auto-rag

Need vectorstores testing for all except faiss

.gitignore

@@ -113,6 +113,7 @@ venv.bak/
 # Spyder project settings
 .spyderproject
 .spyproject
+.jwt_secret_key
 
 # Rope project settings
 .ropeproject

README.md

@@ -48,10 +48,13 @@
 - [x] Add tools (Jan 2025)
 - [x] Manually updating chunks in the app UI (Feb 2025)
 - [x] Devcontainer for easy development (Feb 2025)
-- [ ] Anthropic Tool compatibility
-- [ ] Add triggerable actions / tools (webhook)
+- [x] ReACT agent (March 2025)
+- [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
-- [ ] Chatbots menu re-design to handle tools, scheduling, and more
+- [ ] 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!
 
@@ -92,13 +95,15 @@ A more detailed [Quickstart](https://docs.docsgpt.cloud/quickstart) is available
    ./setup.sh
    ```
 
-This interactive script will guide you through setting up DocsGPT. It offers four options: using the public API, running locally, connecting to a local inference engine, or using a cloud API provider.  The script will automatically configure your `.env` file and handle necessary downloads and installations based on your chosen option.
-
 **For Windows:**
 
-2. **Follow the Docker Deployment Guide:**
+2. **Run the PowerShell setup script:**
 
-   Please refer to the [Docker Deployment documentation](https://docs.docsgpt.cloud/Deploying/Docker-Deploying) for detailed step-by-step instructions on setting up DocsGPT using Docker. 
+   ```powershell
+   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.
 
 **Navigate to http://localhost:5173/**
 
@@ -107,7 +112,7 @@ 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 `setup.sh`).
+(or use the specific `docker compose down` command shown after running the setup script).
 
 > [!Note]
 > For development environment setup instructions, please refer to the [Development Environment Guide](https://docs.docsgpt.cloud/Deploying/Development-Environment).

application/Dockerfile

@@ -84,4 +84,4 @@ EXPOSE 7091
 USER appuser
 
 # Start Gunicorn
-CMD ["gunicorn", "-w", "2", "--timeout", "120", "--bind", "0.0.0.0:7091", "application.wsgi:app"]
+CMD ["gunicorn", "-w", "1", "--timeout", "120", "--bind", "0.0.0.0:7091", "--preload", "application.wsgi:app"]

application/agents/agent_creator.py

@@ -1,9 +1,11 @@
 from application.agents.classic_agent import ClassicAgent
+from application.agents.react_agent import ReActAgent
 
 
 class AgentCreator:
     agents = {
         "classic": ClassicAgent,
+        "react": ReActAgent,
     }
 
     @classmethod

application/agents/base.py

@@ -1,4 +1,6 @@
-from typing import Dict, Generator
+import uuid
+from abc import ABC, abstractmethod
+from typing import Dict, Generator, List, Optional
 
 from application.agents.llm_handler import get_llm_handler
 from application.agents.tools.tool_action_parser import ToolActionParser
@@ -6,19 +8,37 @@ from application.agents.tools.tool_manager import ToolManager
 
 from application.core.mongo_db import MongoDB
 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
 
 
-class BaseAgent:
+class BaseAgent(ABC):
     def __init__(
         self,
-        endpoint,
-        llm_name,
-        gpt_model,
-        api_key,
-        user_api_key=None,
-        decoded_token=None,
+        endpoint: str,
+        llm_name: str,
+        gpt_model: str,
+        api_key: str,
+        user_api_key: Optional[str] = None,
+        prompt: str = "",
+        chat_history: Optional[List[Dict]] = None,
+        decoded_token: Optional[Dict] = None,
+        attachments: Optional[List[Dict]] = None,
     ):
         self.endpoint = endpoint
+        self.llm_name = llm_name
+        self.gpt_model = gpt_model
+        self.api_key = api_key
+        self.user_api_key = user_api_key
+        self.prompt = prompt
+        self.decoded_token = decoded_token or {}
+        self.user: str = decoded_token.get("sub")
+        self.tool_config: Dict = {}
+        self.tools: List[Dict] = []
+        self.tool_calls: List[Dict] = []
+        self.chat_history: List[Dict] = chat_history if chat_history is not None else []
         self.llm = LLMCreator.create_llm(
             llm_name,
             api_key=api_key,
@@ -26,17 +46,44 @@ class BaseAgent:
             decoded_token=decoded_token,
         )
         self.llm_handler = get_llm_handler(llm_name)
-        self.gpt_model = gpt_model
-        self.tools = []
-        self.tool_config = {}
-        self.tool_calls = []
+        self.attachments = attachments or []
 
-    def gen(self, *args, **kwargs) -> Generator[Dict, None, None]:
-        raise NotImplementedError('Method "gen" must be implemented in the child class')
+    @log_activity()
+    def gen(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext = None
+    ) -> Generator[Dict, None, None]:
+        yield from self._gen_inner(query, retriever, log_context)
+
+    @abstractmethod
+    def _gen_inner(
+        self, query: str, retriever: BaseRetriever, log_context: LogContext
+    ) -> Generator[Dict, None, None]:
+        pass
+
+    def _get_tools(self, api_key: str = None) -> Dict[str, Dict]:
+        mongo = MongoDB.get_client()
+        db = mongo[settings.MONGO_DB_NAME]
+        agents_collection = db["agents"]
+        tools_collection = db["user_tools"]
+
+        agent_data = agents_collection.find_one({"key": api_key or self.user_api_key})
+        tool_ids = agent_data.get("tools", []) if agent_data else []
+
+        tools = (
+            tools_collection.find(
+                {"_id": {"$in": [ObjectId(tool_id) for tool_id in tool_ids]}}
+            )
+            if tool_ids
+            else []
+        )
+        tools = list(tools)
+        tools_by_id = {str(tool["_id"]): tool for tool in tools} if tools else {}
+
+        return tools_by_id
 
     def _get_user_tools(self, user="local"):
         mongo = MongoDB.get_client()
-        db = mongo["docsgpt"]
+        db = mongo[settings.MONGO_DB_NAME]
         user_tools_collection = db["user_tools"]
         user_tools = user_tools_collection.find({"user": user, "status": True})
         user_tools = list(user_tools)
@@ -109,14 +156,12 @@ class BaseAgent:
                 for param, details in action_data[param_type]["properties"].items():
                     if param not in call_args and "value" in details:
                         target_dict[param] = details["value"]
-
         for param, value in call_args.items():
             for param_type, target_dict in param_types.items():
                 if param_type in action_data and param in action_data[param_type].get(
                     "properties", {}
                 ):
                     target_dict[param] = value
-
         tm = ToolManager(config={})
         tool = tm.load_tool(
             tool_data["name"],
@@ -151,3 +196,82 @@ class BaseAgent:
         self.tool_calls.append(tool_call_data)
 
         return result, call_id
+
+    def _build_messages(
+        self,
+        system_prompt: str,
+        query: str,
+        retrieved_data: List[Dict],
+    ) -> List[Dict]:
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        p_chat_combine = system_prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+
+        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"]})
+            if "tool_calls" in i:
+                for tool_call in i["tool_calls"]:
+                    call_id = tool_call.get("call_id") or str(uuid.uuid4())
+
+                    function_call_dict = {
+                        "function_call": {
+                            "name": tool_call.get("action_name"),
+                            "args": tool_call.get("arguments"),
+                            "call_id": call_id,
+                        }
+                    }
+                    function_response_dict = {
+                        "function_response": {
+                            "name": tool_call.get("action_name"),
+                            "response": {"result": tool_call.get("result")},
+                            "call_id": call_id,
+                        }
+                    }
+
+                    messages_combine.append(
+                        {"role": "assistant", "content": [function_call_dict]}
+                    )
+                    messages_combine.append(
+                        {"role": "tool", "content": [function_response_dict]}
+                    )
+        messages_combine.append({"role": "user", "content": query})
+        return messages_combine
+
+    def _retriever_search(
+        self,
+        retriever: BaseRetriever,
+        query: str,
+        log_context: Optional[LogContext] = None,
+    ) -> List[Dict]:
+        retrieved_data = retriever.search(query)
+        if log_context:
+            data = build_stack_data(retriever, exclude_attributes=["llm"])
+            log_context.stacks.append({"component": "retriever", "data": data})
+        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
+        )
+        if log_context:
+            data = build_stack_data(self.llm, exclude_attributes=["client"])
+            log_context.stacks.append({"component": "llm", "data": data})
+        return resp
+
+    def _llm_handler(
+        self,
+        resp,
+        tools_dict: Dict,
+        messages: List[Dict],
+        log_context: Optional[LogContext] = None,
+        attachments: Optional[List[Dict]] = None,
+    ):
+        resp = self.llm_handler.handle_response(
+            self, resp, tools_dict, messages, attachments
+        )
+        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

application/agents/classic_agent.py

@@ -1,86 +1,30 @@
-import uuid
 from typing import Dict, Generator
 
 from application.agents.base import BaseAgent
-from application.logging import build_stack_data, log_activity, LogContext
+from application.logging import LogContext
 
 from application.retriever.base import BaseRetriever
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 class ClassicAgent(BaseAgent):
-    def __init__(
-        self,
-        endpoint,
-        llm_name,
-        gpt_model,
-        api_key,
-        user_api_key=None,
-        prompt="",
-        chat_history=None,
-        decoded_token=None,
-    ):
-        super().__init__(
-            endpoint, llm_name, gpt_model, api_key, user_api_key, decoded_token
-        )
-        self.user = decoded_token.get("sub")
-        self.prompt = prompt
-        self.chat_history = chat_history if chat_history is not None else []
-
-    @log_activity()
-    def gen(
-        self, query: str, retriever: BaseRetriever, log_context: LogContext = None
-    ) -> Generator[Dict, None, None]:
-        yield from self._gen_inner(query, retriever, log_context)
-
     def _gen_inner(
         self, query: str, retriever: BaseRetriever, log_context: LogContext
     ) -> Generator[Dict, None, None]:
         retrieved_data = self._retriever_search(retriever, query, log_context)
-
-        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
-        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
-
-        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"]}
-                    )
-                if "tool_calls" in i:
-                    for tool_call in i["tool_calls"]:
-                        call_id = tool_call.get("call_id")
-                        if call_id is None or call_id == "None":
-                            call_id = str(uuid.uuid4())
-
-                        function_call_dict = {
-                            "function_call": {
-                                "name": tool_call.get("action_name"),
-                                "args": tool_call.get("arguments"),
-                                "call_id": call_id,
-                            }
-                        }
-                        function_response_dict = {
-                            "function_response": {
-                                "name": tool_call.get("action_name"),
-                                "response": {"result": tool_call.get("result")},
-                                "call_id": call_id,
-                            }
-                        }
-
-                        messages_combine.append(
-                            {"role": "assistant", "content": [function_call_dict]}
-                        )
-                        messages_combine.append(
-                            {"role": "tool", "content": [function_response_dict]}
-                        )
-        messages_combine.append({"role": "user", "content": query})
-
-        tools_dict = self._get_user_tools(self.user)
+        if self.user_api_key:
+            tools_dict = self._get_tools(self.user_api_key)
+        else:
+            tools_dict = self._get_user_tools(self.user)
         self._prepare_tools(tools_dict)
 
-        resp = self._llm_gen(messages_combine, log_context)
+        messages = self._build_messages(self.prompt, query, retrieved_data)
+
+        resp = self._llm_gen(messages, log_context)
+
+        attachments = self.attachments
 
         if isinstance(resp, str):
             yield {"answer": resp}
@@ -93,7 +37,7 @@ class ClassicAgent(BaseAgent):
             yield {"answer": resp.message.content}
             return
 
-        resp = self._llm_handler(resp, tools_dict, messages_combine, log_context)
+        resp = self._llm_handler(resp, tools_dict, messages, log_context, attachments)
 
         if isinstance(resp, str):
             yield {"answer": resp}
@@ -104,37 +48,17 @@ class ClassicAgent(BaseAgent):
         ):
             yield {"answer": resp.message.content}
         else:
-            completion = self.llm.gen_stream(
-                model=self.gpt_model, messages=messages_combine, tools=self.tools
-            )
-            for line in completion:
+            for line in resp:
                 if isinstance(line, str):
                     yield {"answer": line}
 
+        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()}
-
-    def _retriever_search(self, retriever, query, log_context):
-        retrieved_data = retriever.search(query)
-        if log_context:
-            data = build_stack_data(retriever, exclude_attributes=["llm"])
-            log_context.stacks.append({"component": "retriever", "data": data})
-        return retrieved_data
-
-    def _llm_gen(self, messages_combine, log_context):
-        resp = self.llm.gen_stream(
-            model=self.gpt_model, messages=messages_combine, tools=self.tools
-        )
-        if log_context:
-            data = build_stack_data(self.llm)
-            log_context.stacks.append({"component": "llm", "data": data})
-        return resp
-
-    def _llm_handler(self, resp, tools_dict, messages_combine, log_context):
-        resp = self.llm_handler.handle_response(
-            self, resp, tools_dict, messages_combine
-        )
-        if log_context:
-            data = build_stack_data(self.llm_handler)
-            log_context.stacks.append({"component": "llm_handler", "data": data})
-        return resp

application/agents/llm_handler.py

@@ -1,8 +1,11 @@
 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):
@@ -10,12 +13,90 @@ class LLMHandler(ABC):
         self.tool_calls = []
 
     @abstractmethod
-    def handle_response(self, agent, resp, tools_dict, messages, **kwargs):
+    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, stream: bool = True):
+    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"]
@@ -54,7 +135,9 @@ class OpenAILLMHandler(LLMHandler):
                             {"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",
@@ -69,12 +152,14 @@ class OpenAILLMHandler(LLMHandler):
             return resp
 
         else:
+            text_buffer = ""
             while True:
                 tool_calls = {}
                 for chunk in resp:
                     if isinstance(chunk, str) and len(chunk) > 0:
-                        return
-                    elif hasattr(chunk, "delta"): 
+                        yield chunk
+                        continue
+                    elif hasattr(chunk, "delta"):
                         chunk_delta = chunk.delta
 
                         if (
@@ -145,6 +230,7 @@ class OpenAILLMHandler(LLMHandler):
                                     )
 
                                 except Exception as e:
+                                    logging.error(f"Error executing tool: {str(e)}", exc_info=True)
                                     messages.append(
                                         {
                                             "role": "assistant",
@@ -152,15 +238,21 @@ class OpenAILLMHandler(LLMHandler):
                                         }
                                     )
                             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
+                            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
                 )
@@ -168,9 +260,11 @@ class OpenAILLMHandler(LLMHandler):
 
 
 class GoogleLLMHandler(LLMHandler):
-    def handle_response(self, agent, resp, tools_dict, messages, stream: bool = True):
+    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(
@@ -241,6 +335,9 @@ class GoogleLLMHandler(LLMHandler):
                                 "content": [function_response_part.to_json_dict()],
                             }
                         )
+                    else:
+                        tool_call_found = False
+                        yield result
 
                 if not tool_call_found:
                     return response

application/agents/react_agent.py

@@ -0,0 +1,229 @@
+import os
+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_template = f.read()
+with open(
+    os.path.join(current_dir, "application/prompts", "react_final_prompt.txt"),
+    "r",
+) as f:
+    final_prompt_template = f.read()
+    
+MAX_ITERATIONS_REASONING = 10
+
+class ReActAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        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:
+            tools_dict = self._get_tools(self.user_api_key)
+        else:
+            tools_dict = self._get_user_tools(self.user)
+        self._prepare_tools(tools_dict)
+
+        docs_together = "\n".join([doc["text"] for doc in retrieved_data])
+        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}")
+
+
+            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. "
+            )
+            
+            messages = self._build_messages(execution_prompt_str, query, retrieved_data)
+
+            resp_from_llm_gen = self._llm_gen(messages, log_context)
+
+            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)
+
+            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.")
+
+            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_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_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})
+
+        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
+    ) -> Generator[str, None, None]:
+        observation_string = "\n".join(observations)
+        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_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})
+
+        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/api_tool.py

@@ -25,8 +25,8 @@ class APITool(Tool):
     def _make_api_call(self, url, method, headers, query_params, body):
         if query_params:
             url = f"{url}?{requests.compat.urlencode(query_params)}"
-        if isinstance(body, dict):
-            body = json.dumps(body)
+        # if isinstance(body, dict):
+        #     body = json.dumps(body)
         try:
             print(f"Making API call: {method} {url} with body: {body}")
             if body == "{}":

application/agents/tools/read_webpage.py

@@ -0,0 +1,83 @@
+import requests
+from markdownify import markdownify
+from application.agents.tools.base import Tool
+from urllib.parse import urlparse
+
+class ReadWebpageTool(Tool):
+    """
+    Read Webpage (browser)
+    A tool to fetch the HTML content of a URL and convert it to Markdown.
+    """
+
+    def __init__(self, config=None):
+        """
+        Initializes the tool.
+        :param config: Optional configuration dictionary. Not used by this tool.
+        """
+        self.config = config
+
+    def execute_action(self, action_name: str, **kwargs) -> str:
+        """
+        Executes the specified action. For this tool, the only action is 'read_webpage'.
+
+        :param action_name: The name of the action to execute. Should be 'read_webpage'.
+        :param kwargs: Keyword arguments, must include 'url'.
+        :return: The Markdown content of the webpage or an error message.
+        """
+        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."
+
+        # Ensure the URL has a scheme (if not, default to http)
+        parsed_url = urlparse(url)
+        if not parsed_url.scheme:
+            url = "http://" + url
+        
+        try:
+            response = requests.get(url, timeout=10, headers={'User-Agent': 'DocsGPT-Agent/1.0'})
+            response.raise_for_status()  # Raise an exception for HTTP errors (4xx or 5xx)
+            
+            html_content = response.text
+            #soup = BeautifulSoup(html_content, 'html.parser')
+            
+            
+            markdown_content = markdownify(html_content, heading_style="ATX", newline_style="BACKSLASH")
+            
+            return markdown_content
+
+        except requests.exceptions.RequestException as e:
+            return f"Error fetching URL {url}: {e}"
+        except Exception as e:
+            return f"Error processing URL {url}: {e}"
+
+    def get_actions_metadata(self):
+        """
+        Returns metadata for the actions supported by this tool.
+        """
+        return [
+            {
+                "name": "read_webpage",
+                "description": "Fetches the HTML content of a given URL and returns it as clean Markdown text. Input must be a valid URL.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "url": {
+                            "type": "string",
+                            "description": "The fully qualified URL of the webpage to read (e.g., 'https://www.example.com').",
+                        }
+                    },
+                    "required": ["url"],
+                    "additionalProperties": False,
+                },
+            }
+        ]
+
+    def get_config_requirements(self):
+        """
+        Returns a dictionary describing the configuration requirements for the tool.
+        This tool does not require any specific configuration.
