stream_token_usage]
-        return self._apply_decorator(
-            self._raw_gen_stream,
-            decorators=decorators,
-            model=model,
-            messages=messages,
-            stream=stream,
-            tools=tools,
-            *args,
-            **kwargs
-        )
-
     def supports_tools(self):
         return hasattr(self, "_supports_tools") and callable(
             getattr(self, "_supports_tools")
@@ -55,12 +119,26 @@ class BaseLLM(ABC):
 
     def _supports_tools(self):
         raise NotImplementedError("Subclass must implement _supports_tools method")
-        
+
+    def supports_structured_output(self):
+        """Check if the LLM supports structured output/JSON schema enforcement"""
+        return hasattr(self, "_supports_structured_output") and callable(
+            getattr(self, "_supports_structured_output")
+        )
+
+    def _supports_structured_output(self):
+        return False
+
+    def prepare_structured_output_format(self, json_schema):
+        """Prepare structured output format specific to the LLM provider"""
+        _ = json_schema
+        return None
+
     def get_supported_attachment_types(self):
         """
         Return a list of MIME types supported by this LLM for file uploads.
-        
+
         Returns:
             list: List of supported MIME types
         """
-        return []  # Default: no attachments supported
+        return []

application/llm/google_ai.py

@@ -1,11 +1,13 @@
+import json
+import logging
+
 from google import genai
 from google.genai import types
-import logging
-import json
+
+from application.core.settings import settings
 
 from application.llm.base import BaseLLM
 from application.storage.storage_creator import StorageCreator
-from application.core.settings import settings
 
 
 class GoogleLLM(BaseLLM):
@@ -24,12 +26,12 @@ class GoogleLLM(BaseLLM):
             list: List of supported MIME types
         """
         return [
-            'application/pdf',
-            'image/png',
-            'image/jpeg',
-            'image/jpg',
-            'image/webp',
-            'image/gif'
+            "application/pdf",
+            "image/png",
+            "image/jpeg",
+            "image/jpg",
+            "image/webp",
+            "image/gif",
         ]
 
     def prepare_messages_with_attachments(self, messages, attachments=None):
@@ -70,26 +72,30 @@ class GoogleLLM(BaseLLM):
 
         files = []
         for attachment in attachments:
-            mime_type = attachment.get('mime_type')
+            mime_type = attachment.get("mime_type")
 
             if mime_type in self.get_supported_attachment_types():
                 try:
                     file_uri = self._upload_file_to_google(attachment)
-                    logging.info(f"GoogleLLM: Successfully uploaded file, got URI: {file_uri}")
+                    logging.info(
+                        f"GoogleLLM: Successfully uploaded file, got URI: {file_uri}"
+                    )
                     files.append({"file_uri": file_uri, "mime_type": mime_type})
                 except Exception as e:
-                    logging.error(f"GoogleLLM: Error uploading file: {e}", exc_info=True)
-                    if 'content' in attachment:
-                        prepared_messages[user_message_index]["content"].append({
-                            "type": "text",
-                            "text": f"[File could not be processed: {attachment.get('path', 'unknown')}]"
-                        })
+                    logging.error(
+                        f"GoogleLLM: Error uploading file: {e}", exc_info=True
+                    )
+                    if "content" in attachment:
+                        prepared_messages[user_message_index]["content"].append(
+                            {
+                                "type": "text",
+                                "text": f"[File could not be processed: {attachment.get('path', 'unknown')}]",
+                            }
+                        )
 
         if files:
             logging.info(f"GoogleLLM: Adding {len(files)} files to message")
-            prepared_messages[user_message_index]["content"].append({
-                "files": files
-            })
+            prepared_messages[user_message_index]["content"].append({"files": files})
 
         return prepared_messages
 
@@ -103,10 +109,10 @@ class GoogleLLM(BaseLLM):
         Returns:
             str: Google AI file URI for the uploaded file.
         """
-        if 'google_file_uri' in attachment:
-            return attachment['google_file_uri']
+        if "google_file_uri" in attachment:
+            return attachment["google_file_uri"]
 
-        file_path = attachment.get('path')
+        file_path = attachment.get("path")
         if not file_path:
             raise ValueError("No file path provided in attachment")
 
@@ -116,17 +122,19 @@ class GoogleLLM(BaseLLM):
         try:
             file_uri = self.storage.process_file(
                 file_path,
-                lambda local_path, **kwargs: self.client.files.upload(file=local_path).uri
+                lambda local_path, **kwargs: self.client.files.upload(
+                    file=local_path
+                ).uri,
             )
 
             from application.core.mongo_db import MongoDB
+
             mongo = MongoDB.get_client()
             db = mongo[settings.MONGO_DB_NAME]
             attachments_collection = db["attachments"]
-            if '_id' in attachment:
+            if "_id" in attachment:
                 attachments_collection.update_one(
-                    {"_id": attachment['_id']},
-                    {"$set": {"google_file_uri": file_uri}}
+                    {"_id": attachment["_id"]}, {"$set": {"google_file_uri": file_uri}}
                 )
 
             return file_uri
@@ -166,13 +174,13 @@ class GoogleLLM(BaseLLM):
                                 )
                             )
                         elif "files" in item:
-                                for file_data in item["files"]:
-                                    parts.append(
-                                        types.Part.from_uri(
-                                            file_uri=file_data["file_uri"],
-                                            mime_type=file_data["mime_type"]
-                                        )
+                            for file_data in item["files"]:
+                                parts.append(
+                                    types.Part.from_uri(
+                                        file_uri=file_data["file_uri"],
+                                        mime_type=file_data["mime_type"],
                                     )
+                                )
                         else:
                             raise ValueError(
                                 f"Unexpected content dictionary format:{item}"
@@ -231,6 +239,7 @@ class GoogleLLM(BaseLLM):
         stream=False,
         tools=None,
         formatting="openai",
+        response_schema=None,
         **kwargs,
     ):
         client = genai.Client(api_key=self.api_key)
@@ -244,16 +253,21 @@ class GoogleLLM(BaseLLM):
         if tools:
             cleaned_tools = self._clean_tools_format(tools)
             config.tools = cleaned_tools
-            response = client.models.generate_content(
-                model=model,
-                contents=messages,
-                config=config,
-            )
+
+        # Add response schema for structured output if provided
+        if response_schema:
+            config.response_schema = response_schema
+            config.response_mime_type = "application/json"
+
+        response = client.models.generate_content(
+            model=model,
+            contents=messages,
+            config=config,
+        )
+
+        if tools:
             return response
         else:
-            response = client.models.generate_content(
-                model=model, contents=messages, config=config
-            )
             return response.text
 
     def _raw_gen_stream(
@@ -264,6 +278,7 @@ class GoogleLLM(BaseLLM):
         stream=True,
         tools=None,
         formatting="openai",
+        response_schema=None,
         **kwargs,
     ):
         client = genai.Client(api_key=self.api_key)
@@ -278,17 +293,24 @@ class GoogleLLM(BaseLLM):
             cleaned_tools = self._clean_tools_format(tools)
             config.tools = cleaned_tools
 
+        # Add response schema for structured output if provided
+        if response_schema:
+            config.response_schema = response_schema
+            config.response_mime_type = "application/json"
+
         # Check if we have both tools and file attachments
         has_attachments = False
         for message in messages:
             for part in message.parts:
-                if hasattr(part, 'file_data') and part.file_data is not None:
+                if hasattr(part, "file_data") and part.file_data is not None:
                     has_attachments = True
                     break
             if has_attachments:
                 break
 
-        logging.info(f"GoogleLLM: Starting stream generation. Model: {model}, Messages: {json.dumps(messages, default=str)}, Has attachments: {has_attachments}")
+        logging.info(
+            f"GoogleLLM: Starting stream generation. Model: {model}, Messages: {json.dumps(messages, default=str)}, Has attachments: {has_attachments}"
+        )
 
         response = client.models.generate_content_stream(
             model=model,
@@ -296,7 +318,6 @@ class GoogleLLM(BaseLLM):
             config=config,
         )
 
-
         for chunk in response:
             if hasattr(chunk, "candidates") and chunk.candidates:
                 for candidate in chunk.candidates:
@@ -311,3 +332,75 @@ class GoogleLLM(BaseLLM):
 
     def _supports_tools(self):
         return True
+
+    def _supports_structured_output(self):
+        return True
+
+    def prepare_structured_output_format(self, json_schema):
+        if not json_schema:
+            return None
+
+        type_map = {
+            "object": "OBJECT",
+            "array": "ARRAY",
+            "string": "STRING",
+            "integer": "INTEGER",
+            "number": "NUMBER",
+            "boolean": "BOOLEAN",
+        }
+
+        def convert(schema):
+            if not isinstance(schema, dict):
+                return schema
+
+            result = {}
+            schema_type = schema.get("type")
+            if schema_type:
+                result["type"] = type_map.get(schema_type.lower(), schema_type.upper())
+
+            for key in [
+                "description",
+                "nullable",
+                "enum",
+                "minItems",
+                "maxItems",
+                "required",
+                "propertyOrdering",
+            ]:
+                if key in schema:
+                    result[key] = schema[key]
+
+            if "format" in schema:
+                format_value = schema["format"]
+                if schema_type == "string":
+                    if format_value == "date":
+                        result["format"] = "date-time"
+                    elif format_value in ["enum", "date-time"]:
+                        result["format"] = format_value
+                else:
+                    result["format"] = format_value
+
+            if "properties" in schema:
+                result["properties"] = {
+                    k: convert(v) for k, v in schema["properties"].items()
+                }
+                if "propertyOrdering" not in result and result.get("type") == "OBJECT":
+                    result["propertyOrdering"] = list(result["properties"].keys())
+
+            if "items" in schema:
+                result["items"] = convert(schema["items"])
+
+            for field in ["anyOf", "oneOf", "allOf"]:
+                if field in schema:
+                    result[field] = [convert(s) for s in schema[field]]
+
+            return result
+
+        try:
+            return convert(json_schema)
+        except Exception as e:
+            logging.error(
+                f"Error preparing structured output format for Google: {e}",
+                exc_info=True,
+            )
+            return None

application/llm/handlers/base.py

@@ -0,0 +1,335 @@
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, Generator, List, Optional, Union
+
+from application.logging import build_stack_data
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolCall:
+    """Represents a tool/function call from the LLM."""
+
+    id: str
+    name: str
+    arguments: Union[str, Dict]
+    index: Optional[int] = None
+
+    @classmethod
+    def from_dict(cls, data: Dict) -> "ToolCall":
+        """Create ToolCall from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            name=data.get("name", ""),
+            arguments=data.get("arguments", {}),
+            index=data.get("index"),
+        )
+
+
+@dataclass
+class LLMResponse:
+    """Represents a response from the LLM."""
+
+    content: str
+    tool_calls: List[ToolCall]
+    finish_reason: str
+    raw_response: Any
+
+    @property
+    def requires_tool_call(self) -> bool:
+        """Check if the response requires tool calls."""
+        return bool(self.tool_calls) and self.finish_reason == "tool_calls"
+
+
+class LLMHandler(ABC):
+    """Abstract base class for LLM handlers."""
+
+    def __init__(self):
+        self.llm_calls = []
+        self.tool_calls = []
+
+    @abstractmethod
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse raw LLM response into standardized format."""
+        pass
+
+    @abstractmethod
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create a tool result message for the conversation history."""
+        pass
+
+    @abstractmethod
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through streaming response chunks."""
+        pass
+
+    def process_message_flow(
+        self,
+        agent,
+        initial_response,
+        tools_dict: Dict,
+        messages: List[Dict],
+        attachments: Optional[List] = None,
+        stream: bool = False,
+    ) -> Union[str, Generator]:
+        """
+        Main orchestration method for processing LLM message flow.
+
+        Args:
+            agent: The agent instance
+            initial_response: Initial LLM response
+            tools_dict: Dictionary of available tools
+            messages: Conversation history
+            attachments: Optional attachments
+            stream: Whether to use streaming
+
+        Returns:
+            Final response or generator for streaming
+        """
+        messages = self.prepare_messages(agent, messages, attachments)
+
+        if stream:
+            return self.handle_streaming(agent, initial_response, tools_dict, messages)
+        else:
+            return self.handle_non_streaming(
+                agent, initial_response, tools_dict, messages
+            )
+
+    def prepare_messages(
+        self, agent, messages: List[Dict], attachments: Optional[List] = None
+    ) -> List[Dict]:
+        """
+        Prepare messages with attachments and provider-specific formatting.
+
+        Args:
+            agent: The agent instance
+            messages: Original messages
+            attachments: List of attachments
+
+        Returns:
+            Prepared messages list
+        """
+        if not attachments:
+            return messages
+        logger.info(f"Preparing messages with {len(attachments)} attachments")
+        supported_types = agent.llm.get_supported_attachment_types()
+
+        supported_attachments = [
+            a for a in attachments if a.get("mime_type") in supported_types
+        ]
+        unsupported_attachments = [
+            a for a in attachments if a.get("mime_type") not in supported_types
+        ]
+
+        # Process supported attachments with the LLM's custom method
+
+        if supported_attachments:
+            logger.info(
+                f"Processing {len(supported_attachments)} supported attachments"
+            )
+            messages = agent.llm.prepare_messages_with_attachments(
+                messages, supported_attachments
+            )
+        # Process unsupported attachments with default method
+
+        if unsupported_attachments:
+            logger.info(
+                f"Processing {len(unsupported_attachments)} unsupported attachments"
+            )
+            messages = self._append_unsupported_attachments(
+                messages, unsupported_attachments
+            )
+        return messages
+
+    def _append_unsupported_attachments(
+        self, messages: List[Dict], attachments: List[Dict]
+    ) -> List[Dict]:
+        """
+        Default method to append unsupported attachment content to system prompt.
+
+        Args:
+            messages: Current messages
+            attachments: List of unsupported attachments
+
+        Returns:
+            Updated messages list
+        """
+        prepared_messages = messages.copy()
+        attachment_texts = []
+
+        for attachment in attachments:
+            logger.info(f"Adding attachment {attachment.get('id')} to context")
+            if "content" in attachment:
+                attachment_texts.append(
+                    f"Attached file content:\n\n{attachment['content']}"
+                )
+        if attachment_texts:
+            combined_text = "\n\n".join(attachment_texts)
+
+            system_msg = next(
+                (msg for msg in prepared_messages if msg.get("role") == "system"),
+                {"role": "system", "content": ""},
+            )
+
+            if system_msg not in prepared_messages:
+                prepared_messages.insert(0, system_msg)
+            system_msg["content"] += f"\n\n{combined_text}"
+        return prepared_messages
+
+    def handle_tool_calls(
+        self, agent, tool_calls: List[ToolCall], tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Execute tool calls and update conversation history.
+
+        Args:
+            agent: The agent instance
+            tool_calls: List of tool calls to execute
+            tools_dict: Available tools dictionary
+            messages: Current conversation history
+
+        Returns:
+            Updated messages list
+        """
+        updated_messages = messages.copy()
+
+        for call in tool_calls:
+            try:
+                self.tool_calls.append(call)
+                tool_executor_gen = agent._execute_tool_action(tools_dict, call)
+                while True:
+                    try:
+                        yield next(tool_executor_gen)
+                    except StopIteration as e:
+                        tool_response, call_id = e.value
+                        break
+
+                updated_messages.append(
+                    {
+                        "role": "assistant",
+                        "content": [
+                            {
+                                "function_call": {
+                                    "name": call.name,
+                                    "args": call.arguments,
+                                    "call_id": call_id,
+                                }
+                            }
+                        ],
+                    }
+                )
+
+                updated_messages.append(self.create_tool_message(call, tool_response))
+
+            except Exception as e:
+                logger.error(f"Error executing tool: {str(e)}", exc_info=True)
+                updated_messages.append(
+                    {
+                        "role": "tool",
+                        "content": f"Error executing tool: {str(e)}",
+                        "tool_call_id": call.id,
+                    }
+                )
+
+        return updated_messages
+
+    def handle_non_streaming(
+        self, agent, response: Any, tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Handle non-streaming response flow.
+
+        Args:
+            agent: The agent instance
+            response: Current LLM response
+            tools_dict: Available tools dictionary
+            messages: Conversation history
+
+        Returns:
+            Final response after processing all tool calls
+        """
+        parsed = self.parse_response(response)
+        self.llm_calls.append(build_stack_data(agent.llm))
+
+        while parsed.requires_tool_call:
+            tool_handler_gen = self.handle_tool_calls(
+                agent, parsed.tool_calls, tools_dict, messages
+            )
+            while True:
+                try:
+                    yield next(tool_handler_gen)
+                except StopIteration as e:
+                    messages = e.value
+                    break
+
+            response = agent.llm.gen(
+                model=agent.gpt_model, messages=messages, tools=agent.tools
+            )
+            parsed = self.parse_response(response)
+            self.llm_calls.append(build_stack_data(agent.llm))
+
+        return parsed.content
+
+    def handle_streaming(
+        self, agent, response: Any, tools_dict: Dict, messages: List[Dict]
+    ) -> Generator:
+        """
+        Handle streaming response flow.
+
+        Args:
+            agent: The agent instance
+            response: Current LLM response
+            tools_dict: Available tools dictionary
+            messages: Conversation history
+
+        Yields:
+            Streaming response chunks
+        """
+        buffer = ""
+        tool_calls = {}
+
+        for chunk in self._iterate_stream(response):
+            if isinstance(chunk, str):
+                yield chunk
+                continue
+            parsed = self.parse_response(chunk)
+
+            if parsed.tool_calls:
+                for call in parsed.tool_calls:
+                    if call.index not in tool_calls:
+                        tool_calls[call.index] = call
+                    else:
+                        existing = tool_calls[call.index]
+                        if call.id:
+                            existing.id = call.id
+                        if call.name:
+                            existing.name = call.name
+                        if call.arguments:
+                            existing.arguments += call.arguments
+            if parsed.finish_reason == "tool_calls":
+                tool_handler_gen = self.handle_tool_calls(
+                    agent, list(tool_calls.values()), tools_dict, messages
+                )
+                while True:
+                    try:
+                        yield next(tool_handler_gen)
+                    except StopIteration as e:
+                        messages = e.value
+                        break
+                tool_calls = {}
+
+                response = agent.llm.gen_stream(
+                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                )
+                self.llm_calls.append(build_stack_data(agent.llm))
+
+                yield from self.handle_streaming(agent, response, tools_dict, messages)
+                return
+            if parsed.content:
+                buffer += parsed.content
+                yield buffer
+                buffer = ""
+            if parsed.finish_reason == "stop":
+                return

application/llm/handlers/google.py

@@ -0,0 +1,78 @@
+import uuid
+from typing import Any, Dict, Generator
+
+from application.llm.handlers.base import LLMHandler, LLMResponse, ToolCall
+
+
+class GoogleLLMHandler(LLMHandler):
+    """Handler for Google's GenAI API."""
+
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse Google response into standardized format."""
+
+        if isinstance(response, str):
+            return LLMResponse(
+                content=response,
+                tool_calls=[],
+                finish_reason="stop",
+                raw_response=response,
+            )
+
+        if hasattr(response, "candidates"):
+            parts = response.candidates[0].content.parts if response.candidates else []
+            tool_calls = [
+                ToolCall(
+                    id=str(uuid.uuid4()),
+                    name=part.function_call.name,
+                    arguments=part.function_call.args,
+                )
+                for part in parts
+                if hasattr(part, "function_call") and part.function_call is not None
+            ]
+
+            content = " ".join(
+                part.text
+                for part in parts
+                if hasattr(part, "text") and part.text is not None
+            )
+            return LLMResponse(
+                content=content,
+                tool_calls=tool_calls,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                raw_response=response,
+            )
+
+        else:
+            tool_calls = []
+            if hasattr(response, "function_call"):
+                tool_calls.append(
+                    ToolCall(
+                        id=str(uuid.uuid4()),
+                        name=response.function_call.name,
+                        arguments=response.function_call.args,
+                    )
+                )
+            return LLMResponse(
+                content=response.text if hasattr(response, "text") else "",
+                tool_calls=tool_calls,
+                finish_reason="tool_calls" if tool_calls else "stop",
+                raw_response=response,
+            )
+
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create Google-style tool message."""
+        from google.genai import types
+
+        return {
+            "role": "tool",
+            "content": [
+                types.Part.from_function_response(
+                    name=tool_call.name, response={"result": result}
+                ).to_json_dict()
+            ],
+        }
+
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through Google streaming response."""
+        for chunk in response:
+            yield chunk

application/llm/handlers/handler_creator.py

@@ -0,0 +1,18 @@
+from application.llm.handlers.base import LLMHandler
+from application.llm.handlers.google import GoogleLLMHandler
+from application.llm.handlers.openai import OpenAILLMHandler
+
+
+class LLMHandlerCreator:
+    handlers = {
+        "openai": OpenAILLMHandler,
+        "google": GoogleLLMHandler,
+        "default": OpenAILLMHandler,
+    }
+
+    @classmethod
+    def create_handler(cls, llm_type: str, *args, **kwargs) -> LLMHandler:
+        handler_class = cls.handlers.get(llm_type.lower())
+        if not handler_class:
+            handler_class = OpenAILLMHandler
+        return handler_class(*args, **kwargs)

application/llm/handlers/openai.py

@@ -0,0 +1,57 @@
+from typing import Any, Dict, Generator
+
+from application.llm.handlers.base import LLMHandler, LLMResponse, ToolCall
+
+
+class OpenAILLMHandler(LLMHandler):
+    """Handler for OpenAI API."""
+
+    def parse_response(self, response: Any) -> LLMResponse:
+        """Parse OpenAI response into standardized format."""
+        if isinstance(response, str):
+            return LLMResponse(
+                content=response,
+                tool_calls=[],
+                finish_reason="stop",
+                raw_response=response,
+            )
+
+        message = getattr(response, "message", None) or getattr(response, "delta", None)
+
+        tool_calls = []
+        if hasattr(message, "tool_calls"):
+            tool_calls = [
+                ToolCall(
+                    id=getattr(tc, "id", ""),
+                    name=getattr(tc.function, "name", ""),
+                    arguments=getattr(tc.function, "arguments", ""),
+                    index=getattr(tc, "index", None),
+                )
+                for tc in message.tool_calls or []
+            ]
+        return LLMResponse(
+            content=getattr(message, "content", ""),
+            tool_calls=tool_calls,
+            finish_reason=getattr(response, "finish_reason", ""),
+            raw_response=response,
+        )
+
+    def create_tool_message(self, tool_call: ToolCall, result: Any) -> Dict:
+        """Create OpenAI-style tool message."""
+        return {
+            "role": "tool",
+            "content": [
+                {
+                    "function_response": {
+                        "name": tool_call.name,
+                        "response": {"result": result},
+                        "call_id": tool_call.id,
+                    }
+                }
+            ],
+        }
+
+    def _iterate_stream(self, response: Any) -> Generator:
+        """Iterate through OpenAI streaming response."""
+        for chunk in response:
+            yield chunk

application/llm/llama_cpp.py

@@ -2,6 +2,7 @@ from application.llm.base import BaseLLM
 from application.core.settings import settings
 import threading
 
+
 class LlamaSingleton:
     _instances = {}
     _lock = threading.Lock()  # Add a lock for thread synchronization
@@ -29,7 +30,7 @@ class LlamaCpp(BaseLLM):
         self,
         api_key=None,
         user_api_key=None,
-        llm_name=settings.MODEL_PATH,
+        llm_name=settings.LLM_PATH,
         *args,
         **kwargs,
     ):
@@ -42,14 +43,18 @@ class LlamaCpp(BaseLLM):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False)
+        result = LlamaSingleton.query_model(
+            self.llama, prompt, max_tokens=150, echo=False
+        )
         return result["choices"][0]["text"].split("### Answer \n")[-1]
 
     def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
-        result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False, stream=stream)
+        result = LlamaSingleton.query_model(
+            self.llama, prompt, max_tokens=150, echo=False, stream=stream
+        )
         for item in result:
             for choice in item["choices"]:
-                yield choice["text"]
+                yield choice["text"]

application/llm/openai.py

@@ -1,5 +1,5 @@
-import json
 import base64
+import json
 import logging
 
 from application.core.settings import settings
@@ -13,7 +13,10 @@ class OpenAILLM(BaseLLM):
         from openai import OpenAI
 
         super().__init__(*args, **kwargs)
-        if isinstance(settings.OPENAI_BASE_URL, str) and settings.OPENAI_BASE_URL.strip():
+        if (
+            isinstance(settings.OPENAI_BASE_URL, str)
+            and settings.OPENAI_BASE_URL.strip()
+        ):
             self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
         else:
             DEFAULT_OPENAI_API_BASE = "https://api.openai.com/v1"
@@ -73,14 +76,30 @@ class OpenAILLM(BaseLLM):
                         elif isinstance(item, dict):
                             content_parts = []
                             if "text" in item:
-                                content_parts.append({"type": "text", "text": item["text"]})
-                            elif "type" in item and item["type"] == "text" and "text" in item:
+                                content_parts.append(
+                                    {"type": "text", "text": item["text"]}
+                                )
+                            elif (
+                                "type" in item
+                                and item["type"] == "text"
+                                and "text" in item
+                            ):
                                 content_parts.append(item)
-                            elif "type" in item and item["type"] == "file" and "file" in item:
+                            elif (
+                                "type" in item
+                                and item["type"] == "file"
+                                and "file" in item
+                            ):
                                 content_parts.append(item)
-                            elif "type" in item and item["type"] == "image_url" and "image_url" in item:
+                            elif (
+                                "type" in item
+                                and item["type"] == "image_url"
+                                and "image_url" in item
+                            ):
                                 content_parts.append(item)
-                            cleaned_messages.append({"role": role, "content": content_parts})
+                            cleaned_messages.append(
+                                {"role": role, "content": content_parts}
+                            )
                         else:
                             raise ValueError(
                                 f"Unexpected content dictionary format: {item}"
@@ -98,22 +117,29 @@ class OpenAILLM(BaseLLM):
         stream=False,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
+        response_format=None,
         **kwargs,
     ):
         messages = self._clean_messages_openai(messages)
+
+        request_params = {
+            "model": model,
+            "messages": messages,
+            "stream": stream,
+            **kwargs,
+        }
+
+        if tools:
+            request_params["tools"] = tools
+
+        if response_format:
+            request_params["response_format"] = response_format
+
+        response = self.client.chat.completions.create(**request_params)
+
         if tools:
-            response = self.client.chat.completions.create(
-                model=model,
-                messages=messages,
-                stream=stream,
-                tools=tools,
-                **kwargs,
-            )
             return response.choices[0]
         else:
-            response = self.client.chat.completions.create(
-                model=model, messages=messages, stream=stream, **kwargs
-            )
             return response.choices[0].message.content
 
     def _raw_gen_stream(
@@ -124,24 +150,32 @@ class OpenAILLM(BaseLLM):
         stream=True,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
+        response_format=None,
         **kwargs,
     ):
         messages = self._clean_messages_openai(messages)
+
+        request_params = {
+            "model": model,
+            "messages": messages,
+            "stream": stream,
+            **kwargs,
+        }
+
         if tools:
-            response = self.client.chat.completions.create(
-                model=model,
-                messages=messages,
-                stream=stream,
-                tools=tools,
-                **kwargs,
-            )
-        else:
-            response = self.client.chat.completions.create(
-                model=model, messages=messages, stream=stream, **kwargs
-            )
+            request_params["tools"] = tools
+
+        if response_format:
+            request_params["response_format"] = response_format
+
+        response = self.client.chat.completions.create(**request_params)
 
         for line in response:
-            if len(line.choices) > 0 and line.choices[0].delta.content is not None and len(line.choices[0].delta.content) > 0:
+            if (
+                len(line.choices) > 0
+                and line.choices[0].delta.content is not None
+                and len(line.choices[0].delta.content) > 0
+            ):
                 yield line.choices[0].delta.content
             elif len(line.choices) > 0:
                 yield line.choices[0]
@@ -149,6 +183,66 @@ class OpenAILLM(BaseLLM):
     def _supports_tools(self):
         return True
 