+        """
+        return {}

application/api/answer/routes.py

@@ -23,12 +23,13 @@ from application.utils import check_required_fields, limit_chat_history
 logger = logging.getLogger(__name__)
 
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 conversations_collection = db["conversations"]
 sources_collection = db["sources"]
 prompts_collection = db["prompts"]
-api_key_collection = db["api_keys"]
+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="/")
@@ -85,19 +86,51 @@ def run_async_chain(chain, question, chat_history):
     return result
 
 
-def get_data_from_api_key(api_key):
-    data = api_key_collection.find_one({"key": api_key})
-    # # Raise custom exception if the API key is not found
-    if data is None:
-        raise Exception("Invalid API Key, please generate new key", 401)
+def get_agent_key(agent_id, user_id):
+    if not agent_id:
+        return None, False, None
 
-    if "source" in data and isinstance(data["source"], DBRef):
-        source_doc = db.dereference(data["source"])
+    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"])
-        if "retriever" in source_doc:
-            data["retriever"] = source_doc["retriever"]
+        data["retriever"] = source_doc.get("retriever", data.get("retriever"))
     else:
         data["source"] = {}
+
     return data
 
 
@@ -121,12 +154,16 @@ 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:
@@ -136,6 +173,7 @@ def save_conversation(
                 "$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,
@@ -155,6 +193,7 @@ def save_conversation(
                     "queries": {
                         "prompt": question,
                         "response": response,
+                        "thought": thought,
                         "sources": source_log_docs,
                         "tool_calls": tool_calls,
                         "timestamp": current_time,
@@ -190,6 +229,7 @@ def save_conversation(
                 {
                     "prompt": question,
                     "response": response,
+                    "thought": thought,
                     "sources": source_log_docs,
                     "tool_calls": tool_calls,
                     "timestamp": current_time,
@@ -197,7 +237,12 @@ def save_conversation(
             ],
         }
         if api_key:
-            api_key_doc = api_key_collection.find_one({"key": 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(
@@ -228,11 +273,20 @@ def complete_stream(
     isNoneDoc=False,
     index=None,
     should_save_conversation=True,
+    attachments=None,
+    agent_id=None,
+    is_shared_usage=False,
+    shared_token=None,
 ):
     try:
-        response_full = ""
-        source_log_docs = []
-        tool_calls = []
+        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)
 
@@ -258,6 +312,10 @@ def complete_stream(
                 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:
@@ -275,12 +333,16 @@ def complete_stream(
                 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
@@ -300,14 +362,14 @@ def complete_stream(
                 "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)}")
-        logger.error(traceback.format_exc())
+        logger.error(f"Error in stream: {str(e)}", exc_info=True)
         data = json.dumps(
             {
                 "type": "error",
@@ -348,10 +410,15 @@ class Stream(Resource):
                 required=False, description="Flag indicating if no document is used"
             ),
             "index": fields.Integer(
-                required=False, description="The position where query is to be updated"
+                required=False, description="Index of the query to update"
             ),
             "save_conversation": fields.Boolean(
-                required=False, default=True, description="Flag to save conversation"
+                required=False,
+                default=True,
+                description="Whether to save the conversation",
+            ),
+            "attachments": fields.List(
+                fields.String, required=False, description="List of attachment IDs"
             ),
         },
     )
@@ -372,15 +439,29 @@ class Stream(Resource):
         try:
             question = data["question"]
             history = limit_chat_history(
-                json.loads(data.get("history", [])), gpt_model=gpt_model
+                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))
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             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
+            decoded_token = getattr(request, "decoded_token", None)
+            user_sub = decoded_token.get("sub") if decoded_token else None
+            agent_key, is_shared_usage, shared_token = get_agent_key(
+                agent_id, user_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"])
@@ -389,7 +470,12 @@ class Stream(Resource):
                 source = {"active_docs": data_key.get("source")}
                 retriever_name = data_key.get("retriever", retriever_name)
                 user_api_key = data["api_key"]
-                decoded_token = {"sub": data_key.get("user")}
+                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"]}
@@ -405,8 +491,12 @@ class Stream(Resource):
             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}",
+                f"/stream - request_data: {data}, source: {source}, attachments: {len(attachments)}",
                 extra={"data": json.dumps({"request_data": data, "source": source})},
             )
 
@@ -415,7 +505,7 @@ class Stream(Resource):
                 chunks = 0
 
             agent = AgentCreator.create_agent(
-                settings.AGENT_NAME,
+                agent_type,
                 endpoint="stream",
                 llm_name=settings.LLM_NAME,
                 gpt_model=gpt_model,
@@ -424,6 +514,7 @@ class Stream(Resource):
                 prompt=prompt,
                 chat_history=history,
                 decoded_token=decoded_token,
+                attachments=attachments,
             )
 
             retriever = RetrieverCreator.create_retriever(
@@ -449,6 +540,9 @@ class Stream(Resource):
                     isNoneDoc=data.get("isNoneDoc"),
                     index=index,
                     should_save_conversation=save_conv,
+                    agent_id=agent_id,
+                    is_shared_usage=is_shared_usage,
+                    shared_token=shared_token,
                 ),
                 mimetype="text/event-stream",
             )
@@ -527,9 +621,11 @@ class Answer(Resource):
             )
             conversation_id = data.get("conversation_id")
             prompt_id = data.get("prompt_id", "default")
-            chunks = int(data.get("chunks", 2))
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             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"])
@@ -538,6 +634,7 @@ class Answer(Resource):
                 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:
@@ -562,7 +659,7 @@ class Answer(Resource):
             )
 
             agent = AgentCreator.create_agent(
-                settings.AGENT_NAME,
+                agent_type,
                 endpoint="api/answer",
                 llm_name=settings.LLM_NAME,
                 gpt_model=gpt_model,
@@ -589,6 +686,7 @@ class Answer(Resource):
             source_log_docs = []
             tool_calls = []
             stream_ended = False
+            thought = ""
 
             for line in complete_stream(
                 question=question,
@@ -611,6 +709,8 @@ class Answer(Resource):
                         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"])
@@ -642,6 +742,7 @@ class Answer(Resource):
                     conversation_id,
                     question,
                     response_full,
+                    thought,
                     source_log_docs,
                     tool_calls,
                     llm,
@@ -715,7 +816,8 @@ class Search(Resource):
 
         try:
             question = data["question"]
-            chunks = int(data.get("chunks", 2))
+            chunks_from_request = data.get("chunks", 2)
+            chunks = chunks_from_request if str(chunks_from_request) == 'Auto' else int(chunks_from_request)
             token_limit = data.get("token_limit", settings.DEFAULT_MAX_HISTORY)
             retriever_name = data.get("retriever", "classic")
 
@@ -784,3 +886,34 @@ class Search(Resource):
             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/internal/routes.py

@@ -3,12 +3,15 @@ import datetime
 from flask import Blueprint, request, send_from_directory
 from werkzeug.utils import secure_filename
 from bson.objectid import ObjectId
-
+import logging
 from application.core.mongo_db import MongoDB
 from application.core.settings import settings
+from application.storage.storage_creator import StorageCreator
 
+
+logger = logging.getLogger(__name__)
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 conversations_collection = db["conversations"]
 sources_collection = db["sources"]
 
@@ -45,26 +48,26 @@ def upload_index_files():
     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
 
-    save_dir = os.path.join(current_dir, "indexes", str(id))
+    storage = StorageCreator.get_storage()
+    index_base_path = f"indexes/{id}"
+    
     if settings.VECTOR_STORE == "faiss":
         if "file_faiss" not in request.files:
-            print("No file part")
+            logger.error("No file_faiss part")
             return {"status": "no file"}
         file_faiss = request.files["file_faiss"]
         if file_faiss.filename == "":
             return {"status": "no file name"}
         if "file_pkl" not in request.files:
-            print("No file part")
+            logger.error("No file_pkl part")
             return {"status": "no file"}
         file_pkl = request.files["file_pkl"]
         if file_pkl.filename == "":
             return {"status": "no file name"}
-        # saves index files
-
-        if not os.path.exists(save_dir):
-            os.makedirs(save_dir)
-        file_faiss.save(os.path.join(save_dir, "index.faiss"))
-        file_pkl.save(os.path.join(save_dir, "index.pkl"))
+        
+        # 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")
 
     existing_entry = sources_collection.find_one({"_id": ObjectId(id)})
     if existing_entry:

application/api/user/tasks.py

@@ -1,7 +1,13 @@
 from datetime import timedelta
 
 from application.celery_init import celery
-from application.worker import ingest_worker, remote_worker, sync_worker
+from application.worker import (
+    agent_webhook_worker,
+    attachment_worker,
+    ingest_worker,
+    remote_worker,
+    sync_worker,
+)
 
 
 @celery.task(bind=True)
@@ -22,6 +28,18 @@ def schedule_syncs(self, frequency):
     return resp
 
 
+@celery.task(bind=True)
+def store_attachment(self, file_info, user):
+    resp = attachment_worker(self, file_info, user)
+    return resp
+
+
+@celery.task(bind=True)
+def process_agent_webhook(self, agent_id, payload):
+    resp = agent_webhook_worker(self, agent_id, payload)
+    return resp
+
+
 @celery.on_after_configure.connect
 def setup_periodic_tasks(sender, **kwargs):
     sender.add_periodic_task(

application/cache.py

@@ -61,14 +61,14 @@ def gen_cache(func):
                 if cached_response:
                     return cached_response.decode("utf-8")
             except Exception as e:
-                logger.error(f"Error getting cached response: {e}")
+                logger.error(f"Error getting cached response: {e}", exc_info=True)
 
         result = func(self, model, messages, stream, tools, *args, **kwargs)
         if redis_client and isinstance(result, str):
             try:
                 redis_client.set(cache_key, result, ex=1800)
             except Exception as e:
-                logger.error(f"Error setting cache: {e}")
+                logger.error(f"Error setting cache: {e}", exc_info=True)
 
         return result
 
@@ -100,7 +100,7 @@ def stream_cache(func):
                         time.sleep(0.03)  # Simulate streaming delay
                     return
             except Exception as e:
-                logger.error(f"Error getting cached stream: {e}")
+                logger.error(f"Error getting cached stream: {e}", exc_info=True)
 
         stream_cache_data = []
         for chunk in func(self, model, messages, stream, tools, *args, **kwargs):
@@ -112,6 +112,6 @@ def stream_cache(func):
                 redis_client.set(cache_key, json.dumps(stream_cache_data), ex=1800)
                 logger.info(f"Stream cache saved for key: {cache_key}")
             except Exception as e:
-                logger.error(f"Error setting stream cache: {e}")
+                logger.error(f"Error setting stream cache: {e}", exc_info=True)
 
     return wrapper

application/core/settings.py

@@ -19,6 +19,7 @@ class Settings(BaseSettings):
     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")
     DEFAULT_MAX_HISTORY: int = 150
     MODEL_TOKEN_LIMITS: dict = {
@@ -98,6 +99,8 @@ class Settings(BaseSettings):
     BRAVE_SEARCH_API_KEY: Optional[str] = None
 
     FLASK_DEBUG_MODE: bool = False
+    STORAGE_TYPE: str = "local"  # local or s3 
+
 
     JWT_SECRET_KEY: str = ""
 

application/llm/base.py

@@ -55,3 +55,12 @@ class BaseLLM(ABC):
 
     def _supports_tools(self):
         raise NotImplementedError("Subclass must implement _supports_tools method")
+        
+    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

application/llm/google_ai.py

@@ -1,7 +1,11 @@
 from google import genai
 from google.genai import types
+import logging
+import json
 
 from application.llm.base import BaseLLM
+from application.storage.storage_creator import StorageCreator
+from application.core.settings import settings
 
 
 class GoogleLLM(BaseLLM):
@@ -9,6 +13,126 @@ class GoogleLLM(BaseLLM):
         super().__init__(*args, **kwargs)
         self.api_key = api_key
         self.user_api_key = user_api_key
+        self.client = genai.Client(api_key=self.api_key)
+        self.storage = StorageCreator.get_storage()
+
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by Google Gemini for file uploads.
+
+        Returns:
+            list: List of supported MIME types
+        """
+        return [
+            'application/pdf',
+            'image/png',
+            'image/jpeg',
+            'image/jpg',
+            'image/webp',
+            'image/gif'
+        ]
+
+    def prepare_messages_with_attachments(self, messages, attachments=None):
+        """
+        Process attachments using Google AI's file API for more efficient handling.
+
+        Args:
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content and metadata.
+
+        Returns:
+            list: Messages formatted with file references for Google AI API.
+        """
+        if not attachments:
+            return messages
+
+        prepared_messages = messages.copy()
+
+        # Find the user message to attach files to the last one
+        user_message_index = None
+        for i in range(len(prepared_messages) - 1, -1, -1):
+            if prepared_messages[i].get("role") == "user":
+                user_message_index = i
+                break
+
+        if user_message_index is None:
+            user_message = {"role": "user", "content": []}
+            prepared_messages.append(user_message)
+            user_message_index = len(prepared_messages) - 1
+
+        if isinstance(prepared_messages[user_message_index].get("content"), str):
+            text_content = prepared_messages[user_message_index]["content"]
+            prepared_messages[user_message_index]["content"] = [
+                {"type": "text", "text": text_content}
+            ]
+        elif not isinstance(prepared_messages[user_message_index].get("content"), list):
+            prepared_messages[user_message_index]["content"] = []
+
+        files = []
+        for attachment in attachments:
+            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}")
+                    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')}]"
+                        })
+
+        if files:
+            logging.info(f"GoogleLLM: Adding {len(files)} files to message")
+            prepared_messages[user_message_index]["content"].append({
+                "files": files
+            })
+
+        return prepared_messages
+
+    def _upload_file_to_google(self, attachment):
+        """
+        Upload a file to Google AI and return the file URI.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+
+        Returns:
+            str: Google AI file URI for the uploaded file.
+        """
+        if 'google_file_uri' in attachment:
+            return attachment['google_file_uri']
+
+        file_path = attachment.get('path')
+        if not file_path:
+            raise ValueError("No file path provided in attachment")
+
+        if not self.storage.file_exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        try:
+            file_uri = self.storage.process_file(
+                file_path,
+                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:
+                attachments_collection.update_one(
+                    {"_id": attachment['_id']},
+                    {"$set": {"google_file_uri": file_uri}}
+                )
+
+            return file_uri
+        except Exception as e:
+            logging.error(f"Error uploading file to Google AI: {e}", exc_info=True)
+            raise
 
     def _clean_messages_google(self, messages):
         cleaned_messages = []
@@ -26,7 +150,7 @@ class GoogleLLM(BaseLLM):
                 elif isinstance(content, list):
                     for item in content:
                         if "text" in item:
-                            parts.append(types.Part.from_text(item["text"]))
+                            parts.append(types.Part.from_text(text=item["text"]))
                         elif "function_call" in item:
                             parts.append(
                                 types.Part.from_function_call(
@@ -41,6 +165,14 @@ class GoogleLLM(BaseLLM):
                                     response=item["function_response"]["response"],
                                 )
                             )
+                        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"]
+                                        )
+                                    )
                         else:
                             raise ValueError(
                                 f"Unexpected content dictionary format:{item}"
@@ -146,11 +278,25 @@ class GoogleLLM(BaseLLM):
             cleaned_tools = self._clean_tools_format(tools)
             config.tools = cleaned_tools
 
+        # 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:
+                    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}")
+
         response = client.models.generate_content_stream(
             model=model,
             contents=messages,
             config=config,
         )
+
+
         for chunk in response:
             if hasattr(chunk, "candidates") and chunk.candidates:
                 for candidate in chunk.candidates:

application/llm/openai.py

@@ -1,7 +1,10 @@
 import json
+import base64
+import logging
 
 from application.core.settings import settings
 from application.llm.base import BaseLLM
+from application.storage.storage_creator import StorageCreator
 
 
 class OpenAILLM(BaseLLM):
@@ -10,12 +13,14 @@ class OpenAILLM(BaseLLM):
         from openai import OpenAI
 
         super().__init__(*args, **kwargs)
-        if settings.OPENAI_BASE_URL:
+        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:
-            self.client = OpenAI(api_key=api_key)
+            DEFAULT_OPENAI_API_BASE = "https://api.openai.com/v1"
+            self.client = OpenAI(api_key=api_key, base_url=DEFAULT_OPENAI_API_BASE)
         self.api_key = api_key
         self.user_api_key = user_api_key
+        self.storage = StorageCreator.get_storage()
 
     def _clean_messages_openai(self, messages):
         cleaned_messages = []
@@ -65,6 +70,17 @@ 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(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:
+                                content_parts.append(item)
+                            cleaned_messages.append({"role": role, "content": content_parts})
                         else:
                             raise ValueError(
                                 f"Unexpected content dictionary format: {item}"
@@ -133,11 +149,167 @@ class OpenAILLM(BaseLLM):
     def _supports_tools(self):
         return True
 
+    def get_supported_attachment_types(self):
+        """
+        Return a list of MIME types supported by OpenAI for file uploads.
+
+        Returns:
+            list: List of supported MIME types
+        """
+        return [
+            'application/pdf',
+            'image/png',
+            'image/jpeg',
+            'image/jpg',
+            'image/webp',
+            'image/gif'
+        ]
+
+    def prepare_messages_with_attachments(self, messages, attachments=None):
+        """
+        Process attachments using OpenAI's file API for more efficient handling.
+
+        Args:
+            messages (list): List of message dictionaries.
+            attachments (list): List of attachment dictionaries with content and metadata.
+
+        Returns:
+            list: Messages formatted with file references for OpenAI API.
+        """
+        if not attachments:
+            return messages
+
+        prepared_messages = messages.copy()
+
+        # Find the user message to attach file_id to the last one
+        user_message_index = None
+        for i in range(len(prepared_messages) - 1, -1, -1):
+            if prepared_messages[i].get("role") == "user":
+                user_message_index = i
+                break
+
+        if user_message_index is None:
+            user_message = {"role": "user", "content": []}
+            prepared_messages.append(user_message)
+            user_message_index = len(prepared_messages) - 1
+
+        if isinstance(prepared_messages[user_message_index].get("content"), str):
+            text_content = prepared_messages[user_message_index]["content"]
+            prepared_messages[user_message_index]["content"] = [
+                {"type": "text", "text": text_content}
+            ]
+        elif not isinstance(prepared_messages[user_message_index].get("content"), list):
+            prepared_messages[user_message_index]["content"] = []
+
+        for attachment in attachments:
+            mime_type = attachment.get('mime_type')
+
+            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}"
+                        }
+                    })
+                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')}]"
+                        })
+            # Handle PDFs using the file API
+            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}
+                    })
+                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']}"
+                        })
+
+        return prepared_messages
+
+    def _get_base64_image(self, attachment):
+        """
+        Convert an image file to base64 encoding.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+
+        Returns:
+            str: Base64-encoded image data.
+        """
+        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')
+        except FileNotFoundError:
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+    def _upload_file_to_openai(self, attachment):
+        """
+        Upload a file to OpenAI and return the file_id.
+
+        Args:
+            attachment (dict): Attachment dictionary with path and metadata.
+                Expected keys:
+                - path: Path to the file
+                - id: Optional MongoDB ID for caching
+
+        Returns:
+            str: OpenAI file_id for the uploaded file.
+        """
+        import logging
+
+        if 'openai_file_id' in attachment:
+            return attachment['openai_file_id']
+
+        file_path = attachment.get('path')
+
+        if not self.storage.file_exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        try:
+            file_id = self.storage.process_file(
+                file_path,
+                lambda local_path, **kwargs: self.client.files.create(
+                    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:
+                attachments_collection.update_one(
+                    {"_id": attachment['_id']},
+                    {"$set": {"openai_file_id": file_id}}
+                )
+
+            return file_id
+        except Exception as e:
+            logging.error(f"Error uploading file to OpenAI: {e}", exc_info=True)
+            raise
+
 
 class AzureOpenAILLM(OpenAILLM):
 
     def __init__(
-        self, api_key, user_api_key, *args, **kwargs 
+        self, api_key, user_api_key, *args, **kwargs
     ):
 
         super().__init__(api_key)

application/logging.py

@@ -7,6 +7,7 @@ import uuid
 from typing import Any, Callable, Dict, Generator, List
 
 from application.core.mongo_db import MongoDB
+from application.core.settings import settings
 
 logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
@@ -29,6 +30,8 @@ def build_stack_data(
     exclude_attributes: List[str] = None,
     custom_data: Dict = None,
 ) -> Dict:
+    if obj is None:
+        raise ValueError("The 'obj' parameter cannot be None")
     data = {}
     if include_attributes is None:
         include_attributes = []
@@ -56,8 +59,8 @@ def build_stack_data(
                         data[attr_name] = [str(item) for item in attr_value]
                 elif isinstance(attr_value, dict):
                     data[attr_name] = {k: str(v) for k, v in attr_value.items()}
-                else:
-                    data[attr_name] = str(attr_value)
+        except AttributeError as e:
+            logging.warning(f"AttributeError while accessing {attr_name}: {e}")
         except AttributeError:
             pass
     if custom_data:
@@ -131,7 +134,7 @@ def _log_to_mongodb(
 ) -> None:
     try:
         mongo = MongoDB.get_client()
-        db = mongo["docsgpt"]
+        db = mongo[settings.MONGO_DB_NAME]
         user_logs_collection = db["stack_logs"]
 
         log_entry = {
@@ -148,4 +151,4 @@ def _log_to_mongodb(
         logging.debug(f"Logged activity to MongoDB: {activity_id}")
 
     except Exception as e:
-        logging.error(f"Failed to log to MongoDB: {e}")
+        logging.error(f"Failed to log to MongoDB: {e}", exc_info=True)

application/parser/embedding_pipeline.py

@@ -19,7 +19,7 @@ def add_text_to_store_with_retry(store, doc, source_id):
         doc.metadata["source_id"] = str(source_id)
         store.add_texts([doc.page_content], metadatas=[doc.metadata])
     except Exception as e:
-        logging.error(f"Failed to add document with retry: {e}")
+        logging.error(f"Failed to add document with retry: {e}", exc_info=True)
         raise
 
 
@@ -75,7 +75,7 @@ def embed_and_store_documents(docs, folder_name, source_id, task_status):
             # Add document to vector store
             add_text_to_store_with_retry(store, doc, source_id)
         except Exception as e:
-            logging.error(f"Error embedding document {idx}: {e}")
+            logging.error(f"Error embedding document {idx}: {e}", exc_info=True)
             logging.info(f"Saving progress at document {idx} out of {total_docs}")
             store.save_local(folder_name)
             break

application/parser/file/bulk.py

@@ -158,7 +158,7 @@ class SimpleDirectoryReader(BaseReader):
                     data = f.read()
             # Prepare metadata for this file
             if self.file_metadata is not None:
-                file_metadata = self.file_metadata(str(input_file))
+                file_metadata = self.file_metadata(input_file.name)
             else:
                 # Provide a default empty metadata
                 file_metadata = {'title': '', 'store': ''}

application/parser/file/tabular_parser.py

@@ -73,7 +73,13 @@ class PandasCSVParser(BaseParser):
             for more information.
             Set to empty dict by default, this means pandas will try to figure
             out the separators, table head, etc. on its own.
-
+            
+        header_period (int): Controls how headers are included in output:
+            - 0: Headers only at the beginning
+            - 1: Headers in every row
+            - N > 1: Headers every N rows
+            
+        header_prefix (str): Prefix for header rows. Default is "HEADERS: ".
     """
 
     def __init__(
@@ -83,6 +89,8 @@ class PandasCSVParser(BaseParser):
             col_joiner: str = ", ",
             row_joiner: str = "\n",
             pandas_config: dict = {},
+            header_period: int = 20,
+            header_prefix: str = "HEADERS: ",
             **kwargs: Any
     ) -> None:
         """Init params."""
@@ -91,6 +99,8 @@ class PandasCSVParser(BaseParser):
         self._col_joiner = col_joiner
         self._row_joiner = row_joiner
         self._pandas_config = pandas_config
+        self._header_period = header_period
+        self._header_prefix = header_prefix
 
     def _init_parser(self) -> Dict:
         """Init parser."""
@@ -104,15 +114,26 @@ class PandasCSVParser(BaseParser):
             raise ValueError("pandas module is required to read CSV files.")
 
         df = pd.read_csv(file, **self._pandas_config)
+        headers = df.columns.tolist()
+        header_row = f"{self._header_prefix}{self._col_joiner.join(headers)}"
 
-        text_list = df.apply(
-            lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
-        ).tolist()
+        if not self._concat_rows:
+            return df.apply(
+                lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
+            ).tolist()
+        
+        text_list = []
+        if self._header_period != 1:
+            text_list.append(header_row)
+        
+        for i, row in df.iterrows():
+            if (self._header_period > 1 and i > 0 and i % self._header_period == 0):
+                text_list.append(header_row)
+            text_list.append(self._col_joiner.join(row.astype(str).tolist()))
+            if self._header_period == 1 and i < len(df) - 1:
+                text_list.append(header_row)
 
-        if self._concat_rows:
-            return (self._row_joiner).join(text_list)
-        else:
-            return text_list
+        return self._row_joiner.join(text_list)
 
 
 class ExcelParser(BaseParser):
@@ -138,7 +159,13 @@ class ExcelParser(BaseParser):
             for more information.
             Set to empty dict by default, this means pandas will try to figure
             out the table structure on its own.
-
+            
+        header_period (int): Controls how headers are included in output:
+            - 0: Headers only at the beginning (default)
+            - 1: Headers in every row
+            - N > 1: Headers every N rows
+            
+        header_prefix (str): Prefix for header rows. Default is "HEADERS: ".
     """
 
     def __init__(
@@ -148,6 +175,8 @@ class ExcelParser(BaseParser):
             col_joiner: str = ", ",
             row_joiner: str = "\n",
             pandas_config: dict = {},
+            header_period: int = 20,
+            header_prefix: str = "HEADERS: ",
             **kwargs: Any
     ) -> None:
         """Init params."""
@@ -156,6 +185,8 @@ class ExcelParser(BaseParser):
         self._col_joiner = col_joiner
         self._row_joiner = row_joiner
         self._pandas_config = pandas_config
+        self._header_period = header_period
+        self._header_prefix = header_prefix
 
     def _init_parser(self) -> Dict:
         """Init parser."""
@@ -169,12 +200,22 @@ class ExcelParser(BaseParser):
             raise ValueError("pandas module is required to read Excel files.")
 
         df = pd.read_excel(file, **self._pandas_config)
+        headers = df.columns.tolist()
+        header_row = f"{self._header_prefix}{self._col_joiner.join(headers)}"
+        
+        if not self._concat_rows:
+            return df.apply(
+                lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
+            ).tolist()
+        
+        text_list = []
+        if self._header_period != 1:
+            text_list.append(header_row)
 
-        text_list = df.apply(
-            lambda row: (self._col_joiner).join(row.astype(str).tolist()), axis=1
-        ).tolist()
-
-        if self._concat_rows:
-            return (self._row_joiner).join(text_list)
-        else:
-            return text_list
+        for i, row in df.iterrows():
+            if (self._header_period > 1 and i > 0 and i % self._header_period == 0):
+                text_list.append(header_row)
+            text_list.append(self._col_joiner.join(row.astype(str).tolist()))
+            if self._header_period == 1 and i < len(df) - 1:
+                text_list.append(header_row)
+        return self._row_joiner.join(text_list)

application/parser/remote/crawler_loader.py

@@ -1,3 +1,4 @@
+import logging
 import requests
 from urllib.parse import urlparse, urljoin
 from bs4 import BeautifulSoup
@@ -42,7 +43,7 @@ class CrawlerLoader(BaseRemote):
                         )
                     )
             except Exception as e:
-                print(f"Error processing URL {current_url}: {e}")
+                logging.error(f"Error processing URL {current_url}: {e}", exc_info=True)
                 continue
 
             # Parse the HTML content to extract all links
@@ -61,4 +62,4 @@ class CrawlerLoader(BaseRemote):
             if self.limit is not None and len(visited_urls) >= self.limit:
                 break
 
-        return loaded_content
+        return loaded_content

application/parser/remote/sitemap_loader.py

@@ -1,3 +1,4 @@
+import logging
 import requests
 import re  # Import regular expression library
 import xml.etree.ElementTree as ET
@@ -32,7 +33,7 @@ class SitemapLoader(BaseRemote):
                 documents.extend(loader.load())
                 processed_urls += 1  # Increment the counter after processing each URL
             except Exception as e:
-                print(f"Error processing URL {url}: {e}")
+                logging.error(f"Error processing URL {url}: {e}", exc_info=True)
                 continue
 
         return documents

application/parser/remote/web_loader.py

@@ -1,3 +1,4 @@
+import logging
 from application.parser.remote.base import BaseRemote
 from application.parser.schema.base import Document
 from langchain_community.document_loaders import WebBaseLoader
@@ -39,6 +40,6 @@ class WebLoader(BaseRemote):
                         )
                     )
             except Exception as e:
-                print(f"Error processing URL {url}: {e}")
+                logging.error(f"Error processing URL {url}: {e}", exc_info=True)
                 continue
-        return documents
+        return documents

application/prompts/chat_combine_creative.txt

@@ -1,9 +1,15 @@
-You are a DocsGPT, friendly and helpful AI assistant by Arc53 that provides help with documents. You give thorough answers with code examples if possible.
-Use the following pieces of context to help answer the users question. If its not relevant to the question, provide friendly responses.
-You have access to chat history, and can use it to help answer the question.
-When using code examples, use the following format:
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
+If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+Try to use additional provided context if it's available, otherwise use your knowledge and tool capabilities.
+Allow yourself to be very creative and use your imagination.
 ----------------
+Possible additional context from uploaded sources:
 {summaries}

application/prompts/chat_combine_default.txt

@@ -1,9 +1,14 @@
-You are a helpful AI assistant, DocsGPT, specializing in document assistance, designed to offer detailed and informative responses. 
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
 If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
 You effectively utilize chat history, ensuring relevant and tailored responses. 
-If a question doesn't align with your context, you provide friendly and helpful replies.
+Try to use additional provided context if it's available, otherwise use your knowledge and tool capabilities.
 ----------------
+Possible additional context from uploaded sources:
 {summaries}

application/prompts/chat_combine_strict.txt

@@ -1,13 +1,17 @@
-You are an AI Assistant, DocsGPT, adept at offering document assistance. 
-Your expertise lies in providing answer on top of provided context. 
-You can leverage the chat history if needed.
-Answer the question based on the context below. 
-Keep the answer concise. Respond "Irrelevant context" if not sure about the answer.
-If question is not related to the context, respond "Irrelevant context".
-When using code examples, use the following format:
+You are a helpful AI assistant, DocsGPT. You are proactive and helpful. Try to use tools, if they are available to you, 
+be proactive and fill in missing information.
+Users can Upload documents for your context as attachments or sources via UI using the Conversation input box.
+If appropriate, your answers can include code examples, formatted as follows:
 ```(language)
 (code)
 ```
- ----------------
- Context:
- {summaries}
+Users are also able to see charts and diagrams if you use them with valid mermaid syntax in your responses.
+Try to respond with mermaid charts if visualization helps with users queries.
+You effectively utilize chat history, ensuring relevant and tailored responses. 
+Use context provided below or use available tools tool capabilities to answer user queries.
+If you dont have enough information from the context or tools, answer "I don't know" or "I don't have enough information".
+Never make up information or provide false information!
+Allow yourself to be very creative and use your imagination.
+----------------
+Context from uploaded sources:
+{summaries}

application/prompts/react_final_prompt.txt

@@ -0,0 +1,3 @@
+Query: {query}
+Observations: {observations}
+Now, using the insights from the observations, formulate a well-structured and precise final answer.

application/prompts/react_planning_prompt.txt

@@ -0,0 +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 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}
+Prompt: {prompt}
+Observations(potentially previous tool calls): {observations}

application/requirements.txt

@@ -1,24 +1,20 @@
 anthropic==0.49.0
-boto3==1.35.97
-beautifulsoup4==4.12.3
+boto3==1.38.18
+beautifulsoup4==4.13.4
 celery==5.4.0
 dataclasses-json==0.6.7
 docx2txt==0.8
 duckduckgo-search==7.5.2
 ebooklib==0.18
-elastic-transport==8.17.0
-elasticsearch==8.17.1
 escodegen==1.0.11
 esprima==4.0.1
 esutils==1.0.1
-Flask==3.1.0
+Flask==3.1.1
 faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
 google-genai==1.3.0
-google-generativeai==0.8.3
 gTTS==2.5.4
 gunicorn==23.0.0
-html2text==2024.2.26
 javalang==0.13.0
 jinja2==3.1.6
 jiter==0.8.2
@@ -26,39 +22,33 @@ jmespath==1.0.1
 joblib==1.4.2
 jsonpatch==1.33
 jsonpointer==3.0.0
-jsonschema==4.23.0
-jsonschema-spec==0.2.4
-jsonschema-specifications==2023.7.1
 kombu==5.4.2
 langchain==0.3.20
 langchain-community==0.3.19
-langchain-core==0.3.45
-langchain-openai==0.3.8
-langchain-text-splitters==0.3.6
-langsmith==0.3.19
+langchain-core==0.3.59
+langchain-openai==0.3.16
+langchain-text-splitters==0.3.8
+langsmith==0.3.42
 lazy-object-proxy==1.10.0
 lxml==5.3.1
 markupsafe==3.0.2
 marshmallow==3.26.1
 mpmath==1.3.0
-multidict==6.1.0
+multidict==6.4.3
 mypy-extensions==1.0.0
 networkx==3.4.2
 numpy==2.2.1
-openai==1.66.3
-openapi-schema-validator==0.6.3
-openapi-spec-validator==0.6.0
-openapi3-parser==1.1.19
+openai==1.78.1
+openapi3-parser==1.1.21
 orjson==3.10.14
-packaging==24.1
+packaging==24.2
 pandas==2.2.3
 openpyxl==3.1.5
 pathable==0.4.4
 pillow==11.1.0
-portalocker==2.10.1
+portalocker>=2.7.0,<3.0.0
 prance==23.6.21.0
-primp==0.14.0
-prompt-toolkit==3.0.50
+prompt-toolkit==3.0.51
 protobuf==5.29.3
 psycopg2-binary==2.9.10
 py==1.11.0
@@ -66,23 +56,22 @@ pydantic==2.10.6
 pydantic-core==2.27.2
 pydantic-settings==2.7.1
 pymongo==4.11.3
-pypdf==5.2.0
+pypdf==5.5.0
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
 python-jose==3.4.0
 python-pptx==1.0.2
-qdrant-client==1.13.2
 redis==5.2.1
-referencing==0.30.2
+referencing>=0.28.0,<0.31.0
 regex==2024.11.6
 requests==2.32.3
 retry==0.9.2
 sentence-transformers==3.3.1
 tiktoken==0.8.0
 tokenizers==0.21.0
-torch==2.5.1
+torch==2.7.0
 tqdm==4.67.1
-transformers==4.49.0
+transformers==4.51.3
 typing-extensions==4.12.2
 typing-inspect==0.9.0
 tzdata==2024.2
@@ -90,7 +79,7 @@ urllib3==2.3.0
 vine==5.1.0
 wcwidth==0.2.13
 werkzeug==3.1.3
-yarl==1.18.3
-markdownify==0.14.1
+yarl==1.20.0
+markdownify==1.1.0
 tldextract==5.1.3
 websockets==14.1

application/retriever/classic_rag.py

@@ -1,11 +1,17 @@
+import logging
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
 from application.retriever.base import BaseRetriever
-
 from application.vectorstore.vector_creator import VectorCreator
 
+logger = logging.getLogger(__name__)
 
 class ClassicRAG(BaseRetriever):
+    # Settings for Auto-Chunking
+    AUTO_CHUNK_MIN: int = 0
+    AUTO_CHUNK_MAX: int = 10
+    SIMILARITY_SCORE_THRESHOLD: float = 0.5
+    
     def __init__(
         self,
         source,
@@ -46,6 +52,7 @@ class ClassicRAG(BaseRetriever):
         self.question = self._rephrase_query()
         self.vectorstore = source["active_docs"] if "active_docs" in source else None
         self.decoded_token = decoded_token
+        self.actual_chunks_retrieved = 0
 
     def _rephrase_query(self):
         if (
@@ -72,12 +79,70 @@ class ClassicRAG(BaseRetriever):
             print(f"Rephrased query: {rephrased_query}")
             return rephrased_query if rephrased_query else self.original_question
         except Exception as e:
-            print(f"Error rephrasing query: {e}")
+            logging.error(f"Error rephrasing query: {e}", exc_info=True)
             return self.original_question
 
     def _get_data(self):
+        if self.chunks == 'Auto':
+            return self._get_data_auto()
+        else:
+            return self._get_data_classic()
+
+    def _get_data_auto(self):
+        if not self.vectorstore:
+            self.actual_chunks_retrieved = 0
+            return []
+
+        docsearch = VectorCreator.create_vectorstore(
+            settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY
+        )
+        
+        try:
+            docs_with_scores = docsearch.search_with_scores(self.question, k=self.AUTO_CHUNK_MAX)
+        except Exception as e:
+            logger.error(f"Error during search_with_scores: {e}", exc_info=True)
+            self.actual_chunks_retrieved = 0
+            return []
+        
+        if not docs_with_scores:
+            self.actual_chunks_retrieved = 0
+            return []
+
+        candidate_docs = []
+        for doc, score in docs_with_scores:
+            if score >= self.SIMILARITY_SCORE_THRESHOLD:
+                candidate_docs.append(doc)
+                
+        if len(candidate_docs) < self.AUTO_CHUNK_MIN and self.AUTO_CHUNK_MIN > 0:
+            final_docs_to_format = [doc for doc, score in docs_with_scores[:self.AUTO_CHUNK_MIN]]
+        else:
+            final_docs_to_format = candidate_docs
+            
+        self.actual_chunks_retrieved = len(final_docs_to_format)
+        
+        if not final_docs_to_format:
+            return []
+
+        formatted_docs = [
+            {
+                "title": i.metadata.get(
+                    "title", i.metadata.get("post_title", i.page_content)
+                ).split("/")[-1],
+                "text": i.page_content,
+                "source": (
+                    i.metadata.get("source")
+                    if i.metadata.get("source")
+                    else "local"
+                ),
+            }
+            for i in final_docs_to_format
+        ]
+        logger.info(f"AutoRAG: Retrieved {self.actual_chunks_retrieved} chunks for query '{self.original_question}'.")
+        return formatted_docs
+
+    def _get_data_classic(self):
         if self.chunks == 0:
-            docs = []
+            return []
         else:
             docsearch = VectorCreator.create_vectorstore(
                 settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY
@@ -97,8 +162,7 @@ class ClassicRAG(BaseRetriever):
                 }
                 for i in docs_temp
             ]
-
-        return docs
+            return docs
 
     def gen():
         pass
@@ -110,12 +174,24 @@ class ClassicRAG(BaseRetriever):
         return self._get_data()
 
     def get_params(self):
-        return {
+        params = {
             "question": self.original_question,
             "rephrased_question": self.question,
             "source": self.vectorstore,
-            "chunks": self.chunks,
             "token_limit": self.token_limit,
             "gpt_model": self.gpt_model,
             "user_api_key": self.user_api_key,
         }
+        if self.chunks == 'Auto':
+            params.update({
+                "chunks_mode": "Auto",
+                "chunks_retrieved_auto": self.actual_chunks_retrieved,
+                "auto_chunk_min_setting": self.AUTO_CHUNK_MIN,
+                "auto_chunk_max_setting": self.AUTO_CHUNK_MAX,
+                "similarity_threshold_setting": self.SIMILARITY_SCORE_THRESHOLD,
+            })
+        else:
+            params["chunks_mode"] = "Classic"
+            params["chunks"] = self.chunks
+
+        return params

application/retriever/retriever_creator.py

@@ -2,19 +2,18 @@ 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,
+        "duckduck_search": DuckDuckSearch,
+        "brave_search": BraveRetSearch,
+        "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

@@ -0,0 +1,94 @@
+"""Base storage class for file system abstraction."""
+from abc import ABC, abstractmethod
+from typing import BinaryIO, List, Callable
+
+
+class BaseStorage(ABC):
+    """Abstract base class for storage implementations."""
+
+    @abstractmethod
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """
+        Save a file to storage.
+
+        Args:
+            file_data: File-like object containing the data
+            path: Path where the file should be stored
+
+        Returns:
+            dict: A dictionary containing metadata about the saved file, including:
+                - 'path': The path where the file was saved
+                - 'storage_type': The type of storage (e.g., 'local', 's3')
+                - Other storage-specific metadata (e.g., 'uri', 'bucket_name', etc.)
+        """
+        pass
+
+    @abstractmethod
+    def get_file(self, path: str) -> BinaryIO:
+        """
+        Retrieve a file from storage.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            BinaryIO: File-like object containing the file data
+        """
+        pass
+
+    @abstractmethod
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        This method handles the details of retrieving the file and providing
+        it to the processor function in an appropriate way based on the storage type.
+
+        Args:
+            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
+        """
+        pass
+
+    @abstractmethod
+    def delete_file(self, path: str) -> bool:
+        """
+        Delete a file from storage.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            bool: True if deletion was successful
+        """
+        pass
+
+    @abstractmethod
+    def file_exists(self, path: str) -> bool:
+        """
+        Check if a file exists.
+
+        Args:
+            path: Path to the file
+
+        Returns:
+            bool: True if the file exists
+        """
+        pass
+
+    @abstractmethod
+    def list_files(self, directory: str) -> List[str]:
+        """
+        List all files in a directory.
+
+        Args:
+            directory: Directory path to list
+
+        Returns:
+            List[str]: List of file paths
+        """
+        pass

application/storage/local.py

@@ -0,0 +1,103 @@
+"""Local file system implementation."""
+import os
+import shutil
+from typing import BinaryIO, List, Callable
+
+from application.storage.base import BaseStorage
+
+
+class LocalStorage(BaseStorage):
+    """Local file system storage implementation."""
+
+    def __init__(self, base_dir: str = None):
+        """
+        Initialize local storage.
+
+        Args:
+            base_dir: Base directory for all operations. If None, uses current directory.
+        """
+        self.base_dir = base_dir or os.path.dirname(
+            os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        )
+
+    def _get_full_path(self, path: str) -> str:
+        """Get absolute path by combining base_dir and path."""
+        if os.path.isabs(path):
+            return path
+        return os.path.join(self.base_dir, path)
+
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """Save a file to local storage."""
+        full_path = self._get_full_path(path)
+
+        os.makedirs(os.path.dirname(full_path), exist_ok=True)
+
+        if hasattr(file_data, 'save'):
+            file_data.save(full_path)
+        else:
+            with open(full_path, 'wb') as f:
+                shutil.copyfileobj(file_data, f)
+
+        return {
+            'storage_type': 'local'
+        }
+
+    def get_file(self, path: str) -> BinaryIO:
+        """Get a file from local storage."""
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"File not found: {full_path}")
+
+        return open(full_path, 'rb')
+
+    def delete_file(self, path: str) -> bool:
+        """Delete a file from local storage."""
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            return False
+
+        os.remove(full_path)
+        return True
+
+    def file_exists(self, path: str) -> bool:
+        """Check if a file exists in local storage."""
+        full_path = self._get_full_path(path)
+        return os.path.exists(full_path)
+
+    def list_files(self, directory: str) -> List[str]:
+        """List all files in a directory in local storage."""
+        full_path = self._get_full_path(directory)
+
+        if not os.path.exists(full_path):
+            return []
+
+        result = []
+        for root, _, files in os.walk(full_path):
+            for file in files:
+                rel_path = os.path.relpath(os.path.join(root, file), self.base_dir)
+                result.append(rel_path)
+
+        return result
+
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        For local storage, we can directly pass the full path to the processor.
+
+        Args:
+            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
+        """
+        full_path = self._get_full_path(path)
+
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"File not found: {full_path}")
+
+        return processor_func(local_path=full_path, **kwargs)

application/storage/s3.py

@@ -0,0 +1,120 @@
+"""S3 storage implementation."""
+import io
+from typing import BinaryIO, List, Callable
+import os
+
+import boto3
+from botocore.exceptions import ClientError
+
+from application.storage.base import BaseStorage
+from application.core.settings import settings
+
+
+class S3Storage(BaseStorage):
+    """AWS S3 storage implementation."""
+
+    def __init__(self, bucket_name=None):
+        """
+        Initialize S3 storage.