+    def _supports_structured_output(self):
+        return True
+
+    def prepare_structured_output_format(self, json_schema):
+        if not json_schema:
+            return None
+
+        try:
+
+            def add_additional_properties_false(schema_obj):
+                if isinstance(schema_obj, dict):
+                    schema_copy = schema_obj.copy()
+
+                    if schema_copy.get("type") == "object":
+                        schema_copy["additionalProperties"] = False
+                        # Ensure 'required' includes all properties for OpenAI strict mode
+                        if "properties" in schema_copy:
+                            schema_copy["required"] = list(
+                                schema_copy["properties"].keys()
+                            )
+
+                    for key, value in schema_copy.items():
+                        if key == "properties" and isinstance(value, dict):
+                            schema_copy[key] = {
+                                prop_name: add_additional_properties_false(prop_schema)
+                                for prop_name, prop_schema in value.items()
+                            }
+                        elif key == "items" and isinstance(value, dict):
+                            schema_copy[key] = add_additional_properties_false(value)
+                        elif key in ["anyOf", "oneOf", "allOf"] and isinstance(
+                            value, list
+                        ):
+                            schema_copy[key] = [
+                                add_additional_properties_false(sub_schema)
+                                for sub_schema in value
+                            ]
+
+                    return schema_copy
+                return schema_obj
+
+            processed_schema = add_additional_properties_false(json_schema)
+
+            result = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": processed_schema.get("name", "response"),
+                    "description": processed_schema.get(
+                        "description", "Structured response"
+                    ),
+                    "schema": processed_schema,
+                    "strict": True,
+                },
+            }
+
+            return result
+
+        except Exception as e:
+            logging.error(f"Error preparing structured output format: {e}")
+            return None
+
     def get_supported_attachment_types(self):
         """
         Return a list of MIME types supported by OpenAI for file uploads.
@@ -157,12 +251,12 @@ class OpenAILLM(BaseLLM):
             list: List of supported MIME types
         """
         return [
-            'application/pdf',
-            'image/png',
-            'image/jpeg',
-            'image/jpg',
-            'image/webp',
-            'image/gif'
+            "application/pdf",
+            "image/png",
+            "image/jpeg",
+            "image/jpg",
+            "image/webp",
+            "image/gif",
         ]
 
     def prepare_messages_with_attachments(self, messages, attachments=None):
@@ -202,39 +296,46 @@ class OpenAILLM(BaseLLM):
             prepared_messages[user_message_index]["content"] = []
 
         for attachment in attachments:
-            mime_type = attachment.get('mime_type')
+            mime_type = attachment.get("mime_type")
 
-            if mime_type and mime_type.startswith('image/'):
+            if mime_type and mime_type.startswith("image/"):
                 try:
                     base64_image = self._get_base64_image(attachment)
-                    prepared_messages[user_message_index]["content"].append({
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:{mime_type};base64,{base64_image}"
+                    prepared_messages[user_message_index]["content"].append(
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{mime_type};base64,{base64_image}"
+                            },
                         }
-                    })
+                    )
                 except Exception as e:
-                    logging.error(f"Error processing image attachment: {e}", exc_info=True)
-                    if 'content' in attachment:
-                        prepared_messages[user_message_index]["content"].append({
-                            "type": "text",
-                            "text": f"[Image could not be processed: {attachment.get('path', 'unknown')}]"
-                        })
+                    logging.error(
+                        f"Error processing image attachment: {e}", exc_info=True
+                    )
+                    if "content" in attachment:
+                        prepared_messages[user_message_index]["content"].append(
+                            {
+                                "type": "text",
+                                "text": f"[Image could not be processed: {attachment.get('path', 'unknown')}]",
+                            }
+                        )
             # Handle PDFs using the file API
-            elif mime_type == 'application/pdf':
+            elif mime_type == "application/pdf":
                 try:
                     file_id = self._upload_file_to_openai(attachment)
-                    prepared_messages[user_message_index]["content"].append({
-                        "type": "file",
-                        "file": {"file_id": file_id}
-                    })
+                    prepared_messages[user_message_index]["content"].append(
+                        {"type": "file", "file": {"file_id": file_id}}
+                    )
                 except Exception as e:
                     logging.error(f"Error uploading PDF to OpenAI: {e}", exc_info=True)
-                    if 'content' in attachment:
-                        prepared_messages[user_message_index]["content"].append({
-                            "type": "text",
-                            "text": f"File content:\n\n{attachment['content']}"
-                        })
+                    if "content" in attachment:
+                        prepared_messages[user_message_index]["content"].append(
+                            {
+                                "type": "text",
+                                "text": f"File content:\n\n{attachment['content']}",
+                            }
+                        )
 
         return prepared_messages
 
@@ -248,13 +349,13 @@ class OpenAILLM(BaseLLM):
         Returns:
             str: Base64-encoded image data.
         """
-        file_path = attachment.get('path')
+        file_path = attachment.get("path")
         if not file_path:
             raise ValueError("No file path provided in attachment")
 
         try:
             with self.storage.get_file(file_path) as image_file:
-                return base64.b64encode(image_file.read()).decode('utf-8')
+                return base64.b64encode(image_file.read()).decode("utf-8")
         except FileNotFoundError:
             raise FileNotFoundError(f"File not found: {file_path}")
 
@@ -273,10 +374,10 @@ class OpenAILLM(BaseLLM):
         """
         import logging
 
-        if 'openai_file_id' in attachment:
-            return attachment['openai_file_id']
+        if "openai_file_id" in attachment:
+            return attachment["openai_file_id"]
 
-        file_path = attachment.get('path')
+        file_path = attachment.get("path")
 
         if not self.storage.file_exists(file_path):
             raise FileNotFoundError(f"File not found: {file_path}")
@@ -285,19 +386,18 @@ class OpenAILLM(BaseLLM):
             file_id = self.storage.process_file(
                 file_path,
                 lambda local_path, **kwargs: self.client.files.create(
-                    file=open(local_path, 'rb'),
-                    purpose="assistants"
-                ).id
+                    file=open(local_path, "rb"), purpose="assistants"
+                ).id,
             )
 
             from application.core.mongo_db import MongoDB
+
             mongo = MongoDB.get_client()
             db = mongo[settings.MONGO_DB_NAME]
             attachments_collection = db["attachments"]
-            if '_id' in attachment:
+            if "_id" in attachment:
                 attachments_collection.update_one(
-                    {"_id": attachment['_id']},
-                    {"$set": {"openai_file_id": file_id}}
+                    {"_id": attachment["_id"]}, {"$set": {"openai_file_id": file_id}}
                 )
 
             return file_id
@@ -308,9 +408,7 @@ class OpenAILLM(BaseLLM):
 
 class AzureOpenAILLM(OpenAILLM):
 
-    def __init__(
-        self, api_key, user_api_key, *args, **kwargs
-    ):
+    def __init__(self, api_key, user_api_key, *args, **kwargs):
 
         super().__init__(api_key)
         self.api_base = (settings.OPENAI_API_BASE,)
@@ -321,5 +419,5 @@ class AzureOpenAILLM(OpenAILLM):
         self.client = AzureOpenAI(
             api_key=api_key,
             api_version=settings.OPENAI_API_VERSION,
-            azure_endpoint=settings.OPENAI_API_BASE
+            azure_endpoint=settings.OPENAI_API_BASE,
         )

application/logging.py

@@ -136,6 +136,8 @@ def _log_to_mongodb(
         mongo = MongoDB.get_client()
         db = mongo[settings.MONGO_DB_NAME]
         user_logs_collection = db["stack_logs"]
+        
+
 
         log_entry = {
             "endpoint": endpoint,
@@ -147,6 +149,11 @@ def _log_to_mongodb(
             "stacks": stacks,
             "timestamp": datetime.datetime.now(datetime.timezone.utc),
         }
+        # clean up text fields to be no longer than 10000 characters
+        for key, value in log_entry.items():
+            if isinstance(value, str) and len(value) > 10000:
+                log_entry[key] = value[:10000]
+    
         user_logs_collection.insert_one(log_entry)
         logging.debug(f"Logged activity to MongoDB: {activity_id}")
 

application/parser/chunking.py

@@ -32,16 +32,7 @@ class Chunker:
             header, body = "", text  # No header, treat entire text as body
         return header, body
 
-    def combine_documents(self, doc: Document, next_doc: Document) -> Document:
-        combined_text = doc.text + " " + next_doc.text
-        combined_token_count = len(self.encoding.encode(combined_text))
-        new_doc = Document(
-            text=combined_text,
-            doc_id=doc.doc_id,
-            embedding=doc.embedding,
-            extra_info={**(doc.extra_info or {}), "token_count": combined_token_count}
-        )
-        return new_doc
+
     
     def split_document(self, doc: Document) -> List[Document]:
         split_docs = []
@@ -82,26 +73,11 @@ class Chunker:
                 processed_docs.append(doc)
                 i += 1
             elif token_count < self.min_tokens:
-                if i + 1 < len(documents):
-                    next_doc = documents[i + 1]
-                    next_tokens = self.encoding.encode(next_doc.text)
-                    if token_count + len(next_tokens) <= self.max_tokens:
-                        # Combine small documents
-                        combined_doc = self.combine_documents(doc, next_doc)
-                        processed_docs.append(combined_doc)
-                        i += 2
-                    else:
-                        # Keep the small document as is if adding next_doc would exceed max_tokens
-                        doc.extra_info = doc.extra_info or {}
-                        doc.extra_info["token_count"] = token_count
-                        processed_docs.append(doc)
-                        i += 1
-                else:
-                    # No next document to combine with; add the small document as is
-                    doc.extra_info = doc.extra_info or {}
-                    doc.extra_info["token_count"] = token_count
-                    processed_docs.append(doc)
-                    i += 1
+  
+                doc.extra_info = doc.extra_info or {}
+                doc.extra_info["token_count"] = token_count
+                processed_docs.append(doc)
+                i += 1
             else:
                 # Split large documents
                 processed_docs.extend(self.split_document(doc))

application/parser/connectors/__init__.py

@@ -0,0 +1,18 @@
+"""
+External knowledge base connectors for DocsGPT.
+
+This module contains connectors for external knowledge bases and document storage systems
+that require authentication and specialized handling, separate from simple web scrapers.
+"""
+
+from .base import BaseConnectorAuth, BaseConnectorLoader
+from .connector_creator import ConnectorCreator
+from .google_drive import GoogleDriveAuth, GoogleDriveLoader
+
+__all__ = [
+    'BaseConnectorAuth',
+    'BaseConnectorLoader',
+    'ConnectorCreator',
+    'GoogleDriveAuth',
+    'GoogleDriveLoader'
+]

application/parser/connectors/base.py

@@ -0,0 +1,129 @@
+"""
+Base classes for external knowledge base connectors.
+
+This module provides minimal abstract base classes that define the essential
+interface for external knowledge base connectors.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+from application.parser.schema.base import Document
+
+
+class BaseConnectorAuth(ABC):
+    """
+    Abstract base class for connector authentication.
+    
+    Defines the minimal interface that all connector authentication
+    implementations must follow.
+    """
+    
+    @abstractmethod
+    def get_authorization_url(self, state: Optional[str] = None) -> str:
+        """
+        Generate authorization URL for OAuth flows.
+        
+        Args:
+            state: Optional state parameter for CSRF protection
+            
+        Returns:
+            Authorization URL
+        """
+        pass
+    
+    @abstractmethod
+    def exchange_code_for_tokens(self, authorization_code: str) -> Dict[str, Any]:
+        """
+        Exchange authorization code for access tokens.
+        
+        Args:
+            authorization_code: Authorization code from OAuth callback
+            
+        Returns:
+            Dictionary containing token information
+        """
+        pass
+    
+    @abstractmethod
+    def refresh_access_token(self, refresh_token: str) -> Dict[str, Any]:
+        """
+        Refresh an expired access token.
+        
+        Args:
+            refresh_token: Refresh token
+            
+        Returns:
+            Dictionary containing refreshed token information
+        """
+        pass
+    
+    @abstractmethod
+    def is_token_expired(self, token_info: Dict[str, Any]) -> bool:
+        """
+        Check if a token is expired.
+        
+        Args:
+            token_info: Token information dictionary
+            
+        Returns:
+            True if token is expired, False otherwise
+        """
+        pass
+
+
+class BaseConnectorLoader(ABC):
+    """
+    Abstract base class for connector loaders.
+    
+    Defines the minimal interface that all connector loader
+    implementations must follow.
+    """
+    
+    @abstractmethod
+    def __init__(self, session_token: str):
+        """
+        Initialize the connector loader.
+        
+        Args:
+            session_token: Authentication session token
+        """
+        pass
+    
+    @abstractmethod
+    def load_data(self, inputs: Dict[str, Any]) -> List[Document]:
+        """
+        Load documents from the external knowledge base.
+        
+        Args:
+            inputs: Configuration dictionary containing:
+                - file_ids: Optional list of specific file IDs to load
+                - folder_ids: Optional list of folder IDs to browse/download
+                - limit: Maximum number of items to return
+                - list_only: If True, return metadata without content
+                - recursive: Whether to recursively process folders
+                
+        Returns:
+            List of Document objects
+        """
+        pass
+    
+    @abstractmethod
+    def download_to_directory(self, local_dir: str, source_config: Dict[str, Any] = None) -> Dict[str, Any]:
+        """
+        Download files/folders to a local directory.
+        
+        Args:
+            local_dir: Local directory path to download files to
+            source_config: Configuration for what to download
+            
+        Returns:
+            Dictionary containing download results:
+                - files_downloaded: Number of files downloaded
+                - directory_path: Path where files were downloaded
+                - empty_result: Whether no files were downloaded
+                - source_type: Type of connector
+                - config_used: Configuration that was used
+                - error: Error message if download failed (optional)
+        """
+        pass

application/parser/connectors/connector_creator.py

@@ -0,0 +1,81 @@
+from application.parser.connectors.google_drive.loader import GoogleDriveLoader
+from application.parser.connectors.google_drive.auth import GoogleDriveAuth
+
+
+class ConnectorCreator:
+    """
+    Factory class for creating external knowledge base connectors and auth providers.
+
+    These are different from remote loaders as they typically require
+    authentication and connect to external document storage systems.
+    """
+
+    connectors = {
+        "google_drive": GoogleDriveLoader,
+    }
+
+    auth_providers = {
+        "google_drive": GoogleDriveAuth,
+    }
+
+    @classmethod
+    def create_connector(cls, connector_type, *args, **kwargs):
+        """
+        Create a connector instance for the specified type.
+
+        Args:
+            connector_type: Type of connector to create (e.g., 'google_drive')
+            *args, **kwargs: Arguments to pass to the connector constructor
+
+        Returns:
+            Connector instance
+
+        Raises:
+            ValueError: If connector type is not supported
+        """
+        connector_class = cls.connectors.get(connector_type.lower())
+        if not connector_class:
+            raise ValueError(f"No connector class found for type {connector_type}")
+        return connector_class(*args, **kwargs)
+
+    @classmethod
+    def create_auth(cls, connector_type):
+        """
+        Create an auth provider instance for the specified connector type.
+
+        Args:
+            connector_type: Type of connector auth to create (e.g., 'google_drive')
+
+        Returns:
+            Auth provider instance
+
+        Raises:
+            ValueError: If connector type is not supported for auth
+        """
+        auth_class = cls.auth_providers.get(connector_type.lower())
+        if not auth_class:
+            raise ValueError(f"No auth class found for type {connector_type}")
+        return auth_class()
+
+    @classmethod
+    def get_supported_connectors(cls):
+        """
+        Get list of supported connector types.
+
+        Returns:
+            List of supported connector type strings
+        """
+        return list(cls.connectors.keys())
+
+    @classmethod
+    def is_supported(cls, connector_type):
+        """
+        Check if a connector type is supported.
+
+        Args:
+            connector_type: Type of connector to check
+
+        Returns:
+            True if supported, False otherwise
+        """
+        return connector_type.lower() in cls.connectors

application/parser/connectors/google_drive/__init__.py

@@ -0,0 +1,10 @@
+"""
+Google Drive connector for DocsGPT.
+
+This module provides authentication and document loading capabilities for Google Drive.
+"""
+
+from .auth import GoogleDriveAuth
+from .loader import GoogleDriveLoader
+
+__all__ = ['GoogleDriveAuth', 'GoogleDriveLoader']

application/parser/connectors/google_drive/auth.py

@@ -0,0 +1,268 @@
+import logging
+import datetime
+from typing import Optional, Dict, Any
+
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import Flow
+from googleapiclient.discovery import build
+from googleapiclient.errors import HttpError
+
+from application.core.settings import settings
+from application.parser.connectors.base import BaseConnectorAuth
+
+
+class GoogleDriveAuth(BaseConnectorAuth):
+    """
+    Handles Google OAuth 2.0 authentication for Google Drive access.
+    """
+    
+    SCOPES = [
+        'https://www.googleapis.com/auth/drive.readonly',
+        'https://www.googleapis.com/auth/drive.metadata.readonly'
+    ]
+    
+    def __init__(self):
+        self.client_id = settings.GOOGLE_CLIENT_ID
+        self.client_secret = settings.GOOGLE_CLIENT_SECRET
+        self.redirect_uri = f"{settings.CONNECTOR_REDIRECT_BASE_URI}?provider=google_drive"
+        
+        if not self.client_id or not self.client_secret:
+            raise ValueError("Google OAuth credentials not configured. Please set GOOGLE_CLIENT_ID and GOOGLE_CLIENT_SECRET in settings.")
+
+
+
+    def get_authorization_url(self, state: Optional[str] = None) -> str:
+        try:
+            flow = Flow.from_client_config(
+                {
+                    "web": {
+                        "client_id": self.client_id,
+                        "client_secret": self.client_secret,
+                        "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+                        "token_uri": "https://oauth2.googleapis.com/token",
+                        "redirect_uris": [self.redirect_uri]
+                    }
+                },
+                scopes=self.SCOPES
+            )
+            flow.redirect_uri = self.redirect_uri
+            
+            authorization_url, _ = flow.authorization_url(
+                access_type='offline',
+                prompt='consent',
+                include_granted_scopes='true',
+                state=state
+            )
+            
+            return authorization_url
+            
+        except Exception as e:
+            logging.error(f"Error generating authorization URL: {e}")
+            raise
+    
+    def exchange_code_for_tokens(self, authorization_code: str) -> Dict[str, Any]:
+        try:
+            if not authorization_code:
+                raise ValueError("Authorization code is required")
+
+            flow = Flow.from_client_config(
+                {
+                    "web": {
+                        "client_id": self.client_id,
+                        "client_secret": self.client_secret,
+                        "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+                        "token_uri": "https://oauth2.googleapis.com/token",
+                        "redirect_uris": [self.redirect_uri]
+                    }
+                },
+                scopes=self.SCOPES
+            )
+            flow.redirect_uri = self.redirect_uri
+
+            flow.fetch_token(code=authorization_code)
+
+            credentials = flow.credentials
+
+            if not credentials.refresh_token:
+                logging.warning("OAuth flow did not return a refresh_token.")
+            if not credentials.token:
+                raise ValueError("OAuth flow did not return an access token")
+
+            if not credentials.token_uri:
+                credentials.token_uri = "https://oauth2.googleapis.com/token"
+
+            if not credentials.client_id:
+                credentials.client_id = self.client_id
+
+            if not credentials.client_secret:
+                credentials.client_secret = self.client_secret
+
+            if not credentials.refresh_token:
+                raise ValueError(
+                    "No refresh token received. This typically happens when offline access wasn't granted. "
+                )
+
+            return {
+                'access_token': credentials.token,
+                'refresh_token': credentials.refresh_token,
+                'token_uri': credentials.token_uri,
+                'client_id': credentials.client_id,
+                'client_secret': credentials.client_secret,
+                'scopes': credentials.scopes,
+                'expiry': credentials.expiry.isoformat() if credentials.expiry else None
+            }
+
+        except Exception as e:
+            logging.error(f"Error exchanging code for tokens: {e}")
+            raise
+    
+    def refresh_access_token(self, refresh_token: str) -> Dict[str, Any]:
+        try:
+            if not refresh_token:
+                raise ValueError("Refresh token is required")
+
+            credentials = Credentials(
+                token=None,
+                refresh_token=refresh_token,
+                token_uri="https://oauth2.googleapis.com/token",
+                client_id=self.client_id,
+                client_secret=self.client_secret
+            )
+
+            from google.auth.transport.requests import Request
+            credentials.refresh(Request())
+
+            return {
+                'access_token': credentials.token,
+                'refresh_token': refresh_token,
+                'token_uri': credentials.token_uri,
+                'client_id': credentials.client_id,
+                'client_secret': credentials.client_secret,
+                'scopes': credentials.scopes,
+                'expiry': credentials.expiry.isoformat() if credentials.expiry else None
+            }
+        except Exception as e:
+            logging.error(f"Error refreshing access token: {e}", exc_info=True)
+            raise
+    
+    def create_credentials_from_token_info(self, token_info: Dict[str, Any]) -> Credentials:
+        from application.core.settings import settings
+
+        access_token = token_info.get('access_token')
+        if not access_token:
+            raise ValueError("No access token found in token_info")
+
+        credentials = Credentials(
+            token=access_token,
+            refresh_token=token_info.get('refresh_token'),
+            token_uri= 'https://oauth2.googleapis.com/token',
+            client_id=settings.GOOGLE_CLIENT_ID,
+            client_secret=settings.GOOGLE_CLIENT_SECRET,
+            scopes=token_info.get('scopes', ['https://www.googleapis.com/auth/drive.readonly'])
+        )
+
+        if not credentials.token:
+            raise ValueError("Credentials created without valid access token")
+
+        return credentials
+    
+    def build_drive_service(self, credentials: Credentials):
+        try:
+            if not credentials:
+                raise ValueError("No credentials provided")
+
+            if not credentials.token and not credentials.refresh_token:
+                raise ValueError("No access token or refresh token available. User must re-authorize with offline access.")
+
+            needs_refresh = credentials.expired or not credentials.token
+            if needs_refresh:
+                if credentials.refresh_token:
+                    try:
+                        from google.auth.transport.requests import Request
+                        credentials.refresh(Request())
+                    except Exception as refresh_error:
+                        raise ValueError(f"Failed to refresh credentials: {refresh_error}")
+                else:
+                    raise ValueError("No access token or refresh token available. User must re-authorize with offline access.")
+
+            return build('drive', 'v3', credentials=credentials)
+
+        except HttpError as e:
+            raise ValueError(f"Failed to build Google Drive service: HTTP {e.resp.status}")
+        except Exception as e:
+            raise ValueError(f"Failed to build Google Drive service: {str(e)}")
+        
+    def is_token_expired(self, token_info):
+        if 'expiry' in token_info and token_info['expiry']:
+            try:
+                from dateutil import parser
+                # Google Drive provides timezone-aware ISO8601 dates
+                expiry_dt = parser.parse(token_info['expiry'])
+                current_time = datetime.datetime.now(datetime.timezone.utc)
+                return current_time >= expiry_dt - datetime.timedelta(seconds=60)
+            except Exception:
+                return True
+
+        if 'access_token' in token_info and token_info['access_token']:
+            return False
+
+        return True
+    
+    def get_token_info_from_session(self, session_token: str) -> Dict[str, Any]:
+        try:
+            from application.core.mongo_db import MongoDB
+            from application.core.settings import settings
+
+            mongo = MongoDB.get_client()
+            db = mongo[settings.MONGO_DB_NAME]
+            
+            sessions_collection = db["connector_sessions"]
+            session = sessions_collection.find_one({"session_token": session_token})   
+            if not session:
+                raise ValueError(f"Invalid session token: {session_token}")
+
+            if "token_info" not in session:
+                raise ValueError("Session missing token information")
+
+            token_info = session["token_info"]
+            if not token_info:
+                raise ValueError("Invalid token information")
+
+            required_fields = ["access_token", "refresh_token"]
+            missing_fields = [field for field in required_fields if field not in token_info or not token_info.get(field)]
+            if missing_fields:
+                raise ValueError(f"Missing required token fields: {missing_fields}")
+
+            if 'client_id' not in token_info:
+                token_info['client_id'] = settings.GOOGLE_CLIENT_ID
+            if 'client_secret' not in token_info:
+                token_info['client_secret'] = settings.GOOGLE_CLIENT_SECRET
+            if 'token_uri' not in token_info:
+                token_info['token_uri'] = 'https://oauth2.googleapis.com/token'
+
+            return token_info
+
+        except Exception as e:
+            raise ValueError(f"Failed to retrieve Google Drive token information: {str(e)}")
+
+    def validate_credentials(self, credentials: Credentials) -> bool:
+        """
+        Validate Google Drive credentials by making a test API call.
+
+        Args:
+            credentials: Google credentials object
+
+        Returns:
+            True if credentials are valid, False otherwise
+        """
+        try:
+            service = self.build_drive_service(credentials)
+            service.about().get(fields="user").execute()
+            return True
+
+        except HttpError as e:
+            logging.error(f"HTTP error validating credentials: {e}")
+            return False
+        except Exception as e:
+            logging.error(f"Error validating credentials: {e}")
+            return False