+
+        Args:
+            bucket_name: S3 bucket name (optional, defaults to settings)
+        """
+        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',
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            region_name=region_name
+        )
+
+    def save_file(self, file_data: BinaryIO, path: str) -> dict:
+        """Save a file to S3 storage."""
+        self.s3.upload_fileobj(file_data, self.bucket_name, path)
+
+        region = getattr(settings, "SAGEMAKER_REGION", None)
+
+        return {
+            '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)
+        return file_obj
+
+    def delete_file(self, path: str) -> bool:
+        """Delete a file from S3 storage."""
+        try:
+            self.s3.delete_object(Bucket=self.bucket_name, Key=path)
+            return True
+        except ClientError:
+            return False
+
+    def file_exists(self, path: str) -> bool:
+        """Check if a file exists in S3 storage."""
+        try:
+            self.s3.head_object(Bucket=self.bucket_name, Key=path)
+            return True
+        except ClientError:
+            return False
+
+    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 += '/'
+
+        result = []
+        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'])
+
+        return result
+
+    def process_file(self, path: str, processor_func: Callable, **kwargs):
+        """
+        Process a file using the provided processor function.
+
+        Args:
+            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
+        
+        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:
+            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

application/storage/storage_creator.py

@@ -0,0 +1,32 @@
+"""Storage factory for creating different storage implementations."""
+from typing import Dict, Type
+
+from application.storage.base import BaseStorage
+from application.storage.local import LocalStorage
+from application.storage.s3 import S3Storage
+from application.core.settings import settings
+
+
+class StorageCreator:
+    storages: Dict[str, Type[BaseStorage]] = {
+        "local": LocalStorage,
+        "s3": S3Storage,
+    }
+    
+    _instance = None
+    
+    @classmethod
+    def get_storage(cls) -> BaseStorage:
+        if cls._instance is None:
+            storage_type = getattr(settings, "STORAGE_TYPE", "local")
+            cls._instance = cls.create_storage(storage_type)
+        
+        return cls._instance
+    
+    @classmethod
+    def create_storage(cls, type_name: str, *args, **kwargs) -> BaseStorage:
+        storage_class = cls.storages.get(type_name.lower())
+        if not storage_class:
+            raise ValueError(f"No storage implementation found for type {type_name}")
+        
+        return storage_class(*args, **kwargs)

application/usage.py

@@ -2,10 +2,11 @@ import sys
 from datetime import datetime
 
 from application.core.mongo_db import MongoDB
+from application.core.settings import settings
 from application.utils import num_tokens_from_object_or_list, num_tokens_from_string
 
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 usage_collection = db["token_usage"]
 
 

application/vectorstore/base.py

@@ -58,6 +58,10 @@ class BaseVectorStore(ABC):
     def search(self, *args, **kwargs):
         pass
 
+    @abstractmethod
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        pass
+
     def is_azure_configured(self):
         return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 

application/vectorstore/elasticsearch.py

@@ -1,9 +1,6 @@
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
 from application.vectorstore.document_class import Document
-import elasticsearch
-
-
 
 
 class ElasticsearchStore(BaseVectorStore):
@@ -26,8 +23,7 @@ class ElasticsearchStore(BaseVectorStore):
             else:
                 raise ValueError("Please provide either elasticsearch_url or cloud_id.")
 
-            
-
+            import elasticsearch
             ElasticsearchStore._es_connection = elasticsearch.Elasticsearch(**connection_params)
             
         self.docsearch = ElasticsearchStore._es_connection
@@ -112,6 +108,46 @@ class ElasticsearchStore(BaseVectorStore):
             
             doc_list.append(Document(page_content = hit['_source']['text'], metadata = hit['_source']['metadata']))
         return doc_list
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key)
+        vector = embeddings.embed_query(query)
+        knn = {
+            "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
+            "field": "vector",
+            "k": k,
+            "num_candidates": 100,
+            "query_vector": vector,
+        }
+        full_query = {
+            "knn": knn,
+            "query": {
+                "bool": {
+                    "must": [
+                        {
+                            "match": {
+                                "text": {
+                                    "query": question,
+                                }
+                            }
+                        }
+                    ],
+                    "filter": [{"match": {"metadata.source_id.keyword": self.source_id}}],
+                }
+            },
+            "rank": {"rrf": {}},
+        }
+        resp = self.docsearch.search(index=self.index_name, query=full_query['query'], size=k, knn=full_query['knn'])
+
+        docs_with_scores = []
+        for hit in resp['hits']['hits']:
+            score = hit['_score']
+            # Normalize the score. Elasticsearch returns a score of 1.0 + cosine similarity.
+            similarity = max(0, score - 1.0)
+            doc = Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata'])
+            docs_with_scores.append((doc, similarity))
+
+        return docs_with_scores
 
     def _create_index_if_not_exists(
             self, index_name, dims_length
@@ -155,8 +191,6 @@ class ElasticsearchStore(BaseVectorStore):
         **kwargs,
         ):
         
-        from elasticsearch.helpers import BulkIndexError, bulk
-
         bulk_kwargs = bulk_kwargs or {}
         import uuid
         embeddings = []
@@ -189,6 +223,7 @@ class ElasticsearchStore(BaseVectorStore):
 
 
         if len(requests) > 0:
+            from elasticsearch.helpers import BulkIndexError, bulk
             try:
                 success, failed = bulk(
                     self._es_connection,

application/vectorstore/faiss.py

@@ -1,17 +1,19 @@
 import os
+import tempfile
 
 from langchain_community.vectorstores import FAISS
 
 from application.core.settings import settings
 from application.parser.schema.base import Document
 from application.vectorstore.base import BaseVectorStore
+from application.storage.storage_creator import StorageCreator
 
 
 def get_vectorstore(path: str) -> str:
     if path:
-        vectorstore = os.path.join("application", "indexes", path)
+        vectorstore = f"indexes/{path}"
     else:
-        vectorstore = os.path.join("application")
+        vectorstore = "indexes"
     return vectorstore
 
 
@@ -21,21 +23,57 @@ class FaissStore(BaseVectorStore):
         self.source_id = source_id
         self.path = get_vectorstore(source_id)
         self.embeddings = self._get_embeddings(settings.EMBEDDINGS_NAME, embeddings_key)
+        self.storage = StorageCreator.get_storage()
 
         try:
             if docs_init:
                 self.docsearch = FAISS.from_documents(docs_init, self.embeddings)
             else:
-                self.docsearch = FAISS.load_local(
-                    self.path, self.embeddings, allow_dangerous_deserialization=True
-                )
-        except Exception:
-            raise
+                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}"
+                        )
+
+                    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:
+                        f.write(faiss_file.read())
+
+                    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
+                    )
+        except Exception as e:
+            raise Exception(f"Error loading FAISS index: {str(e)}")
 
         self.assert_embedding_dimensions(self.embeddings)
 
     def search(self, *args, **kwargs):
         return self.docsearch.similarity_search(*args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        docs_and_distances = self.docsearch.similarity_search_with_score(query, k, *args, **kwargs)
+        
+        # Convert L2 distance to a normalized similarity score (0-1, higher is better)
+        docs_and_similarities = []
+        for doc, distance in docs_and_distances:
+            if distance < 0: distance = 0
+            similarity = 1 / (1 + distance)
+            docs_and_similarities.append((doc, similarity))
+            
+        return docs_and_similarities
 
     def add_texts(self, *args, **kwargs):
         return self.docsearch.add_texts(*args, **kwargs)

application/vectorstore/lancedb.py

@@ -2,6 +2,8 @@ from typing import List, Optional
 import importlib
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
+from application.vectorstore.document_class import Document
+
 
 class LanceDBVectorStore(BaseVectorStore):
     """Class for LanceDB Vector Store integration."""
@@ -87,6 +89,23 @@ class LanceDBVectorStore(BaseVectorStore):
         results = self.docsearch.search(query_embedding).limit(k).to_list()
         return [(result["_distance"], result["text"], result["metadata"]) for result in results]
 
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        """Perform a similarity search with scores."""
+        self.ensure_table_exists()
+        query_embedding = self._get_embeddings(settings.EMBEDDINGS_NAME, self.embeddings_key).embed_query(query)
+        results = self.docsearch.search(query_embedding).limit(k).to_list()
+
+        docs_with_scores = []
+        for result in results:
+            distance = result.get('_distance', float('inf'))
+            if distance < 0: distance = 0
+            # Convert L2 distance to a normalized similarity score
+            similarity = 1 / (1 + distance)
+            doc = Document(page_content=result['text'], metadata=result["metadata"])
+            docs_with_scores.append((doc, similarity))
+            
+        return docs_with_scores
+    
     def delete_index(self):
         """Delete the entire LanceDB index (table)."""
         if self.table:

application/vectorstore/milvus.py

@@ -25,6 +25,16 @@ class MilvusStore(BaseVectorStore):
     def search(self, question, k=2, *args, **kwargs):
         expr = f"source_id == '{self._source_id}'"
         return self._docsearch.similarity_search(query=question, k=k, expr=expr, *args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        expr = f"source_id == '{self._source_id}'"
+        docs_and_distances = self._docsearch.similarity_search_with_score(query, k, expr=expr, *args, **kwargs)
+        docs_with_scores = []
+        for doc, distance in docs_and_distances:
+            similarity = 1.0 - distance
+            docs_with_scores.append((doc, max(0, similarity)))
+        
+        return docs_with_scores
 
     def add_texts(self, texts: List[str], metadatas: Optional[List[dict]], *args, **kwargs):
         ids = [str(uuid4()) for _ in range(len(texts))]

application/vectorstore/mongodb.py

@@ -1,3 +1,4 @@
+import logging
 from application.core.settings import settings
 from application.vectorstore.base import BaseVectorStore
 from application.vectorstore.document_class import Document
@@ -61,6 +62,40 @@ class MongoDBVectorStore(BaseVectorStore):
             metadata = doc
             results.append(Document(text, metadata))
         return results
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        query_vector = self._embedding.embed_query(query)
+
+        pipeline = [
+            {
+                "$vectorSearch": {
+                    "queryVector": query_vector,
+                    "path": self._embedding_key,
+                    "limit": k,
+                    "numCandidates": k * 10,
+                    "index": self._index_name,
+                    "filter": {"source_id": {"$eq": self._source_id}},
+                }
+            },
+            {
+                "$addFields": {
+                    "score": {"$meta": "vectorSearchScore"}
+                }
+            }
+        ]
+
+        cursor = self._collection.aggregate(pipeline)
+
+        results = []
+        for doc in cursor:
+            score = doc.pop("score", 0.0)
+            text = doc.pop(self._text_key)
+            doc.pop("_id")
+            doc.pop(self._embedding_key, None)
+            metadata = doc
+            doc = Document(page_content=text, metadata=metadata)
+            results.append((doc, score))
+        return results
 
     def _insert_texts(self, texts, metadatas):
         if not texts:
@@ -146,7 +181,7 @@ class MongoDBVectorStore(BaseVectorStore):
 
             return chunks
         except Exception as e:
-            print(f"Error getting chunks: {e}")
+            logging.error(f"Error getting chunks: {e}", exc_info=True)
             return []
 
     def add_chunk(self, text, metadata=None):
@@ -172,5 +207,5 @@ class MongoDBVectorStore(BaseVectorStore):
             result = self._collection.delete_one({"_id": object_id})
             return result.deleted_count > 0
         except Exception as e:
-            print(f"Error deleting chunk: {e}")
+            logging.error(f"Error deleting chunk: {e}", exc_info=True)
             return False

application/vectorstore/qdrant.py

@@ -1,11 +1,12 @@
-from langchain_community.vectorstores.qdrant import Qdrant
 from application.vectorstore.base import BaseVectorStore
 from application.core.settings import settings
-from qdrant_client import models
 
 
 class QdrantStore(BaseVectorStore):
     def __init__(self, source_id: str = "", embeddings_key: str = "embeddings"):
+        from qdrant_client import models
+        from langchain_community.vectorstores.qdrant import Qdrant
+
         self._filter = models.Filter(
             must=[
                 models.FieldCondition(
@@ -34,6 +35,9 @@ class QdrantStore(BaseVectorStore):
 
     def search(self, *args, **kwargs):
         return self._docsearch.similarity_search(filter=self._filter, *args, **kwargs)
+    
+    def search_with_scores(self, query: str, k: int, *args, **kwargs):
+        return self._docsearch.similarity_search_with_score(query=query, k=k, filter=self._filter, *args, **kwargs)
 
     def add_texts(self, *args, **kwargs):
         return self._docsearch.add_texts(*args, **kwargs)

application/worker.py

@@ -1,25 +1,37 @@
+import datetime
+import json
 import logging
+import mimetypes
 import os
 import shutil
 import string
+import tempfile
 import zipfile
+
 from collections import Counter
 from urllib.parse import urljoin
 
 import requests
+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.core.mongo_db import MongoDB
 from application.core.settings import settings
-from application.parser.file.bulk import SimpleDirectoryReader
+from application.parser.chunking import Chunker
 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
 from application.parser.schema.base import Document
-from application.parser.chunking import Chunker
-from application.utils import count_tokens_docs
+from application.retriever.retriever_creator import RetrieverCreator
+
+from application.storage.storage_creator import StorageCreator
+from application.utils import count_tokens_docs, num_tokens_from_string
 
 mongo = MongoDB.get_client()
-db = mongo["docsgpt"]
+db = mongo[settings.MONGO_DB_NAME]
 sources_collection = db["sources"]
 
 # Constants
@@ -27,18 +39,22 @@ 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)])
 
+
 current_dir = os.path.dirname(
     os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 )
 
+
 def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
     """
     Recursively extract zip files with a limit on recursion depth.
@@ -58,7 +74,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
             zip_ref.extractall(extract_to)
         os.remove(zip_path)  # Remove the zip file after extracting
     except Exception as e:
-        logging.error(f"Error extracting zip file {zip_path}: {e}")
+        logging.error(f"Error extracting zip file {zip_path}: {e}", exc_info=True)
         return
 
     # Check for nested zip files and extract them
@@ -69,6 +85,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
                 file_path = os.path.join(root, file)
                 extract_zip_recursive(file_path, root, current_depth + 1, max_depth)
 
+
 def download_file(url, params, dest_path):
     try:
         response = requests.get(url, params=params)
@@ -79,6 +96,7 @@ def download_file(url, params, dest_path):
         logging.error(f"Error downloading file: {e}")
         raise
 
+
 def upload_index(full_path, file_data):
     try:
         if settings.VECTOR_STORE == "faiss":
@@ -87,7 +105,9 @@ def upload_index(full_path, file_data):
                 "file_pkl": open(full_path + "/index.pkl", "rb"),
             }
             response = requests.post(
-                urljoin(settings.API_URL, "/api/upload_index"), files=files, data=file_data
+                urljoin(settings.API_URL, "/api/upload_index"),
+                files=files,
+                data=file_data,
             )
         else:
             response = requests.post(
@@ -102,6 +122,76 @@ def upload_index(full_path, file_data):
             for file in files.values():
                 file.close()
 
+
+def run_agent_logic(agent_config, input_data):
+    try:
+        source = agent_config.get("source")
+        retriever = agent_config.get("retriever", "classic")
+        if isinstance(source, DBRef):
+            source_doc = db.dereference(source)
+            source = str(source_doc["_id"])
+            retriever = source_doc.get("retriever", agent_config.get("retriever"))
+        else:
+            source = {}
+        source = {"active_docs": source}
+        chunks = int(agent_config.get("chunks", 2))
+        prompt_id = agent_config.get("prompt_id", "default")
+        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)
+        agent = AgentCreator.create_agent(
+            agent_type,
+            endpoint="webhook",
+            llm_name=settings.LLM_NAME,
+            gpt_model=settings.MODEL_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=user_api_key,
+            prompt=prompt,
+            chat_history=[],
+            decoded_token=decoded_token,
+            attachments=[],
+        )
+        retriever = RetrieverCreator.create_retriever(
+            retriever,
+            source=source,
+            chat_history=[],
+            prompt=prompt,
+            chunks=chunks,
+            token_limit=settings.DEFAULT_MAX_HISTORY,
+            gpt_model=settings.MODEL_NAME,
+            user_api_key=user_api_key,
+            decoded_token=decoded_token,
+        )
+        answer = agent.gen(query=input_data, retriever=retriever)
+        response_full = ""
+        thought = ""
+        source_log_docs = []
+        tool_calls = []
+
+        for line in answer:
+            if "answer" in line:
+                response_full += str(line["answer"])
+            elif "sources" in line:
+                source_log_docs.extend(line["sources"])
+            elif "tool_calls" in line:
+                tool_calls.extend(line["tool_calls"])
+            elif "thought" in line:
+                thought += line["thought"]
+
+        result = {
+            "answer": response_full,
+            "sources": source_log_docs,
+            "tool_calls": tool_calls,
+            "thought": thought,
+        }
+        logging.info(f"Agent response: {result}")
+        return result
+    except Exception as e:
+        logging.error(f"Error in run_agent_logic: {e}", exc_info=True)
+        raise
+
+
 # Define the main function for ingesting and processing documents.
 def ingest_worker(
     self, directory, formats, name_job, filename, user, retriever="classic"
@@ -126,62 +216,87 @@ def ingest_worker(
     limit = None
     exclude = True
     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})
-    file_data = {"name": name_job, "file": filename, "user": user}
 
-    if not os.path.exists(full_path):
-        os.makedirs(full_path)
-    download_file(urljoin(settings.API_URL, "/api/download"), file_data, os.path.join(full_path, filename))
+    # Create temporary working directory
+    with tempfile.TemporaryDirectory() as temp_dir:
+        try:
+            os.makedirs(temp_dir, exist_ok=True)
 
-    # check if file is .zip and extract it
-    if filename.endswith(".zip"):
-        extract_zip_recursive(
-            os.path.join(full_path, filename), full_path, 0, RECURSION_DEPTH
-        )
+            # Download file from storage to temp directory
+            temp_file_path = os.path.join(temp_dir, filename)
+            file_data = storage.get_file(source_file_path)
 
-    self.update_state(state="PROGRESS", meta={"current": 1})
+            with open(temp_file_path, "wb") as f:
+                f.write(file_data.read())
 
-    raw_docs = SimpleDirectoryReader(
-        input_dir=full_path,
-        input_files=input_files,
-        recursive=recursive,
-        required_exts=formats,
-        num_files_limit=limit,
-        exclude_hidden=exclude,
-        file_metadata=metadata_from_filename,
-    ).load_data()
+            self.update_state(state="PROGRESS", meta={"current": 1})
 
-    chunker = Chunker(
-        chunking_strategy="classic_chunk",
-        max_tokens=MAX_TOKENS,
-        min_tokens=MIN_TOKENS,
-        duplicate_headers=False
-    )
-    raw_docs = chunker.chunk(documents=raw_docs)
+            # 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
+                )
 
-    docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
-    id = ObjectId()
+            if sample:
+                logging.info(f"Sample mode enabled. Using {limit} documents.")
 
-    embed_and_store_documents(docs, full_path, id, self)
-    tokens = count_tokens_docs(docs)
-    self.update_state(state="PROGRESS", meta={"current": 100})
+            reader = SimpleDirectoryReader(
+                input_dir=temp_dir,
+                input_files=input_files,
+                recursive=recursive,
+                required_exts=formats,
+                exclude_hidden=exclude,
+                file_metadata=metadata_from_filename,
+            )
+            raw_docs = reader.load_data()
 
-    if sample:
-        for i in range(min(5, len(raw_docs))):
-            logging.info(f"Sample document {i}: {raw_docs[i]}")
+            chunker = Chunker(
+                chunking_strategy="classic_chunk",
+                max_tokens=MAX_TOKENS,
+                min_tokens=MIN_TOKENS,
+                duplicate_headers=False,
+            )
+            raw_docs = chunker.chunk(documents=raw_docs)
 
-    file_data.update({
-        "tokens": tokens,
-        "retriever": retriever,
-        "id": str(id),
-        "type": "local",
-    })
-    upload_index(full_path, file_data)
+            docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
 
-    # delete local
-    shutil.rmtree(full_path)
+            id = ObjectId()
+
+            vector_store_path = os.path.join(temp_dir, "vector_store")
+            os.makedirs(vector_store_path, exist_ok=True)
+
+            embed_and_store_documents(docs, vector_store_path, id, self)
+
+            tokens = count_tokens_docs(docs)
+
+            self.update_state(state="PROGRESS", meta={"current": 100})
+
+            if sample:
+                for i in range(min(5, len(raw_docs))):
+                    logging.info(f"Sample document {i}: {raw_docs[i]}")
+            file_data = {
+                "name": name_job,
+                "file": filename,
+                "user": user,
+                "tokens": tokens,
+                "retriever": retriever,
+                "id": str(id),
+                "type": "local",
+            }
+
+            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,
@@ -192,6 +307,7 @@ def ingest_worker(
         "limited": False,
     }
 
+
 def remote_worker(
     self,
     source_data,
@@ -203,7 +319,7 @@ def remote_worker(
     sync_frequency="never",
     operation_mode="upload",
     doc_id=None,
-):  
+):
     full_path = os.path.join(directory, user, name_job)
     if not os.path.exists(full_path):
         os.makedirs(full_path)
@@ -218,7 +334,7 @@ def remote_worker(
             chunking_strategy="classic_chunk",
             max_tokens=MAX_TOKENS,
             min_tokens=MIN_TOKENS,
-            duplicate_headers=False
+            duplicate_headers=False,
         )
         docs = chunker.chunk(documents=raw_docs)
         docs = [Document.to_langchain_format(raw_doc) for raw_doc in raw_docs]
@@ -260,6 +376,7 @@ def remote_worker(
     logging.info("remote_worker task completed successfully")
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
 
+
 def sync(
     self,
     source_data,
@@ -285,10 +402,11 @@ def sync(
             doc_id,
         )
     except Exception as e:
-        logging.error(f"Error during sync: {e}")
+        logging.error(f"Error during sync: {e}", exc_info=True)
         return {"status": "error", "error": str(e)}
     return {"status": "success"}
 
+
 def sync_worker(self, frequency):
     sync_counts = Counter()
     sources = sources_collection.find()
@@ -312,3 +430,121 @@ def sync_worker(self, frequency):
         key: sync_counts[key]
         for key in ["total_sync_count", "sync_success", "sync_failure"]
     }
+
+
+def attachment_worker(self, file_info, user):
+    """
+    Process and store a single attachment without vectorization.
+    """
+
+    mongo = MongoDB.get_client()
+    db = mongo[settings.MONGO_DB_NAME]
+    attachments_collection = db["attachments"]
+
+    filename = file_info["filename"]
+    attachment_id = file_info["attachment_id"]
+    relative_path = file_info["path"]
+    metadata = file_info.get("metadata", {})
+
+    try:
+        self.update_state(state="PROGRESS", meta={"current": 10})
+        storage = StorageCreator.get_storage()
+        
+        self.update_state(
+            state="PROGRESS", meta={"current": 30, "status": "Processing content"}
+        )
+
+        content = storage.process_file(
+            relative_path,
+            lambda local_path, **kwargs: SimpleDirectoryReader(
+                input_files=[local_path], exclude_hidden=True, errors="ignore"
+            ).load_data()[0].text
+        )
+            
+        token_count = num_tokens_from_string(content)
+
+        self.update_state(
+            state="PROGRESS", meta={"current": 80, "status": "Storing in database"}
+        )
+
+        mime_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
+
+        doc_id = ObjectId(attachment_id)
+        attachments_collection.insert_one(
+            {
+                "_id": doc_id,
+                "user": user,
+                "path": relative_path,
+                "content": content,
+                "token_count": token_count,
+                "mime_type": mime_type,
+                "date": datetime.datetime.now(),
+                "metadata": metadata,
+            }
+        )
+
+        logging.info(
+            f"Stored attachment with ID: {attachment_id}", extra={"user": user}
+        )
+
+        self.update_state(
+            state="PROGRESS", meta={"current": 100, "status": "Complete"}
+        )
+
+        return {
+            "filename": filename,
+            "path": relative_path,
+            "token_count": token_count,
+            "attachment_id": attachment_id,
+            "mime_type": mime_type,
+            "metadata": metadata,
+        }
+
+    except Exception as e:
+        logging.error(
+            f"Error processing file {filename}: {e}",
+            extra={"user": user},
+            exc_info=True,
+        )
+        raise
+
+
+def agent_webhook_worker(self, agent_id, payload):
+    """
+    Process the webhook payload for an agent.
+
+    Args:
+        self: Reference to the instance of the task.
+        agent_id (str): Unique identifier for the agent.
+        payload (dict): The payload data from the webhook.
+
+    Returns:
+        dict: Information about the processed webhook.
+    """
+    mongo = MongoDB.get_client()
+    db = mongo["docsgpt"]
+    agents_collection = db["agents"]
+
+    self.update_state(state="PROGRESS", meta={"current": 1})
+    try:
+        agent_oid = ObjectId(agent_id)
+        agent_config = agents_collection.find_one({"_id": agent_oid})
+        if not agent_config:
+            raise ValueError(f"Agent with ID {agent_id} not found.")
+        input_data = json.dumps(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)
+    except Exception as e:
+        logging.error(f"Error running agent logic: {e}", exc_info=True)
+        return {"status": "error", "error": str(e)}
+    finally:
+        self.update_state(state="PROGRESS", meta={"current": 100})
+        logging.info(
+            f"Webhook processed for agent {agent_id}", extra={"agent_id": agent_id}
+        )
+        return {"status": "success", "result": result}

deployment/docker-compose.yaml

@@ -12,6 +12,7 @@ services:
       - backend
 
   backend:
+    user: root
     build: ../application
     environment:
       - API_KEY=$API_KEY
@@ -27,13 +28,14 @@ services:
       - "7091:7091"
     volumes:
       - ../application/indexes:/app/application/indexes
-      - ../application/inputs:/app/application/inputs
+      - ../application/inputs:/app/inputs
       - ../application/vectors:/app/application/vectors
     depends_on:
       - redis
       - mongo
 
   worker:
+    user: root
     build: ../application
     command: celery -A application.app.celery worker -l INFO -B
     environment:
@@ -45,6 +47,10 @@ services:
       - MONGO_URI=mongodb://mongo:27017/docsgpt
       - API_URL=http://backend:7091
       - CACHE_REDIS_URL=redis://redis:6379/2
+    volumes:
+      - ../application/indexes:/app/application/indexes
+      - ../application/inputs:/app/inputs
+      - ../application/vectors:/app/application/vectors
     depends_on:
       - redis
       - mongo

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 +1,6 @@
+{
+  "basics": {
+    "title": "🤖 Agent Basics",
+    "href": "/Agents/basics"
+  }
+}

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/Deploying/DocsGPT-Settings.mdx

@@ -95,6 +95,49 @@ EMBEDDINGS_NAME=huggingface_sentence-transformers/all-mpnet-base-v2 # You can al
 
 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.
 
+## Authentication Settings
+
+DocsGPT includes a JWT (JSON Web Token) based authentication feature for managing sessions or securing local deployments while allowing access.
+
+- **`AUTH_TYPE`**: This setting in your `.env` file or `settings.py` determines the authentication method.
+    
+    - **Possible values:**
+        - `None` (or not set): No authentication is used.
+        - `simple_jwt`: A single, long-lived JWT token is generated and used for all authenticated requests. This is useful for securing a local deployment with a shared secret.
+        - `session_jwt`: Unique JWT tokens are generated for sessions, typically for individual users or temporary access.
+    - If `AUTH_TYPE` is set to `simple_jwt` or `session_jwt`, then a `JWT_SECRET_KEY` is required.
+- **`JWT_SECRET_KEY`**: This is a crucial secret key used to sign and verify JWTs.
+    
+    - It can be set directly in your `.env` file or `settings.py`.
+    - **Automatic Key Generation**: If `AUTH_TYPE` is `simple_jwt` or `session_jwt` and `JWT_SECRET_KEY` is _not_ set in your environment variables or `settings.py`, DocsGPT will attempt to:
+        1. Read the key from a file named `.jwt_secret_key` in the project's root directory.
+        2. If the file doesn't exist, it will generate a new 32-byte random key, save it to `.jwt_secret_key`, and use it for the session. This ensures that the key persists across application restarts.
+    - **Security Note**: It's vital to keep this key secure. If you set it manually, choose a strong, random string.
+
+**How it works:**
+
+- When `AUTH_TYPE` is set to `simple_jwt`, a token is generated at startup (if not already present or configured) and printed to the console. This token should be included in the `Authorization` header of your API requests as a Bearer token (e.g., `Authorization: Bearer YOUR_SIMPLE_JWT_TOKEN`).
+- When `AUTH_TYPE` is set to `session_jwt`:
+    - Clients can request a new token from the `/api/generate_token` endpoint.
+    - This token should then be included in the `Authorization` header for subsequent requests.
+- The backend verifies the JWT token provided in the `Authorization` header for protected routes.
+- The `/api/config` endpoint can be used to check the current `auth_type` and whether authentication is required.
+
+**Frontend Token Input for `simple_jwt`:**
+
+<img 
+  src="/jwt-input.png" 
+  alt="Frontend prompt for JWT Token" 
+  style={{ 
+    width: '500px', 
+    maxWidth: '100%', 
+    display: 'block', 
+    margin: '1em auto' 
+  }} 
+/>
+
+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. You'll need to paste the `SIMPLE_JWT_TOKEN` (which is printed to your console when the backend starts) into this field to access the application.
+
 ## Exploring More Settings
 
 These are just the basic settings to get you started. The `settings.py` file contains many more advanced options that you can explore to further customize DocsGPT, such as:

docs/pages/Extensions/chat-widget copy.md

@@ -1,212 +0,0 @@
-# Setting up the DocsGPT Widget in Your React Project
-
-## Introduction:
-The DocsGPT Widget is a powerful tool that allows you to integrate AI-powered documentation assistance into your web applications. This guide will walk you through the installation and usage of the DocsGPT Widget in your React project. Whether you're building a web app or a knowledge base, this widget can enhance your user experience.
-
-## Installation
-First, make sure you have Node.js and npm installed in your project. Then go to your project and install a new dependency: `npm install docsgpt`.
-
-## Usage
-In the file where you want to use the widget, import it and include the CSS file:
-```js
-import { DocsGPTWidget } from "docsgpt";
-```
-
-
-Now, you can use the widget in your component like this :
-```jsx
-<DocsGPTWidget
-  apiHost="https://your-docsgpt-api.com"
-  apiKey=""
-  avatar = "https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"
-  title = "Get AI assistance"
-  description = "DocsGPT's AI Chatbot is here to help"
-  heroTitle = "Welcome to DocsGPT !"
-  heroDescription="This chatbot is built with DocsGPT and utilises GenAI, 
-  please review important information using sources."
-  theme = "dark"
-  buttonIcon = "https://your-icon"
-  buttonBg = "#222327"
-/>
-```
-## Props Table for DocsGPT Widget
-
-| **Prop**          | **Type**         | **Default Value**                                           | **Description**                                                                                     |
-|--------------------|------------------|-------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
-| **`apiHost`**      | `string`         | `"https://gptcloud.arc53.com"`                                     | The URL of your DocsGPT API for vector search and chatbot queries.                                 |
-| **`apiKey`**       | `string`         | `""`                                                        | Your API key for authentication. Can be left empty if authentication is not required.              |
-| **`avatar`**       | `string`         | `"https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"`   | Specifies the URL of the avatar or image representing the chatbot.                                 |
-| **`title`**        | `string`         | `"Get AI assistance"`                                       | Sets the title text displayed in the chatbot interface.                                            |
-| **`description`**  | `string`         | `"DocsGPT's AI Chatbot is here to help"`                     | Provides a brief description of the chatbot's purpose or functionality.                            |
-| **`heroTitle`**    | `string`         | `"Welcome to DocsGPT !"`                                    | Displays a welcome title when users interact with the chatbot.                                     |
-| **`heroDescription`** | `string`     | `"This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources."` | Provides additional introductory text or information about the chatbot's capabilities.             |
-| **`theme`**        | `"dark" \| "light"` | `"dark"`                                                  | Allows you to select the theme for the chatbot interface. Accepts `"dark"` or `"light"`.           |
-| **`buttonIcon`**   | `string`         | `"https://your-icon"`                                        | Specifies the URL of the icon image for the widget's launch button.                                |
-| **`buttonBg`**     | `string`         | `"#222327"`                                                 | Sets the background color of the widget's launch button.                                           |
-| **`size`**         | `"small" \| "medium"` | `"medium"`                                              | Sets the size of the widget. Options are `"small"` or `"medium"`.                                  |
-
----
-
-## Notes
-- **Customizing Props:** All properties can be overridden when embedding the widget. For example, you can provide a unique avatar, title, or color scheme to better align with your brand.
-- **Default Theme:** The widget defaults to the dark theme unless explicitly set to `"light"`. 
-- **API Key:** If the `apiKey` is not required for your application, leave it empty.
-
-This table provides a clear overview of the customization options available for tailoring the DocsGPT widget to fit your application.
-
-
-## How to use DocsGPTWidget with [Nextra](https://nextra.site/) (Next.js + MDX)
-Install your widget as described above and then go to your `pages/` folder and create a new file `_app.js` with the following content:
-```js
-import { DocsGPTWidget } from "docsgpt";
-
-export default function MyApp({ Component, pageProps }) {
-    return (
-        <>
-            <Component {...pageProps} />
-            <DocsGPTWidget selectDocs="local/docsgpt-sep.zip/"/>
-        </>
-    )
-}
-```  
-## How to use DocsGPTWidget with HTML
-```html
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <meta http-equiv="X-UA-Compatible" content="ie=edge" />
-    <title>HTML + CSS</title>
-    <link rel="stylesheet" href="styles.css" />
-  </head>
-  <body>
-    <h1>This is a simple HTML + CSS template!</h1>
-    <div id="app"></div>
-    <!-- Include the widget script from dist/modern or dist/legacy -->
-    <script
-      src="https://unpkg.com/docsgpt/dist/modern/main.js"
-      type="module"
-    ></script>
-    <script type="module">
-      window.onload = function () {
-        renderDocsGPTWidget("app", {
-          apiKey: "",
-          size: "medium",
-        });
-      };
-    </script>
-  </body>
-</html>
-```
-To link the widget to your api and your documents you can pass parameters to the renderDocsGPTWidget('div id', { parameters }).
-```html
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8" />
-  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>DocsGPT Widget</title>
-  <script src="https://unpkg.com/docsgpt/dist/modern/main.js" type="module"></script>  
-</head>
-<body>
-  <div id="app"></div>
-  <!-- Include the widget script from dist/modern or dist/legacy -->
-  <script type="module">
-    window.onload = function() {
-      renderDocsGPTWidget('app', {
-        apiHost: 'http://localhost:7001',
-        apiKey:"",
-        avatar: 'https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png',
-        title: 'Get AI assistance',
-        description: "DocsGPT's AI Chatbot is here to help",
-        heroTitle: 'Welcome to DocsGPT!',
-        heroDescription: 'This chatbot is built with DocsGPT and utilises GenAI, please review important information using sources.',
-        theme:"dark",
-        buttonIcon:"https://your-icon",
-        buttonBg:"#222327"
-      });
-    }
-  </script>
-</body>
-</html>
-```
-
-# SearchBar
-
-The `SearchBar` component is an interactive search bar designed to provide search results based on **vector similarity search**. It also includes the capability to open the AI Chatbot, enabling users to query.
-
----
-
-### Importing the Component
-```tsx
-import { SearchBar } from "docsgpt-react";
-```
-
----
-
-### Usage Example
-```tsx
-<SearchBar 
-    apiKey="your-api-key"
-    apiHost="https://gptcloud.arc53.com"
-    theme="light"
-    placeholder="Search or Ask AI..."
-    width="300px"
-/>
-```
-
----
-
-## HTML embedding for Search bar
-
-```html
-<!DOCTYPE html>
-<html lang="en">
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <title>SearchBar Embedding</title>
-  <script src="https://unpkg.com/docsgpt/dist/modern/main.js"></script> <!-- The bundled JavaScript file -->
-</head>
-<body>
-  <!-- Element where the SearchBar will render -->
-  <div id="search-bar-container"></div>
-
-  <script>
-    // Render the SearchBar into the specified element
-    renderSearchBar('search-bar-container', {
-      apiKey: 'your-api-key-here',
-      apiHost: 'https://your-api-host.com',
-      theme: 'light',
-      placeholder: 'Search here...',
-      width: '300px'
-    });
-  </script>
-</body>
-</html>
-
-```
-
-### Props
-
-| **Prop**       | **Type**  | **Default Value**                   | **Description**                                                                                  |
-|-----------------|-----------|-------------------------------------|--------------------------------------------------------------------------------------------------|
-| **`apiKey`**    | `string`  | `"74039c6d-bff7-44ce-ae55-2973cbf13837"` | Your API key generated from the app. Used for authenticating requests.                         |
-| **`apiHost`**   | `string`  | `"https://gptcloud.arc53.com"`       | The base URL of the server hosting the vector similarity search and chatbot services.           |
-| **`theme`**     | `"dark" \| "light"` | `"dark"`                            | The theme of the search bar. Accepts `"dark"` or `"light"`.                                     |
-| **`placeholder`** | `string` | `"Search or Ask AI..."`             | Placeholder text displayed in the search input field.                                           |
-| **`width`**     | `string`  | `"256px"`                          | Width of the search bar. Accepts any valid CSS width value (e.g., `"300px"`, `"100%"`, `"20rem"`). |
-
-
-Feel free to reach out if you need help customizing or extending the `SearchBar`!
-
-## Our github
-
-[DocsGPT](https://github.com/arc53/DocsGPT)
-
-You can find the source code in the extensions/react-widget folder.
-
-For more information about React, refer to this [link here](https://react.dev/learn)
-

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/_app.mdx

@@ -4,7 +4,7 @@ export default function MyApp({ Component, pageProps }) {
   return (
     <>
       <Component {...pageProps} />
-        <DocsGPTWidget showSources={true} apiKey="6dd66edf-d374-4904-93af-ab0c6d41ee56" theme="dark" size="medium" />
+        <DocsGPTWidget showSources={true} apiKey="5d8270cb-735f-484e-9dc9-5b407f24e652" theme="dark" size="medium" />
     </>
   )
 }

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",

docs/pages/quickstart.mdx

@@ -73,9 +73,44 @@ The easiest way to launch DocsGPT is using the provided `setup.sh` script. This
 
 ## Launching DocsGPT (Windows)
 
-For Windows users, we recommend following the Docker deployment guide for detailed instructions. Please refer to the [Docker Deployment documentation](/Deploying/Docker-Deploying) for step-by-step instructions on setting up DocsGPT on Windows using Docker.
+For Windows users, we provide a PowerShell script that offers the same functionality as the macOS/Linux setup script.
 
-**Important for Windows:** Ensure Docker Desktop is installed and running correctly on your Windows system before proceeding.
+**Steps:**
+
+1. **Download the DocsGPT Repository:**
+
+   First, you need to download the DocsGPT repository to your local machine. You can do this using Git:
+
+   ```powershell
+   git clone https://github.com/arc53/DocsGPT.git
+   cd DocsGPT
+   ```
+
+2. **Run the `setup.ps1` script:**
+
+   Execute the PowerShell setup script:
+
+   ```powershell
+   PowerShell -ExecutionPolicy Bypass -File .\setup.ps1
+   ```
+
+3. **Follow the interactive setup:**
+
+   Just like the Linux/macOS script, the PowerShell script will guide you through setting DocsGPT.
+   The script will handle environment configuration and start DocsGPT based on your selections.
+
+4. **Access DocsGPT in your browser:**
+
+   Once the setup is complete and Docker containers are running, navigate to [http://localhost:5173/](http://localhost:5173/) in your web browser to access the DocsGPT web application.
+
+5. **Stopping DocsGPT:**
+
+   To stop DocsGPT run the Docker Compose down command displayed at the end of the setup script's execution.
+
+**Important for Windows:** Ensure Docker Desktop is installed and running correctly on your Windows system before proceeding. The script will attempt to start Docker if it's not running, but you may need to start it manually if there are issues.
+
+**Alternative Method:**
+If you prefer a more manual approach, you can follow our [Docker Deployment documentation](/Deploying/Docker-Deploying) for detailed instructions on setting up DocsGPT on Windows using Docker commands directly.