application/parser/connectors/google_drive/loader.py

@@ -0,0 +1,536 @@
+"""
+Google Drive loader for DocsGPT.
+Loads documents from Google Drive using Google Drive API.
+"""
+
+import io
+import logging
+import os
+from typing import List, Dict, Any, Optional
+
+from googleapiclient.http import MediaIoBaseDownload
+from googleapiclient.errors import HttpError
+
+from application.parser.connectors.base import BaseConnectorLoader
+from application.parser.connectors.google_drive.auth import GoogleDriveAuth
+from application.parser.schema.base import Document
+
+
+class GoogleDriveLoader(BaseConnectorLoader):
+
+    SUPPORTED_MIME_TYPES = {
+        'application/pdf': '.pdf',
+        'application/vnd.google-apps.document': '.docx',
+        'application/vnd.google-apps.presentation': '.pptx',
+        'application/vnd.google-apps.spreadsheet': '.xlsx',
+        'application/vnd.openxmlformats-officedocument.wordprocessingml.document': '.docx',
+        'application/vnd.openxmlformats-officedocument.presentationml.presentation': '.pptx',
+        'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet': '.xlsx',
+        'application/msword': '.doc',
+        'application/vnd.ms-powerpoint': '.ppt',
+        'application/vnd.ms-excel': '.xls',
+        'text/plain': '.txt',
+        'text/csv': '.csv',
+        'text/html': '.html',
+        'application/rtf': '.rtf',
+        'image/jpeg': '.jpg',
+        'image/jpg': '.jpg',
+        'image/png': '.png',
+    }
+
+    EXPORT_FORMATS = {
+        'application/vnd.google-apps.document': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document',
+        'application/vnd.google-apps.presentation': 'application/vnd.openxmlformats-officedocument.presentationml.presentation',
+        'application/vnd.google-apps.spreadsheet': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+    }
+
+    def __init__(self, session_token: str):
+        self.auth = GoogleDriveAuth()
+        self.session_token = session_token
+
+        token_info = self.auth.get_token_info_from_session(session_token)
+        self.credentials = self.auth.create_credentials_from_token_info(token_info)
+
+        try:
+            self.service = self.auth.build_drive_service(self.credentials)
+        except Exception as e:
+            logging.warning(f"Could not build Google Drive service: {e}")
+            self.service = None
+
+        self.next_page_token = None
+
+
+
+    def _process_file(self, file_metadata: Dict[str, Any], load_content: bool = True) -> Optional[Document]:
+        try:
+            file_id = file_metadata.get('id')
+            file_name = file_metadata.get('name', 'Unknown')
+            mime_type = file_metadata.get('mimeType', 'application/octet-stream')
+
+            if mime_type not in self.SUPPORTED_MIME_TYPES and not mime_type.startswith('application/vnd.google-apps.'):
+                return None
+            if mime_type not in self.SUPPORTED_MIME_TYPES and not mime_type.startswith('application/vnd.google-apps.'):
+                logging.info(f"Skipping unsupported file type: {mime_type} for file {file_name}")
+                return None
+            # Google Drive provides timezone-aware ISO8601 dates
+            doc_metadata = {
+                'file_name': file_name,
+                'mime_type': mime_type,
+                'size': file_metadata.get('size', None),
+                'created_time': file_metadata.get('createdTime'),
+                'modified_time': file_metadata.get('modifiedTime'),
+                'parents': file_metadata.get('parents', []),
+                'source': 'google_drive'
+            }
+
+            if not load_content:
+                return Document(
+                    text="",
+                    doc_id=file_id,
+                    extra_info=doc_metadata
+                )
+
+            content = self._download_file_content(file_id, mime_type)
+            if content is None:
+                logging.warning(f"Could not load content for file {file_name} ({file_id})")
+                return None
+
+            return Document(
+                text=content,
+                doc_id=file_id,
+                extra_info=doc_metadata
+            )
+
+        except Exception as e:
+            logging.error(f"Error processing file: {e}")
+            return None
+
+    def load_data(self, inputs: Dict[str, Any]) -> List[Document]:
+        session_token = inputs.get('session_token')
+        if session_token and session_token != self.session_token:
+            logging.warning("Session token in inputs differs from loader's session token. Using loader's session token.")
+        self.config = inputs
+
+        try:
+            documents: List[Document] = []
+
+            folder_id = inputs.get('folder_id')
+            file_ids = inputs.get('file_ids', [])
+            limit = inputs.get('limit', 100)
+            list_only = inputs.get('list_only', False)
+            load_content = not list_only
+            page_token = inputs.get('page_token')
+            self.next_page_token = None
+
+            if file_ids:
+                # Specific files requested: load them
+                for file_id in file_ids:
+                    try:
+                        doc = self._load_file_by_id(file_id, load_content=load_content)
+                        if doc:
+                            documents.append(doc)
+                        elif hasattr(self, '_credential_refreshed') and self._credential_refreshed:
+                            self._credential_refreshed = False
+                            logging.info(f"Retrying load of file {file_id} after credential refresh")
+                            doc = self._load_file_by_id(file_id, load_content=load_content)
+                            if doc:
+                                documents.append(doc)
+                    except Exception as e:
+                        logging.error(f"Error loading file {file_id}: {e}")
+                        continue
+            else:
+                # Browsing mode: list immediate children of provided folder or root
+                parent_id = folder_id if folder_id else 'root'
+                documents = self._list_items_in_parent(parent_id, limit=limit, load_content=load_content, page_token=page_token)
+
+            logging.info(f"Loaded {len(documents)} documents from Google Drive")
+            return documents
+
+        except Exception as e:
+            logging.error(f"Error loading data from Google Drive: {e}", exc_info=True)
+            raise
+
+
+
+    def _load_file_by_id(self, file_id: str, load_content: bool = True) -> Optional[Document]:
+        self._ensure_service()
+
+        try:
+            file_metadata = self.service.files().get(
+                fileId=file_id,
+                fields='id,name,mimeType,size,createdTime,modifiedTime,parents'
+            ).execute()
+
+            return self._process_file(file_metadata, load_content=load_content)
+
+        except HttpError as e:
+            logging.error(f"HTTP error loading file {file_id}: {e.resp.status} - {e.content}")
+
+            if e.resp.status in [401, 403]:
+                if hasattr(self.credentials, 'refresh_token') and self.credentials.refresh_token:
+                    try:
+                        from google.auth.transport.requests import Request
+                        self.credentials.refresh(Request())
+                        self._ensure_service()
+                        return None
+                    except Exception as refresh_error:
+                        raise ValueError(f"Authentication failed and could not be refreshed: {refresh_error}")
+                else:
+                    raise ValueError("Authentication failed and cannot be refreshed: missing refresh_token")
+
+            return None
+        except Exception as e:
+            logging.error(f"Error loading file {file_id}: {e}")
+            return None
+
+
+    def _list_items_in_parent(self, parent_id: str, limit: int = 100, load_content: bool = False, page_token: Optional[str] = None) -> List[Document]:
+        self._ensure_service()
+
+        documents: List[Document] = []
+
+        try:
+            query = f"'{parent_id}' in parents and trashed=false"
+            next_token_out: Optional[str] = None
+
+            while True:
+                page_size = 100
+                if limit:
+                    remaining = max(0, limit - len(documents))
+                    if remaining == 0:
+                        break
+                    page_size = min(100, remaining)
+
+                results = self.service.files().list(
+                    q=query,
+                    fields='nextPageToken,files(id,name,mimeType,size,createdTime,modifiedTime,parents)',
+                    pageToken=page_token,
+                    pageSize=page_size
+                ).execute()
+
+                items = results.get('files', [])
+                for item in items:
+                    mime_type = item.get('mimeType')
+                    if mime_type == 'application/vnd.google-apps.folder':
+                        doc_metadata = {
+                            'file_name': item.get('name', 'Unknown'),
+                            'mime_type': mime_type,
+                            'size': item.get('size', None),
+                            'created_time': item.get('createdTime'),
+                            'modified_time': item.get('modifiedTime'),
+                            'parents': item.get('parents', []),
+                            'source': 'google_drive',
+                            'is_folder': True
+                        }
+                        documents.append(Document(text="", doc_id=item.get('id'), extra_info=doc_metadata))
+                    else:
+                        doc = self._process_file(item, load_content=load_content)
+                        if doc:
+                            documents.append(doc)
+
+                    if limit and len(documents) >= limit:
+                        self.next_page_token = results.get('nextPageToken')
+                        return documents
+
+                page_token = results.get('nextPageToken')
+                next_token_out = page_token
+                if not page_token:
+                    break
+
+            self.next_page_token = next_token_out
+            return documents
+        except Exception as e:
+            logging.error(f"Error listing items under parent {parent_id}: {e}")
+            return documents
+
+
+
+
+    def _download_file_content(self, file_id: str, mime_type: str) -> Optional[str]:
+        if not self.credentials.token:
+            logging.warning("No access token in credentials, attempting to refresh")
+            if hasattr(self.credentials, 'refresh_token') and self.credentials.refresh_token:
+                try:
+                    from google.auth.transport.requests import Request
+                    self.credentials.refresh(Request())
+                    logging.info("Credentials refreshed successfully")
+                    self._ensure_service()
+                except Exception as e:
+                    logging.error(f"Failed to refresh credentials: {e}")
+                    raise ValueError("Authentication failed and cannot be refreshed: missing or invalid refresh_token")
+            else:
+                logging.error("No access token and no refresh_token available")
+                raise ValueError("Authentication failed and cannot be refreshed: missing refresh_token")
+
+        if self.credentials.expired:
+            logging.warning("Credentials are expired, attempting to refresh")
+            if hasattr(self.credentials, 'refresh_token') and self.credentials.refresh_token:
+                try:
+                    from google.auth.transport.requests import Request
+                    self.credentials.refresh(Request())
+                    logging.info("Credentials refreshed successfully")
+                    self._ensure_service()
+                except Exception as e:
+                    logging.error(f"Failed to refresh expired credentials: {e}")
+                    raise ValueError("Authentication failed and cannot be refreshed: expired credentials")
+            else:
+                logging.error("Credentials expired and no refresh_token available")
+                raise ValueError("Authentication failed and cannot be refreshed: missing refresh_token")
+
+        try:
+            if mime_type in self.EXPORT_FORMATS:
+                export_mime_type = self.EXPORT_FORMATS[mime_type]
+                request = self.service.files().export_media(
+                    fileId=file_id,
+                    mimeType=export_mime_type
+                )
+            else:
+                request = self.service.files().get_media(fileId=file_id)
+
+            file_io = io.BytesIO()
+            downloader = MediaIoBaseDownload(file_io, request)
+
+            done = False
+            while done is False:
+                try:
+                    _, done = downloader.next_chunk()
+                except HttpError as e:
+                    logging.error(f"HTTP error downloading file {file_id}: {e.resp.status} - {e.content}")
+                    return None
+                except Exception as e:
+                    logging.error(f"Error during download of file {file_id}: {e}")
+                    return None
+
+            content_bytes = file_io.getvalue()
+
+            try:
+                content = content_bytes.decode('utf-8')
+            except UnicodeDecodeError:
+                try:
+                    content = content_bytes.decode('latin-1')
+                except UnicodeDecodeError:
+                    logging.error(f"Could not decode file {file_id} as text")
+                    return None
+
+            return content
+
+        except HttpError as e:
+            logging.error(f"HTTP error downloading file {file_id}: {e.resp.status} - {e.content}")
+
+            if e.resp.status in [401, 403]:
+                logging.error(f"Authentication error downloading file {file_id}")
+
+                if hasattr(self.credentials, 'refresh_token') and self.credentials.refresh_token:
+                    logging.info(f"Attempting to refresh credentials for file {file_id}")
+                    try:
+                        from google.auth.transport.requests import Request
+                        self.credentials.refresh(Request())
+                        logging.info("Credentials refreshed successfully")
+                        self._credential_refreshed = True
+                        self._ensure_service()
+                        return None
+                    except Exception as refresh_error:
+                        logging.error(f"Error refreshing credentials: {refresh_error}")
+                        raise ValueError(f"Authentication failed and could not be refreshed: {refresh_error}")
+                else:
+                    logging.error("Cannot refresh credentials: missing refresh_token")
+                    raise ValueError("Authentication failed and cannot be refreshed: missing refresh_token")
+
+            return None
+        except Exception as e:
+            logging.error(f"Error downloading file {file_id}: {e}")
+            return None
+
+
+    def _download_file_to_directory(self, file_id: str, local_dir: str) -> bool:
+        try:
+            self._ensure_service()
+            return self._download_single_file(file_id, local_dir)
+        except Exception as e:
+            logging.error(f"Error downloading file {file_id}: {e}", exc_info=True)
+            return False
+
+    def _ensure_service(self):
+        if not self.service:
+            try:
+                self.service = self.auth.build_drive_service(self.credentials)
+            except Exception as e:
+                raise ValueError(f"Cannot access Google Drive: {e}")
+
+    def _download_single_file(self, file_id: str, local_dir: str) -> bool:
+        file_metadata = self.service.files().get(
+            fileId=file_id,
+            fields='name,mimeType'
+        ).execute()
+
+        file_name = file_metadata['name']
+        mime_type = file_metadata['mimeType']
+
+        if mime_type not in self.SUPPORTED_MIME_TYPES and not mime_type.startswith('application/vnd.google-apps.'):
+            return False
+
+        os.makedirs(local_dir, exist_ok=True)
+        full_path = os.path.join(local_dir, file_name)
+
+        if mime_type in self.EXPORT_FORMATS:
+            export_mime_type = self.EXPORT_FORMATS[mime_type]
+            request = self.service.files().export_media(
+                fileId=file_id,
+                mimeType=export_mime_type
+            )
+            extension = self._get_extension_for_mime_type(export_mime_type)
+            if not full_path.endswith(extension):
+                full_path += extension
+        else:
+            request = self.service.files().get_media(fileId=file_id)
+
+        with open(full_path, 'wb') as f:
+            downloader = MediaIoBaseDownload(f, request)
+            done = False
+            while not done:
+                _, done = downloader.next_chunk()
+
+        return True
+
+    def _download_folder_recursive(self, folder_id: str, local_dir: str, recursive: bool = True) -> int:
+        files_downloaded = 0
+        try:
+            os.makedirs(local_dir, exist_ok=True)
+
+            query = f"'{folder_id}' in parents and trashed=false"
+            page_token = None
+
+            while True:
+                results = self.service.files().list(
+                    q=query,
+                    fields='nextPageToken, files(id, name, mimeType)',
+                    pageToken=page_token,
+                    pageSize=1000
+                ).execute()
+
+                items = results.get('files', [])
+                logging.info(f"Found {len(items)} items in folder {folder_id}")
+
+                for item in items:
+                    item_name = item['name']
+                    item_id = item['id']
+                    mime_type = item['mimeType']
+
+                    if mime_type == 'application/vnd.google-apps.folder':
+                        if recursive:
+                            # Create subfolder and recurse
+                            subfolder_path = os.path.join(local_dir, item_name)
+                            os.makedirs(subfolder_path, exist_ok=True)
+                            subfolder_files = self._download_folder_recursive(
+                                item_id,
+                                subfolder_path,
+                                recursive
+                            )
+                            files_downloaded += subfolder_files
+                            logging.info(f"Downloaded {subfolder_files} files from subfolder {item_name}")
+                    else:
+                        # Download file
+                        success = self._download_single_file(item_id, local_dir)
+                        if success:
+                            files_downloaded += 1
+                            logging.info(f"Downloaded file: {item_name}")
+                        else:
+                            logging.warning(f"Failed to download file: {item_name}")
+
+                page_token = results.get('nextPageToken')
+                if not page_token:
+                    break
+
+            return files_downloaded
+
+        except Exception as e:
+            logging.error(f"Error in _download_folder_recursive for folder {folder_id}: {e}", exc_info=True)
+            return files_downloaded
+
+    def _get_extension_for_mime_type(self, mime_type: str) -> str:
+        extensions = {
+            'application/pdf': '.pdf',
+            'text/plain': '.txt',
+            'application/vnd.openxmlformats-officedocument.wordprocessingml.document': '.docx',
+            'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet': '.xlsx',
+            'application/vnd.openxmlformats-officedocument.presentationml.presentation': '.pptx',
+            'text/html': '.html',
+            'text/markdown': '.md',
+        }
+        return extensions.get(mime_type, '.bin')
+
+    def _download_folder_contents(self, folder_id: str, local_dir: str, recursive: bool = True) -> int:
+        try:
+            self._ensure_service()
+            return self._download_folder_recursive(folder_id, local_dir, recursive)
+        except Exception as e:
+            logging.error(f"Error downloading folder {folder_id}: {e}", exc_info=True)
+            return 0
+
+    def download_to_directory(self, local_dir: str, source_config: dict = None) -> dict:
+        if source_config is None:
+            source_config = {}
+
+        config = source_config if source_config else getattr(self, 'config', {})
+        files_downloaded = 0
+
+        try:
+            folder_ids = config.get('folder_ids', [])
+            file_ids = config.get('file_ids', [])
+            recursive = config.get('recursive', True)
+
+            self._ensure_service()
+
+            if file_ids:
+                if isinstance(file_ids, str):
+                    file_ids = [file_ids]
+
+                for file_id in file_ids:
+                    if self._download_file_to_directory(file_id, local_dir):
+                        files_downloaded += 1
+
+            # Process folders
+            if folder_ids:
+                if isinstance(folder_ids, str):
+                    folder_ids = [folder_ids]
+
+                for folder_id in folder_ids:
+                    try:
+                        folder_metadata = self.service.files().get(
+                            fileId=folder_id,
+                            fields='name'
+                        ).execute()
+                        folder_name = folder_metadata.get('name', '')
+                        folder_path = os.path.join(local_dir, folder_name)
+                        os.makedirs(folder_path, exist_ok=True)
+
+                        folder_files = self._download_folder_recursive(
+                            folder_id,
+                            folder_path,
+                            recursive
+                        )
+                        files_downloaded += folder_files
+                        logging.info(f"Downloaded {folder_files} files from folder {folder_name}")
+                    except Exception as e:
+                        logging.error(f"Error downloading folder {folder_id}: {e}", exc_info=True)
+
+            if not file_ids and not folder_ids:
+                raise ValueError("No folder_ids or file_ids provided for download")
+
+            return {
+                "files_downloaded": files_downloaded,
+                "directory_path": local_dir,
+                "empty_result": files_downloaded == 0,
+                "source_type": "google_drive",
+                "config_used": config
+            }
+
+        except Exception as e:
+            return {
+                "files_downloaded": files_downloaded,
+                "directory_path": local_dir,
+                "empty_result": True,
+                "source_type": "google_drive",
+                "config_used": config,
+                "error": str(e)
+            }

application/parser/embedding_pipeline.py

@@ -6,6 +6,21 @@ from application.core.settings import settings
 from application.vectorstore.vector_creator import VectorCreator
 
 
+def sanitize_content(content: str) -> str:
+    """
+    Remove NUL characters that can cause vector store ingestion to fail.
+    
+    Args:
+        content (str): Raw content that may contain NUL characters
+        
+    Returns:
+        str: Sanitized content with NUL characters removed
+    """
+    if not content:
+        return content
+    return content.replace('\x00', '')
+
+
 @retry(tries=10, delay=60)
 def add_text_to_store_with_retry(store, doc, source_id):
     """
@@ -16,6 +31,9 @@ def add_text_to_store_with_retry(store, doc, source_id):
         source_id: Unique identifier for the source.
     """
     try:
+        # Sanitize content to remove NUL characters that cause ingestion failures
+        doc.page_content = sanitize_content(doc.page_content)
+        
         doc.metadata["source_id"] = str(source_id)
         store.add_texts([doc.page_content], metadatas=[doc.metadata])
     except Exception as e:
@@ -46,7 +64,7 @@ def embed_and_store_documents(docs, folder_name, source_id, task_status):
         store = VectorCreator.create_vectorstore(
             settings.VECTOR_STORE,
             docs_init=docs_init,
-            source_id=folder_name,
+            source_id=source_id,
             embeddings_key=os.getenv("EMBEDDINGS_KEY"),
         )
     else:

application/parser/file/bulk.py

@@ -15,6 +15,7 @@ from application.parser.file.json_parser import JSONParser
 from application.parser.file.pptx_parser import PPTXParser
 from application.parser.file.image_parser import ImageParser
 from application.parser.schema.base import Document
+from application.utils import num_tokens_from_string
 
 DEFAULT_FILE_EXTRACTOR: Dict[str, BaseParser] = {
     ".pdf": PDFParser(),
@@ -141,11 +142,12 @@ class SimpleDirectoryReader(BaseReader):
 
         Returns:
             List[Document]: A list of documents.
-
         """
         data: Union[str, List[str]] = ""
         data_list: List[str] = []
         metadata_list = []
+        self.file_token_counts = {}
+        
         for input_file in self.input_files:
             if input_file.suffix in self.file_extractor:
                 parser = self.file_extractor[input_file.suffix]
@@ -156,24 +158,48 @@ class SimpleDirectoryReader(BaseReader):
                 # do standard read
                 with open(input_file, "r", errors=self.errors) as f:
                     data = f.read()
-            # Prepare metadata for this file
-            if self.file_metadata is not None:
-                file_metadata = self.file_metadata(input_file.name)
+            
+            # Calculate token count for this file
+            if isinstance(data, List):
+                file_tokens = sum(num_tokens_from_string(str(d)) for d in data)
             else:
-                # Provide a default empty metadata
-                file_metadata = {'title': '', 'store': ''}
-                # TODO: Find a case with no metadata and check if breaks anything 
+                file_tokens = num_tokens_from_string(str(data))
+            
+            full_path = str(input_file.resolve())
+            self.file_token_counts[full_path] = file_tokens
+            
+            base_metadata = {
+                'title': input_file.name,
+                'token_count': file_tokens,
+            }
+            
+            if hasattr(self, 'input_dir'):
+                try:
+                    relative_path = str(input_file.relative_to(self.input_dir))
+                    base_metadata['source'] = relative_path
+                except ValueError:
+                    base_metadata['source'] = str(input_file)
+            else:
+                base_metadata['source'] = str(input_file)
+
+            if self.file_metadata is not None:
+                custom_metadata = self.file_metadata(input_file.name)
+                base_metadata.update(custom_metadata)
 
             if isinstance(data, List):
                 # Extend data_list with each item in the data list
                 data_list.extend([str(d) for d in data])
-                # For each item in the data list, add the file's metadata to metadata_list
-                metadata_list.extend([file_metadata for _ in data])
+                metadata_list.extend([base_metadata for _ in data])
             else:
-                # Add the single piece of data to data_list
                 data_list.append(str(data))
-                # Add the file's metadata to metadata_list
-                metadata_list.append(file_metadata)
+                metadata_list.append(base_metadata)
+        
+        # Build directory structure if input_dir is provided
+        if hasattr(self, 'input_dir'):
+            self.directory_structure = self.build_directory_structure(self.input_dir)
+            logging.info("Directory structure built successfully")
+        else:
+            self.directory_structure = {}
 
         if concatenate:
             return [Document("\n".join(data_list))]
@@ -181,3 +207,48 @@ class SimpleDirectoryReader(BaseReader):
             return [Document(d, extra_info=m) for d, m in zip(data_list, metadata_list)]
         else:
             return [Document(d) for d in data_list]
+
+    def build_directory_structure(self, base_path):
+        """Build a dictionary representing the directory structure.
+
+        Args:
+            base_path: The base path to start building the structure from.
+
+        Returns:
+            dict: A nested dictionary representing the directory structure.
+        """
+        import mimetypes
+        
+        def build_tree(path):
+            """Helper function to recursively build the directory tree."""
+            result = {}
+            
+            for item in path.iterdir():
+                if self.exclude_hidden and item.name.startswith('.'):
+                    continue
+                    
+                if item.is_dir():
+                    subtree = build_tree(item)
+                    if subtree:
+                        result[item.name] = subtree
+                else:
+                    if self.required_exts is not None and item.suffix not in self.required_exts:
+                        continue
+                    
+                    full_path = str(item.resolve())
+                    file_size_bytes = item.stat().st_size
+                    mime_type = mimetypes.guess_type(item.name)[0] or "application/octet-stream"
+                    
+                    file_info = {
+                        "type": mime_type,
+                        "size_bytes": file_size_bytes
+                    }
+                    
+                    if hasattr(self, 'file_token_counts') and full_path in self.file_token_counts:
+                        file_info["token_count"] = self.file_token_counts[full_path]
+                        
+                    result[item.name] = file_info
+                    
+            return result
+        
+        return build_tree(Path(base_path))

application/parser/file/image_parser.py

@@ -8,6 +8,7 @@ import requests
 from typing import Dict, Union
 
 from application.parser.file.base_parser import BaseParser
+from application.core.settings import settings
 
 
 class ImageParser(BaseParser):
@@ -18,10 +19,13 @@ class ImageParser(BaseParser):
         return {}
 
     def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
-        doc2md_service = "https://llm.arc53.com/doc2md"
-        # alternatively you can use local vision capable LLM
-        with open(file, "rb") as file_loaded:
-            files = {'file': file_loaded}
-            response = requests.post(doc2md_service, files=files)   
-            data = response.json()["markdown"] 
+        if settings.PARSE_IMAGE_REMOTE:
+            doc2md_service = "https://llm.arc53.com/doc2md"
+            # alternatively you can use local vision capable LLM
+            with open(file, "rb") as file_loaded:
+                files = {'file': file_loaded}
+                response = requests.post(doc2md_service, files=files)   
+                data = response.json()["markdown"] 
+        else:
+            data = ""
         return data

application/parser/remote/remote_creator.py

@@ -6,6 +6,16 @@ from application.parser.remote.github_loader import GitHubLoader
 
 
 class RemoteCreator:
+    """
+    Factory class for creating remote content loaders.
+
+    These loaders fetch content from remote web sources like URLs,
+    sitemaps, web crawlers, social media platforms, etc.
+
+    For external knowledge base connectors (like Google Drive),
+    use ConnectorCreator instead.
+    """
+
     loaders = {
         "url": WebLoader,
         "sitemap": SitemapLoader,
@@ -18,5 +28,5 @@ class RemoteCreator:
     def create_loader(cls, type, *args, **kwargs):
         loader_class = cls.loaders.get(type.lower())
         if not loader_class:
-            raise ValueError(f"No LLM class found for type {type}")
+            raise ValueError(f"No loader class found for type {type}")
         return loader_class(*args, **kwargs)

application/prompts/react_planning_prompt.txt

@@ -1,10 +1,13 @@
 You are an AI assistant and talk like you're thinking out loud. Given the following query, outline a concise thought process that includes key steps and considerations necessary for effective analysis and response. Avoid pointwise formatting. The goal is to break down the query into manageable components without excessive detail, focusing on clarity and logical progression.
 
-Include the following elements in your thought process:
+Include the following elements in your thought and execution process:
 1. Identify the main objective of the query.
 2. Determine any relevant context or background information needed to understand the query.
 3. List potential approaches or methods to address the query.
 4. Highlight any critical factors or constraints that may influence the outcome.
+5. Plan with available tools to help you with the analysis but dont execute them. Tools will be executed by another AI.
 