 
 ## Advanced Configuration
 

docs/public/toolIcons/tool_api_tool.svg

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+</svg>

docs/public/toolIcons/tool_brave.svg

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docs/public/toolIcons/tool_cryptoprice.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 122.88 122.88"><path d="M17.89 0h88.9c8.85 0 16.1 7.24 16.1 16.1v90.68c0 8.85-7.24 16.1-16.1 16.1H16.1c-8.85 0-16.1-7.24-16.1-16.1v-88.9C0 8.05 8.05 0 17.89 0zm57.04 66.96l16.46 4.96c-1.1 4.61-2.84 8.47-5.23 11.56-2.38 3.1-5.32 5.43-8.85 7-3.52 1.57-8.01 2.36-13.45 2.36-6.62 0-12.01-.96-16.21-2.87-4.19-1.92-7.79-5.3-10.83-10.13-3.04-4.82-4.57-11.02-4.57-18.54 0-10.04 2.67-17.76 8.02-23.17 5.36-5.39 12.93-8.09 22.71-8.09 7.65 0 13.68 1.54 18.06 4.64 4.37 3.1 7.64 7.85 9.76 14.27l-16.55 3.66c-.58-1.84-1.19-3.18-1.82-4.03-1.06-1.43-2.35-2.53-3.86-3.3-1.53-.78-3.22-1.16-5.11-1.16-4.27 0-7.54 1.71-9.8 5.12-1.71 2.53-2.57 6.52-2.57 11.94 0 6.73 1.02 11.33 3.07 13.83 2.05 2.49 4.92 3.73 8.63 3.73 3.59 0 6.31-1 8.15-3.03 1.83-1.99 3.16-4.92 3.99-8.75z" fill-rule="evenodd" clip-rule="evenodd"/></svg>

docs/public/toolIcons/tool_postgres.svg

@@ -0,0 +1,29 @@
+<?xml version="1.0" encoding="utf-8"?><!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="-8.78 0 70 70" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://www.w3.org/2000/svg">
+    <metadata>
+        <rdf:RDF>
+            <cc:Work>
+                <dc:subject>
+                    Data
+                </dc:subject>
+                <dc:identifier>
+                    sql-database-generic
+                </dc:identifier>
+                <dc:title>
+                    SQL Database (Generic)
+                </dc:title>
+                <dc:format>
+                    image/svg+xml
+                </dc:format>
+                <dc:publisher>
+                    Amido Limited
+                </dc:publisher>
+                <dc:creator>
+                    Richard Slater
+                </dc:creator>
+                <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+            </cc:Work>
+        </rdf:RDF>
+    </metadata>
+    <path d="m 852.97077,1013.9363 c -6.55238,-0.4723 -13.02857,-2.1216 -17.00034,-4.3296 -2.26232,-1.2576 -3.98589,-2.8032 -4.66223,-4.1807 l -0.4024,-0.8196 0,-25.70807 0,-25.7081 0.31843,-0.6465 c 1.42297,-2.889 5.96432,-5.4935 12.30378,-7.0562 2.15195,-0.5305 5.2586,-1.0588 7.79304,-1.3252 2.58797,-0.2721 9.44765,-0.2307 12.02919,0.073 6.86123,0.8061 12.69967,2.6108 16.29768,5.0377 1.38756,0.9359 2.81137,2.4334 3.29371,3.4642 l 0.41358,0.8838 -0.0354,25.6303 -0.0354,25.63047 -0.33195,0.6744 c -0.18257,0.3709 -0.73406,1.1007 -1.22553,1.6216 -2.99181,3.1715 -9.40919,5.5176 -17.8267,6.5172 -1.71567,0.2038 -9.16916,0.3686 -10.92937,0.2417 z m 12.07501,-22.02839 c -0.0252,-0.0657 -1.00472,-0.93831 -2.17671,-1.93922 -1.17199,-1.00091 -2.18138,-1.86687 -2.24309,-1.92436 -0.0617,-0.0575 0.15481,-0.26106 0.48117,-0.45237 0.32635,-0.19131 0.95163,-0.7235 1.3895,-1.18265 1.2805,-1.34272 1.88466,-3.00131 1.88466,-5.17388 0,-2.1388 -0.65162,-3.8645 -1.95671,-5.1818 -1.31533,-1.3278 -2.82554,-1.8983 -5.02486,-1.8983 -3.39007,0 -5.99368,1.9781 -6.82468,5.1851 -0.28586,1.1031 -0.28432,3.33211 0.003,4.31023 0.74941,2.55136 2.79044,4.40434 5.33062,4.83946 0.8596,0.14724 0.97605,0.21071 1.5621,0.85144 0.34829,0.38078 1.06301,1.14085 1.58827,1.68904 l 0.95501,0.9967 2.53878,0 c 1.39633,0 2.51816,-0.0537 2.49296,-0.11939 z m -8.70653,-7.10848 c -0.61119,-0.31868 -0.84225,-0.56599 -1.19079,-1.27453 -0.26919,-0.54724 -0.31522,-0.85851 -0.31824,-2.15197 -0.003,-1.3143 0.0388,-1.5983 0.31987,-2.169 0.45985,-0.9339 1.09355,-1.376 2.07384,-1.4469 1.36454,-0.099 2.15217,0.5707 2.56498,2.1801 0.50612,1.97321 -0.0504,4.07107 -1.26471,4.76729 -0.63707,0.36527 -1.58737,0.40659 -2.18495,0.095 z m -11.25315,3.66269 c 2.66179,-0.5048 4.1728,-2.0528 4.1728,-4.27495 0,-1.97137 -0.97548,-3.12004 -3.6716,-4.32364 -1.54338,-0.689 -2.10241,-1.1215 -2.10241,-1.6268 0,-0.4188 0.53052,-0.8777 1.14813,-0.993 0.60302,-0.1126 2.20237,0.1652 3.14683,0.5467 l 0.79167,0.3198 0,-1.7524 0,-1.7525 -0.85923,-0.1906 c -0.53103,-0.1178 -1.64689,-0.1885 -2.92137,-0.1849 -1.80528,0 -2.15881,0.044 -2.83818,0.3138 -1.98445,0.7878 -2.92613,2.1298 -2.91107,4.1485 0.0141,1.8898 1.01108,3.06864 3.49227,4.12912 1.46399,0.62572 2.05076,1.10218 2.05076,1.66522 0,1.1965 -1.99362,1.34375 -4.10437,0.30315 -0.57805,-0.28498 -1.09739,-0.54137 -1.1541,-0.56976 -0.0567,-0.0284 -0.10311,0.79023 -0.10311,1.81917 0,1.86239 0.002,1.87137 0.33919,1.99974 1.26979,0.48278 4.07626,0.69787 5.52379,0.42335 z m 30.4308,-1.72766 0,-1.58098 -2.40584,0 -2.40583,0 0,-5.43035 0,-5.4303 -2.13089,0 -2.13088,0 0,7.0113 0,7.01131 4.53672,0 4.53672,0 0,-1.58098 z m -14.84745,-27.70503 c 4.23447,-0.2937 7.4086,-0.8482 10.20178,-1.7821 2.78264,-0.9304 4.42643,-2.0562 4.79413,-3.2834 0.14166,-0.4729 0.13146,-0.6523 -0.0665,-1.1708 -0.88775,-2.3245 -5.84694,-4.1104 -13.42493,-4.8345 -3.24154,-0.3098 -9.13671,-0.2094 -12.22745,0.2081 -4.71604,0.6372 -8.54333,1.8208 -10.2451,3.1683 -3.44251,2.726 0.19793,5.7242 8.66397,7.1354 3.67084,0.6119 8.42674,0.828 12.30414,0.559 z" fill="#00bcf2" transform="translate(-830.906 -943.981)"/>
+</svg>

docs/public/toolIcons/tool_read_webpage.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#e3e3e3"><path d="M480-80q-82 0-155-31.5t-127.5-86Q143-252 111.5-325T80-480q0-83 31.5-155.5t86-127Q252-817 325-848.5T480-880q83 0 155.5 31.5t127 86q54.5 54.5 86 127T880-480q0 82-31.5 155t-86 127.5q-54.5 54.5-127 86T480-80Zm0-82q26-36 45-75t31-83H404q12 44 31 83t45 75Zm-104-16q-18-33-31.5-68.5T322-320H204q29 50 72.5 87t99.5 55Zm208 0q56-18 99.5-55t72.5-87H638q-9 38-22.5 73.5T584-178ZM170-400h136q-3-20-4.5-39.5T300-480q0-21 1.5-40.5T306-560H170q-5 20-7.5 39.5T160-480q0 21 2.5 40.5T170-400Zm216 0h188q3-20 4.5-39.5T580-480q0-21-1.5-40.5T574-560H386q-3 20-4.5 39.5T380-480q0 21 1.5 40.5T386-400Zm268 0h136q5-20 7.5-39.5T800-480q0-21-2.5-40.5T790-560H654q3 20 4.5 39.5T660-480q0 21-1.5 40.5T654-400Zm-16-240h118q-29-50-72.5-87T584-782q18 33 31.5 68.5T638-640Zm-234 0h152q-12-44-31-83t-45-75q-26 36-45 75t-31 83Zm-200 0h118q9-38 22.5-73.5T376-782q-56 18-99.5 55T204-640Z"/></svg>

docs/public/toolIcons/tool_telegram.svg

@@ -0,0 +1,10 @@
+<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)"/>
+<path d="M5.43282 12.373C8.93157 10.849 11.2641 9.8443 12.4303 9.3588C15.7641 7.97261 16.4559 7.73186 16.9078 7.7237C17.0072 7.72211 17.2284 7.74667 17.3728 7.86339C17.4928 7.96183 17.5266 8.09495 17.5434 8.18842C17.5584 8.2818 17.5791 8.49461 17.5622 8.66074C17.3822 10.5582 16.6003 15.1629 16.2028 17.2882C16.0359 18.1874 15.7041 18.4889 15.3834 18.5184C14.6859 18.5825 14.1572 18.0579 13.4822 17.6155C12.4266 16.9231 11.8303 16.4922 10.8047 15.8167C9.6197 15.0359 10.3884 14.6067 11.0634 13.9055C11.2397 13.7219 14.3109 10.9291 14.3691 10.6758C14.3766 10.6441 14.3841 10.526 14.3128 10.4637C14.2434 10.4013 14.1403 10.4227 14.0653 10.4395C13.9584 10.4635 12.2728 11.5788 9.00282 13.7851C8.52469 14.114 8.09157 14.2743 7.70157 14.2659C7.27407 14.2567 6.44907 14.0236 5.83595 13.8245C5.08595 13.5802 4.48782 13.451 4.54032 13.036C4.56657 12.82 4.8647 12.599 5.43282 12.373Z" fill="white"/>
+<defs>
+<linearGradient id="paint0_linear_5586_9958" x1="1200" y1="0.5" x2="1200" y2="2400.5" gradientUnits="userSpaceOnUse">
+<stop stop-color="#2AABEE"/>
+<stop offset="1" stop-color="#229ED9"/>
+</linearGradient>
+</defs>
+</svg>

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",

extensions/react-widget/src/components/SearchBar.tsx

@@ -4,19 +4,19 @@ import { WidgetCore } from './DocsGPTWidget';
 import { SearchBarProps } from '@/types';
 import { getSearchResults } from '../requests/searchAPI';
 import { Result } from '@/types';
-import MarkdownIt from 'markdown-it';
 import { getOS, processMarkdownString } from '../utils/helper';
 import DOMPurify from 'dompurify';
-import { 
-    CodeIcon, 
+import {
+    CodeIcon,
     TextAlignLeftIcon,
     HeadingIcon,
-    ReaderIcon, 
-    ListBulletIcon, 
-    QuoteIcon 
+    ReaderIcon,
+    ListBulletIcon,
+    QuoteIcon
 } from '@radix-ui/react-icons';
 const themes = {
     dark: {
+        name: 'dark',
         bg: '#202124',
         text: '#EDEDED',
         primary: {
@@ -29,6 +29,7 @@ const themes = {
         }
     },
     light: {
+        name: 'light',
         bg: '#EAEAEA',
         text: '#171717',
         primary: {
@@ -44,15 +45,16 @@ const themes = {
 
 const GlobalStyle = createGlobalStyle`
   .highlight {
-    color:#007EE6;
+    color: ${props => props.theme.name === 'dark' ? '#4B9EFF' : '#0066CC'};
+    font-weight: 500;
   }
 `;
 
 const loadGeistFont = () => {
-  const link = document.createElement('link');
-  link.href = 'https://fonts.googleapis.com/css2?family=Geist:wght@100..900&display=swap'; 
-  link.rel = 'stylesheet';
-  document.head.appendChild(link);
+    const link = document.createElement('link');
+    link.href = 'https://fonts.googleapis.com/css2?family=Geist:wght@100..900&display=swap';
+    link.rel = 'stylesheet';
+    document.head.appendChild(link);
 };
 
 const Main = styled.div`
@@ -81,12 +83,27 @@ const Container = styled.div`
     position: relative;
     display: inline-block;
 `
+const SearchOverlay = styled.div`
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background-color: #0000001A;
+    backdrop-filter: blur(8px);
+    -webkit-backdrop-filter: blur(8px);
+    z-index: 99;
+`;
+
+
 const SearchResults = styled.div`
     position: fixed;
     display: flex;
     flex-direction: column;
-    background-color: ${props => props.theme.primary.bg};
-    border: 1px solid ${props => props.theme.bg};
+    background-color: ${props => props.theme.name === 'dark' ? 
+        'rgba(0, 0, 0, 0.15)' : 
+        'rgba(255, 255, 255, 0.4)'};
+    border: 1px solid rgba(255, 255, 255, 0.18);
     border-radius: 15px;
     padding: 8px 0px 8px 0px;
     width: 792px;
@@ -97,8 +114,12 @@ const SearchResults = styled.div`
     top: 50%;
     transform: translate(-50%, -50%);
     color: ${props => props.theme.primary.text};
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1), 0 2px 4px rgba(0, 0, 0, 0.1);
-    backdrop-filter: blur(16px);
+    
+    box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37);
+    backdrop-filter: blur(82px);
+    -webkit-backdrop-filter: blur(82px);
+    border-radius: 10px;
+    
     box-sizing: border-box;
 
     @media only screen and (max-width: 768px) {
@@ -142,6 +163,33 @@ const ContentWrapper = styled.div`
     flex-direction: column;
     gap: 12px; 
 `;
+
+
+
+const ResultWrapper = styled.div`
+    display: flex;
+    align-items: flex-start;
+    width: 100%;
+    box-sizing: border-box;
+    padding: 8px 16px;
+    cursor: pointer;
+    background-color: transparent;
+    font-family: 'Geist', sans-serif;
+    border-radius: 8px;
+
+    word-wrap: break-word;
+    overflow-wrap: break-word;
+    word-break: break-word;
+    white-space: normal;
+    overflow: hidden;
+    text-overflow: ellipsis;
+
+    &:hover {
+        backdrop-filter: blur(8px);
+        -webkit-backdrop-filter: blur(8px);
+    }
+`;
+
 const Content = styled.div`
     display: flex;
     margin-left: 8px;
@@ -151,9 +199,10 @@ const Content = styled.div`
     font-size: 15px;
     color: ${props => props.theme.primary.text};
     line-height: 1.6;
-    border-left: 2px solid #585858;
+    border-left: 2px solid ${props => props.theme.primary.text}CC;
     overflow: hidden;
-`
+    
+`;
 const ContentSegment = styled.div`
     display: flex;
     align-items: flex-start;
@@ -165,80 +214,6 @@ const ContentSegment = styled.div`
     text-overflow: ellipsis;
 `
 
-const ResultWrapper = styled.div`
-    display: flex;
-    align-items: flex-start;
-    width: 100%;
-    box-sizing: border-box;
-    padding: 8px 16px;
-    cursor: pointer;
-    background-color: ${props => props.theme.primary.bg};
-    font-family: 'Geist', sans-serif;
-    transition: background-color 0.2s;
-    border-radius: 8px;
-
-    word-wrap: break-word;
-    overflow-wrap: break-word;
-    word-break: break-word;
-    white-space: normal;
-    overflow: hidden;
-    text-overflow: ellipsis;
-
-    &:hover {
-        background-color: ${props => props.theme.bg};
-    }
-`
-const Markdown = styled.div`
-line-height:18px;
-font-size: 11px;
-white-space: pre-wrap;
- pre {
-      padding: 8px;
-      width: 90%;
-      font-size: 11px;
-      border-radius: 6px;
-      overflow-x: auto;
-      background-color: #1B1C1F;
-      color: #fff ;
-    }
-
-    h1,h2 {
-      font-size: 14px;
-      font-weight: 600;
-      color: ${(props) => props.theme.text};
-      opacity: 0.8;
-    }
-
-
-    h3 {
-      font-size: 12px;
-    }
-
-    p {
-      margin: 0px;
-      line-height: 1.35rem;
-      font-size: 11px;
-    }
-
-    code:not(pre code) {
-      border-radius: 6px;
-      padding: 2px 2px;
-      margin: 2px;
-      font-size: 9px;
-      display: inline;
-      background-color: #646464;
-      color: #fff ;
-    }
-    img{
-        max-width: 50%;
-    }
-    code {
-      overflow-x: auto;
-    }
-    a{
-        color: #007ee6;
-    }
-`
 const Toolkit = styled.kbd`
     position: absolute;
     right: 4px;
@@ -259,8 +234,8 @@ const Toolkit = styled.kbd`
 `
 const Loader = styled.div`
   margin: 2rem auto;
-  border: 4px solid ${props => props.theme.secondary.text};
-  border-top: 4px solid ${props => props.theme.primary.bg};
+  border: 4px solid ${props => props.theme.name === 'dark' ? 'rgba(255, 255, 255, 0.2)' : 'rgba(0, 0, 0, 0.1)'};
+  border-top: 4px solid ${props => props.theme.name === 'dark' ? '#FFFFFF' : props.theme.primary.bg};
   border-radius: 50%;
   width: 12px;
   height: 12px;
@@ -280,7 +255,8 @@ const NoResults = styled.div`
   margin-top: 2rem;
   text-align: center;
   font-size: 14px;
-  color: #888;
+  color: ${props => props.theme.name === 'dark' ? '#E0E0E0' : '#505050'};
+  font-weight: 500;
 `;
 const AskAIButton = styled.button`
     display: flex;
@@ -293,25 +269,35 @@ const AskAIButton = styled.button`
     height: 50px;
     padding: 8px 24px;
     border: none;
-    border-radius: 6px;
-    background-color: ${props => props.theme.bg};
+    border-radius: 8px;
     color: ${props => props.theme.text}; 
     cursor: pointer;
-    transition: background-color 0.2s, box-shadow 0.2s;
     font-size: 16px;
+    backdrop-filter: blur(16px);
+    -webkit-backdrop-filter: blur(16px);
+    background-color: ${props => props.theme.name === 'dark' ? 
+        'rgba(255, 255, 255, 0.05)' : 
+        'rgba(0, 0, 0, 0.03)'};
 
     &:hover {
-        opacity: 0.8;
+        backdrop-filter: blur(20px);
+        -webkit-backdrop-filter: blur(20px);
+        background-color: ${props => props.theme.name === 'dark' ? 
+            'rgba(255, 255, 255, 0.1)' : 
+            'rgba(0, 0, 0, 0.06)'}; 
     }
-`
+`;
+
 const SearchHeader = styled.div`
     display: flex;
     align-items: center;
     gap: 8px;
     margin-bottom: 12px;
     padding-bottom: 12px;
-    border-bottom: 1px solid ${props => props.theme.bg};
-`
+    border-bottom: 1px solid ${props => props.theme.name === 'dark' ? '#FFFFFF24' : 'rgba(0, 0, 0, 0.14)'};
+`;
+
+
 
 const TextField = styled.input`
     width: calc(100% - 32px);
@@ -327,8 +313,16 @@ const TextField = styled.input`
     &:focus {
         border-color: none;
     }
+
+    &::placeholder {
+        color: ${props => props.theme.name === 'dark' ? 'rgba(255, 255, 255, 0.6)' : 'rgba(0, 0, 0, 0.5)'} !important;
+        opacity: 100%; /* Force opacity to ensure placeholder is visible */
+        font-weight: 500;
+    }
 `
 
+
+
 const EscapeInstruction = styled.kbd`
     display: flex;
     align-items: center;
@@ -337,17 +331,21 @@ const EscapeInstruction = styled.kbd`
     padding: 4px 8px;
     border-radius: 4px;
     background-color: transparent;
-    border: 1px solid ${props => props.theme.secondary.text};
-    color: ${props => props.theme.text};
+    border: 1px solid ${props => props.theme.name === 'dark' ? 