 Query: {query}  
-Summaries: {summaries}
+Summaries: {summaries}
+Prompt: {prompt}
+Observations(potentially previous tool calls): {observations}

application/requirements.txt

@@ -13,6 +13,9 @@ Flask==3.1.1
 faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
 google-genai==1.3.0
+google-api-python-client==2.179.0
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.2
 gTTS==2.5.4
 gunicorn==23.0.0
 javalang==0.13.0
@@ -46,7 +49,7 @@ pandas==2.2.3
 openpyxl==3.1.5
 pathable==0.4.4
 pillow==11.1.0
-portalocker==3.1.1
+portalocker>=2.7.0,<3.0.0
 prance==23.6.21.0
 prompt-toolkit==3.0.51
 protobuf==5.29.3
@@ -62,7 +65,7 @@ python-dotenv==1.0.1
 python-jose==3.4.0
 python-pptx==1.0.2
 redis==5.2.1
-referencing==0.36.2
+referencing>=0.28.0,<0.31.0
 regex==2024.11.6
 requests==2.32.3
 retry==0.9.2

application/retriever/brave_search.py

@@ -1,112 +0,0 @@
-import json
-
-from langchain_community.tools import BraveSearch
-
-from application.core.settings import settings
-from application.llm.llm_creator import LLMCreator
-from application.retriever.base import BaseRetriever
-
-
-class BraveRetSearch(BaseRetriever):
-
-    def __init__(
-        self,
-        source,
-        chat_history,
-        prompt,
-        chunks=2,
-        token_limit=150,
-        gpt_model="docsgpt",
-        user_api_key=None,
-        decoded_token=None,
-    ):
-        self.question = ""
-        self.source = source
-        self.chat_history = chat_history
-        self.prompt = prompt
-        self.chunks = chunks
-        self.gpt_model = gpt_model
-        self.token_limit = (
-            token_limit
-            if token_limit
-            < settings.MODEL_TOKEN_LIMITS.get(
-                self.gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-            else settings.MODEL_TOKEN_LIMITS.get(
-                self.gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-        )
-        self.user_api_key = user_api_key
-        self.decoded_token = decoded_token
-
-    def _get_data(self):
-        if self.chunks == 0:
-            docs = []
-        else:
-            search = BraveSearch.from_api_key(
-                api_key=settings.BRAVE_SEARCH_API_KEY,
-                search_kwargs={"count": int(self.chunks)},
-            )
-            results = search.run(self.question)
-            results = json.loads(results)
-
-            docs = []
-            for i in results:
-                try:
-                    title = i["title"]
-                    link = i["link"]
-                    snippet = i["snippet"]
-                    docs.append({"text": snippet, "title": title, "link": link})
-                except IndexError:
-                    pass
-        if settings.LLM_NAME == "llama.cpp":
-            docs = [docs[0]]
-
-        return docs
-
-    def gen(self):
-        docs = self._get_data()
-
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc["text"] for doc in docs])
-        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-        for doc in docs:
-            yield {"source": doc}
-
-        if len(self.chat_history) > 0:
-            for i in self.chat_history:
-                if "prompt" in i and "response" in i:
-                    messages_combine.append({"role": "user", "content": i["prompt"]})
-                    messages_combine.append(
-                        {"role": "assistant", "content": i["response"]}
-                    )
-        messages_combine.append({"role": "user", "content": self.question})
-
-        llm = LLMCreator.create_llm(
-            settings.LLM_NAME,
-            api_key=settings.API_KEY,
-            user_api_key=self.user_api_key,
-            decoded_token=self.decoded_token,
-        )
-
-        completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
-        for line in completion:
-            yield {"answer": str(line)}
-
-    def search(self, query: str = ""):
-        if query:
-            self.question = query
-        return self._get_data()
-
-    def get_params(self):
-        return {
-            "question": self.question,
-            "source": self.source,
-            "chat_history": self.chat_history,
-            "prompt": self.prompt,
-            "chunks": self.chunks,
-            "token_limit": self.token_limit,
-            "gpt_model": self.gpt_model,
-            "user_api_key": self.user_api_key,
-        }

application/retriever/classic_rag.py

@@ -16,7 +16,7 @@ class ClassicRAG(BaseRetriever):
         token_limit=150,
         gpt_model="docsgpt",
         user_api_key=None,
-        llm_name=settings.LLM_NAME,
+        llm_name=settings.LLM_PROVIDER,
         api_key=settings.API_KEY,
         decoded_token=None,
     ):
@@ -28,10 +28,10 @@ class ClassicRAG(BaseRetriever):
         self.token_limit = (
             token_limit
             if token_limit
-            < settings.MODEL_TOKEN_LIMITS.get(
+            < settings.LLM_TOKEN_LIMITS.get(
                 self.gpt_model, settings.DEFAULT_MAX_HISTORY
             )
-            else settings.MODEL_TOKEN_LIMITS.get(
+            else settings.LLM_TOKEN_LIMITS.get(
                 self.gpt_model, settings.DEFAULT_MAX_HISTORY
             )
         )
@@ -44,8 +44,8 @@ class ClassicRAG(BaseRetriever):
             user_api_key=self.user_api_key,
             decoded_token=decoded_token,
         )
-        self.question = self._rephrase_query()
         self.vectorstore = source["active_docs"] if "active_docs" in source else None
+        self.question = self._rephrase_query()
         self.decoded_token = decoded_token
 
     def _rephrase_query(self):
@@ -53,6 +53,8 @@ class ClassicRAG(BaseRetriever):
             not self.original_question
             or not self.chat_history
             or self.chat_history == []
+            or self.chunks == 0
+            or self.vectorstore is None
         ):
             return self.original_question
 
@@ -77,7 +79,7 @@ class ClassicRAG(BaseRetriever):
             return self.original_question
 
     def _get_data(self):
-        if self.chunks == 0:
+        if self.chunks == 0 or self.vectorstore is None:
             docs = []
         else:
             docsearch = VectorCreator.create_vectorstore(

application/retriever/duckduck_search.py

@@ -1,111 +0,0 @@
-from langchain_community.tools import DuckDuckGoSearchResults
-from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
-
-from application.core.settings import settings
-from application.llm.llm_creator import LLMCreator
-from application.retriever.base import BaseRetriever
-
-
-class DuckDuckSearch(BaseRetriever):
-
-    def __init__(
-        self,
-        source,
-        chat_history,
-        prompt,
-        chunks=2,
-        token_limit=150,
-        gpt_model="docsgpt",
-        user_api_key=None,
-        decoded_token=None,
-    ):
-        self.question = ""
-        self.source = source
-        self.chat_history = chat_history
-        self.prompt = prompt
-        self.chunks = chunks
-        self.gpt_model = gpt_model
-        self.token_limit = (
-            token_limit
-            if token_limit
-            < settings.MODEL_TOKEN_LIMITS.get(
-                self.gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-            else settings.MODEL_TOKEN_LIMITS.get(
-                self.gpt_model, settings.DEFAULT_MAX_HISTORY
-            )
-        )
-        self.user_api_key = user_api_key
-        self.decoded_token = decoded_token
-
-    def _get_data(self):
-        if self.chunks == 0:
-            docs = []
-        else:
-            wrapper = DuckDuckGoSearchAPIWrapper(max_results=self.chunks)
-            search = DuckDuckGoSearchResults(api_wrapper=wrapper, output_format="list")
-            results = search.run(self.question)
-
-            docs = []
-            for i in results:
-                try:
-                    docs.append(
-                        {
-                            "text": i.get("snippet", "").strip(),
-                            "title": i.get("title", "").strip(),
-                            "link": i.get("link", "").strip(),
-                        }
-                    )
-                except IndexError:
-                    pass
-        if settings.LLM_NAME == "llama.cpp":
-            docs = [docs[0]]
-
-        return docs
-
-    def gen(self):
-        docs = self._get_data()
-
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc["text"] for doc in docs])
-        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-        for doc in docs:
-            yield {"source": doc}
-
-        if len(self.chat_history) > 0:
-            for i in self.chat_history:
-                if "prompt" in i and "response" in i:
-                    messages_combine.append({"role": "user", "content": i["prompt"]})
-                    messages_combine.append(
-                        {"role": "assistant", "content": i["response"]}
-                    )
-        messages_combine.append({"role": "user", "content": self.question})
-
-        llm = LLMCreator.create_llm(
-            settings.LLM_NAME,
-            api_key=settings.API_KEY,
-            user_api_key=self.user_api_key,
-            decoded_token=self.decoded_token,
-        )
-
-        completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
-        for line in completion:
-            yield {"answer": str(line)}
-
-    def search(self, query: str = ""):
-        if query:
-            self.question = query
-        return self._get_data()
-
-    def get_params(self):
-        return {
-            "question": self.question,
-            "source": self.source,
-            "chat_history": self.chat_history,
-            "prompt": self.prompt,
-            "chunks": self.chunks,
-            "token_limit": self.token_limit,
-            "gpt_model": self.gpt_model,
-            "user_api_key": self.user_api_key,
-        }

application/retriever/retriever_creator.py

@@ -1,20 +1,16 @@
 from application.retriever.classic_rag import ClassicRAG
-from application.retriever.duckduck_search import DuckDuckSearch
-from application.retriever.brave_search import BraveRetSearch
-
 
 
 class RetrieverCreator:
     retrievers = {
-        'classic': ClassicRAG,
-        'duckduck_search': DuckDuckSearch,
-        'brave_search': BraveRetSearch,
-        'default': ClassicRAG
+        "classic": ClassicRAG,
+        "default": ClassicRAG,
     }
 
     @classmethod
     def create_retriever(cls, type, *args, **kwargs):
-        retiever_class = cls.retrievers.get(type.lower())
+        retriever_type = (type or "default").lower()
+        retiever_class = cls.retrievers.get(retriever_type)
         if not retiever_class:
             raise ValueError(f"No retievers class found for type {type}")
-        return retiever_class(*args, **kwargs)
+        return retiever_class(*args, **kwargs)

application/storage/base.py

@@ -1,4 +1,5 @@
 """Base storage class for file system abstraction."""
+
 from abc import ABC, abstractmethod
 from typing import BinaryIO, List, Callable
 
@@ -7,7 +8,7 @@ class BaseStorage(ABC):
     """Abstract base class for storage implementations."""
 
     @abstractmethod
-    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+    def save_file(self, file_data: BinaryIO, path: str, **kwargs) -> dict:
         """
         Save a file to storage.
 
@@ -92,3 +93,32 @@ class BaseStorage(ABC):
             List[str]: List of file paths
         """
         pass
+        
+    @abstractmethod
+    def is_directory(self, path: str) -> bool:
+        """
+        Check if a path is a directory.
+
+        Args:
+            path: Path to check
+
+        Returns:
+            bool: True if the path is a directory
+        """
+        pass
+
+    @abstractmethod
+    def remove_directory(self, directory: str) -> bool:
+        """
+        Remove a directory and all its contents.
+
+        For local storage, this removes the directory and all files/subdirectories within it.
+        For S3 storage, this removes all objects with the directory path as a prefix.
+
+        Args:
+            directory: Directory path to remove
+
+        Returns:
+            bool: True if removal was successful, False otherwise
+        """
+        pass

application/storage/local.py

@@ -101,3 +101,40 @@ class LocalStorage(BaseStorage):
             raise FileNotFoundError(f"File not found: {full_path}")
 
         return processor_func(local_path=full_path, **kwargs)
+
+    def is_directory(self, path: str) -> bool:
+        """
+        Check if a path is a directory in local storage.
+        
+        Args:
+            path: Path to check
+        
+        Returns:
+            bool: True if the path is a directory, False otherwise
+        """
+        full_path = self._get_full_path(path)
+        return os.path.isdir(full_path)
+
+    def remove_directory(self, directory: str) -> bool:
+        """
+        Remove a directory and all its contents from local storage.
+
+        Args:
+            directory: Directory path to remove
+
+        Returns:
+            bool: True if removal was successful, False otherwise
+        """
+        full_path = self._get_full_path(directory)
+
+        if not os.path.exists(full_path):
+            return False
+
+        if not os.path.isdir(full_path):
+            return False
+
+        try:
+            shutil.rmtree(full_path)
+            return True
+        except (OSError, PermissionError):
+            return False

application/storage/s3.py

@@ -1,13 +1,14 @@
 """S3 storage implementation."""
+
 import io
-from typing import BinaryIO, List, Callable
 import os
+from typing import BinaryIO, Callable, List
 
 import boto3
-from botocore.exceptions import ClientError
+from application.core.settings import settings
 
 from application.storage.base import BaseStorage
-from application.core.settings import settings
+from botocore.exceptions import ClientError
 
 
 class S3Storage(BaseStorage):
@@ -20,38 +21,48 @@ class S3Storage(BaseStorage):
         Args:
             bucket_name: S3 bucket name (optional, defaults to settings)
         """
-        self.bucket_name = bucket_name or getattr(settings, "S3_BUCKET_NAME", "docsgpt-test-bucket")
+        self.bucket_name = bucket_name or getattr(
+            settings, "S3_BUCKET_NAME", "docsgpt-test-bucket"
+        )
 
         # Get credentials from settings
+
         aws_access_key_id = getattr(settings, "SAGEMAKER_ACCESS_KEY", None)
         aws_secret_access_key = getattr(settings, "SAGEMAKER_SECRET_KEY", None)
         region_name = getattr(settings, "SAGEMAKER_REGION", None)
 
         self.s3 = boto3.client(
-            's3',
+            "s3",
             aws_access_key_id=aws_access_key_id,
             aws_secret_access_key=aws_secret_access_key,
-            region_name=region_name
+            region_name=region_name,
         )
 
-    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+    def save_file(
+        self,
+        file_data: BinaryIO,
+        path: str,
+        storage_class: str = "INTELLIGENT_TIERING",
+        **kwargs,
+    ) -> dict:
         """Save a file to S3 storage."""
-        self.s3.upload_fileobj(file_data, self.bucket_name, path)
+        self.s3.upload_fileobj(
+            file_data, self.bucket_name, path, ExtraArgs={"StorageClass": storage_class}
+        )
 
         region = getattr(settings, "SAGEMAKER_REGION", None)
 
         return {
-            'storage_type': 's3',
-            'bucket_name': self.bucket_name,
-            'uri': f's3://{self.bucket_name}/{path}',
-            'region': region
+            "storage_type": "s3",
+            "bucket_name": self.bucket_name,
+            "uri": f"s3://{self.bucket_name}/{path}",
+            "region": region,
         }
 
     def get_file(self, path: str) -> BinaryIO:
         """Get a file from S3 storage."""
         if not self.file_exists(path):
             raise FileNotFoundError(f"File not found: {path}")
-
         file_obj = io.BytesIO()
         self.s3.download_fileobj(self.bucket_name, path, file_obj)
         file_obj.seek(0)
@@ -76,18 +87,17 @@ class S3Storage(BaseStorage):
     def list_files(self, directory: str) -> List[str]:
         """List all files in a directory in S3 storage."""
         # Ensure directory ends with a slash if it's not empty
-        if directory and not directory.endswith('/'):
-            directory += '/'
 
+        if directory and not directory.endswith("/"):
+            directory += "/"
         result = []
-        paginator = self.s3.get_paginator('list_objects_v2')
+        paginator = self.s3.get_paginator("list_objects_v2")
         pages = paginator.paginate(Bucket=self.bucket_name, Prefix=directory)
 
         for page in pages:
-            if 'Contents' in page:
-                for obj in page['Contents']:
-                    result.append(obj['Key'])
-
+            if "Contents" in page:
+                for obj in page["Contents"]:
+                    result.append(obj["Key"])
         return result
 
     def process_file(self, path: str, processor_func: Callable, **kwargs):
@@ -98,23 +108,99 @@ class S3Storage(BaseStorage):
             path: Path to the file
             processor_func: Function that processes the file
             **kwargs: Additional arguments to pass to the processor function
-        
+
         Returns:
             The result of the processor function
         """
-        import tempfile
         import logging
-        
+        import tempfile
+
         if not self.file_exists(path):
             raise FileNotFoundError(f"File not found in S3: {path}")
-        
-        with tempfile.NamedTemporaryFile(suffix=os.path.splitext(path)[1], delete=True) as temp_file:
+        with tempfile.NamedTemporaryFile(
+            suffix=os.path.splitext(path)[1], delete=True
+        ) as temp_file:
             try:
                 # Download the file from S3 to the temporary file
+
                 self.s3.download_fileobj(self.bucket_name, path, temp_file)
                 temp_file.flush()
-                
+
                 return processor_func(local_path=temp_file.name, **kwargs)
             except Exception as e:
                 logging.error(f"Error processing S3 file {path}: {e}", exc_info=True)
                 raise
+
+    def is_directory(self, path: str) -> bool:
+        """
+        Check if a path is a directory in S3 storage.
+
+        In S3, directories are virtual concepts. A path is considered a directory
+        if there are objects with the path as a prefix.
+
+        Args:
+            path: Path to check
+
+        Returns:
+            bool: True if the path is a directory, False otherwise
+        """
+        # Ensure path ends with a slash if not empty
+        if path and not path.endswith('/'):
+            path += '/'
+
+        response = self.s3.list_objects_v2(
+            Bucket=self.bucket_name,
+            Prefix=path,
+            MaxKeys=1
+        )
+
+        return 'Contents' in response
+
+    def remove_directory(self, directory: str) -> bool:
+        """
+        Remove a directory and all its contents from S3 storage.
+
+        In S3, this removes all objects with the directory path as a prefix.
+        Since S3 doesn't have actual directories, this effectively removes
+        all files within the virtual directory structure.
+
+        Args:
+            directory: Directory path to remove
+
+        Returns:
+            bool: True if removal was successful, False otherwise
+        """
+        # Ensure directory ends with a slash if not empty
+        if directory and not directory.endswith('/'):
+            directory += '/'
+
+        try:
+            # Get all objects with the directory prefix
+            objects_to_delete = []
+            paginator = self.s3.get_paginator('list_objects_v2')
+            pages = paginator.paginate(Bucket=self.bucket_name, Prefix=directory)
+
+            for page in pages:
+                if 'Contents' in page:
+                    for obj in page['Contents']:
+                        objects_to_delete.append({'Key': obj['Key']})
+
+            if not objects_to_delete:
+                return False
+
+            batch_size = 1000
+            for i in range(0, len(objects_to_delete), batch_size):
+                batch = objects_to_delete[i:i + batch_size]
+
+                response = self.s3.delete_objects(
+                    Bucket=self.bucket_name,
+                    Delete={'Objects': batch}
+                )
+
+                if 'Errors' in response and response['Errors']:
+                    return False
+
+            return True
+
+        except ClientError:
+            return False

application/utils.py

@@ -1,8 +1,13 @@
 import hashlib
+import os
 import re
+import uuid
 
 import tiktoken
 from flask import jsonify, make_response
+from werkzeug.utils import secure_filename
+
+from application.core.settings import settings
 
 
 _encoding = None
@@ -15,6 +20,41 @@ def get_encoding():
     return _encoding
 
 
+def get_gpt_model() -> str:
+    """Get the appropriate GPT model based on provider"""
+    model_map = {
+        "openai": "gpt-4o-mini",
+        "anthropic": "claude-2",
+        "groq": "llama3-8b-8192",
+        "novita": "deepseek/deepseek-r1",
+    }
+    return settings.LLM_NAME or model_map.get(settings.LLM_PROVIDER, "")
+
+
+def safe_filename(filename):
+    """
+    Creates a safe filename that preserves the original extension.
+    Uses secure_filename, but ensures a proper filename is returned even with non-Latin characters.
+
+    Args:
+        filename (str): The original filename
+
+    Returns:
+        str: A safe filename that can be used for storage
+    """
+    if not filename:
+        return str(uuid.uuid4())
+    _, extension = os.path.splitext(filename)
+
+    safe_name = secure_filename(filename)
+
+    # If secure_filename returns just the extension or an empty string
+
+    if not safe_name or safe_name == extension.lstrip("."):
+        return f"{str(uuid.uuid4())}{extension}"
+    return safe_name
+
+
 def num_tokens_from_string(string: str) -> int:
     encoding = get_encoding()
     if isinstance(string, str):
@@ -39,7 +79,6 @@ def count_tokens_docs(docs):
     docs_content = ""
     for doc in docs:
         docs_content += doc.page_content
-
     tokens = num_tokens_from_string(docs_content)
     return tokens
 
@@ -51,7 +90,7 @@ def check_required_fields(data, required_fields):
             jsonify(
                 {
                     "success": False,
-                    "message": f"Missing fields: {', '.join(missing_fields)}",
+                    "message": f"Missing required fields: {', '.join(missing_fields)}",
                 }
             ),
             400,
@@ -59,6 +98,27 @@ def check_required_fields(data, required_fields):
     return None
 
 
+def validate_required_fields(data, required_fields):
+    missing_fields = []
+    empty_fields = []
+
+    for field in required_fields:
+        if field not in data:
+            missing_fields.append(field)
+        elif not data[field]:
+            empty_fields.append(field)
+    errors = []
+    if missing_fields:
+        errors.append(f"Missing required fields: {', '.join(missing_fields)}")
+    if empty_fields:
+        errors.append(f"Empty values in required fields: {', '.join(empty_fields)}")
+    if errors:
+        return make_response(
+            jsonify({"success": False, "message": " | ".join(errors)}), 400
+        )
+    return None
+
+
 def get_hash(data):
     return hashlib.md5(data.encode(), usedforsecurity=False).hexdigest()
 
@@ -74,13 +134,12 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
         max_token_limit
         if max_token_limit
         and max_token_limit
-        < settings.MODEL_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
-        else settings.MODEL_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
+        < settings.LLM_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
+        else settings.LLM_TOKEN_LIMITS.get(gpt_model, settings.DEFAULT_MAX_HISTORY)
     )
 
     if not history:
         return []
-
     trimmed_history = []
     tokens_current_history = 0
 
@@ -89,18 +148,15 @@ def limit_chat_history(history, max_token_limit=None, gpt_model="docsgpt"):
         if "prompt" in message and "response" in message:
             tokens_batch += num_tokens_from_string(message["prompt"])
             tokens_batch += num_tokens_from_string(message["response"])
-
         if "tool_calls" in message:
             for tool_call in message["tool_calls"]:
                 tool_call_string = f"Tool: {tool_call.get('tool_name')} | Action: {tool_call.get('action_name')} | Args: {tool_call.get('arguments')} | Response: {tool_call.get('result')}"
                 tokens_batch += num_tokens_from_string(tool_call_string)
-
         if tokens_current_history + tokens_batch < max_token_limit:
             tokens_current_history += tokens_batch
             trimmed_history.insert(0, message)
         else:
             break
-
     return trimmed_history
 
 
@@ -109,3 +165,14 @@ def validate_function_name(function_name):
     if not re.match(r"^[a-zA-Z0-9_-]+$", function_name):
         return False
     return True
+
+
+def generate_image_url(image_path):
+    strategy = getattr(settings, "URL_STRATEGY", "backend")
+    if strategy == "s3":
+        bucket_name = getattr(settings, "S3_BUCKET_NAME", "docsgpt-test-bucket")
+        region_name = getattr(settings, "SAGEMAKER_REGION", "eu-central-1")
+        return f"https://{bucket_name}.s3.{region_name}.amazonaws.com/{image_path}"
+    else:
+        base_url = getattr(settings, "API_URL", "http://localhost:7091")
+        return f"{base_url}/api/images/{image_path}"

application/vectorstore/faiss.py

@@ -1,5 +1,6 @@
 import os
 import tempfile
+import io
 
 from langchain_community.vectorstores import FAISS
 
@@ -32,22 +33,26 @@ class FaissStore(BaseVectorStore):
                 with tempfile.TemporaryDirectory() as temp_dir:
                     faiss_path = f"{self.path}/index.faiss"
                     pkl_path = f"{self.path}/index.pkl"
-                    
-                    if not self.storage.file_exists(faiss_path) or not self.storage.file_exists(pkl_path):
-                        raise FileNotFoundError(f"Index files not found in storage at {self.path}")
-                    
+
+                    if not self.storage.file_exists(
+                        faiss_path
+                    ) or not self.storage.file_exists(pkl_path):
+                        raise FileNotFoundError(
+                            f"Index files not found in storage at {self.path}"
+                        )
+
                     faiss_file = self.storage.get_file(faiss_path)
                     pkl_file = self.storage.get_file(pkl_path)
-                    
+
                     local_faiss_path = os.path.join(temp_dir, "index.faiss")
                     local_pkl_path = os.path.join(temp_dir, "index.pkl")
-                    
-                    with open(local_faiss_path, 'wb') as f:
+
+                    with open(local_faiss_path, "wb") as f:
                         f.write(faiss_file.read())
-                    
-                    with open(local_pkl_path, 'wb') as f:
+
+                    with open(local_pkl_path, "wb") as f:
                         f.write(pkl_file.read())
-                    
+
                     self.docsearch = FAISS.load_local(
                         temp_dir, self.embeddings, allow_dangerous_deserialization=True
                     )
@@ -62,8 +67,37 @@ class FaissStore(BaseVectorStore):
     def add_texts(self, *args, **kwargs):
         return self.docsearch.add_texts(*args, **kwargs)
 
-    def save_local(self, *args, **kwargs):
-        return self.docsearch.save_local(*args, **kwargs)
+    def _save_to_storage(self):
+        """
+        Save the FAISS index to storage using temporary directory pattern.
+        Works consistently for both local and S3 storage.
+        """
+        with tempfile.TemporaryDirectory() as temp_dir:
+            self.docsearch.save_local(temp_dir)
+
+            faiss_path = os.path.join(temp_dir, "index.faiss")
+            pkl_path = os.path.join(temp_dir, "index.pkl")
+
+            with open(faiss_path, "rb") as f_faiss:
+                faiss_data = f_faiss.read()
+
+            with open(pkl_path, "rb") as f_pkl:
+                pkl_data = f_pkl.read()
+
+            storage_path = get_vectorstore(self.source_id)
+            self.storage.save_file(io.BytesIO(faiss_data), f"{storage_path}/index.faiss")
+            self.storage.save_file(io.BytesIO(pkl_data), f"{storage_path}/index.pkl")
+
+        return True
+
+    def save_local(self, path=None):
+        if path:
+            os.makedirs(path, exist_ok=True)
+            self.docsearch.save_local(path)
+
+        self._save_to_storage()
+
+        return True
 
     def delete_index(self, *args, **kwargs):
         return self.docsearch.delete(*args, **kwargs)
@@ -99,13 +133,17 @@ class FaissStore(BaseVectorStore):
         return chunks
 
     def add_chunk(self, text, metadata=None):
+        """Add a new chunk and save to storage."""
         metadata = metadata or {}
         doc = Document(text=text, extra_info=metadata).to_langchain_format()
         doc_id = self.docsearch.add_documents([doc])
-        self.save_local(self.path)
+        self._save_to_storage()
         return doc_id
 
+
+
     def delete_chunk(self, chunk_id):
+        """Delete a chunk and save to storage."""
         self.delete_index([chunk_id])
-        self.save_local(self.path)
+        self._save_to_storage()
         return True

application/vectorstore/pgvector.py

@@ -0,0 +1,303 @@
+import logging
+from typing import List, Optional, Any, Dict
+from application.core.settings import settings
+from application.vectorstore.base import BaseVectorStore
+from application.vectorstore.document_class import Document
+
+
+class PGVectorStore(BaseVectorStore):
+    def __init__(
+        self,
+        source_id: str = "",
+        embeddings_key: str = "embeddings",
+        table_name: str = "documents",
+        vector_column: str = "embedding",
+        text_column: str = "text",
+        metadata_column: str = "metadata",
+        connection_string: str = None,
+    ):
+        super().__init__()
+        # Store the source_id for use in add_chunk
+        self._source_id = str(source_id).replace("application/indexes/", "").rstrip("/")
+        self._embeddings_key = embeddings_key
+        self._table_name = table_name
+        self._vector_column = vector_column
+        self._text_column = text_column
+        self._metadata_column = metadata_column
+        self._embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        
+        # Use provided connection string or fall back to settings
+        self._connection_string = connection_string or getattr(settings, 'PGVECTOR_CONNECTION_STRING', None)
+        
+        if not self._connection_string:
+            raise ValueError(
+                "PostgreSQL connection string is required. "
+                "Set PGVECTOR_CONNECTION_STRING in settings or pass connection_string parameter."
+            )
+
+        try:
+            import psycopg2
+            from psycopg2.extras import Json
+            import pgvector.psycopg2
+        except ImportError:
+            raise ImportError(
+                "Could not import required packages. "
+                "Please install with `pip install psycopg2-binary pgvector`."
+            )
+
+        self._psycopg2 = psycopg2
+        self._Json = Json
+        self._pgvector = pgvector.psycopg2
+        self._connection = None
+        self._ensure_table_exists()
+
+    def _get_connection(self):
+        """Get or create database connection"""
+        if self._connection is None or self._connection.closed:
+            self._connection = self._psycopg2.connect(self._connection_string)
+            # Register pgvector types
+            self._pgvector.register_vector(self._connection)
+        return self._connection
+
+    def _ensure_table_exists(self):
+        """Create table and enable pgvector extension if they don't exist"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            # Enable pgvector extension
+            cursor.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+            
+            # Get embedding dimension
+            embedding_dim = getattr(self._embedding, 'dimension', 1536)  # Default to OpenAI dimension
+            
+            # Create table with vector column
+            create_table_query = f"""
+            CREATE TABLE IF NOT EXISTS {self._table_name} (
+                id SERIAL PRIMARY KEY,
+                {self._text_column} TEXT NOT NULL,
+                {self._vector_column} vector({embedding_dim}),
+                {self._metadata_column} JSONB,
+                source_id TEXT NOT NULL,
+                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            );
+            """
+            cursor.execute(create_table_query)
+            
+            # Create index for vector similarity search
+            index_query = f"""
+            CREATE INDEX IF NOT EXISTS {self._table_name}_{self._vector_column}_idx 
+            ON {self._table_name} USING ivfflat ({self._vector_column} vector_cosine_ops)
+            WITH (lists = 100);
+            """
+            cursor.execute(index_query)
+            
+            # Create index for source_id filtering
+            source_index_query = f"""
+            CREATE INDEX IF NOT EXISTS {self._table_name}_source_id_idx 
+            ON {self._table_name} (source_id);
+            """
+            cursor.execute(source_index_query)
+            
+            conn.commit()
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error creating table: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def search(self, question: str, k: int = 2, *args, **kwargs) -> List[Document]:
+        """Search for similar documents using vector similarity"""
+        query_vector = self._embedding.embed_query(question)
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            # Use cosine distance for similarity search with proper vector formatting
+            search_query = f"""
+            SELECT {self._text_column}, {self._metadata_column}, 
+                   ({self._vector_column} <=> %s::vector) as distance
+            FROM {self._table_name}
+            WHERE source_id = %s
+            ORDER BY {self._vector_column} <=> %s::vector
+            LIMIT %s;
+            """
+            
+            cursor.execute(search_query, (query_vector, self._source_id, query_vector, k))
+            results = cursor.fetchall()
+            
+            
+            documents = []
+            for text, metadata, distance in results:
+                metadata = metadata or {}
+                documents.append(Document(page_content=text, metadata=metadata))
+            
+            return documents
+            
+        except Exception as e:
+            logging.error(f"Error searching documents: {e}", exc_info=True)
+            return []
+        finally:
+            cursor.close()
+
+    def add_texts(
+        self,
+        texts: List[str],
+        metadatas: Optional[List[Dict[str, Any]]] = None,
+        *args,
+        **kwargs,
+    ) -> List[str]:
+        """Add texts with their embeddings to the vector store"""
+        if not texts:
+            return []
+        
+        embeddings = self._embedding.embed_documents(texts)
+        metadatas = metadatas or [{}] * len(texts)
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            insert_query = f"""
+            INSERT INTO {self._table_name} ({self._text_column}, {self._vector_column}, {self._metadata_column}, source_id)
+            VALUES (%s, %s, %s, %s)
+            RETURNING id;
+            """
+            
+            inserted_ids = []
+            for text, embedding, metadata in zip(texts, embeddings, metadatas):
+                cursor.execute(
+                    insert_query,
+                    (text, embedding, self._Json(metadata), self._source_id)
+                )
+                inserted_id = cursor.fetchone()[0]
+                inserted_ids.append(str(inserted_id))
+            
+            conn.commit()
+            return inserted_ids
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error adding texts: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def delete_index(self, *args, **kwargs):
+        """Delete all documents for this source_id"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            delete_query = f"DELETE FROM {self._table_name} WHERE source_id = %s;"
+            cursor.execute(delete_query, (self._source_id,))
+            conn.commit()
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error deleting index: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def save_local(self, *args, **kwargs):
+        """No-op for PostgreSQL - data is already persisted"""
+        pass
+
+    def get_chunks(self) -> List[Dict[str, Any]]:
+        """Get all chunks for this source_id"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            select_query = f"""
+            SELECT id, {self._text_column}, {self._metadata_column}
+            FROM {self._table_name}
+            WHERE source_id = %s;
+            """
+            cursor.execute(select_query, (self._source_id,))
+            results = cursor.fetchall()
+            
+            chunks = []
+            for doc_id, text, metadata in results:
+                chunks.append({
+                    "doc_id": str(doc_id),
+                    "text": text,
+                    "metadata": metadata or {}
+                })
+            
+            return chunks
+            
+        except Exception as e:
+            logging.error(f"Error getting chunks: {e}")
+            return []
+        finally:
+            cursor.close()
+
+    def add_chunk(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> str:
+        """Add a single chunk to the vector store"""
+        metadata = metadata or {}
+        
+        # Create a copy to avoid modifying the original metadata
+        final_metadata = metadata.copy()
+        
+        # Ensure the source_id is in the metadata so the chunk can be found by filters
+        final_metadata["source_id"] = self._source_id
+        
+        embeddings = self._embedding.embed_documents([text])
+        
+        if not embeddings:
+            raise ValueError("Could not generate embedding for chunk")
+        
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            insert_query = f"""
+            INSERT INTO {self._table_name} ({self._text_column}, {self._vector_column}, {self._metadata_column}, source_id)
+            VALUES (%s, %s, %s, %s)
+            RETURNING id;
+            """
+            
+            cursor.execute(
+                insert_query,
+                (text, embeddings[0], self._Json(final_metadata), self._source_id)
+            )
+            inserted_id = cursor.fetchone()[0]
+            conn.commit()
+            
+            return str(inserted_id)
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error adding chunk: {e}")
+            raise
+        finally:
+            cursor.close()
+
+    def delete_chunk(self, chunk_id: str) -> bool:
+        """Delete a specific chunk by its ID"""
+        conn = self._get_connection()
+        cursor = conn.cursor()
+        
+        try:
+            delete_query = f"DELETE FROM {self._table_name} WHERE id = %s AND source_id = %s;"
+            cursor.execute(delete_query, (int(chunk_id), self._source_id))
+            deleted_count = cursor.rowcount
+            conn.commit()
+            
+            return deleted_count > 0
+            
+        except Exception as e:
+            conn.rollback()
+            logging.error(f"Error deleting chunk: {e}")
+            return False
+        finally:
+            cursor.close()
+
+    def __del__(self):
+        """Close database connection when object is destroyed"""
+        if hasattr(self, '_connection') and self._connection and not self._connection.closed:
+            self._connection.close()

application/vectorstore/qdrant.py

@@ -1,5 +1,7 @@
+import logging
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
 
 
 class QdrantStore(BaseVectorStore):
@@ -7,18 +9,22 @@ class QdrantStore(BaseVectorStore):
         from qdrant_client import models
         from langchain_community.vectorstores.qdrant import Qdrant
 