+        'rgba(237, 237, 237, 0.6)' : 
+        'rgba(23, 23, 23, 0.6)'};
+    color: ${props => props.theme.name === 'dark' ? '#EDEDED' : '#171717'};
     font-size: 12px;
     font-family: 'Geist', sans-serif;
     white-space: nowrap;
     cursor: pointer;
     width: fit-content;
-    &:hover {
-        background-color: rgba(255, 255, 255, 0.1);
-    }
-`
+    -webkit-appearance: none;
+    -moz-appearance: none;
+    appearance: none;
+`;
+
+
 export const SearchBar = ({
     apiKey = "74039c6d-bff7-44ce-ae55-2973cbf13837",
     apiHost = "https://gptcloud.arc53.com",
@@ -367,7 +365,7 @@ export const SearchBar = ({
     const abortControllerRef = React.useRef<AbortController | null>(null);
     const browserOS = getOS();
     const isTouch = 'ontouchstart' in window;
-    
+
     const getKeyboardInstruction = () => {
         if (isResultVisible) return "Enter";
         return browserOS === 'mac' ? '⌘ + K' : 'Ctrl + K';
@@ -394,7 +392,7 @@ export const SearchBar = ({
             }
         };
 
-     
+
         document.addEventListener('mousedown', handleClickOutside);
         document.addEventListener('keydown', handleKeyDown);
         return () => {
@@ -404,33 +402,34 @@ export const SearchBar = ({
     }, []);
 
     React.useEffect(() => {
-        if (!input) {
-            setResults([]);
-            return;
-        }
-        setLoading(true);
-        if (debounceTimeout.current) {
-            clearTimeout(debounceTimeout.current);
-        }
+    if (!input) {
+        setResults([]);
+        setLoading(false);
+        return;
+    }
+    setLoading(true);
+    if (debounceTimeout.current) {
+        clearTimeout(debounceTimeout.current);
+    }
 
-        if (abortControllerRef.current) {
-            abortControllerRef.current.abort();
-        }
-        const abortController = new AbortController();
-        abortControllerRef.current = abortController;
+    if (abortControllerRef.current) {
+        abortControllerRef.current.abort();
+    }
+    const abortController = new AbortController();
+    abortControllerRef.current = abortController;
 
-        debounceTimeout.current = setTimeout(() => {
-            getSearchResults(input, apiKey, apiHost, abortController.signal)
-                .then((data) => setResults(data))
-                .catch((err) => !abortController.signal.aborted && console.log(err))
-                .finally(() => setLoading(false));
-        }, 500);
+    debounceTimeout.current = setTimeout(() => {
+        getSearchResults(input, apiKey, apiHost, abortController.signal)
+            .then((data) => setResults(data))
+            .catch((err) => !abortController.signal.aborted && console.log(err))
+            .finally(() => setLoading(false));
+    }, 500);
 
-        return () => {
-            abortController.abort();
-            clearTimeout(debounceTimeout.current ?? undefined);
-        };
-    }, [input])
+    return () => {
+        abortController.abort();
+        clearTimeout(debounceTimeout.current ?? undefined);
+    };
+}, [input])
 
     const handleKeyDown = (event: React.KeyboardEvent<HTMLInputElement>) => {
         if (event.key === 'Enter') {
@@ -462,6 +461,8 @@ export const SearchBar = ({
                     </SearchButton>
                     {
                         isResultVisible && (
+                            <>
+                            <SearchOverlay onClick={() => setIsResultVisible(false)} />
                             <SearchResults>
                                 <SearchHeader>
                                     <TextField
@@ -477,8 +478,8 @@ export const SearchBar = ({
                                     </EscapeInstruction>
                                 </SearchHeader>
                                 <AskAIButton onClick={openWidget}>
-                                    <img 
-                                        src="https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png" 
+                                    <img
+                                        src="https://d3dg1063dc54p9.cloudfront.net/cute-docsgpt.png"
                                         alt="DocsGPT"
                                         width={24}
                                         height={24}
@@ -539,6 +540,7 @@ export const SearchBar = ({
                                     )}
                                 </SearchResultsScroll>
                             </SearchResults>
+                            </>
                         )
                     }
                     {

frontend/.env.development

@@ -1,3 +1,4 @@
 # Please put appropriate value
-VITE_API_HOST=http://0.0.0.0:7091
+VITE_BASE_URL=http://localhost:5173
+VITE_API_HOST=http://127.0.0.1:7091
 VITE_API_STREAMING=true

frontend/package.json

@@ -21,25 +21,28 @@
   "dependencies": {
     "@reduxjs/toolkit": "^2.5.1",
     "chart.js": "^4.4.4",
+    "clsx": "^2.1.1",
     "i18next": "^24.2.0",
     "i18next-browser-languagedetector": "^8.0.2",
+    "mermaid": "^11.6.0",
     "prop-types": "^15.8.1",
     "react": "^18.2.0",
     "react-chartjs-2": "^5.3.0",
     "react-copy-to-clipboard": "^5.1.0",
     "react-dom": "^18.3.1",
-    "react-helmet": "^6.1.0",
     "react-dropzone": "^14.3.5",
+    "react-helmet": "^6.1.0",
     "react-i18next": "^15.4.0",
     "react-markdown": "^9.0.1",
-    "react-redux": "^8.0.5",
-    "react-router-dom": "^7.1.1",
-    "react-syntax-highlighter": "^15.5.0",
+    "react-redux": "^9.2.0",
+    "react-router-dom": "^7.6.1",
+    "react-syntax-highlighter": "^15.6.1",
     "rehype-katex": "^7.0.1",
     "remark-gfm": "^4.0.0",
     "remark-math": "^6.0.0"
   },
   "devDependencies": {
+    "@types/mermaid": "^9.1.0",
     "@types/react": "^18.0.27",
     "@types/react-dom": "^18.3.0",
     "@types/react-helmet": "^6.1.11",
@@ -49,22 +52,22 @@
     "@vitejs/plugin-react": "^4.3.4",
     "autoprefixer": "^10.4.13",
     "eslint": "^8.57.1",
-    "eslint-config-prettier": "^9.1.0",
+    "eslint-config-prettier": "^10.1.5",
     "eslint-config-standard-with-typescript": "^34.0.0",
     "eslint-plugin-import": "^2.31.0",
     "eslint-plugin-n": "^15.7.0",
     "eslint-plugin-prettier": "^5.2.1",
     "eslint-plugin-promise": "^6.6.0",
-    "eslint-plugin-react": "^7.37.3",
+    "eslint-plugin-react": "^7.37.5",
     "eslint-plugin-unused-imports": "^4.1.4",
     "husky": "^8.0.0",
     "lint-staged": "^15.3.0",
     "postcss": "^8.4.49",
-    "prettier": "^3.4.2",
-    "prettier-plugin-tailwindcss": "^0.6.9",
+    "prettier": "^3.5.3",
+    "prettier-plugin-tailwindcss": "^0.6.11",
     "tailwindcss": "^3.4.17",
-    "typescript": "^5.7.2",
-    "vite": "^5.4.14",
-    "vite-plugin-svgr": "^4.2.0"
+    "typescript": "^5.8.3",
+    "vite": "^6.3.5",
+    "vite-plugin-svgr": "^4.3.0"
   }
 }

frontend/prettier.config.cjs

@@ -4,4 +4,5 @@ module.exports = {
   semi: true,
   singleQuote: true,
   printWidth: 80,
-}
+  plugins: ['prettier-plugin-tailwindcss'],
+};

frontend/public/toolIcons/tool_read_webpage.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" height="24px" viewBox="0 -960 960 960" width="24px" fill="#e3e3e3"><path d="M480-80q-82 0-155-31.5t-127.5-86Q143-252 111.5-325T80-480q0-83 31.5-155.5t86-127Q252-817 325-848.5T480-880q83 0 155.5 31.5t127 86q54.5 54.5 86 127T880-480q0 82-31.5 155t-86 127.5q-54.5 54.5-127 86T480-80Zm0-82q26-36 45-75t31-83H404q12 44 31 83t45 75Zm-104-16q-18-33-31.5-68.5T322-320H204q29 50 72.5 87t99.5 55Zm208 0q56-18 99.5-55t72.5-87H638q-9 38-22.5 73.5T584-178ZM170-400h136q-3-20-4.5-39.5T300-480q0-21 1.5-40.5T306-560H170q-5 20-7.5 39.5T160-480q0 21 2.5 40.5T170-400Zm216 0h188q3-20 4.5-39.5T580-480q0-21-1.5-40.5T574-560H386q-3 20-4.5 39.5T380-480q0 21 1.5 40.5T386-400Zm268 0h136q5-20 7.5-39.5T800-480q0-21-2.5-40.5T790-560H654q3 20 4.5 39.5T660-480q0 21-1.5 40.5T654-400Zm-16-240h118q-29-50-72.5-87T584-782q18 33 31.5 68.5T638-640Zm-234 0h152q-12-44-31-83t-45-75q-26 36-45 75t-31 83Zm-200 0h118q9-38 22.5-73.5T376-782q-56 18-99.5 55T204-640Z"/></svg>

frontend/src/About.tsx

@@ -1,99 +0,0 @@
-//TODO - Add hyperlinks to text
-//TODO - Styling
-import DocsGPT3 from './assets/cute_docsgpt3.svg';
-
-export default function About() {
-  return (
-    <div className="mx-5 grid min-h-screen md:mx-36">
-      <article className="place-items-left mx-auto my-auto flex w-full max-w-6xl flex-col gap-4 rounded-3xl bg-gray-100 p-6 text-jet dark:bg-gun-metal dark:text-bright-gray lg:p-6 xl:p-10">
-        <div className="flex items-center">
-          <p className="mr-2 text-3xl">About DocsGPT</p>
-          <img className="h14 mb-2" src={DocsGPT3} alt="DocsGPT" />
-        </div>
-        <p className="mt-4">
-          Find the information in your documentation through AI-powered
-          <a
-            className="text-blue-500"
-            href="https://github.com/arc53/DocsGPT"
-            target="_blank"
-            rel="noreferrer"
-          >
-            {' '}
-            open-source{' '}
-          </a>
-          chatbot. Powered by GPT-3, Faiss and LangChain.
-        </p>
-
-        <div>
-          <p>
-            If you want to add your own documentation, please follow the
-            instruction below:
-          </p>
-          <p className="mt-4 ml-2">
-            1. Navigate to{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">
-              {' '}
-              /application
-            </span>{' '}
-            folder
-          </p>
-          <p className="mt-4 ml-2">
-            2. Install dependencies from{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">
-              pip install -r requirements.txt
-            </span>
-          </p>
-          <p className="mt-4 ml-2">
-            3. Prepare a{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">.env</span>{' '}
-            file. Copy{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">
-              .env_sample
-            </span>{' '}
-            and create{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">.env</span>{' '}
-            with your OpenAI API token
-          </p>
-          <p className="mt-4 ml-2">
-            4. Run the app with{' '}
-            <span className="bg-gray-200 italic dark:bg-outer-space">
-              python app.py
-            </span>
-          </p>
-        </div>
-
-        <p>
-          Currently It uses{' '}
-          <span className="text-blue-950 font-medium">DocsGPT</span>{' '}
-          documentation, so it will respond to information relevant to{' '}
-          <span className="text-blue-950 font-medium">DocsGPT</span>. If you
-          want to train it on different documentation - please follow
-          <a
-            className="text-blue-500"
-            href="https://github.com/arc53/DocsGPT/wiki/How-to-train-on-other-documentation"
-            target="_blank"
-            rel="noreferrer"
-          >
-            {' '}
-            this guide
-          </a>
-          .
-        </p>
-
-        <p className="mt-4 text-left">
-          If you want to launch it on your own server - follow
-          <a
-            className="text-blue-500"
-            href="https://github.com/arc53/DocsGPT/wiki/Hosting-the-app"
-            target="_blank"
-            rel="noreferrer"
-          >
-            {' '}
-            this guide
-          </a>
-          .
-        </p>
-      </article>
-    </div>
-  );
-}

frontend/src/App.tsx

@@ -3,7 +3,9 @@ import './locale/i18n';
 import { useState } from 'react';
 import { Outlet, Route, Routes } from 'react-router-dom';
 
-import About from './About';
+import Agents from './agents';
+import SharedAgentGate from './agents/SharedAgentGate';
+import ActionButtons from './components/ActionButtons';
 import Spinner from './components/Spinner';
 import Conversation from './conversation/Conversation';
 import { SharedConversation } from './conversation/SharedConversation';
@@ -18,7 +20,7 @@ function AuthWrapper({ children }: { children: React.ReactNode }) {
 
   if (isAuthLoading) {
     return (
-      <div className="h-screen flex items-center justify-center">
+      <div className="flex h-screen items-center justify-center">
         <Spinner />
       </div>
     );
@@ -27,17 +29,18 @@ function AuthWrapper({ children }: { children: React.ReactNode }) {
 }
 
 function MainLayout() {
-  const { isMobile } = useMediaQuery();
-  const [navOpen, setNavOpen] = useState(!isMobile);
+  const { isMobile, isTablet } = useMediaQuery();
+  const [navOpen, setNavOpen] = useState(!(isMobile || isTablet));
 
   return (
-    <div className="dark:bg-raisin-black relative h-screen overflow-auto">
+    <div className="relative h-screen overflow-hidden dark:bg-raisin-black">
       <Navigation navOpen={navOpen} setNavOpen={setNavOpen} />
+      <ActionButtons showNewChat={true} showShare={true} />
       <div
-        className={`h-[calc(100dvh-64px)] md:h-screen ${
-          !isMobile
-            ? `ml-0 ${!navOpen ? 'md:mx-auto lg:mx-auto' : 'md:ml-72'}`
-            : 'ml-0 md:ml-16'
+        className={`h-[calc(100dvh-64px)] overflow-auto lg:h-screen ${
+          !(isMobile || isTablet)
+            ? `ml-0 ${!navOpen ? 'lg:mx-auto' : 'lg:ml-72'}`
+            : 'ml-0 lg:ml-16'
         }`}
       >
         <Outlet />
@@ -45,14 +48,13 @@ function MainLayout() {
     </div>
   );
 }
-
 export default function App() {
   const [, , componentMounted] = useDarkTheme();
   if (!componentMounted) {
     return <div />;
   }
   return (
-    <div className="h-full relative overflow-auto">
+    <div className="relative h-full overflow-hidden">
       <Routes>
         <Route
           element={
@@ -62,10 +64,11 @@ export default function App() {
           }
         >
           <Route index element={<Conversation />} />
-          <Route path="/about" element={<About />} />
-          <Route path="/settings" element={<Setting />} />
+          <Route path="/settings/*" element={<Setting />} />
+          <Route path="/agents/*" element={<Agents />} />
         </Route>
         <Route path="/share/:identifier" element={<SharedConversation />} />
+        <Route path="/shared/agent/:agentId" element={<SharedAgentGate />} />
         <Route path="/*" element={<PageNotFound />} />
       </Routes>
     </div>

frontend/src/Hero.tsx

@@ -19,18 +19,18 @@ export default function Hero({
   }>;
 
   return (
-    <div className="flex h-full w-full flex-col text-black-1000 dark:text-bright-gray items-center justify-between">
+    <div className="flex h-full w-full flex-col items-center justify-between text-black-1000 dark:text-bright-gray">
       {/* Header Section */}
-      <div className="flex flex-col items-center justify-center flex-grow pt-8 md:pt-0">
-        <div className="flex items-center mb-4">
+      <div className="flex flex-grow flex-col items-center justify-center pt-8 md:pt-0">
+        <div className="mb-4 flex items-center">
           <span className="text-4xl font-semibold">DocsGPT</span>
           <img className="mb-1 inline w-14" src={DocsGPT3} alt="docsgpt" />
         </div>
       </div>
 
       {/* Demo Buttons Section */}
-      <div className="w-full max-w-full mb-8 md:mb-16">
-        <div className="grid grid-cols-1 md:grid-cols-1 lg:grid-cols-2 gap-3 md:gap-4 text-xs">
+      <div className="mb-8 w-full max-w-full md:mb-16">
+        <div className="grid grid-cols-1 gap-3 text-xs md:grid-cols-1 md:gap-4 lg:grid-cols-2">
           {demos?.map(
             (demo: { header: string; query: string }, key: number) =>
               demo.header &&
@@ -38,14 +38,12 @@ export default function Hero({
                 <button
                   key={key}
                   onClick={() => handleQuestion({ question: demo.query })}
-                  className="w-full rounded-[66px] border bg-transparent px-6 py-[14px] text-left transition-colors
-                    border-dark-gray text-just-black hover:bg-cultured
-                    dark:border-dim-gray dark:text-chinese-white dark:hover:bg-charleston-green"
+                  className={`w-full rounded-[66px] border border-dark-gray bg-transparent px-6 py-[14px] text-left text-just-black transition-colors hover:bg-cultured dark:border-dim-gray dark:text-chinese-white dark:hover:bg-charleston-green ${key >= 2 ? 'hidden md:block' : ''} // Show only 2 buttons on mobile`}
                 >
                   <p className="mb-2 font-semibold text-black-1000 dark:text-bright-gray">
                     {demo.header}
                   </p>
-                  <span className="text-gray-700 dark:text-gray-300 opacity-60 line-clamp-2">
+                  <span className="line-clamp-2 text-gray-700 opacity-60 dark:text-gray-300">
                     {demo.query}
                   </span>
                 </button>

frontend/src/Navigation.tsx

@@ -3,6 +3,7 @@ import { useTranslation } from 'react-i18next';
 import { useDispatch, useSelector } from 'react-redux';
 import { NavLink, useNavigate } from 'react-router-dom';
 
+import { Agent } from './agents/types';
 import conversationService from './api/services/conversationService';
 import userService from './api/services/userService';
 import Add from './assets/add.svg';
@@ -12,13 +13,15 @@ import Expand from './assets/expand.svg';
 import Github from './assets/github.svg';
 import Hamburger from './assets/hamburger.svg';
 import openNewChat from './assets/openNewChat.svg';
+import Pin from './assets/pin.svg';
+import Robot from './assets/robot.svg';
 import SettingGear from './assets/settingGear.svg';
+import Spark from './assets/spark.svg';
 import SpinnerDark from './assets/spinner-dark.svg';
 import Spinner from './assets/spinner.svg';
 import Twitter from './assets/TwitterX.svg';
-import UploadIcon from './assets/upload.svg';
+import UnPin from './assets/unpin.svg';
 import Help from './components/Help';
-import SourceDropdown from './components/SourceDropdown';
 import {
   handleAbort,
   selectQueries,
@@ -31,22 +34,20 @@ import useDefaultDocument from './hooks/useDefaultDocument';
 import useTokenAuth from './hooks/useTokenAuth';
 import DeleteConvModal from './modals/DeleteConvModal';
 import JWTModal from './modals/JWTModal';
-import { ActiveState, Doc } from './models/misc';
-import { getConversations, getDocs } from './preferences/preferenceApi';
+import { ActiveState } from './models/misc';
+import { getConversations } from './preferences/preferenceApi';
 import {
-  selectApiKeyStatus,
+  selectAgents,
   selectConversationId,
   selectConversations,
   selectModalStateDeleteConv,
-  selectPaginatedDocuments,
-  selectSelectedDocs,
-  selectSourceDocs,
+  selectSelectedAgent,
+  selectSharedAgents,
   selectToken,
+  setAgents,
   setConversations,
   setModalStateDeleteConv,
-  setPaginatedDocuments,
-  setSelectedDocs,
-  setSourceDocs,
+  setSelectedAgent,
 } from './preferences/preferenceSlice';
 import Upload from './upload/Upload';
 
@@ -57,39 +58,64 @@ interface NavigationProps {
 
 export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
   const dispatch = useDispatch();
+  const navigate = useNavigate();
+
+  const { t } = useTranslation();
+
   const token = useSelector(selectToken);
   const queries = useSelector(selectQueries);
-  const docs = useSelector(selectSourceDocs);
-  const selectedDocs = useSelector(selectSelectedDocs);
   const conversations = useSelector(selectConversations);
-  const modalStateDeleteConv = useSelector(selectModalStateDeleteConv);
   const conversationId = useSelector(selectConversationId);
-  const paginatedDocuments = useSelector(selectPaginatedDocuments);
-  const [isDeletingConversation, setIsDeletingConversation] = useState(false);
+  const modalStateDeleteConv = useSelector(selectModalStateDeleteConv);
+  const agents = useSelector(selectAgents);
+  const sharedAgents = useSelector(selectSharedAgents);
+  const selectedAgent = useSelector(selectSelectedAgent);
 
-  const { isMobile } = useMediaQuery();
+  const { isMobile, isTablet } = useMediaQuery();
   const [isDarkTheme] = useDarkTheme();
-  const [isDocsListOpen, setIsDocsListOpen] = useState(false);
-  const { t } = useTranslation();
-  const isApiKeySet = useSelector(selectApiKeyStatus);
-
   const { showTokenModal, handleTokenSubmit } = useTokenAuth();
 
+  const [isDeletingConversation, setIsDeletingConversation] = useState(false);
   const [uploadModalState, setUploadModalState] =
     useState<ActiveState>('INACTIVE');
+  const [recentAgents, setRecentAgents] = useState<Agent[]>([]);
 
   const navRef = useRef(null);
 
-  const navigate = useNavigate();
-
-  useEffect(() => {
-    if (!conversations?.data) {
-      fetchConversations();
+  async function fetchRecentAgents() {
+    try {
+      const response = await userService.getPinnedAgents(token);
+      if (!response.ok) throw new Error('Failed to fetch pinned agents');
+      const pinnedAgents: Agent[] = await response.json();
+      if (pinnedAgents.length >= 3) {
+        setRecentAgents(pinnedAgents);
+        return;
+      }
+      let tempAgents: Agent[] = [];
+      if (!agents) {
+        const response = await userService.getAgents(token);
+        if (!response.ok) throw new Error('Failed to fetch agents');
+        const data: Agent[] = await response.json();
+        dispatch(setAgents(data));
+        tempAgents = data;
+      } else tempAgents = agents;
+      const additionalAgents = tempAgents
+        .filter(
+          (agent: Agent) =>
+            agent.status === 'published' &&
+            !pinnedAgents.some((pinned) => pinned.id === agent.id),
+        )
+        .sort(
+          (a: Agent, b: Agent) =>
+            new Date(b.last_used_at ?? 0).getTime() -
+            new Date(a.last_used_at ?? 0).getTime(),
+        )
+        .slice(0, 3 - pinnedAgents.length);
+      setRecentAgents([...pinnedAgents, ...additionalAgents]);
+    } catch (error) {
+      console.error('Failed to fetch recent agents: ', error);
     }
-    if (queries.length === 0) {
-      resetConversation();
-    }
-  }, [conversations?.data, dispatch]);
+  }
 
   async function fetchConversations() {
     dispatch(setConversations({ ...conversations, loading: true }));
@@ -103,6 +129,15 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       });
   }
 
+  useEffect(() => {
+    fetchRecentAgents();
+  }, [agents, sharedAgents, token, dispatch]);
+
+  useEffect(() => {
+    if (!conversations?.data) fetchConversations();
+    if (queries.length === 0) resetConversation();
+  }, [conversations?.data, dispatch]);
+
   const handleDeleteAllConversations = () => {
     setIsDeletingConversation(true);
     conversationService
@@ -124,44 +159,82 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       .catch((error) => console.error(error));
   };
 
-  const handleDeleteClick = (doc: Doc) => {
-    userService
-      .deletePath(doc.id ?? '', token)
-      .then(() => {
-        return getDocs(token);
-      })