+        # Store the source_id for use in add_chunk
+        self._source_id = str(source_id).replace("application/indexes/", "").rstrip("/")
+        
         self._filter = models.Filter(
             must=[
                 models.FieldCondition(
                     key="metadata.source_id",
-                    match=models.MatchValue(value=source_id.replace("application/indexes/", "").rstrip("/")),
+                    match=models.MatchValue(value=self._source_id),
                 )
             ]
         )
 
+        embedding=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
         self._docsearch = Qdrant.construct_instance(
             ["TEXT_TO_OBTAIN_EMBEDDINGS_DIMENSION"],
-            embedding=self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key),
+            embedding=embedding,
             collection_name=settings.QDRANT_COLLECTION_NAME,
             location=settings.QDRANT_LOCATION,
             url=settings.QDRANT_URL,
@@ -32,6 +38,32 @@ class QdrantStore(BaseVectorStore):
             path=settings.QDRANT_PATH,
             distance_func=settings.QDRANT_DISTANCE_FUNC,
         )
+        try:
+            collections = self._docsearch.client.get_collections()
+            collection_exists = settings.QDRANT_COLLECTION_NAME in [
+                collection.name for collection in collections.collections
+            ]
+            
+            if not collection_exists:
+                self._docsearch.client.recreate_collection(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    vectors_config=models.VectorParams(size=embedding.client[1].word_embedding_dimension, distance=models.Distance.COSINE),
+                )
+            
+            # Ensure the required index exists for metadata.source_id
+            try:
+                self._docsearch.client.create_payload_index(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    field_name="metadata.source_id",
+                    field_schema=models.PayloadSchemaType.KEYWORD,
+                )
+            except Exception as index_error:
+                # Index might already exist, which is fine
+                if "already exists" not in str(index_error).lower():
+                    logging.warning(f"Could not create index for metadata.source_id: {index_error}")
+                    
+        except Exception as e:
+            logging.warning(f"Could not check for collection: {e}")
 
     def search(self, *args, **kwargs):
         return self._docsearch.similarity_search(filter=self._filter, *args, **kwargs)
@@ -46,3 +78,59 @@ class QdrantStore(BaseVectorStore):
         return self._docsearch.client.delete(
             collection_name=settings.QDRANT_COLLECTION_NAME, points_selector=self._filter
         )
+
+    def get_chunks(self):
+        try:
+
+            chunks = []
+            offset = None
+            while True:
+                records, offset = self._docsearch.client.scroll(
+                    collection_name=settings.QDRANT_COLLECTION_NAME,
+                    scroll_filter=self._filter,
+                    limit=10,
+                    with_payload=True,
+                    with_vectors=False,
+                    offset=offset,
+                )
+                for record in records:
+                    doc_id = record.id
+                    text = record.payload.get("page_content")
+                    metadata = record.payload.get("metadata")
+                    chunks.append(
+                        {"doc_id": doc_id, "text": text, "metadata": metadata}
+                    )
+                if offset is None:
+                    break
+            return chunks
+        except Exception as e:
+            logging.error(f"Error getting chunks: {e}", exc_info=True)
+            return []
+
+    def add_chunk(self, text, metadata=None):
+        import uuid
+        metadata = metadata or {}
+        
+        # Create a copy to avoid modifying the original metadata
+        final_metadata = metadata.copy()
+        
+        # Ensure the source_id is in the metadata so the chunk can be found by filters
+        final_metadata["source_id"] = self._source_id
+        
+        doc = Document(page_content=text, metadata=final_metadata)
+        # Generate a unique ID for the document
+        doc_id = str(uuid.uuid4())
+        doc.id = doc_id
+        doc_ids = self._docsearch.add_documents([doc])
+        return doc_ids[0] if doc_ids else doc_id
+
+    def delete_chunk(self, chunk_id):
+        try:
+            self._docsearch.client.delete(
+                collection_name=settings.QDRANT_COLLECTION_NAME,
+                points_selector=[chunk_id],
+            )
+            return True
+        except Exception as e:
+            logging.error(f"Error deleting chunk: {e}", exc_info=True)
+            return False

application/vectorstore/vector_creator.py

@@ -3,6 +3,7 @@ from application.vectorstore.elasticsearch import ElasticsearchStore
 from application.vectorstore.milvus import MilvusStore
 from application.vectorstore.mongodb import MongoDBVectorStore
 from application.vectorstore.qdrant import QdrantStore
+from application.vectorstore.pgvector import PGVectorStore
 
 
 class VectorCreator:
@@ -12,6 +13,7 @@ class VectorCreator:
         "mongodb": MongoDBVectorStore,
         "qdrant": QdrantStore,
         "milvus": MilvusStore,
+        "pgvector": PGVectorStore
     }
 
     @classmethod

application/worker.py

@@ -6,6 +6,7 @@ import os
 import shutil
 import string
 import tempfile
+from typing import Any, Dict
 import zipfile
 
 from collections import Counter
@@ -16,11 +17,12 @@ from bson.dbref import DBRef
 from bson.objectid import ObjectId
 
 from application.agents.agent_creator import AgentCreator
-from application.api.answer.routes import get_prompt
+from application.api.answer.services.stream_processor import get_prompt
 
 from application.core.mongo_db import MongoDB
 from application.core.settings import settings
 from application.parser.chunking import Chunker
+from application.parser.connectors.connector_creator import ConnectorCreator
 from application.parser.embedding_pipeline import embed_and_store_documents
 from application.parser.file.bulk import SimpleDirectoryReader
 from application.parser.remote.remote_creator import RemoteCreator
@@ -35,17 +37,22 @@ db = mongo[settings.MONGO_DB_NAME]
 sources_collection = db["sources"]
 
 # Constants
+
 MIN_TOKENS = 150
 MAX_TOKENS = 1250
 RECURSION_DEPTH = 2
 
 
 # Define a function to extract metadata from a given filename.
+
+
 def metadata_from_filename(title):
     return {"title": title}
 
 
 # Define a function to generate a random string of a given length.
+
+
 def generate_random_string(length):
     return "".join([string.ascii_letters[i % 52] for i in range(length)])
 
@@ -68,7 +75,6 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
     if current_depth > max_depth:
         logging.warning(f"Reached maximum recursion depth of {max_depth}")
         return
-
     try:
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             zip_ref.extractall(extract_to)
@@ -76,12 +82,13 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
     except Exception as e:
         logging.error(f"Error extracting zip file {zip_path}: {e}", exc_info=True)
         return
-
     # Check for nested zip files and extract them
+
     for root, dirs, files in os.walk(extract_to):
         for file in files:
             if file.endswith(".zip"):
                 # If a nested zip file is found, extract it recursively
+
                 file_path = os.path.join(root, file)
                 extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
 
@@ -98,11 +105,23 @@ def download_file(url, params, dest_path):
 
 
 def upload_index(full_path, file_data):
+    files = None
     try:
         if settings.VECTOR_STORE == "faiss":
+            faiss_path = full_path + "/index.faiss"
+            pkl_path = full_path + "/index.pkl"
+
+            if not os.path.exists(faiss_path):
+                logging.error(f"FAISS index file not found: {faiss_path}")
+                raise FileNotFoundError(f"FAISS index file not found: {faiss_path}")
+
+            if not os.path.exists(pkl_path):
+                logging.error(f"FAISS pickle file not found: {pkl_path}")
+                raise FileNotFoundError(f"FAISS pickle file not found: {pkl_path}")
+
             files = {
-                "file_faiss": open(full_path + "/index.faiss", "rb"),
-                "file_pkl": open(full_path + "/index.pkl", "rb"),
+                "file_faiss": open(faiss_path, "rb"),
+                "file_pkl": open(pkl_path, "rb"),
             }
             response = requests.post(
                 urljoin(settings.API_URL, "/api/upload_index"),
@@ -114,11 +133,11 @@ def upload_index(full_path, file_data):
                 urljoin(settings.API_URL, "/api/upload_index"), data=file_data
             )
         response.raise_for_status()
-    except requests.RequestException as e:
+    except (requests.RequestException, FileNotFoundError) as e:
         logging.error(f"Error uploading index: {e}")
         raise
     finally:
-        if settings.VECTOR_STORE == "faiss":
+        if settings.VECTOR_STORE == "faiss" and files is not None:
             for file in files.values():
                 file.close()
 
@@ -139,12 +158,12 @@ def run_agent_logic(agent_config, input_data):
         user_api_key = agent_config["key"]
         agent_type = agent_config.get("agent_type", "classic")
         decoded_token = {"sub": agent_config.get("user")}
-        prompt = get_prompt(prompt_id)
+        prompt = get_prompt(prompt_id, db["prompts"])
         agent = AgentCreator.create_agent(
             agent_type,
             endpoint="webhook",
-            llm_name=settings.LLM_NAME,
-            gpt_model=settings.MODEL_NAME,
+            llm_name=settings.LLM_PROVIDER,
+            gpt_model=settings.LLM_NAME,
             api_key=settings.API_KEY,
             user_api_key=user_api_key,
             prompt=prompt,
@@ -159,7 +178,7 @@ def run_agent_logic(agent_config, input_data):
             prompt=prompt,
             chunks=chunks,
             token_limit=settings.DEFAULT_MAX_HISTORY,
-            gpt_model=settings.MODEL_NAME,
+            gpt_model=settings.LLM_NAME,
             user_api_key=user_api_key,
             decoded_token=decoded_token,
         )
@@ -178,7 +197,6 @@ def run_agent_logic(agent_config, input_data):
                 tool_calls.extend(line["tool_calls"])
             elif "thought" in line:
                 thought += line["thought"]
-
         result = {
             "answer": response_full,
             "sources": source_log_docs,
@@ -193,8 +211,11 @@ def run_agent_logic(agent_config, input_data):
 
 
 # Define the main function for ingesting and processing documents.
+
+
 def ingest_worker(
-    self, directory, formats, name_job, filename, user, retriever="classic"
+    self, directory, formats, job_name, file_path, filename, user, 
+    retriever="classic"
 ):
     """
     Ingest and process documents.
@@ -203,9 +224,10 @@ def ingest_worker(
         self: Reference to the instance of the task.
         directory (str): Specifies the directory for ingesting ('inputs' or 'temp').
         formats (list of str): List of file extensions to consider for ingestion (e.g., [".rst", ".md"]).
-        name_job (str): Name of the job for this ingestion task.
-        filename (str): Name of the file to be ingested.
-        user (str): Identifier for the user initiating the ingestion.
+        job_name (str): Name of the job for this ingestion task (original, unsanitized).
+        file_path (str): Complete file path to use consistently throughout the pipeline.
+        filename (str): Original unsanitized filename provided by the user.
+        user (str): Identifier for the user initiating the ingestion (original, unsanitized).
         retriever (str): Type of retriever to use for processing the documents.
 
     Returns:
@@ -218,36 +240,57 @@ def ingest_worker(
     sample = False
 
     storage = StorageCreator.get_storage()
-
-    full_path = os.path.join(directory, user, name_job)
-    source_file_path = os.path.join(full_path, filename)
-
-    logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": name_job})
+    
+    logging.info(f"Ingest path: {file_path}", extra={"user": user, "job": job_name})
 
     # Create temporary working directory
+
     with tempfile.TemporaryDirectory() as temp_dir:
         try:
             os.makedirs(temp_dir, exist_ok=True)
 
-            # Download file from storage to temp directory
-            temp_file_path = os.path.join(temp_dir, filename)
-            file_data = storage.get_file(source_file_path)
+            if storage.is_directory(file_path):
+                # Handle directory case
+                logging.info(f"Processing directory: {file_path}")
+                files_list = storage.list_files(file_path)
+                
+                for storage_file_path in files_list:
+                    if storage.is_directory(storage_file_path):
+                        continue
+                        
+                    # Create relative path structure in temp directory
+                    rel_path = os.path.relpath(storage_file_path, file_path)
+                    local_file_path = os.path.join(temp_dir, rel_path)
+                    
+                    os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
+                    
+                    # Download file
+                    try:
+                        file_data = storage.get_file(storage_file_path)
+                        with open(local_file_path, "wb") as f:
+                            f.write(file_data.read())
+                    except Exception as e:
+                        logging.error(f"Error downloading file {storage_file_path}: {e}")
+                        continue
+            else:
+                # Handle single file case
+                temp_filename = os.path.basename(file_path)
+                temp_file_path = os.path.join(temp_dir, temp_filename)
+                
+                file_data = storage.get_file(file_path)
+                with open(temp_file_path, "wb") as f:
+                    f.write(file_data.read())
 
-            with open(temp_file_path, "wb") as f:
-                f.write(file_data.read())
+                # Handle zip files
+                if temp_filename.endswith(".zip"):
+                    logging.info(f"Extracting zip file: {temp_filename}")
+                    extract_zip_recursive(
+                        temp_file_path, temp_dir, current_depth=0, max_depth=RECURSION_DEPTH
+                    )
 
             self.update_state(state="PROGRESS", meta={"current": 1})
-
-            # Handle zip files
-            if filename.endswith(".zip"):
-                logging.info(f"Extracting zip file: {filename}")
-                extract_zip_recursive(
-                    temp_file_path, temp_dir, current_depth=0, max_depth=RECURSION_DEPTH
-                )
-
             if sample:
                 logging.info(f"Sample mode enabled. Using {limit} documents.")
-
             reader = SimpleDirectoryReader(
                 input_dir=temp_dir,
                 input_files=input_files,
@@ -257,6 +300,9 @@ def ingest_worker(
                 file_metadata=metadata_from_filename,
             )
             raw_docs = reader.load_data()
+            
+            directory_structure = getattr(reader, 'directory_structure', {})
+            logging.info(f"Directory structure from reader: {directory_structure}")
 
             chunker = Chunker(
                 chunking_strategy="classic_chunk",
@@ -283,31 +329,277 @@ def ingest_worker(
                 for i in range(min(5, len(raw_docs))):
                     logging.info(f"Sample document {i}: {raw_docs[i]}")
             file_data = {
-                "name": name_job,
+                "name": job_name,
                 "file": filename,
                 "user": user,
                 "tokens": tokens,
                 "retriever": retriever,
                 "id": str(id),
                 "type": "local",
+                "file_path": file_path,
+                "directory_structure": json.dumps(directory_structure),
             }
 
             upload_index(vector_store_path, file_data)
-
         except Exception as e:
             logging.error(f"Error in ingest_worker: {e}", exc_info=True)
             raise
-
     return {
         "directory": directory,
         "formats": formats,
-        "name_job": name_job,
+        "name_job": job_name,  # Use original job_name
         "filename": filename,
-        "user": user,
+        "user": user,  # Use original user
         "limited": False,
     }
 
 
+def reingest_source_worker(self, source_id, user):
+    """
+    Re-ingestion worker that handles incremental updates by:
+    1. Adding chunks from newly added files
+    2. Removing chunks from deleted files
+
+    Args:
+        self: Task instance
+        source_id: ID of the source to re-ingest
+        user: User identifier
+
+    Returns:
+        dict: Information about the re-ingestion task
+    """
+    try:
+        from application.vectorstore.vector_creator import VectorCreator
+
+        self.update_state(state="PROGRESS", meta={"current": 10, "status": "Initializing re-ingestion scan"})
+
+        source = sources_collection.find_one({"_id": ObjectId(source_id), "user": user})
+        if not source:
+            raise ValueError(f"Source {source_id} not found or access denied")
+
+        storage = StorageCreator.get_storage()
+        source_file_path = source.get("file_path", "")
+
+        self.update_state(state="PROGRESS", meta={"current": 20, "status": "Scanning current files"})
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # Download all files from storage to temp directory, preserving directory structure
+            if storage.is_directory(source_file_path):
+                files_list = storage.list_files(source_file_path)
+
+                for storage_file_path in files_list:
+                    if storage.is_directory(storage_file_path):
+                        continue
+
+
+                    rel_path = os.path.relpath(storage_file_path, source_file_path)
+                    local_file_path = os.path.join(temp_dir, rel_path)
+
+                    os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
+
+                    # Download file
+                    try:
+                        file_data = storage.get_file(storage_file_path)
+                        with open(local_file_path, "wb") as f:
+                            f.write(file_data.read())
+                    except Exception as e:
+                        logging.error(f"Error downloading file {storage_file_path}: {e}")
+                        continue
+
+            reader = SimpleDirectoryReader(
+                input_dir=temp_dir,
+                recursive=True,
+                required_exts=[
+                    ".rst", ".md", ".pdf", ".txt", ".docx", ".csv", ".epub",
+                    ".html", ".mdx", ".json", ".xlsx", ".pptx", ".png",
+                    ".jpg", ".jpeg",
+                ],
+                exclude_hidden=True,
+                file_metadata=metadata_from_filename,
+            )
+            reader.load_data()
+            directory_structure = reader.directory_structure
+            logging.info(f"Directory structure built with token counts: {directory_structure}")
+
+            try:
+                old_directory_structure = source.get("directory_structure") or {}
+                if isinstance(old_directory_structure, str):
+                    try:
+                        old_directory_structure = json.loads(old_directory_structure)
+                    except Exception:
+                        old_directory_structure = {}
+
+                def _flatten_directory_structure(struct, prefix=""):
+                    files = set()
+                    if isinstance(struct, dict):
+                        for name, meta in struct.items():
+                            current_path = os.path.join(prefix, name) if prefix else name
+                            if isinstance(meta, dict) and ("type" in meta and "size_bytes" in meta):
+                                files.add(current_path)
+                            elif isinstance(meta, dict):
+                                files |= _flatten_directory_structure(meta, current_path)
+                    return files
+
+                old_files = _flatten_directory_structure(old_directory_structure)
+                new_files = _flatten_directory_structure(directory_structure)
+
+                added_files = sorted(new_files - old_files)
+                removed_files = sorted(old_files - new_files)
+
+                if added_files:
+                    logging.info(f"Files added since last ingest: {added_files}")
+                else:
+                    logging.info("No files added since last ingest.")
+
+                if removed_files:
+                    logging.info(f"Files removed since last ingest: {removed_files}")
+                else:
+                    logging.info("No files removed since last ingest.")
+
+            except Exception as e:
+                logging.error(f"Error comparing directory structures: {e}", exc_info=True)
+                added_files = []
+                removed_files = []
+            try:
+                if not added_files and not removed_files:
+                    logging.info("No changes detected.")
+                    return {
+                        "source_id": source_id,
+                        "user": user,
+                        "status": "no_changes",
+                        "added_files": [],
+                        "removed_files": [],
+                    }
+
+                vector_store = VectorCreator.create_vectorstore(
+                    settings.VECTOR_STORE,
+                    source_id,
+                    settings.EMBEDDINGS_KEY,
+                )
+
+                self.update_state(state="PROGRESS", meta={"current": 40, "status": "Processing file changes"})
+
+                # 1) Delete chunks from removed files
+                deleted = 0
+                if removed_files:
+                    try:
+                        for ch in vector_store.get_chunks() or []:
+                            metadata = ch.get("metadata", {}) if isinstance(ch, dict) else getattr(ch, "metadata", {})
+                            raw_source = metadata.get("source")
+
+                            source_file = str(raw_source) if raw_source else ""
+
+                            if source_file in removed_files:
+                                cid = ch.get("doc_id")
+                                if cid:
+                                    try:
+                                        vector_store.delete_chunk(cid)
+                                        deleted += 1
+                                    except Exception as de:
+                                        logging.error(f"Failed deleting chunk {cid}: {de}")
+                        logging.info(f"Deleted {deleted} chunks from {len(removed_files)} removed files")
+                    except Exception as e:
+                        logging.error(f"Error during deletion of removed file chunks: {e}", exc_info=True)
+
+                # 2) Add chunks from new files
+                added = 0
+                if added_files:
+                    try:
+                        # Build list of local files for added files only
+                        added_local_files = []
+                        for rel_path in added_files:
+                            local_path = os.path.join(temp_dir, rel_path)
+                            if os.path.isfile(local_path):
+                                added_local_files.append(local_path)
+
+                        if added_local_files:
+                            reader_new = SimpleDirectoryReader(
+                                input_files=added_local_files,
+                                exclude_hidden=True,
+                                errors="ignore",
+                                file_metadata=metadata_from_filename,
+                            )
+                            raw_docs_new = reader_new.load_data()
+                            chunker_new = Chunker(
+                                chunking_strategy="classic_chunk",
+                                max_tokens=MAX_TOKENS,
+                                min_tokens=MIN_TOKENS,
+                                duplicate_headers=False,
+                            )
+                            chunked_new = chunker_new.chunk(documents=raw_docs_new)
+
+                            for file_path, token_count in reader_new.file_token_counts.items():
+                                try:
+                                    rel_path = os.path.relpath(file_path, start=temp_dir)
+                                    path_parts = rel_path.split(os.sep)
+                                    current_dir = directory_structure
+
+                                    for part in path_parts[:-1]:
+                                        if part in current_dir and isinstance(current_dir[part], dict):
+                                            current_dir = current_dir[part]
+                                        else:
+                                            break
+
+                                    filename = path_parts[-1]
+                                    if filename in current_dir and isinstance(current_dir[filename], dict):
+                                        current_dir[filename]["token_count"] = token_count
+                                        logging.info(f"Updated token count for {rel_path}: {token_count}")
+                                except Exception as e:
+                                    logging.warning(f"Could not update token count for {file_path}: {e}")
+
+                            for d in chunked_new:
+                                meta = dict(d.extra_info or {})
+                                try:
+                                    raw_src = meta.get("source")
+                                    if isinstance(raw_src, str) and os.path.isabs(raw_src):
+                                        meta["source"] = os.path.relpath(raw_src, start=temp_dir)
+                                except Exception:
+                                    pass
+
+                                vector_store.add_chunk(d.text, metadata=meta)
+                                added += 1
+                            logging.info(f"Added {added} chunks from {len(added_files)} new files")
+                    except Exception as e:
+                        logging.error(f"Error during ingestion of new files: {e}", exc_info=True)
+
+                # 3) Update source directory structure timestamp
+                try:
+                    total_tokens = sum(reader.file_token_counts.values())
+                    
+                    sources_collection.update_one(
+                        {"_id": ObjectId(source_id)},
+                        {
+                            "$set": {
+                                "directory_structure": directory_structure,
+                                "date": datetime.datetime.now(),
+                                "tokens": total_tokens
+                            }
+                        },
+                    )
+                except Exception as e:
+                    logging.error(f"Error updating directory_structure in DB: {e}", exc_info=True)
+
+                self.update_state(state="PROGRESS", meta={"current": 100, "status": "Re-ingestion completed"})
+
+                return {
+                    "source_id": source_id,
+                    "user": user,
+                    "status": "completed",
+                    "added_files": added_files,
+                    "removed_files": removed_files,
+                    "chunks_added": added,
+                    "chunks_deleted": deleted,
+                }
+            except Exception as e:
+                logging.error(f"Error while processing file changes: {e}", exc_info=True)
+                raise
+
+
+
+    except Exception as e:
+        logging.error(f"Error in reingest_source_worker: {e}", exc_info=True)
+        raise
+
 def remote_worker(
     self,
     source_data,
@@ -323,7 +615,6 @@ def remote_worker(
     full_path = os.path.join(directory, user, name_job)
     if not os.path.exists(full_path):
         os.makedirs(full_path)
-
     self.update_state(state="PROGRESS", meta={"current": 1})
     try:
         logging.info("Initializing remote loader with type: %s", loader)
@@ -350,7 +641,6 @@ def remote_worker(
                 raise ValueError("doc_id must be provided for sync operation.")
             id = ObjectId(doc_id)
             embed_and_store_documents(docs, full_path, id, self)
-
         self.update_state(state="PROGRESS", meta={"current": 100})
 
         file_data = {
@@ -361,18 +651,18 @@ def remote_worker(
             "id": str(id),
             "type": loader,
             "remote_data": source_data,
-            "sync_frequency": sync_frequency,
+            "sync_frequency": sync_frequency
         }
-        upload_index(full_path, file_data)
 
+        if operation_mode == "sync":
+            file_data["last_sync"] = datetime.datetime.now()
+        upload_index(full_path, file_data)
     except Exception as e:
         logging.error("Error in remote_worker task: %s", str(e), exc_info=True)
         raise
-
     finally:
         if os.path.exists(full_path):
             shutil.rmtree(full_path)
-
     logging.info("remote_worker task completed successfully")
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
 
@@ -422,10 +712,9 @@ def sync_worker(self, frequency):
                 self, source_data, name, user, source_type, frequency, retriever, doc_id
             )
             sync_counts["total_sync_count"] += 1
-            sync_counts[
+            sync_counts[ 
                 "sync_success" if resp["status"] == "success" else "sync_failure"
             ] += 1
-
     return {
         key: sync_counts[key]
         for key in ["total_sync_count", "sync_success", "sync_failure"]
@@ -449,7 +738,7 @@ def attachment_worker(self, file_info, user):
     try:
         self.update_state(state="PROGRESS", meta={"current": 10})
         storage = StorageCreator.get_storage()
-        
+
         self.update_state(
             state="PROGRESS", meta={"current": 30, "status": "Processing content"}
         )
@@ -458,11 +747,17 @@ def attachment_worker(self, file_info, user):
             relative_path,
             lambda local_path, **kwargs: SimpleDirectoryReader(
                 input_files=[local_path], exclude_hidden=True, errors="ignore"
-            ).load_data()[0].text
+            )
+            .load_data()[0]
+            .text, 
         )
-            
+        
+        
         token_count = num_tokens_from_string(content)
-
+        if token_count > 100000:
+            content = content[:250000]
+            token_count = num_tokens_from_string(content)
+        
         self.update_state(
             state="PROGRESS", meta={"current": 80, "status": "Storing in database"}
         )
@@ -475,6 +770,7 @@ def attachment_worker(self, file_info, user):
                 "_id": doc_id,
                 "user": user,
                 "path": relative_path,
+                "filename": filename,
                 "content": content,
                 "token_count": token_count,
                 "mime_type": mime_type,
@@ -487,9 +783,7 @@ def attachment_worker(self, file_info, user):
             f"Stored attachment with ID: {attachment_id}", extra={"user": user}
         )
 
-        self.update_state(
-            state="PROGRESS", meta={"current": 100, "status": "Complete"}
-        )
+        self.update_state(state="PROGRESS", meta={"current": 100, "status": "Complete"})
 
         return {
             "filename": filename,
@@ -499,7 +793,6 @@ def attachment_worker(self, file_info, user):
             "mime_type": mime_type,
             "metadata": metadata,
         }
-
     except Exception as e:
         logging.error(
             f"Error processing file {filename}: {e}",
@@ -535,7 +828,6 @@ def agent_webhook_worker(self, agent_id, payload):
     except Exception as e:
         logging.error(f"Error processing agent webhook: {e}", exc_info=True)
         return {"status": "error", "error": str(e)}
-
     self.update_state(state="PROGRESS", meta={"current": 50})
     try:
         result = run_agent_logic(agent_config, input_data)
@@ -548,3 +840,174 @@ def agent_webhook_worker(self, agent_id, payload):
             f"Webhook processed for agent {agent_id}", extra={"agent_id": agent_id}
         )
         return {"status": "success", "result": result}
+
+
+def ingest_connector(
+    self,
+    job_name: str,
+    user: str,
+    source_type: str,
+    session_token=None,
+    file_ids=None,
+    folder_ids=None,
+    recursive=True,
+    retriever: str = "classic",
+    operation_mode: str = "upload",
+    doc_id=None,
+    sync_frequency: str = "never",
+) -> Dict[str, Any]:
+    """
+    Ingestion for internal knowledge bases (GoogleDrive, etc.).
+
+    Args:
+        job_name: Name of the ingestion job
+        user: User identifier
+        source_type: Type of remote source ("google_drive", "dropbox", etc.)
+        session_token: Authentication token for the service
+        file_ids: List of file IDs to download
+        folder_ids: List of folder IDs to download
+        recursive: Whether to recursively download folders
+        retriever: Type of retriever to use
+        operation_mode: "upload" for initial ingestion, "sync" for incremental sync
+        doc_id: Document ID for sync operations (required when operation_mode="sync")
+        sync_frequency: How often to sync ("never", "daily", "weekly", "monthly")
+    """
+    logging.info(f"Starting remote ingestion from {source_type} for user: {user}, job: {job_name}")
+    self.update_state(state="PROGRESS", meta={"current": 1})
+    
+    with tempfile.TemporaryDirectory() as temp_dir:
+        try:
+            # Step 1: Initialize the appropriate loader
+            self.update_state(state="PROGRESS", meta={"current": 10, "status": "Initializing connector"})
+
+            if not session_token:
+                raise ValueError(f"{source_type} connector requires session_token")
+
+            if not ConnectorCreator.is_supported(source_type):
+                raise ValueError(f"Unsupported connector type: {source_type}. Supported types: {ConnectorCreator.get_supported_connectors()}")
+
+            remote_loader = ConnectorCreator.create_connector(source_type, session_token)
+
+            # Create a clean config for storage
+            api_source_config = {
+                "file_ids": file_ids or [],
+                "folder_ids": folder_ids or [],
+                "recursive": recursive
+            }
+
+            # Step 2: Download files to temp directory
+            self.update_state(state="PROGRESS", meta={"current": 20, "status": "Downloading files"})
+            download_info = remote_loader.download_to_directory(
+                temp_dir,
+                api_source_config
+            )
+            
+            if download_info.get("empty_result", False) or not download_info.get("files_downloaded", 0):
+                logging.warning(f"No files were downloaded from {source_type}")
+                # Create empty result directly instead of calling a separate method
+                return {
+                    "name": job_name,
+                    "user": user,
+                    "tokens": 0,
+                    "type": source_type,
+                    "source_config": api_source_config,
+                    "directory_structure": "{}",
+                }
+            
+            # Step 3: Use SimpleDirectoryReader to process downloaded files
+            self.update_state(state="PROGRESS", meta={"current": 40, "status": "Processing files"})
+            reader = SimpleDirectoryReader(
+                input_dir=temp_dir,
+                recursive=True,
+                required_exts=[
+                    ".rst", ".md", ".pdf", ".txt", ".docx", ".csv", ".epub",
+                    ".html", ".mdx", ".json", ".xlsx", ".pptx", ".png",
+                    ".jpg", ".jpeg",
+                ],
+                exclude_hidden=True,
+                file_metadata=metadata_from_filename,
+            )
+            raw_docs = reader.load_data()
+            directory_structure = getattr(reader, 'directory_structure', {})
+
+
+            
+            # Step 4: Process documents (chunking, embedding, etc.)
+            self.update_state(state="PROGRESS", meta={"current": 60, "status": "Processing documents"})
+            
+            chunker = Chunker(
+                chunking_strategy="classic_chunk",
+                max_tokens=MAX_TOKENS,
+                min_tokens=MIN_TOKENS,
+                duplicate_headers=False,
+            )
+            raw_docs = chunker.chunk(documents=raw_docs)
+            
+            # Preserve source information in document metadata
+            for doc in raw_docs:
+                if hasattr(doc, 'extra_info') and doc.extra_info:
+                    source = doc.extra_info.get('source')
+                    if source and os.path.isabs(source):
+                        # Convert absolute path to relative path
+                        doc.extra_info['source'] = os.path.relpath(source, start=temp_dir)
+            
+            docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
+            
+            if operation_mode == "upload":
+                id = ObjectId()
+            elif operation_mode == "sync":
+                if not doc_id or not ObjectId.is_valid(doc_id):
+                    logging.error("Invalid doc_id provided for sync operation: %s", doc_id)
+                    raise ValueError("doc_id must be provided for sync operation.")
+                id = ObjectId(doc_id)
+            else:
+                raise ValueError(f"Invalid operation_mode: {operation_mode}")
+
+            vector_store_path = os.path.join(temp_dir, "vector_store")
+            os.makedirs(vector_store_path, exist_ok=True)
+
+            self.update_state(state="PROGRESS", meta={"current": 80, "status": "Storing documents"})
+            embed_and_store_documents(docs, vector_store_path, id, self)
+
+            tokens = count_tokens_docs(docs)
+
+            # Step 6: Upload index files
+            file_data = {
+                "user": user,
+                "name": job_name,
+                "tokens": tokens,
+                "retriever": retriever,
+                "id": str(id),
+                "type": "connector",
+                "remote_data": json.dumps({
+                    "provider": source_type,
+                    **api_source_config
+                }),
+                "directory_structure": json.dumps(directory_structure),
+                "sync_frequency": sync_frequency 
+            }
+
+            if operation_mode == "sync":
+                file_data["last_sync"] = datetime.datetime.now()
+            else:
+                file_data["last_sync"] = datetime.datetime.now()
+
+            upload_index(vector_store_path, file_data)
+
+            # Ensure we mark the task as complete
+            self.update_state(state="PROGRESS", meta={"current": 100, "status": "Complete"})
+
+            logging.info(f"Remote ingestion completed: {job_name}")
+
+            return {
+                "user": user,
+                "name": job_name,
+                "tokens": tokens,
+                "type": source_type,
+                "id": str(id),
+                "status": "complete"
+            }
+            
+        except Exception as e:
+            logging.error(f"Error during remote ingestion: {e}", exc_info=True)
+            raise

deployment/docker-compose-dev.yaml

@@ -1,3 +1,4 @@
+name: docsgpt-oss
 services:
 
   redis:

deployment/docker-compose-hub.yaml

@@ -0,0 +1,74 @@
+name: docsgpt-oss
+services:
+
+  frontend:
+    image: arc53/docsgpt-fe:develop
+    environment:
+      - VITE_API_HOST=http://localhost:7091
+      - VITE_API_STREAMING=$VITE_API_STREAMING
+    ports:
+      - "5173:5173"
+    depends_on:
+      - backend
+
+
+  backend:
+    user: root
+    image: arc53/docsgpt:develop
+    environment:
+      - API_KEY=$API_KEY
+      - EMBEDDINGS_KEY=$API_KEY
+      - LLM_PROVIDER=$LLM_PROVIDER
+      - LLM_NAME=$LLM_NAME
+      - CELERY_BROKER_URL=redis://redis:6379/0
+      - CELERY_RESULT_BACKEND=redis://redis:6379/1
+      - MONGO_URI=mongodb://mongo:27017/docsgpt
+      - CACHE_REDIS_URL=redis://redis:6379/2
+      - OPENAI_BASE_URL=$OPENAI_BASE_URL
+    ports:
+      - "7091:7091"
+    volumes:
+      - ../application/indexes:/app/indexes
+      - ../application/inputs:/app/inputs
+      - ../application/vectors:/app/vectors
+    depends_on:
+      - redis
+      - mongo
+
+
+  worker:
+    user: root
+    image: arc53/docsgpt:develop
+    command: celery -A application.app.celery worker -l INFO -B
+    environment:
+      - API_KEY=$API_KEY
+      - EMBEDDINGS_KEY=$API_KEY
+      - LLM_PROVIDER=$LLM_PROVIDER
+      - LLM_NAME=$LLM_NAME
+      - CELERY_BROKER_URL=redis://redis:6379/0
+      - CELERY_RESULT_BACKEND=redis://redis:6379/1
+      - MONGO_URI=mongodb://mongo:27017/docsgpt
+      - API_URL=http://backend:7091
+      - CACHE_REDIS_URL=redis://redis:6379/2
+    volumes:
+      - ../application/indexes:/app/indexes
+      - ../application/inputs:/app/inputs
+      - ../application/vectors:/app/vectors
+    depends_on:
+      - redis
+      - mongo
+
+  redis:
+    image: redis:6-alpine
+    ports:
+      - 6379:6379
+
+  mongo:
+    image: mongo:6
+    ports:
+      - 27017:27017
+    volumes:
+      - mongodb_data_container:/data/db
+
+volumes:
+  mongodb_data_container:

deployment/docker-compose.yaml

@@ -1,3 +1,4 @@
+name: docsgpt-oss
 services:
   frontend:
     build: ../frontend
@@ -17,19 +18,19 @@ services:
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
+      - LLM_PROVIDER=$LLM_PROVIDER
       - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
       - MONGO_URI=mongodb://mongo:27017/docsgpt
       - CACHE_REDIS_URL=redis://redis:6379/2
       - OPENAI_BASE_URL=$OPENAI_BASE_URL
-      - MODEL_NAME=$MODEL_NAME
     ports:
       - "7091:7091"
     volumes:
-      - ../application/indexes:/app/application/indexes
+      - ../application/indexes:/app/indexes
       - ../application/inputs:/app/inputs
-      - ../application/vectors:/app/application/vectors
+      - ../application/vectors:/app/vectors
     depends_on:
       - redis
       - mongo
@@ -41,6 +42,7 @@ services:
     environment:
       - API_KEY=$API_KEY
       - EMBEDDINGS_KEY=$API_KEY
+      - LLM_PROVIDER=$LLM_PROVIDER
       - LLM_NAME=$LLM_NAME
       - CELERY_BROKER_URL=redis://redis:6379/0
       - CELERY_RESULT_BACKEND=redis://redis:6379/1
@@ -48,9 +50,9 @@ services:
       - API_URL=http://backend:7091
       - CACHE_REDIS_URL=redis://redis:6379/2
     volumes:
-      - ../application/indexes:/app/application/indexes
+      - ../application/indexes:/app/indexes
       - ../application/inputs:/app/inputs
-      - ../application/vectors:/app/application/vectors
+      - ../application/vectors:/app/vectors
     depends_on:
       - redis
       - mongo

deployment/k8s/docsgpt-secrets.yaml

@@ -4,7 +4,7 @@ metadata:
   name: docsgpt-secrets
 type: Opaque
 data:
-  LLM_NAME: ZG9jc2dwdA==
+  LLM_PROVIDER: ZG9jc2dwdA==
   INTERNAL_KEY: aW50ZXJuYWw=
   CELERY_BROKER_URL: cmVkaXM6Ly9yZWRpcy1zZXJ2aWNlOjYzNzkvMA==
   CELERY_RESULT_BACKEND: cmVkaXM6Ly9yZWRpcy1zZXJ2aWNlOjYzNzkvMA==

docs/components/ToolCards.jsx

@@ -0,0 +1,117 @@
+import Image from 'next/image';
+
+const iconMap = {
+    'API Tool': '/toolIcons/tool_api_tool.svg',
+    'Brave Search Tool': '/toolIcons/tool_brave.svg',
+    'Cryptoprice Tool': '/toolIcons/tool_cryptoprice.svg',
+    'Ntfy Tool': '/toolIcons/tool_ntfy.svg',
+    'PostgreSQL Tool': '/toolIcons/tool_postgres.svg',
+    'Read Webpage Tool': '/toolIcons/tool_read_webpage.svg',
+    'Telegram Tool': '/toolIcons/tool_telegram.svg'
+};
+
+
+export function ToolCards({ items }) {
+    return (
+        <>
+            <div className="tool-cards">
+                {items.map(({ title, link, description }) => {
+                    const isExternal = link.startsWith('https://');
+                    const iconSrc = iconMap[title] || '/default-icon.png'; // Default icon if not found
+
+                    return (
+                        <div
+                            key={title}
+                            className={`card${isExternal ? ' external' : ''}`}
+                        >
+                            <a href={link} target={isExternal ? '_blank' : undefined} rel="noopener noreferrer" className="card-link-wrapper">
+                                <div className="card-icon-container">
+                                    {iconSrc && <div className="card-icon"><Image src={iconSrc} alt={title} width={32} height={32} /></div>} {/* Reduced icon size */}
+                                </div>
+                                <h3 className="card-title">{title}</h3>
+                                {description && <p className="card-description">{description}</p>}
+                                {/* Card URL element removed from here */}
+                            </a>
+                        </div>
+                    );
+                })}
+            </div>
+
+            <style jsx>{`
+               .tool-cards {
+                    margin-top: 24px;
+                    display: grid;
+                    grid-template-columns: 1fr;
+                    gap: 16px;
+                }
+                @media (min-width: 768px) {
+                    .tool-cards {
+                        grid-template-columns: 1fr 1fr; /* Keeps two columns on wider screens */
+                    }
+                }
+                .card {
+                    background-color: #222222;
+                    border-radius: 8px;
+                    padding: 16px; /* Existing padding */
+                    transition: background-color 0.3s;
+                    position: relative;
+                    color: #ffffff;
+                    display: flex; /* Using flex to help with alignment */
+                    flex-direction: column;
+                    /* align-items: center; // Alignment for items inside card-link-wrapper is better */
+                    /* justify-content: center; // We want content to flow from top */
+                    height: 100%; /* Fill the height of the grid cell, ensures cards in a row are same height */
+                }
+                .card:hover {
+                    background-color: #333333;
+                }
+                .card.external::after {
+                    content: "↗";
+                    position: absolute;
+                    top: 12px;
+                    right: 12px;
+                    color: #ffffff;
+                    font-size: 0.7em;
+                    opacity: 0.8;
+                }
+                .card-link-wrapper {
+                    display: flex;
+                    flex-direction: column;
+                    align-items:center; /* Centers icon, title, description horizontally */
+                    text-align: center; /* Ensures text within p and h3 is centered */
+                    color: inherit;
+                    text-decoration: none;
+                    width:100%;
+                    height: 100%; /* Make the link wrapper take full card height */
+                    justify-content: flex-start; /* Align content to the top */
+                }
+               .card-icon-container{
+                    display:flex;
+                    justify-content:center;
+                    width: 100%;
+                    margin-top: 8px; /* Added some margin at the top if needed */
+                    margin-bottom: 12px; /* Increased space between icon and title */
+               }
+                .card-icon {
+                   display: block;
+                   /* margin: 0 auto; // Center handled by card-icon-container */
+                }
+                .card-title {
+                    font-weight: 600;
+                    margin-bottom: 8px; /* Increased space below title */
+                    font-size: 16px; /* Consider increasing slightly if descriptions are longer e.g. 17px or 18px */
+                    color: #f0f0f0;
+                }
+                .card-description {
+                    /* margin-bottom: 0; // Original value */
+                    font-size: 14px; /* Slightly increased font size for better readability */
+                    color: #aaaaaa;
+                    line-height: 1.5; /* Slightly increased line height */
+                    flex-grow: 1; /* Allows description to take available space */
+                    overflow-y: auto; /* Adds scroll if description is too long, though ideally content fits */
+                    padding-bottom: 8px; /* Add some padding at the bottom of the description area */
+                }
+            `}</style>
+        </>
+    );
+}

docs/package.json

@@ -7,8 +7,8 @@
   "license": "MIT",
   "dependencies": {
     "@vercel/analytics": "^1.1.1",
-    "docsgpt-react": "^0.5.0",
-    "next": "^14.2.26",
+    "docsgpt-react": "^0.5.1",
+    "next": "^15.3.3",
     "nextra": "^2.13.2",
     "nextra-theme-docs": "^2.13.2",
     "react": "^18.2.0",

docs/pages/Agents/_meta.json

@@ -0,0 +1,14 @@
+{
+  "basics": {
+    "title": "🤖 Agent Basics",
+    "href": "/Agents/basics"
+  },
+  "api": {
+    "title": "🔌 Agent API",
+    "href": "/Agents/api"
+  },
+  "webhooks": {
+    "title": "🪝 Agent Webhooks",
+    "href": "/Agents/webhooks"
+  }
+}

docs/pages/Agents/api.mdx

@@ -0,0 +1,227 @@
+---
+title: Interacting with Agents via API
+description: Learn how to programmatically interact with DocsGPT Agents using the streaming and non-streaming API endpoints.
+---
+
+import { Callout, Tabs } from 'nextra/components';
+
+# Interacting with Agents via API
+
+DocsGPT Agents can be accessed programmatically through a dedicated API, allowing you to integrate their specialized capabilities into your own applications, scripts, and workflows. This guide covers the two primary methods for interacting with an agent: the streaming API for real-time responses and the non-streaming API for a single, consolidated answer.
+
+When you use an API key generated for a specific agent, you do not need to pass `prompt`, `tools` etc. The agent's configuration (including its prompt, selected tools, and knowledge sources) is already associated with its unique API key. 
+
+### API Endpoints
+
+- **Non-Streaming:** `http://localhost:7091/api/answer`
+- **Streaming:** `http://localhost:7091/stream`
+
+<Callout type="info">
+For DocsGPT Cloud, use `https://gptcloud.arc53.com/` as the base URL.
+</Callout>
+
+For more technical details, you can explore the API swagger documentation available for the cloud version or your local instance.
+
+---
+
+## Non-Streaming API (`/api/answer`)
+
+This is a standard synchronous endpoint. It waits for the agent to fully process the request and returns a single JSON object with the complete answer. This is the simplest method and is ideal for backend processes where a real-time feed is not required.
+
+### Request
+
+-   **Endpoint:** `/api/answer`
+-   **Method:** `POST`
+-   **Payload:**
+    -   `question` (string, required): The user's query or input for the agent.
+    -   `api_key` (string, required): The unique API key for the agent you wish to interact with.
+    -   `history` (string, optional): A JSON string representing the conversation history, e.g., `[{\"prompt\": \"first question\", \"answer\": \"first answer\"}]`.
+
+### Response
+
+A single JSON object containing:
+-   `answer`: The complete, final answer from the agent.
+-   `sources`: A list of sources the agent consulted.
+-   `conversation_id`: The unique ID for the interaction.
+
+### Examples
+
+<Tabs items={['cURL', 'Python', 'JavaScript']}>
+  <Tabs.Tab>
+    ```bash
+    curl -X POST http://localhost:7091/api/answer \
+    -H "Content-Type: application/json" \
+    -d '{
+        "question": "your question here",
+        "api_key": "your_agent_api_key"
+    }'
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```python
+    import requests
+
+    API_URL = "http://localhost:7091/api/answer"
+    API_KEY = "your_agent_api_key"
+    QUESTION = "your question here"
+
+    response = requests.post(
+        API_URL,
+        json={"question": QUESTION, "api_key": API_KEY}
+    )
+
+    if response.status_code == 200:
+        print(response.json())
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```javascript
+    const apiUrl = 'http://localhost:7091/api/answer';
+    const apiKey = 'your_agent_api_key';
+    const question = 'your question here';
+
+    async function getAnswer() {
+    try {
+        const response = await fetch(apiUrl, {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({ question, api_key: apiKey }),
+        });
+
+        if (!response.ok) {
+        throw new Error(`HTTP error! Status: ${response.status}`);
+        }
+
+        const data = await response.json();
+        console.log(data);
+    } catch (error) {
+        console.error("Failed to fetch answer:", error);
+    }
+    }
+
+    getAnswer();
+    ```
+  </Tabs.Tab>
+</Tabs>
+
+---
+
+## Streaming API (`/stream`)
+
+The `/stream` endpoint uses Server-Sent Events (SSE) to push data in real-time. This is ideal for applications where you want to display the response as it's being generated, such as in a live chatbot interface.
+
+### Request
+
+-   **Endpoint:** `/stream`
+-   **Method:** `POST`
+-   **Payload:** Same as the non-streaming API.
+
+### Response (SSE Stream)
+
+The stream consists of multiple `data:` events, each containing a JSON object. Your client should listen for these events and process them based on their `type`.
+
+**Event Types:**
+-   `answer`: A chunk of the agent's final answer.
+-   `source`: A document or source used by the agent.
+-   `thought`: A reasoning step from the agent (for ReAct agents).
+-   `id`: The unique `conversation_id` for the interaction.
+-   `error`: An error message.
+-   `end`: A final message indicating the stream has concluded.
+
+### Examples
+
+<Tabs items={['cURL', 'Python', 'JavaScript']}>
+  <Tabs.Tab>
+    ```bash
+    curl -X POST http://localhost:7091/stream \
+    -H "Content-Type: application/json" \
+    -H "Accept: text/event-stream" \
+    -d '{
+        "question": "your question here",
+        "api_key": "your_agent_api_key"
+    }'
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```python
+    import requests
+    import json
+
+    API_URL = "http://localhost:7091/stream"
+    payload = {
+        "question": "your question here",
+        "api_key": "your_agent_api_key"
+    }
+
+    with requests.post(API_URL, json=payload, stream=True) as r:
+        for line in r.iter_lines():
+            if line:
+                decoded_line = line.decode('utf-8')
+                if decoded_line.startswith('data: '):
+                    try:
+                        data = json.loads(decoded_line[6:])
+                        print(data)
+                    except json.JSONDecodeError:
+                        pass
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```javascript
+    const apiUrl = 'http://localhost:7091/stream';
+    const apiKey = 'your_agent_api_key';
+    const question = 'your question here';
+
+    async function getStream() {
+    try {
+        const response = await fetch(apiUrl, {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/json',
+            'Accept': 'text/event-stream'
+        },
+        // Corrected line: 'apiKey' is changed to 'api_key'
+        body: JSON.stringify({ question, api_key: apiKey }),
+        });
+
+        if (!response.ok) {
+        throw new Error(`HTTP error! Status: ${response.status}`);
+        }
+
+        const reader = response.body.getReader();
+        const decoder = new TextDecoder();
+
+        while (true) {
+        const { done, value } = await reader.read();
+        if (done) break;
+
+        const chunk = decoder.decode(value, { stream: true });
+        // Note: This parsing method assumes each chunk contains whole lines.
+        // For a more robust production implementation, buffer the chunks
+        // and process them line by line.
+        const lines = chunk.split('\n');
+        
+        for (const line of lines) {
+            if (line.startsWith('data: ')) {
+            try {
+                const data = JSON.parse(line.substring(6));
+                console.log(data);
+            } catch (e) {
+                console.error("Failed to parse JSON from SSE event:", e);
+            }
+            }
+        }
+        }
+    } catch (error) {
+        console.error("Failed to fetch stream:", error);
+    }
+    }
+
+    getStream();
+    ```
+  </Tabs.Tab>
+</Tabs>

docs/pages/Agents/basics.mdx

@@ -0,0 +1,109 @@
+---
+title: Understanding DocsGPT Agents
+description: Learn about DocsGPT Agents, their types, how to create and manage them, and how they can enhance your interaction with documents and tools.
+---
+
+import { Callout } from 'nextra/components';
+import Image from 'next/image'; // Assuming you might want to embed images later, like the ones you uploaded.
+
+# Understanding DocsGPT Agents 🤖
+
+DocsGPT Agents are advanced, configurable AI entities designed to go beyond simple question-answering. They act as specialized assistants or workers that combine instructions (prompts), knowledge (document sources), and capabilities (tools) to perform a wide range of tasks, automate workflows, and provide tailored interactions.
+
+Think of an Agent as a pre-configured version of DocsGPT, fine-tuned for a specific purpose, such as classifying documents, responding to new form submissions, or validating emails.
+
+## Why Use Agents?
+
+* **Personalization:** Create AI assistants that behave and respond according to specific roles or personas.
+* **Task Specialization:** Design agents focused on particular tasks, like customer support, data extraction, or content generation.
+* **Knowledge Integration:** Equip agents with specific document sources, making them experts in particular domains.
+* **Tool Utilization:** Grant agents access to various tools, allowing them to interact with external services, fetch live data, or perform actions.
+* **Automation:** Automate repetitive tasks by defining an agent's behavior and integrating it via webhooks or other means.
+* **Shareability:** Share your custom-configured agents with others or use agents shared with you.
+
+Agents provide a more structured and powerful way to leverage LLMs compared to a standard chat interface, as they come with a pre-defined context, instruction set, and set of capabilities.
+
+## Core Components of an Agent
+
+When you create or configure an agent, you'll work with these key components:  
+
+**Meta:**
+    * **Agent Name:** A user-friendly name to identify the agent (e.g., "Support Ticket Classifier," "Product Spec Expert").
+    * **Describe your agent:** A brief description for you or users to understand the agent's purpose.
+
+**Source:**
+    * **Select source:** The knowledge base for the agent. You can select from previously uploaded documents or data sources. This is what the agent will "know."
+    * **Chunks per query:** A numerical value determining how many relevant text chunks from the selected source are sent to the LLM with each query. This helps manage context length and relevance.
+
+**Prompt:**
+The main set of instructions or system [prompt](/Guides/Customising-prompts) that defines the agent's persona, objectives, constraints, and how it should behave or respond.
+
+**Tools:** A selection of available [DocsGPT Tools](/Tools/basics) that the agent can use to perform actions or access external information.
+
+**Agent type:** The underlying operational logic or architecture the agent uses. DocsGPT supports different types of agents, each suited for different kinds of tasks.
+
+## Understanding Agent Types
+
+DocsGPT allows for different "types" of agents, each with a distinct way of processing information and generating responses. The code for these agent types can be found in the `application/agents/` directory.
+
+### 1. Classic Agent (`classic_agent.py`)
+
+**How it works:** The Classic Agent follows a traditional Retrieval Augmented Generation (RAG) approach.
+    1.  **Retrieve:** When a query is made, it first searches the selected Source documents for relevant information.
+    2.  **Augment:** This retrieved data is then added to the context, along with the main Prompt and the user's query.
+    3.  **Generate:** The LLM generates a response based on this augmented context. It can also utilize any configured tools if the LLM decides they are necessary.
+
+**Best for:**
+    * Direct question-answering over a specific set of documents.
+    * Tasks where the primary goal is to extract and synthesize information from the provided sources.
+    * Simpler tool integrations where the decision to use a tool is straightforward.
+
+### 2. ReAct Agent (`react_agent.py`)
+
+**How it works:** The ReAct Agent employs a more sophisticated "Reason and Act" framework. This involves a multi-step process:
+    1.  **Plan (Thought):** Based on the query, its prompt, and available tools/sources, the LLM first generates a plan or a sequence of thoughts on how to approach the problem. You might see this output as a "thought" process during generation.
+    2.  **Act:** The agent then executes actions based on this plan. This might involve querying its sources, using a tool, or performing internal reasoning.
+    3.  **Observe:** It gathers observations from the results of its actions (e.g., data from a tool, snippets from documents).
+    4.  **Repeat (if necessary):** Steps 2 and 3 can be repeated as the agent refines its approach or gathers more information.
+    5.  **Conclude:** Finally, it generates the final answer based on the initial query and all accumulated observations.
+
+**Best for:**
+    * More complex tasks that require multi-step reasoning or problem-solving.
+    * Scenarios where the agent needs to dynamically decide which tools to use and in what order, based on intermediate results.
+    * Interactive tasks where the agent needs to "think" through a problem.
+
+<Callout type="info">
+Developers looking to introduce new agent architectures can explore the `application/agents/` directory. `classic_agent.py` and `react_agent.py` serve as excellent starting points, demonstrating how to inherit from `BaseAgent` and structure agent logic.
+</Callout>
+
+## Navigating and Managing Agents in DocsGPT
+
+You can easily access and manage your agents through the DocsGPT user interface. Recently used agents appear at the top of the left sidebar for quick access. Below these, the "Manage Agents" button will take you to the main Agents page.
+
+### Creating a New Agent
+
+1.  Navigate to the "Agents" page.
+2.  Click the **"New Agent"** button.
+3.  You will be presented with the "New Agent" configuration screen:
+
+<Image
+  src="/new-agent.png"
+  alt="API Tool configuration example for phone validation"
+  width={800}
+  height={450}
+  style={{ margin: '1em auto', display: 'block', borderRadius: '8px' }}
+/>
+
+4.  Fill in the fields as described in the "Core Components of an Agent" section.
+5.  Once configured, you can **"Save Draft"** to continue editing later or **"Publish"** to make the agent active.
+
+## Interacting with and Editing Agents
+
+Once an agent is created, you can:
+
+* **Chat with it:** Select the agent to start an interaction.
+* **View Logs:** Access usage statistics, monitor token consumption per interaction, and review user message feedbacks. This is crucial for understanding how your agent is being used and performing.
+* **Edit an Agent:**
+    * Modify any of its configuration settings (name, description, source, prompt, tools, type).
+    * **Generate a Public Link:** From the edit screen, you can create a shareable public link that allows others to import and use your agent.
+    * **Get a Webhook URL:** You can also obtain a Webhook URL for the agent. This allows external applications or services to trigger the agent and receive responses programmatically, enabling powerful integrations and automations.

docs/pages/Agents/webhooks.mdx

@@ -0,0 +1,152 @@
+---
+title: Triggering Agents with Webhooks
+description: Learn how to automate and integrate DocsGPT Agents using webhooks for asynchronous task execution.
+---
+
+import { Callout, Tabs } from 'nextra/components';
+
+# Triggering Agents with Webhooks
+
+Agent Webhooks provide a powerful mechanism to trigger an agent's execution from external systems. Unlike the direct API which provides an immediate response, webhooks are designed for **asynchronous** operations. When you call a webhook, DocsGPT enqueues the agent's task for background processing and immediately returns a `task_id`. You then use this ID to poll for the result.
+
+This workflow is ideal for integrating with services that expect a quick initial response (e.g., form submissions) or for triggering long-running tasks without tying up a client connection.
+
+Each agent has its own unique webhook URL, which can be generated from the agent's edit page in the DocsGPT UI. This URL includes a secure token for authentication.
+
+### API Endpoints
+
+- **Webhook URL:** `http://localhost:7091/api/webhooks/agents/{AGENT_WEBHOOK_TOKEN}`
+- **Task Status URL:** `http://localhost:7091/api/task_status`
+
+<Callout type="info">
+For DocsGPT Cloud, use `https://gptcloud.arc53.com/` as the base URL.
+</Callout>
+
+For more technical details, you can explore the API swagger documentation available for the cloud version or your local instance.
+
+---
+
+## The Webhook Workflow
+
+The process involves two main steps: triggering the task and polling for the result.
+
+### Step 1: Trigger the Webhook
+
+Send an HTTP `POST` request to the agent's unique webhook URL with the required payload. The structure of this payload should match what the agent's prompt and tools are designed to handle.
+
+-   **Method:** `POST`
+-   **Response:** A JSON object with a `task_id`. `{"task_id": "a1b2c3d4-e5f6-..."}`
+
+<Tabs items={['cURL', 'Python', 'JavaScript']}>
+  <Tabs.Tab>
+    ```bash
+    curl -X POST \
+      http://localhost:7091/api/webhooks/agents/your_webhook_token \
+      -H "Content-Type: application/json" \
+      -d '{"question": "Your message to agent"}'
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```python
+    import requests
+
+    WEBHOOK_URL = "http://localhost:7091/api/webhooks/agents/your_webhook_token"
+    payload = {"question": "Your message to agent"}
+
+    try:
+        response = requests.post(WEBHOOK_URL, json=payload)
+        response.raise_for_status()
+        task_id = response.json().get("task_id")
+        print(f"Task successfully created with ID: {task_id}")
+    except requests.exceptions.RequestException as e:
+        print(f"Error triggering webhook: {e}")
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```javascript
+    const webhookUrl = 'http://localhost:7091/api/webhooks/agents/your_webhook_token';
+    const payload = { question: 'Your message to agent' };
+
+    async function triggerWebhook() {
+      try {
+        const response = await fetch(webhookUrl, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify(payload)
+        });
+        if (!response.ok) throw new Error(`HTTP error! ${response.status}`);
+        const data = await response.json();
+        console.log(`Task successfully created with ID: ${data.task_id}`);
+        return data.task_id;
+      } catch (error) {
+        console.error('Error triggering webhook:', error);
+      }
+    }
+    
+    triggerWebhook();
+    ```
+  </Tabs.Tab>
+</Tabs>
+
+### Step 2: Poll for the Result
+
+Once you have the `task_id`, periodically send a `GET` request to the `/api/task_status` endpoint until the task `status` is `SUCCESS` or `FAILURE`.
+
+- **`status`**: The current state of the task (`PENDING`, `STARTED`, `SUCCESS`, `FAILURE`).
+- **`result`**: The final output from the agent, available when the status is `SUCCESS` or `FAILURE`.
+
+<Tabs items={['cURL', 'Python', 'JavaScript']}>
+  <Tabs.Tab>
+    ```bash
+    # Replace the task_id with the one you received
+    curl http://localhost:7091/api/task_status?task_id=YOUR_TASK_ID
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```python
+    import requests
+    import time
+
+    STATUS_URL = "http://localhost:7091/api/task_status"
+    task_id = "YOUR_TASK_ID"
+
+    while True:
+        response = requests.get(STATUS_URL, params={"task_id": task_id})
+        data = response.json()
+        status = data.get("status")
+        print(f"Current task status: {status}")
+
+        if status in ["SUCCESS", "FAILURE"]:
+            print("Final Result:")
+            print(data.get("result"))
+            break
+        
+        time.sleep(2)
+    ```
+  </Tabs.Tab>
+  <Tabs.Tab>
+    ```javascript
+    const statusUrl = 'http://localhost:7091/api/task_status';
+    const taskId = 'YOUR_TASK_ID';
+
+    const sleep = (ms) => new Promise(resolve => setTimeout(resolve, ms));
+
+    async function pollForResult() {
+      while (true) {
+        const response = await fetch(`${statusUrl}?task_id=${taskId}`);
+        const data = await response.json();
+        const status = data.status;
+        console.log(`Current task status: ${status}`);
+
+        if (status === 'SUCCESS' || status === 'FAILURE') {
+          console.log('Final Result:', data.result);
+          break;
+        }
+        await sleep(2000);
+      }
+    }
+
+    pollForResult();
+    ```
+  </Tabs.Tab>
+</Tabs>

docs/pages/Deploying/Docker-Deploying.mdx

@@ -37,7 +37,7 @@ The fastest way to try out DocsGPT is by using the public API endpoint. This req
     Open the `.env` file and add the following lines:
 
     ```
-    LLM_NAME=docsgpt
+    LLM_PROVIDER=docsgpt
     VITE_API_STREAMING=true
     ```
 
@@ -93,16 +93,16 @@ There are two Ollama optional files:
 
 3.  **Pull the Ollama Model:**
 
-    **Crucially, after launching with Ollama, you need to pull the desired model into the Ollama container.**  Find the `MODEL_NAME` you configured in your `.env` file (e.g., `llama3.2:1b`). Then execute the following command to pull the model *inside* the running Ollama container:
+    **Crucially, after launching with Ollama, you need to pull the desired model into the Ollama container.**  Find the `LLM_NAME` you configured in your `.env` file (e.g., `llama3.2:1b`). Then execute the following command to pull the model *inside* the running Ollama container:
 
     ```bash
-    docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml exec -it ollama ollama pull <MODEL_NAME>
+    docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-cpu.yaml exec -it ollama ollama pull <LLM_NAME>
     ```
     or (for GPU):
      ```bash
-    docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml exec -it ollama ollama pull <MODEL_NAME>
+    docker compose -f deployment/docker-compose.yaml -f deployment/optional/docker-compose.optional.ollama-gpu.yaml exec -it ollama ollama pull <LLM_NAME>
     ```
-    Replace `<MODEL_NAME>` with the actual model name from your `.env` file.
+    Replace `<LLM_NAME>` with the actual model name from your `.env` file.
 
 4.  **Access DocsGPT in your browser:**
 

docs/pages/Deploying/DocsGPT-Settings.mdx

@@ -20,9 +20,9 @@ The easiest and recommended way to configure basic settings is by using a `.env`
 **Example `.env` file structure:**
 
 ```
-LLM_NAME=openai
+LLM_PROVIDER=openai
 API_KEY=YOUR_OPENAI_API_KEY
-MODEL_NAME=gpt-4o
+LLM_NAME=gpt-4o
 ```
 
 ### 2. Configuration via `settings.py` file (Advanced)
@@ -37,33 +37,33 @@ While modifying `settings.py` offers more flexibility, it's generally recommende
 
 Here are some of the most fundamental settings you'll likely want to configure:
 
-- **`LLM_NAME`**: This setting determines which Large Language Model (LLM) provider DocsGPT will use.  It tells DocsGPT which API to interact with.
+- **`LLM_PROVIDER`**: This setting determines which Large Language Model (LLM) provider DocsGPT will use. It tells DocsGPT which API to interact with.
 
-    - **Common values:**
-        - `docsgpt`:  Use the DocsGPT Public API Endpoint (simple and free, as offered in `setup.sh` option 1).
-        - `openai`: Use OpenAI's API (requires an API key).
-        - `google`: Use Google's Vertex AI or Gemini models.
-        - `anthropic`: Use Anthropic's Claude models.
-        - `groq`: Use Groq's models.
-        - `huggingface`: Use HuggingFace Inference API.
-        - `azure_openai`: Use Azure OpenAI Service.
-        - `openai` (when using local inference engines like Ollama, Llama.cpp, TGI, etc.):  This signals DocsGPT to use an OpenAI-compatible API format, even if the actual LLM is running locally.
+  - **Common values:**
+    - `docsgpt`: Use the DocsGPT Public API Endpoint (simple and free, as offered in `setup.sh` option 1).
+    - `openai`: Use OpenAI's API (requires an API key).
+    - `google`: Use Google's Vertex AI or Gemini models.
+    - `anthropic`: Use Anthropic's Claude models.
+    - `groq`: Use Groq's models.
+    - `huggingface`: Use HuggingFace Inference API.
+    - `azure_openai`: Use Azure OpenAI Service.
+    - `openai` (when using local inference engines like Ollama, Llama.cpp, TGI, etc.): This signals DocsGPT to use an OpenAI-compatible API format, even if the actual LLM is running locally.
 
-- **`MODEL_NAME`**:  Specifies the specific model to use from the chosen LLM provider. The available models depend on the `LLM_NAME` you've selected.
+- **`LLM_NAME`**: Specifies the specific model to use from the chosen LLM provider. The available models depend on the `LLM_PROVIDER` you've selected.
 
-    - **Examples:**
-        - For `LLM_NAME=openai`: `gpt-4o`
-        - For `LLM_NAME=google`: `gemini-2.0-flash`
-        - For local models (e.g., Ollama): `llama3.2:1b` (or any model name available in your setup).
+  - **Examples:**
+    - For `LLM_PROVIDER=openai`: `gpt-4o`
+    - For `LLM_PROVIDER=google`: `gemini-2.0-flash`
+    - For local models (e.g., Ollama): `llama3.2:1b` (or any model name available in your setup).
 
-- **`EMBEDDINGS_NAME`**:  This setting defines which embedding model DocsGPT will use to generate vector embeddings for your documents. Embeddings are numerical representations of text that allow DocsGPT to understand the semantic meaning of your documents for efficient search and retrieval.
+- **`EMBEDDINGS_NAME`**: This setting defines which embedding model DocsGPT will use to generate vector embeddings for your documents. Embeddings are numerical representations of text that allow DocsGPT to understand the semantic meaning of your documents for efficient search and retrieval.
 
-    - **Default value:** `huggingface_sentence-transformers/all-mpnet-base-v2` (a good general-purpose embedding model).
-    - **Other options:** You can explore other embedding models from Hugging Face Sentence Transformers or other providers if needed.
+  - **Default value:** `huggingface_sentence-transformers/all-mpnet-base-v2` (a good general-purpose embedding model).
+  - **Other options:** You can explore other embedding models from Hugging Face Sentence Transformers or other providers if needed.
 
-- **`API_KEY`**:  Required for most cloud-based LLM providers.  This is your authentication key to access the LLM provider's API. You'll need to obtain this key from your chosen provider's platform.
+- **`API_KEY`**: Required for most cloud-based LLM providers. This is your authentication key to access the LLM provider's API. You'll need to obtain this key from your chosen provider's platform.
 
-- **`OPENAI_BASE_URL`**:  Specifically used when `LLM_NAME` is set to `openai` but you are connecting to a local inference engine (like Ollama, Llama.cpp, etc.) that exposes an OpenAI-compatible API.  This setting tells DocsGPT where to find your local LLM server.
+- **`OPENAI_BASE_URL`**: Specifically used when `LLM_PROVIDER` is set to `openai` but you are connecting to a local inference engine (like Ollama, Llama.cpp, etc.) that exposes an OpenAI-compatible API. This setting tells DocsGPT where to find your local LLM server.
 
 ## Configuration Examples
 
@@ -74,9 +74,9 @@ Let's look at some concrete examples of how to configure these settings in your
 To use OpenAI's `gpt-4o` model, you would configure your `.env` file like this:
 
 ```
-LLM_NAME=openai
+LLM_PROVIDER=openai
 API_KEY=YOUR_OPENAI_API_KEY  # Replace with your actual OpenAI API key
-MODEL_NAME=gpt-4o
+LLM_NAME=gpt-4o
 ```
 
 Make sure to replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key.
@@ -86,14 +86,88 @@ Make sure to replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key.
 To use a local Ollama server with the `llama3.2:1b` model, you would configure your `.env` file like this:
 
 ```
-LLM_NAME=openai # Using OpenAI compatible API format for local models
+LLM_PROVIDER=openai # Using OpenAI compatible API format for local models
 API_KEY=None      # API Key is not needed for local Ollama
-MODEL_NAME=llama3.2:1b
+LLM_NAME=llama3.2:1b
 OPENAI_BASE_URL=http://host.docker.internal:11434/v1 # Default Ollama API URL within Docker
 EMBEDDINGS_NAME=huggingface_sentence-transformers/all-mpnet-base-v2 # You can also run embeddings locally if needed
 ```
 
-In this case, even though you are using Ollama locally, `LLM_NAME` is set to `openai` because Ollama (and many other local inference engines) are designed to be API-compatible with OpenAI.  `OPENAI_BASE_URL` points DocsGPT to the local Ollama server.
+In this case, even though you are using Ollama locally, `LLM_PROVIDER` is set to `openai` because Ollama (and many other local inference engines) are designed to be API-compatible with OpenAI. `OPENAI_BASE_URL` points DocsGPT to the local Ollama server.
+
+## Authentication Settings
+
+DocsGPT includes a JWT (JSON Web Token) based authentication feature for managing sessions or securing local deployments while allowing access.
+
+### `AUTH_TYPE` Overview
+
+The `AUTH_TYPE` setting in your `.env` file or `settings.py` determines the authentication method used by DocsGPT. This allows you to control how users authenticate with your DocsGPT instance.
+
+| Value         | Description                                                                                 |
+| ------------- | ------------------------------------------------------------------------------------------- |
+| `None`        | No authentication is used. Anyone can access the app.                                       |
+| `simple_jwt`  | A single, long-lived JWT token is generated at startup. All requests use this shared token. |
+| `session_jwt` | Unique JWT tokens are generated for each session/user.                                      |
+
+#### How to Configure
+
+Add the following to your `.env` file (or set in `settings.py`):
+
+```env
+# No authentication (default)
+AUTH_TYPE=None
+
+# OR: Simple JWT (shared token)
+AUTH_TYPE=simple_jwt
+JWT_SECRET_KEY=your_secret_key_here
+
+# OR: Session JWT (per-user/session tokens)
+AUTH_TYPE=session_jwt
+JWT_SECRET_KEY=your_secret_key_here
+```
+
+- If `AUTH_TYPE` is set to `simple_jwt` or `session_jwt`, a `JWT_SECRET_KEY` is required.
+- If `JWT_SECRET_KEY` is not set, DocsGPT will generate one and store it in `.jwt_secret_key` in the project root.
+
+#### How Each Method Works
+
+- **None**: No authentication. All API and UI access is open.
+- **simple_jwt**:
+  - A single JWT token is generated at startup and printed to the console.
+  - Use this token in the `Authorization` header for all API requests:
+    ```http
+    Authorization: Bearer <SIMPLE_JWT_TOKEN>
+    ```
+  - The frontend will prompt for this token if not already set.
+- **session_jwt**:
+  - Clients can request a new token from `/api/generate_token`.
+  - Use the received token in the `Authorization` header for subsequent requests.
+  - Each user/session gets a unique token.
+
+#### Security Notes
+
+- Always keep your `JWT_SECRET_KEY` secure and private.
+- If you set it manually, use a strong, random string.
+- If not set, DocsGPT will generate a secure key and persist it in `.jwt_secret_key`.
+
+#### Checking Current Auth Type
+
+- Use the `/api/config` endpoint to check the current `auth_type` and whether authentication is required.
+
+#### Frontend Token Input for `simple_jwt`
+
+If you have configured `AUTH_TYPE=simple_jwt`, the DocsGPT frontend will prompt you to enter the JWT token if it's not already set or is invalid. Paste the `SIMPLE_JWT_TOKEN` (printed to your console when the backend starts) into this field to access the application.
+
+<img
+  src="/jwt-input.png"
+  alt="Frontend prompt for JWT Token"
+  style={{
+    width: "500px",
+    maxWidth: "100%",
+    display: "block",
+    margin: "1em auto",
+  }}
+/>
 
 ## Exploring More Settings
 
@@ -104,4 +178,4 @@ These are just the basic settings to get you started. The `settings.py` file con
 - Cache settings (`CACHE_REDIS_URL`)
 - And many more!
 
-For a complete list of available settings and their descriptions, refer to the `settings.py` file in `application/core`. Remember to restart your Docker containers after making changes to your `.env` file or `settings.py` for the changes to take effect.
+For a complete list of available settings and their descriptions, refer to the `settings.py` file in `application/core`. Remember to restart your Docker containers after making changes to your `.env` file or `settings.py` for the changes to take effect.

docs/pages/Guides/How-to-use-different-LLM.mdx

@@ -32,9 +32,9 @@ Choose the LLM of your choice.
 ### For Open source llm change:
 <Steps>
 ### Step 1
-For open source version please edit `LLM_NAME`, `MODEL_NAME` and others in the .env file. Refer to [⚙️ App Configuration](/Deploying/DocsGPT-Settings) for more information.
+For open source version please edit `LLM_PROVIDER`, `LLM_NAME` and others in the .env file. Refer to [⚙️ App Configuration](/Deploying/DocsGPT-Settings) for more information.
 ### Step 2
-Visit [☁️ Cloud Providers](/Models/cloud-providers) for the updated list of online models. Make sure you have the right API_KEY and correct LLM_NAME.
+Visit [☁️ Cloud Providers](/Models/cloud-providers) for the updated list of online models. Make sure you have the right API_KEY and correct LLM_PROVIDER.
 For self-hosted please visit [🖥️ Local Inference](/Models/local-inference). 
 </Steps>
 

docs/pages/Models/cloud-providers.mdx

@@ -13,15 +13,15 @@ The primary method for configuring your LLM provider in DocsGPT is through the `
 
 To connect to a cloud LLM provider, you will typically need to configure the following basic settings in your `.env` file:
 
-*   **`LLM_NAME`**:  This setting is essential and identifies the specific cloud provider you wish to use (e.g., `openai`, `google`, `anthropic`).
-*   **`MODEL_NAME`**:  Specifies the exact model you want to utilize from your chosen provider (e.g., `gpt-4o`, `gemini-2.0-flash`, `claude-3-5-sonnet-latest`).  Refer to your provider's documentation for a list of available models.
+*   **`LLM_PROVIDER`**:  This setting is essential and identifies the specific cloud provider you wish to use (e.g., `openai`, `google`, `anthropic`).
+*   **`LLM_NAME`**:  Specifies the exact model you want to utilize from your chosen provider (e.g., `gpt-4o`, `gemini-2.0-flash`, `claude-3-5-sonnet-latest`).  Refer to your provider's documentation for a list of available models.
 *   **`API_KEY`**:  Almost all cloud LLM providers require an API key for authentication. Obtain your API key from your chosen provider's platform and securely store it in your `.env` file.
 
 ## Explicitly Supported Cloud Providers
 
-DocsGPT offers direct, streamlined support for the following cloud LLM providers, making configuration straightforward.  The table below outlines the `LLM_NAME` and example `MODEL_NAME` values to use for each provider in your `.env` file.
+DocsGPT offers direct, streamlined support for the following cloud LLM providers, making configuration straightforward.  The table below outlines the `LLM_PROVIDER` and example `LLM_NAME` values to use for each provider in your `.env` file.
 
-| Provider                     | `LLM_NAME`     | Example `MODEL_NAME`        |
+| Provider                     | `LLM_PROVIDER` | Example `LLM_NAME`          |
 | :--------------------------- | :------------- | :-------------------------- |
 | DocsGPT Public API           | `docsgpt`      | `None`                      |
 | OpenAI                       | `openai`       | `gpt-4o`                    |
@@ -35,16 +35,16 @@ DocsGPT offers direct, streamlined support for the following cloud LLM providers
 
 DocsGPT's flexible architecture allows you to connect to any cloud provider that offers an API compatible with the OpenAI API standard. This opens up a vast ecosystem of LLM services.
 
-To connect to an OpenAI-compatible cloud provider, you will still use `LLM_NAME=openai` in your `.env` file.  However, you will also need to specify the API endpoint of your chosen provider using the `OPENAI_BASE_URL` setting.  You will also likely need to provide an `API_KEY` and `MODEL_NAME` as required by that provider.
+To connect to an OpenAI-compatible cloud provider, you will still use `LLM_PROVIDER=openai` in your `.env` file.  However, you will also need to specify the API endpoint of your chosen provider using the `OPENAI_BASE_URL` setting.  You will also likely need to provide an `API_KEY` and `LLM_NAME` as required by that provider.
 
 **Example for DeepSeek (OpenAI-Compatible API):**
 
 To connect to DeepSeek, which offers an OpenAI-compatible API, your `.env` file could be configured as follows:
 
 ```
-LLM_NAME=openai
+LLM_PROVIDER=openai
 API_KEY=YOUR_API_KEY # Your DeepSeek API key
-MODEL_NAME=deepseek-chat # Or your desired DeepSeek model name
+LLM_NAME=deepseek-chat # Or your desired DeepSeek model name
 OPENAI_BASE_URL=https://api.deepseek.com/v1 # DeepSeek's OpenAI API URL
 ```
 

docs/pages/Models/embeddings.md

@@ -60,7 +60,7 @@ To use OpenAI's `text-embedding-ada-002` embedding model, you need to set `EMBED
 **Example `.env` configuration for OpenAI Embeddings:**
 
 ```
-LLM_NAME=openai
+LLM_PROVIDER=openai
 API_KEY=YOUR_OPENAI_API_KEY # Your OpenAI API Key
 EMBEDDINGS_NAME=openai_text-embedding-ada-002
 ```

docs/pages/Models/local-inference.mdx

@@ -15,8 +15,8 @@ Setting up a local inference engine with DocsGPT is configured through environme
 
 To connect to a local inference engine, you will generally need to configure these settings in your `.env` file:
 
-*   **`LLM_NAME`**:  Crucially set this to `openai`. This tells DocsGPT to use the OpenAI-compatible API format for communication, even though the LLM is local.
-*   **`MODEL_NAME`**:  Specify the model name as recognized by your local inference engine. This might be a model identifier or left as `None` if the engine doesn't require explicit model naming in the API request.
+*   **`LLM_PROVIDER`**:  Crucially set this to `openai`. This tells DocsGPT to use the OpenAI-compatible API format for communication, even though the LLM is local.
+*   **`LLM_NAME`**:  Specify the model name as recognized by your local inference engine. This might be a model identifier or left as `None` if the engine doesn't require explicit model naming in the API request.
 *   **`OPENAI_BASE_URL`**:  This is essential. Set this to the base URL of your local inference engine's API endpoint. This tells DocsGPT where to find your local LLM server.
 *   **`API_KEY`**:  Generally, for local inference engines, you can set `API_KEY=None` as authentication is usually not required in local setups.
 
@@ -24,16 +24,16 @@ To connect to a local inference engine, you will generally need to configure the
 
 DocsGPT is readily configurable to work with the following local inference engines, all communicating via the OpenAI API format. Here are example `OPENAI_BASE_URL` values for each, based on default setups:
 
-| Inference Engine              | `LLM_NAME` | `OPENAI_BASE_URL`          |
-| :---------------------------- | :--------- | :------------------------- |
-| LLaMa.cpp                     | `openai`   | `http://localhost:8000/v1`   |
-| Ollama                        | `openai`   | `http://localhost:11434/v1`  |
-| Text Generation Inference (TGI)| `openai`   | `http://localhost:8080/v1`   |
-| SGLang                        | `openai`   | `http://localhost:30000/v1`  |
-| vLLM                          | `openai`   | `http://localhost:8000/v1`   |
-| Aphrodite                     | `openai`   | `http://localhost:2242/v1`   |
-| FriendliAI                    | `openai`   | `http://localhost:8997/v1`   |
-| LMDeploy                      | `openai`   | `http://localhost:23333/v1` |
+| Inference Engine              | `LLM_PROVIDER` | `OPENAI_BASE_URL`          |
+| :---------------------------- | :------------- | :------------------------- |
+| LLaMa.cpp                     | `openai`       | `http://localhost:8000/v1`   |
+| Ollama                        | `openai`       | `http://localhost:11434/v1`  |
+| Text Generation Inference (TGI)| `openai`      | `http://localhost:8080/v1`   |
+| SGLang                        | `openai`       | `http://localhost:30000/v1`  |
+| vLLM                          | `openai`       | `http://localhost:8000/v1`   |
+| Aphrodite                     | `openai`       | `http://localhost:2242/v1`   |
+| FriendliAI                    | `openai`       | `http://localhost:8997/v1`   |
+| LMDeploy                      | `openai`       | `http://localhost:23333/v1` |
 
 **Important Note on `localhost` vs `host.docker.internal`:**
 

docs/pages/Tools/_meta.json

@@ -0,0 +1,14 @@
+{
+  "basics": {
+    "title": "🔧 Tools Basics",
+    "href": "/Tools/basics"
+  },
+  "api-tool": {
+    "title": "🗝️ API Tool",
+    "href": "/Tools/api-tool"
+  },
+  "creating-a-tool": {
+    "title": "🛠️ Creating a Custom Tool",
+    "href": "/Tools/creating-a-tool"
+  }
+}

docs/pages/Tools/api-tool.mdx

@@ -0,0 +1,153 @@
+---
+title: 🗝️ Generic API Tool
+description: Learn how to configure and use the API Tool in DocsGPT to connect with any RESTful API without writing custom code.
+---
+
+import { Callout } from 'nextra/components';
+import Image from 'next/image';
+
+# Using the Generic API Tool
+
+The API Tool provides a no-code/low-code solution to make DocsGPT interact with third-party or internal RESTful APIs. It acts as a bridge, allowing the Large Language Model (LLM) to leverage external services based on your chat interactions.
+ This guide will walk you through its capabilities, configuration, and best practices.
+
+## Introduction to the Generic API Tool
+
+**When to Use It:**
+    * Ideal for quickly integrating existing APIs where the interaction involves standard HTTP requests (GET, POST, PUT, DELETE).
+    * Suitable for fetching data to enrich answers (e.g., current weather, stock prices, product details).
+    * Useful for triggering simple actions in other systems (e.g., sending a notification, creating a basic task).
+
+**Contrast with Custom Python Tools:**
+    * **API Tool:** Best for straightforward API calls. Configuration is done through the DocsGPT UI.
+    * **Custom Python Tools:** Preferable when you need complex logic before or after the API call, handle non-standard authentication (like complex OAuth flows), manage multi-step API interactions, or require intricate data processing not easily managed by the LLM alone. See [Creating a Custom Tool](/Tools/creating-a-tool) for more.
+
+## Capabilities of the API Tool
+
+**Supported HTTP Methods:** You can configure actions using standard HTTP methods such as:
+    * `GET`: To retrieve data.
+    * `POST`: To submit data to create a new resource.
+    * `PUT`: To update an existing resource.
+    * `DELETE`: To remove a resource.
+
+**Request Configuration:**
+    * **Headers:** Define static or dynamic HTTP headers for authentication (e.g., API keys), content type specification, etc.
+    * **Query Parameters:** Specify URL query parameters, which can be static or dynamically filled by the LLM based on user input.
+    * **Request Body:** Define the structure of the request body (e.g., JSON), with fields that can be static or dynamically populated by the LLM.
+
+**Response Handling:**
+    * The API Tool executes the request and receives the raw response from the API (typically JSON or plain text).
+    * This raw response is then passed back to the LLM.
+    * The LLM uses this response, along with the context of your query and the description of the API tool action, to formulate an answer or decide on follow-up actions. The API tool itself doesn't deeply parse or transform the response beyond basic content type detection (e.g., loading JSON into a parsable object).
+
+## Configuring an API as a Tool
+
+You can configure the API Tool through the DocsGPT user interface, found in **Settings -> Tools**. When you add or modify an API Tool, you'll define specific actions that DocsGPT can perform.
+
+<Callout type="info">
+  The configuration involves defining how DocsGPT should call an API endpoint. Each configured API call essentially becomes a distinct "action" the LLM can choose to use.
+</Callout>
+
+Below is an example of how you might configure an API action, inspired by setting up a phone number validation service:
+
+<Image
+  src="/toolIcons/api-tool-example.png"
+  alt="API Tool configuration example for phone validation"
+  width={800}
+  height={450}
+  style={{ margin: '1em auto', display: 'block', borderRadius: '8px' }}
+/>
+_Figure 1: Example configuration for an API Tool action to validate phone numbers._
+
+**Defining an API Endpoint/Action:**
+
+When you configure a new API action, you'll fill in the following fields:
+
+-   **`Name`:** A user-friendly name for this specific API action (e.g., "Phone-check" as in the image, or more specific like "ValidateUSPhoneNumber"). This helps in managing your tools.
+-   **`Description`:** This is a **critical field**. Provide a clear and concise description of what the API action does, what kind of input it expects (implicitly), and what kind of output it provides. The LLM uses this description to understand when and how to use this action.
+-   **`URL`:** The full endpoint URL for the API request.
+-   **`HTTP Method`:** Select the appropriate HTTP method (e.g., GET, POST) from a dropdown.
+-   **`Headers`:** You can add custom HTTP headers as key-value pairs (Name, Value). Indicate if the value should be `Filled by LLM` or is static. If filled by LLM, provide a `Description` for the LLM.
+
+-   **`Query Parameters`:** For `GET` requests or when parameters are sent in the URL.
+    * **`Name`:** The name of the query parameter (e.g., `api_key`, `phone`).
+    * **`Type`:** The data type of the parameter (e.g., `string`).
+    * **`Filled by LLM` (Checkbox):**
+        - **Unchecked (Static):** The `Value` you provide will be used for every call (e.g., for an `api_key` that doesn't change).
+        - **Checked (Dynamic):** The LLM will extract the appropriate value from the user's chat query based on the `Description` you provide for this parameter. The `Value` field is typically left empty or contains a placeholder if `Filled by LLM` is checked.
+    * `Description`: Context for the LLM if the parameter is to be filled dynamically, or for your own reference if static.
+    * `Value`: The static value if not filled by LLM.
+
+-   **`Request Body`:** Used to send data (commonly JSON) to the API. Similar to Query Parameters, you define fields with `Name`, `Type`, whether it's `Filled by LLM`, a `Description` for dynamic fields, and a static `Value` if applicable.
+
+**Response Handling Guidance for the LLM:**
+
+While the API Tool configuration UI doesn't have explicit fields for defining response parsing rules (like JSONPath extractors), you significantly influence how the LLM handles the response through:
+    * **Tool Action `Description`:** Clearly state what kind of information the API returns (e.g., "This API returns a JSON object with 'status' and 'location' fields for the phone number."). This helps the LLM know what to look for in the API's output.
+    * **Prompt Engineering:** For more complex scenarios, you might need to adjust your global or agent-specific prompts to guide DocsGPT on how to interpret and present information from API tool responses. See [Customising Prompts](/Guides/Customising-prompts).
+
+## Using the Configured API Tool in Chat
+
+Once an API action is configured and enabled, DocsGPT's LLM can decide to use it based on your natural language queries.
+
+**Example (based on the phone validation tool in Figure 1):**
+
+1.  **User Query:** "Hey DocsGPT, can you check if +14155555555 is a valid phone number?"
+
+2.  **DocsGPT (LLM Orchestration):**
+    * The LLM analyzes the query.
+    * It matches the intent ("check if ... is a valid phone number") with the description of the "Phone-check" API action.
+    * It identifies `+14155555555` as the value for the `phone` parameter (which was marked as `Filled by LLM` with the description "Phone number to check").
+    * DocsGPT constructs the GET API request.
+3.  **API Tool Execution:**
+    * The API Tool makes the HTTP GET request.
+    * The external API (AbstractAPI) processes the request and returns a JSON response, e.g.:
+        ```json
+        {
+          "phone": "+14155555555",
+          "valid": true,
+          "format": {
+            "international": "+1 415-555-5555",
+            "national": "(415) 555-5555"
+          },
+          "country": {
+            "code": "US",
+            "name": "United States",
+            "prefix": "+1"
+          },
+          "location": "California",
+          "type": "Landline"
+        }
+        ```
+
+4.  **DocsGPT Response Formulation:**
+    * The API Tool passes this JSON response back to the LLM.
+    * The LLM, guided by the tool's description and the user's original query, extracts relevant information and formulates a user-friendly answer.
+    * **DocsGPT Chat Response:** "Yes, +14155555555 appears to be a valid landline phone number in California, United States."
+
+## Advanced Tips and Best Practices
+
+**Clear Description is the Key:** The LLM relies heavily on the `Description` field of the API action and its parameters. Make them unambiguous and action-oriented. Clearly state what the tool does and what kind of input it expects (even if implicitly through parameter descriptions). 
+
+**Iterative Testing:** After configuring an API tool, test it with various phrasings of user queries to ensure the LLM triggers it correctly and interprets the response as expected.
+  
+**Error Handling:**
+    * If an API call fails, the API Tool will return an error message and status code from the `requests` library or the API itself. The LLM may relay this error or try to explain it.
+    * Check DocsGPT's backend logs for more detailed error information if you encounter issues.
+  
+**Security Considerations:**
+    * **API Keys:** Be mindful of API keys and other sensitive credentials. The example image shows an API key directly in the configuration. For production or shared environments avoid exposing configurations with sensitive keys.
+    * **Rate Limits:** Be aware of the rate limits of the APIs you are integrating. Frequent calls from DocsGPT could exceed these limits.
+    * **Data Privacy:** Consider the data privacy implications of sending user query data to third-party APIs.
+-   **Idempotency:** For tools that modify data (POST, PUT, DELETE), be aware of whether the API operations are idempotent to avoid unintended consequences from repeated calls if the LLM retries an action.
+
+## Limitations
+
+While powerful, the Generic API Tool has some limitations:
+
+-   **Complex Authentication:** Advanced authentication flows like OAuth 2.0 (especially 3-legged OAuth requiring user redirection) or custom signature-based authentication often require custom Python tools.
+-   **Multi-Step API Interactions:** If a task requires multiple API calls that depend on each other (e.g., fetch a list, then for each item, fetch details), this kind of complex chaining and logic is better handled by a custom Python tool.
+-   **Complex Data Transformations:** If the API response needs significant transformation or processing before being useful to the LLM, a custom Python tool offers more flexibility.
+-   **Real-time Streaming (SSE, WebSockets):** The tool is designed for request-response interactions, not for maintaining persistent streaming connections.
+
+For scenarios that exceed these limitations, developing a [Custom Python Tool](/Tools/creating-a-tool) is the recommended approach.

docs/pages/Tools/basics.mdx

@@ -0,0 +1,92 @@
+---
+title: Tools Basics - Enhancing DocsGPT Capabilities
+description: Understand what DocsGPT Tools are, how they work, and explore the built-in tools available to extend DocsGPT's functionality.
+---
+
+import { Callout } from 'nextra/components';
+import Image from 'next/image';
+import { ToolCards } from '../../components/ToolCards';
+
+# Understanding DocsGPT Tools
+
+DocsGPT Tools are powerful extensions that significantly enhance the capabilities of your DocsGPT application. 
+They allow DocsGPT to move beyond its core function of retrieving information from your documents and enable it to perform actions, 
+interact with external data sources, and integrate with other services. You can find and configure available tools within 
+the "Tools" section of the DocsGPT application settings in the user interface.
+
+## What are Tools?
+
+-   **Purpose:** The primary purpose of Tools is to bridge the gap between understanding a user's request (natural language processing by the LLM) and executing a tangible action. This could involve fetching live data from the web, sending notifications, running code snippets, querying databases, or interacting with third-party APIs.
+
+-   **LLM as an Orchestrator:** The Large Language Model (LLM) at the heart of DocsGPT is designed to act as an intelligent orchestrator. Based on your query and the declared capabilities of the available tools (defined in their metadata), the LLM decides if a tool is needed, which tool to use, and what parameters to pass to it.
+
+-   **Action-Oriented Interactions:** Tools enable more dynamic and action-oriented interactions. For example:
+    * *"What's the latest news on renewable energy?"* - This might trigger a web search tool to fetch current articles.
+    * *"Fetch the order status for customer ID 12345 from our database."* - This could use a database tool.
+    * *"Summarize the content of this webpage and send the summary to the #general channel on Telegram."* - This might involve a web scraping tool followed by a Telegram notification tool.
+
+## Overview of Built-in Tools
+
+DocsGPT includes a suite of pre-built tools designed to expand its capabilities out-of-the-box. Below is an overview of the currently available tools. 
+
+<ToolCards
+  items={[
+    {
+      title: 'API Tool',
+      link: '/Tools/api-tool',
+      description: 'A highly flexible tool that allows DocsGPT to interact with virtually any API without needing to write custom Python code.'
+    },
+    {
+      title: 'Brave Search Tool',
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/brave.py',
+      description: 'Enables DocsGPT to perform real-time web and image searches using the Brave Search API for up-to-date information.'
+    },
+    {
+      title: 'Cryptoprice Tool',
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/cryptoprice.py',
+      description: 'Fetches the current price of specified cryptocurrencies.'
+    },
+    {
+      title: 'Ntfy Tool',
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/ntfy.py',
+      description: 'Allows DocsGPT to send push notifications to Ntfy.sh channels, ideal for alerts and updates.'
+    },
+    {
+      title: 'PostgreSQL Tool',
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/postgres.py',
+      description: 'Provides capabilities to connect to a PostgreSQL database, execute SQL queries, and retrieve schema information.'
+    },
+    {
+      title: 'Read Webpage Tool', // Renamed from Scraper Tool
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/read_webpage.py',
+      description: 'Enables DocsGPT to fetch and extract (scrape) textual content from specified web page URLs.'
+    },
+    {
+      title: 'Telegram Tool',
+      link: 'https://github.com/arc53/DocsGPT/blob/main/application/agents/tools/telegram.py',
+      description: 'Allows DocsGPT to send messages or images to Telegram chats via a Telegram Bot.'
+    }
+  ]}
+/>
+
+## Using Tools in DocsGPT (User Perspective)
+
+Interacting with tools in DocsGPT is designed to be intuitive:
+
+1.  **Natural Language Interaction:** As a user, you typically interact with DocsGPT using natural language queries or commands. The LLM within DocsGPT analyzes your input to determine if a specific task can or should be handled by one of the available and configured tools.
+
+2.  **Configuration in UI:**
+    * Tools are generally managed and configured within the DocsGPT application's settings, found under a "Tools" section in the GUI.
+    * For tools that interact with external services (like Brave Search, Telegram, or any service via the API Tool), you might need to provide authentication credentials (e.g., API keys, tokens) or specific endpoint information during the tool's setup in the UI.
+
+3.  **Prompt Engineering for Tools:** While the LLM aims to intelligently use tools, for more complex or reliable agent-like behaviors, you might need to customize the system prompts. Modifying the prompt can guide the LLM on when and how to prioritize or chain tools to achieve specific outcomes, especially if you're building an agent designed to perform a certain sequence of actions every time. For more on this, see [Customising Prompts](/Guides/Customising-prompts).
+
+## Advancing with Tools
+
+Understanding the basics of DocsGPT Tools opens up many possibilities:
+
+* **Leverage the API Tool:** For quick integrations with numerous external services, explore the [API Tool Detailed Guide](/Tools/api-tool).
+* **Develop Custom Tools:** If you have specific needs not covered by built-in tools or the generic API tool, you can develop your own. See our guide on `[Developing Custom Tools](/Tools/creating-a-tool)` (placeholder for now).
+* **Build AI Agents:** Tools are the fundamental building blocks for creating sophisticated AI agents within DocsGPT. Explore how these can be combined by looking into the `[Agents section/tab concept - link to be added once available]`.
+
+By harnessing the power of Tools, you can transform DocsGPT into a more versatile and proactive assistant tailored to your unique workflows.

docs/pages/Tools/creating-a-tool.mdx

@@ -0,0 +1,186 @@
+---
+title: 🛠️ Creating a Custom Tool
+description: Learn how to create custom Python tools to extend DocsGPT's functionality and integrate with various services or perform specific actions.
+---
+
+import { Callout } from 'nextra/components';
+import { Steps } from 'nextra/components';
+
+# 🛠️ Creating a Custom Python Tool
+
+This guide provides developers with a comprehensive, step-by-step approach to creating their own custom tools for DocsGPT. By developing custom tools, you can significantly extend DocsGPT's capabilities, enabling it to interact with new data sources, services, and perform specialized actions tailored to your unique needs.
+
+## Introduction to Custom Tool Development
+
+### Why Create Custom Tools?
+
+While DocsGPT offers a range of built-in tools and a versatile API Tool, there are many scenarios where a custom Python tool is the best solution:
+
+* **Integrating with Proprietary Systems:** Connect to internal APIs, databases, or services that are not publicly accessible or require complex authentication.
+* **Adding Domain-Specific Functionalities:** Implement logic specific to your industry or use case that isn't covered by general-purpose tools.
+* **Automating Unique Workflows:** Create tools that orchestrate multiple steps or interact with systems in a way unique to your operational needs.
+* **Connecting to Any System with an Accessible Interface:** If you can interact with a system programmatically using Python (e.g., through libraries, SDKs, or direct HTTP requests), you can likely build a DocsGPT tool for it.
+* **Complex Logic or Data Transformation:** When API interactions require intricate logic before sending a request or after receiving a response, or when data needs significant transformation that is difficult for an LLM to handle directly.
+
+### Prerequisites
+
+Before you begin, ensure you have:
+
+* A solid understanding of Python programming.
+* Familiarity with the DocsGPT project structure, particularly the `application/agents/tools/` directory where custom tools reside.
+* Basic knowledge of how APIs work, as many tools involve interacting with external or internal APIs.
+* Your DocsGPT development environment set up. If not, please refer to the [Setting Up a Development Environment](/Deploying/Development-Environment) guide.
+
+## The Anatomy of a DocsGPT Tool
+
+Custom tools in DocsGPT are Python classes that inherit from a base `Tool` class and implement specific methods to define their behavior, capabilities, and configuration needs.
+
+The **foundation** for all custom tools is the abstract base class, located in `application/agents/tools/base.py`. Your custom tool class **must** inherit from this class.
+
+### Essential Methods to Implement
+
+Your custom tool class needs to implement the following methods:
+
+1. **`__init__(self, config: dict)`**
+    
+    - **Purpose:** The constructor for your tool. It's called when DocsGPT initializes the tool.
+    - **Usage:** This method is typically used to receive and store tool-specific configurations passed via the `config` dictionary. This dictionary is populated based on the tool's settings, often configured through the DocsGPT UI or environment variables. For example, you would store API keys, base URLs, or database connection strings here.
+    - **Example** (`brave.py`)**:**
+        ``` python
+        class BraveSearchTool(Tool):
+            def __init__(self, config):
+                self.config = config
+                self.token = config.get("token", "") # API Key for Brave Search
+                self.base_url = "https://api.search.brave.com/res/v1"
+        ```
+
+2. **`execute_action(self, action_name: str, **kwargs) -> dict`**
+    
+    - **Purpose:** This is the workhorse of your tool. The LLM, acting as an agent, calls this method when it decides to use one of the actions your tool provides.
+    - **Parameters:**
+        - `action_name` (str): A string specifying which of the tool's actions to run (e.g., "brave_web_search").
+        - `**kwargs` (dict): A dictionary containing the parameters for that specific action. These parameters are defined in the tool's metadata (`get_actions_metadata()`) and are extracted or inferred by the LLM from the user's query.
+    - **Return Value:** A dictionary containing the result of the action. It's good practice to include keys like:
+        - `status_code` (int): An HTTP-like status code (e.g., 200 for success, 500 for error).
+        - `message` (str): A human-readable message describing the outcome.
+        - `data` (any): The actual data payload returned by the action (if applicable).
+        - `error` (str): An error message if the action failed.
+    - **Example (`read_webpage.py`):**
+        
+        ``` python
+        def execute_action(self, action_name: str, **kwargs) -> str:
+            if action_name != "read_webpage":
+                return f"Error: Unknown action '{action_name}'. This tool only supports 'read_webpage'."
+        
+            url = kwargs.get("url")
+            if not url:
+                return "Error: URL parameter is missing."
+            # ... (logic to fetch and parse webpage) ...
+            try:
+                # ...
+                return markdown_content 
+            except Exception as e:
+                return f"Error processing URL {url}: {e}"
+        ```
+        
+        A more structured return:
+        
+        ``` python
+        # ... inside execute_action
+        try:
+            # ... logic ...
+            return {"status_code": 200, "message": "Webpage read successfully", "data": markdown_content}
+        except Exception as e:
+            return {"status_code": 500, "message": f"Error processing URL {url}", "error": str(e)}
+        ```
+        
+3. **`get_actions_metadata(self) -> list`**
+    
+    - **Purpose:** This method is **critical** for the LLM to understand what your tool can do, when to use it, and what parameters it needs. It effectively advertises your tool's capabilities.
+    - **Return Value:** A list of dictionaries. Each dictionary describes one distinct action the tool can perform and must follow a specific JSON schema structure.
+        - `name` (str): A unique and descriptive name for the action (e.g., `mytool_get_user_details`). It's a common convention to prefix with the tool name to avoid collisions.
+        - `description` (str): A clear, concise, and unambiguous description of what the action does. **Write this for the LLM.** The LLM uses this description to decide if this action is appropriate for a given user query.
+        - `parameters` (dict): A JSON Schema object defining the parameters that the action expects. This schema tells the LLM what arguments are needed, their types, and which are required.
+            - `type`: Should always be `"object"`.
+            - `properties`: A dictionary where each key is a parameter name, and the value is an object defining its `type` (e.g., "string", "integer", "boolean") and `description`.
+            - `required`: A list of strings, where each string is the name of a parameter that is mandatory for the action.
+    - **Example (`postgres.py` - partial):**
+        
+        ``` python
+        def get_actions_metadata(self):
+            return [
+                {
+                    "name": "postgres_execute_sql",
+                    "description": "Execute an SQL query against the PostgreSQL database...",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "sql_query": {
+                                "type": "string",
+                                "description": "The SQL query to execute.",
+                            },
+                        },
+                        "required": ["sql_query"],
+                        "additionalProperties": False, # Good practice to prevent unexpected params
+                    },
+                },
+                # ... other actions like postgres_get_schema
+            ]
+        ```
+        
+4. **`get_config_requirements(self) -> dict`**
+    
+    - **Purpose:** Defines the configuration parameters that your tool needs to function (e.g., API keys, specific base URLs, connection strings, default settings). This information can be used by the DocsGPT UI to dynamically render configuration fields for your tool or for validation.
+    - **Return Value:** A dictionary where keys are the configuration item names (which will be keys in the `config` dict passed to `__init__`) and values are dictionaries describing each requirement:
+        - `type` (str): The expected data type of the config value (e.g., "string", "boolean", "integer").
+        - `description` (str): A human-readable description of what this configuration item is for.
+        - `secret` (bool, optional): Set to `True` if the value is sensitive (e.g., an API key) and should be masked or handled specially in UIs. Defaults to `False`.
+    - **Example (`brave.py`):**
+        
+        ``` python
+        def get_config_requirements(self):
+            return {
+                "token": { # This 'token' will be a key in the config dict for __init__
+                    "type": "string",
+                    "description": "Brave Search API key for authentication",
+                    "secret": True
+                },
+            }
+        ```    
+
+## Tool Registration and Discovery
+
+DocsGPT's ToolManager (located in application/agents/tools/tool_manager.py) automatically discovers and loads tools.
+
+As long as your custom tool:
+
+1. Is placed in a Python file within the `application/agents/tools/` directory (and the filename is not `base.py` or starts with `__`).
+2. Correctly inherits from the `Tool` base class.
+3. Implements all the abstract methods (`execute_action`, `get_actions_metadata`, `get_config_requirements`).
+
+The `ToolManager` should be able to load it when DocsGPT starts. 
+
+## Configuration & Secrets Management
+
+- **Configuration Source:** The `config` dictionary passed to your tool's `__init__` method is typically populated from settings defined in the DocsGPT UI (if available for the tool) or from environment variables/configuration files that DocsGPT loads (see [⚙️ App Configuration](/Deploying/DocsGPT-Settings)). The keys in this dictionary should match the names you define in `get_config_requirements()`.
+- **Secrets:** Never hardcode secrets (like API keys or passwords) directly into your tool's Python code. Instead, define them as configuration requirements (using `secret: True` in `get_config_requirements()`) and let DocsGPT's configuration system inject them via the `config` dictionary at runtime. This ensures that secrets are managed securely and are not exposed in your codebase.
+
+## Best Practices for Tool Development
+
+- **Atomicity:** Design tool actions to be as atomic (single, well-defined purpose) as possible. This makes them easier for the LLM to understand and combine.
+- **Clarity in Metadata:** Ensure action names and descriptions in `get_actions_metadata()` are extremely clear, specific, and unambiguous. This is the primary way the LLM understands your tool.
+- **Robust Error Handling:** Implement comprehensive error handling within your `execute_action` logic (and the private methods it calls). Return informative error messages in the result dictionary so the LLM or user can understand what went wrong.
+- **Security:**
+    - Be mindful of the security implications of your tool, especially if it interacts with sensitive systems or can execute arbitrary code/queries.
+    - Validate and sanitize any inputs, especially if they are used to construct database queries or shell commands, to prevent injection attacks.
+- **Performance:** Consider the performance implications of your tool's actions. If an action is slow, it will impact the user experience. Optimize where possible.
+
+## (Optional) Contributing Your Tool
+
+If you develop a custom tool that you believe could be valuable to the broader DocsGPT community and is general-purpose:
+
+1. Ensure it's well-documented (both in code and with clear metadata).
+2. Make sure it adheres to the best practices outlined above.
+3. Consider opening a Pull Request to the [DocsGPT GitHub repository](https://github.com/arc53/DocsGPT) with your new tool, including any necessary documentation updates.
+
+By following this guide, you can create powerful custom tools that extend DocsGPT's capabilities to your specific operational environment.

docs/pages/_meta.json

@@ -4,6 +4,8 @@
   "quickstart": "Quickstart",
   "Deploying": "Deploying",
   "Models": "Models",
+  "Tools": "Tools",
+  "Agents": "Agents",
   "Extensions": "Extensions",
   "https://gptcloud.arc53.com/": {
     "title": "API",

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docs/public/toolIcons/tool_read_webpage.svg

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docs/public/toolIcons/tool_telegram.svg

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+<svg width="24" height="25" viewBox="0 0 24 25" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M12 0.5C8.81812 0.5 5.76375 1.76506 3.51562 4.01469C1.2652 6.26522 0.000643966 9.31734 0 12.5C0 15.6813 1.26562 18.7357 3.51562 20.9853C5.76375 23.2349 8.81812 24.5 12 24.5C15.1819 24.5 18.2362 23.2349 20.4844 20.9853C22.7344 18.7357 24 15.6813 24 12.5C24 9.31869 22.7344 6.26431 20.4844 4.01469C18.2362 1.76506 15.1819 0.5 12 0.5Z" fill="url(#paint0_linear_5586_9958)"/>
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+<stop offset="1" stop-color="#229ED9"/>
+</linearGradient>
+</defs>
+</svg>

docs/theme.config.jsx

@@ -60,7 +60,7 @@ const config = {
           GitHub
         </a>
         {' | '}
-        <a href="https://www.blog.docsgpt.cloud/" target="_blank">
+        <a href="https://blog.docsgpt.cloud/" target="_blank">
           Blog
         </a>
       </div>

extensions/react-widget/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docsgpt",
-  "version": "0.5.0",
+  "version": "0.5.1",
   "private": false,
   "description": "DocsGPT 🦖 is an innovative open-source tool designed to simplify the retrieval of information from project documentation using advanced GPT models 🤖.",
   "source": "./src/index.html",