-      .then((updatedDocs) => {
-        dispatch(setSourceDocs(updatedDocs));
-        const updatedPaginatedDocs = paginatedDocuments?.filter(
-          (document) => document.id !== doc.id,
+  const handleAgentClick = (agent: Agent) => {
+    resetConversation();
+    dispatch(setSelectedAgent(agent));
+    if (isMobile || isTablet) setNavOpen(!navOpen);
+    navigate('/');
+  };
+
+  const handleTogglePin = (agent: Agent) => {
+    userService.togglePinAgent(agent.id ?? '', token).then((response) => {
+      if (response.ok) {
+        const updatedAgents = agents?.map((a) =>
+          a.id === agent.id ? { ...a, pinned: !a.pinned } : a,
         );
-        dispatch(
-          setPaginatedDocuments(updatedPaginatedDocs || paginatedDocuments),
-        );
-        dispatch(
-          setSelectedDocs(
-            Array.isArray(updatedDocs) &&
-              updatedDocs?.find(
-                (doc: Doc) => doc.name.toLowerCase() === 'default',
-              ),
-          ),
-        );
-      })
-      .catch((error) => console.error(error));
+        dispatch(setAgents(updatedAgents));
+      }
+    });
   };
 
   const handleConversationClick = (index: string) => {
+    dispatch(setSelectedAgent(null));
     conversationService
       .getConversation(index, token)
-      .then((response) => response.json())
+      .then((response) => {
+        if (!response.ok) {
+          navigate('/');
+          dispatch(setSelectedAgent(null));
+          return null;
+        }
+        return response.json();
+      })
       .then((data) => {
-        navigate('/');
-        dispatch(setConversation(data));
+        if (!data) return;
+        dispatch(setConversation(data.queries));
         dispatch(
           updateConversationId({
             query: { conversationId: index },
           }),
         );
+        if (isMobile || isTablet) {
+          setNavOpen(false);
+        }
+        if (data.agent_id) {
+          if (data.is_shared_usage) {
+            userService
+              .getSharedAgent(data.shared_token, token)
+              .then((response) => {
+                if (!response.ok) {
+                  navigate('/');
+                  dispatch(setSelectedAgent(null));
+                  return;
+                }
+                response.json().then((agent: Agent) => {
+                  navigate(`/agents/shared/${agent.shared_token}`);
+                });
+              });
+          } else {
+            userService.getAgent(data.agent_id, token).then((response) => {
+              if (!response.ok) {
+                navigate('/');
+                dispatch(setSelectedAgent(null));
+                return;
+              }
+              response.json().then((agent: Agent) => {
+                if (agent.shared_token)
+                  navigate(`/agents/shared/${agent.shared_token}`);
+                else {
+                  dispatch(setSelectedAgent(agent));
+                  navigate('/');
+                }
+              });
+            });
+          }
+        } else {
+          navigate('/');
+          dispatch(setSelectedAgent(null));
+        }
       });
   };
 
@@ -173,12 +246,15 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
         query: { conversationId: null },
       }),
     );
+    dispatch(setSelectedAgent(null));
   };
+
   const newChat = () => {
     if (queries && queries?.length > 0) {
       resetConversation();
     }
   };
+
   async function updateConversationName(updatedConversation: {
     name: string;
     id: string;
@@ -197,20 +273,16 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
       });
   }
 
-  /*
-    Needed to fix bug where if mobile nav was closed and then window was resized to desktop, nav would still be closed but the button to open would be gone, as per #1 on issue #146
-  */
-
   useEffect(() => {
-    setNavOpen(!isMobile);
-  }, [isMobile]);
+    setNavOpen(!(isMobile || isTablet));
+  }, [isMobile, isTablet]);
 
   useDefaultDocument();
   return (
     <>
       {!navOpen && (
-        <div className="duration-25 absolute  top-3 left-3 z-20 hidden transition-all md:block">
-          <div className="flex gap-3 items-center">
+        <div className="duration-25 absolute left-3 top-3 z-20 hidden transition-all lg:block">
+          <div className="flex items-center gap-3">
             <button
               onClick={() => {
                 setNavOpen(!navOpen);
@@ -237,7 +309,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
                 />
               </button>
             )}
-            <div className="text-[#949494] font-medium text-[20px]">
+            <div className="text-[20px] font-medium text-[#949494]">
               DocsGPT
             </div>
           </div>
@@ -247,13 +319,13 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
         ref={navRef}
         className={`${
           !navOpen && '-ml-96 md:-ml-[18rem]'
-        } duration-20 fixed top-0 z-20 flex h-full w-72 flex-col border-r-[1px] border-b-0 bg-lotion dark:bg-chinese-black transition-all dark:border-r-purple-taupe  dark:text-white`}
+        } duration-20 fixed top-0 z-20 flex h-full w-72 flex-col border-b-0 border-r-[1px] bg-lotion transition-all dark:border-r-purple-taupe dark:bg-chinese-black dark:text-white`}
       >
         <div
           className={'visible mt-2 flex h-[6vh] w-full justify-between md:h-12'}
         >
           <div
-            className="my-auto mx-4 flex cursor-pointer gap-1.5"
+            className="mx-4 my-auto flex cursor-pointer gap-1.5"
             onClick={() => {
               if (isMobile) {
                 setNavOpen(!navOpen);
@@ -283,7 +355,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
         <NavLink
           to={'/'}
           onClick={() => {
-            if (isMobile) {
+            if (isMobile || isTablet) {
               setNavOpen(!navOpen);
             }
             resetConversation();
@@ -291,7 +363,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
           className={({ isActive }) =>
             `${
               isActive ? 'bg-transparent' : ''
-            } group sticky mx-4 mt-4 flex cursor-pointer gap-2.5 rounded-3xl border border-silver p-3 hover:border-rainy-gray dark:border-purple-taupe dark:text-white hover:bg-transparent`
+            } group sticky mx-4 mt-4 flex cursor-pointer gap-2.5 rounded-3xl border border-silver p-3 hover:border-rainy-gray hover:bg-transparent dark:border-purple-taupe dark:text-white`
           }
         >
           <img
@@ -299,7 +371,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
             alt="Create new chat"
             className="opacity-80 group-hover:opacity-100"
           />
-          <p className=" text-sm text-dove-gray group-hover:text-neutral-600 dark:text-chinese-silver dark:group-hover:text-bright-gray">
+          <p className="text-sm text-dove-gray group-hover:text-neutral-600 dark:text-chinese-silver dark:group-hover:text-bright-gray">
             {t('newChat')}
           </p>
         </NavLink>
@@ -308,7 +380,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
           className="mb-auto h-[78vh] overflow-y-auto overflow-x-hidden dark:text-white"
         >
           {conversations?.loading && !isDeletingConversation && (
-            <div className="absolute top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2">
+            <div className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2 transform">
               <img
                 src={isDarkTheme ? SpinnerDark : Spinner}
                 className="animate-spin cursor-pointer bg-transparent"
@@ -316,10 +388,104 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
               />
             </div>
           )}
-          {conversations?.data && conversations.data.length > 0 ? (
+          {recentAgents?.length > 0 ? (
             <div>
-              <div className=" my-auto mx-4 mt-2 flex h-6 items-center justify-between gap-4 rounded-3xl">
-                <p className="mt-1 ml-4 text-sm font-semibold">{t('chats')}</p>
+              <div className="mx-4 my-auto mt-2 flex h-6 items-center">
+                <p className="ml-4 mt-1 text-sm font-semibold">Agents</p>
+              </div>
+              <div className="agents-container">
+                <div>
+                  {recentAgents.map((agent, idx) => (
+                    <div
+                      key={idx}
+                      className={`group mx-4 my-auto mt-4 flex h-9 cursor-pointer items-center justify-between rounded-3xl pl-4 hover:bg-bright-gray dark:hover:bg-dark-charcoal ${
+                        agent.id === selectedAgent?.id && !conversationId
+                          ? 'bg-bright-gray dark:bg-dark-charcoal'
+                          : ''
+                      }`}
+                      onClick={() => handleAgentClick(agent)}
+                    >
+                      <div className="flex items-center gap-2">
+                        <div className="flex w-6 justify-center">
+                          <img
+                            src={agent.image ?? Robot}
+                            alt="agent-logo"
+                            className="h-6 w-6"
+                          />
+                        </div>
+                        <p className="overflow-hidden overflow-ellipsis whitespace-nowrap text-sm leading-6 text-eerie-black dark:text-bright-gray">
+                          {agent.name}
+                        </p>
+                      </div>
+                      <div
+                        className={`${isMobile || isTablet ? 'flex' : 'invisible flex group-hover:visible'} items-center px-3`}
+                      >
+                        <button
+                          className="rounded-full hover:opacity-75"
+                          onClick={(e) => {
+                            e.stopPropagation();
+                            handleTogglePin(agent);
+                          }}
+                        >
+                          <img
+                            src={agent.pinned ? UnPin : Pin}
+                            className="h-4 w-4"
+                          ></img>
+                        </button>
+                      </div>
+                    </div>
+                  ))}
+                </div>
+                <div
+                  className="mx-4 my-auto mt-2 flex h-9 cursor-pointer items-center gap-2 rounded-3xl pl-4 hover:bg-bright-gray dark:hover:bg-dark-charcoal"
+                  onClick={() => {
+                    dispatch(setSelectedAgent(null));
+                    if (isMobile || isTablet) {
+                      setNavOpen(false);
+                    }
+                    navigate('/agents');
+                  }}
+                >
+                  <div className="flex w-6 justify-center">
+                    <img
+                      src={Spark}
+                      alt="manage-agents"
+                      className="h-[18px] w-[18px]"
+                    />
+                  </div>
+                  <p className="overflow-hidden overflow-ellipsis whitespace-nowrap text-sm leading-6 text-eerie-black dark:text-bright-gray">
+                    {t('manageAgents')}
+                  </p>
+                </div>
+              </div>
+            </div>
+          ) : (
+            <div
+              className="mx-4 my-auto mt-2 flex h-9 cursor-pointer items-center gap-2 rounded-3xl pl-4 hover:bg-bright-gray dark:hover:bg-dark-charcoal"
+              onClick={() => {
+                if (isMobile || isTablet) {
+                  setNavOpen(false);
+                }
+                dispatch(setSelectedAgent(null));
+                navigate('/agents');
+              }}
+            >
+              <div className="flex w-6 justify-center">
+                <img
+                  src={Spark}
+                  alt="manage-agents"
+                  className="h-[18px] w-[18px]"
+                />
+              </div>
+              <p className="overflow-hidden overflow-ellipsis whitespace-nowrap text-sm leading-6 text-eerie-black dark:text-bright-gray">
+                {t('manageAgents')}
+              </p>
+            </div>
+          )}
+          {conversations?.data && conversations.data.length > 0 ? (
+            <div className="mt-7">
+              <div className="mx-4 my-auto mt-2 flex h-6 items-center justify-between gap-4 rounded-3xl">
+                <p className="ml-4 mt-1 text-sm font-semibold">{t('chats')}</p>
               </div>
               <div className="conversations-container">
                 {conversations.data?.map((conversation) => (
@@ -345,48 +511,17 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
           )}
         </div>
         <div className="flex h-auto flex-col justify-end text-eerie-black dark:text-white">
-          <div className="flex flex-col-reverse border-b-[1px] dark:border-b-purple-taupe">
-            <div className="relative my-4 mx-4 flex gap-4 items-center">
-              <SourceDropdown
-                options={docs}
-                selectedDocs={selectedDocs}
-                setSelectedDocs={setSelectedDocs}
-                isDocsListOpen={isDocsListOpen}
-                setIsDocsListOpen={setIsDocsListOpen}
-                handleDeleteClick={handleDeleteClick}
-                handlePostDocumentSelect={(option?: string) => {
-                  if (isMobile) {
-                    setNavOpen(!navOpen);
-                  }
-                }}
-              />
-              <img
-                className="hover:cursor-pointer"
-                src={UploadIcon}
-                width={28}
-                height={25}
-                alt="Upload document"
-                onClick={() => {
-                  setUploadModalState('ACTIVE');
-                  if (isMobile) {
-                    setNavOpen(!navOpen);
-                  }
-                }}
-              ></img>
-            </div>
-            <p className="ml-5 mt-3 text-sm font-semibold">{t('sourceDocs')}</p>
-          </div>
           <div className="flex flex-col gap-2 border-b-[1px] py-2 dark:border-b-purple-taupe">
             <NavLink
               onClick={() => {
-                if (isMobile) {
-                  setNavOpen(!navOpen);
+                if (isMobile || isTablet) {
+                  setNavOpen(false);
                 }
                 resetConversation();
               }}
               to="/settings"
               className={({ isActive }) =>
-                `my-auto mx-4 flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-[#28292E] ${
+                `mx-4 my-auto flex h-9 cursor-pointer gap-4 rounded-3xl hover:bg-gray-100 dark:hover:bg-[#28292E] ${
                   isActive ? 'bg-gray-3000 dark:bg-transparent' : ''
                 }`
               }
@@ -394,15 +529,15 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
               <img
                 src={SettingGear}
                 alt="Settings"
-                className="ml-2 w- filter dark:invert"
+                className="w- ml-2 filter dark:invert"
               />
-              <p className="my-auto text-sm text-eerie-black  dark:text-white">
+              <p className="my-auto text-sm text-eerie-black dark:text-white">
                 {t('settings.label')}
               </p>
             </NavLink>
           </div>
           <div className="flex flex-col justify-end text-eerie-black dark:text-white">
-            <div className="flex justify-between items-center py-1">
+            <div className="flex items-center justify-between py-1">
               <Help />
 
               <div className="flex items-center gap-1 pr-4">
@@ -450,10 +585,10 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
           </div>
         </div>
       </div>
-      <div className="sticky z-10 h-16 w-full border-b-2 bg-gray-50 dark:border-b-purple-taupe dark:bg-chinese-black md:hidden">
-        <div className="flex gap-6 items-center h-full ml-6 ">
+      <div className="sticky z-10 h-16 w-full border-b-2 bg-gray-50 dark:border-b-purple-taupe dark:bg-chinese-black lg:hidden">
+        <div className="ml-6 flex h-full items-center gap-6">
           <button
-            className=" h-6 w-6 md:hidden"
+            className="h-6 w-6 lg:hidden"
             onClick={() => setNavOpen(true)}
           >
             <img
@@ -462,7 +597,7 @@ export default function Navigation({ navOpen, setNavOpen }: NavigationProps) {
               className="w-7 filter dark:invert"
             />
           </button>
-          <div className="text-[#949494] font-medium text-[20px]">DocsGPT</div>
+          <div className="text-[20px] font-medium text-[#949494]">DocsGPT</div>
         </div>
       </div>
       <DeleteConvModal

frontend/src/PageNotFound.tsx

@@ -6,7 +6,7 @@ export default function PageNotFound() {
       <p className="mx-auto my-auto mt-20 flex w-full max-w-6xl flex-col place-items-center gap-6 rounded-3xl bg-gray-100 p-6 text-jet dark:bg-outer-space dark:text-gray-100 lg:p-10 xl:p-16">
         <h1>404</h1>
         <p>The page you are looking for does not exist.</p>
-        <button className="pointer-cursor mr-4 flex cursor-pointer items-center justify-center rounded-full bg-blue-1000 py-2 px-4 text-white transition-colors duration-100 hover:bg-blue-3000">
+        <button className="pointer-cursor mr-4 flex cursor-pointer items-center justify-center rounded-full bg-blue-1000 px-4 py-2 text-white transition-colors duration-100 hover:bg-blue-3000">
           <Link to="/">Go Back Home</Link>
         </button>
       </p>

frontend/src/agents/AgentCard.tsx

@@ -0,0 +1,123 @@
+import { useRef, useState } from 'react';
+import { useDispatch, useSelector } from 'react-redux';
+import { useNavigate } from 'react-router-dom';
+
+import userService from '../api/services/userService';
+import Robot from '../assets/robot.svg';
+import ThreeDots from '../assets/three-dots.svg';
+import ContextMenu, { MenuOption } from '../components/ContextMenu';
+import ConfirmationModal from '../modals/ConfirmationModal';
+import { ActiveState } from '../models/misc';
+import {
+  selectToken,
+  setAgents,
+  setSelectedAgent,
+} from '../preferences/preferenceSlice';
+import { Agent } from './types';
+
+type AgentCardProps = {
+  agent: Agent;
+  agents: Agent[];
+  menuOptions?: MenuOption[];
+  onDelete?: (agentId: string) => void;
+};
+
+export default function AgentCard({
+  agent,
+  agents,
+  menuOptions,
+  onDelete,
+}: AgentCardProps) {
+  const navigate = useNavigate();
+  const dispatch = useDispatch();
+  const token = useSelector(selectToken);
+
+  const [isMenuOpen, setIsMenuOpen] = useState(false);
+  const [deleteConfirmation, setDeleteConfirmation] =
+    useState<ActiveState>('INACTIVE');
+
+  const menuRef = useRef<HTMLDivElement>(null);
+
+  const handleCardClick = () => {
+    if (agent.status === 'published') {
+      dispatch(setSelectedAgent(agent));
+      navigate('/');
+    }
+  };
+
+  const defaultDelete = async (agentId: string) => {
+    const response = await userService.deleteAgent(agentId, token);
+    if (!response.ok) throw new Error('Failed to delete agent');
+    const data = await response.json();
+    dispatch(setAgents(agents.filter((prevAgent) => prevAgent.id !== data.id)));
+  };
+
+  return (
+    <div
+      className={`relative flex h-44 w-48 flex-col justify-between rounded-[1.2rem] bg-[#F6F6F6] px-6 py-5 hover:bg-[#ECECEC] dark:bg-[#383838] hover:dark:bg-[#383838]/80 ${
+        agent.status === 'published' ? 'cursor-pointer' : ''
+      }`}
+      onClick={handleCardClick}
+    >
+      <div
+        ref={menuRef}
+        onClick={(e) => {
+          e.stopPropagation();
+          setIsMenuOpen(true);
+        }}
+        className="absolute right-4 top-4 z-10 cursor-pointer"
+      >
+        <img src={ThreeDots} alt="options" className="h-[19px] w-[19px]" />
+        {menuOptions && (
+          <ContextMenu
+            isOpen={isMenuOpen}
+            setIsOpen={setIsMenuOpen}
+            options={menuOptions}
+            anchorRef={menuRef}
+            position="top-right"
+            offset={{ x: 0, y: 0 }}
+          />
+        )}
+      </div>
+
+      <div className="w-full">
+        <div className="flex w-full items-center gap-1 px-1">
+          <img
+            src={agent.image ?? Robot}
+            alt={`${agent.name}`}
+            className="h-7 w-7 rounded-full"
+          />
+          {agent.status === 'draft' && (
+            <p className="text-xs text-black opacity-50 dark:text-[#E0E0E0]">
+              (Draft)
+            </p>
+          )}
+        </div>
+        <div className="mt-2">
+          <p
+            title={agent.name}
+            className="truncate px-1 text-[13px] font-semibold capitalize leading-relaxed text-[#020617] dark:text-[#E0E0E0]"
+          >
+            {agent.name}
+          </p>
+          <p className="mt-1 h-20 overflow-auto px-1 text-[12px] leading-relaxed text-[#64748B] dark:text-sonic-silver-light">
+            {agent.description}
+          </p>
+        </div>
+      </div>
+
+      <ConfirmationModal
+        message="Are you sure you want to delete this agent?"
+        modalState={deleteConfirmation}
+        setModalState={setDeleteConfirmation}
+        submitLabel="Delete"
+        handleSubmit={() => {
+          onDelete ? onDelete(agent.id || '') : defaultDelete(agent.id || '');
+          setDeleteConfirmation('INACTIVE');
+        }}
+        cancelLabel="Cancel"
+        variant="danger"
+      />
+    </div>
+  );
+}

frontend/src/agents/AgentLogs.tsx

@@ -0,0 +1,86 @@
+import { useEffect, useState } from 'react';
+import { useSelector } from 'react-redux';
+import { useNavigate, useParams } from 'react-router-dom';
+
+import userService from '../api/services/userService';
+import ArrowLeft from '../assets/arrow-left.svg';
+import Spinner from '../components/Spinner';
+import { selectToken } from '../preferences/preferenceSlice';
+import Analytics from '../settings/Analytics';
+import Logs from '../settings/Logs';
+import { Agent } from './types';
+
+export default function AgentLogs() {
+  const navigate = useNavigate();
+  const { agentId } = useParams();
+  const token = useSelector(selectToken);
+
+  const [agent, setAgent] = useState<Agent>();
+  const [loadingAgent, setLoadingAgent] = useState<boolean>(true);
+
+  const fetchAgent = async (agentId: string) => {
+    setLoadingAgent(true);
+    try {
+      const response = await userService.getAgent(agentId ?? '', token);
+      if (!response.ok) throw new Error('Failed to fetch Chatbots');
+      const agent = await response.json();
+      setAgent(agent);
+    } catch (error) {
+      console.error(error);
+    } finally {
+      setLoadingAgent(false);
+    }
+  };
+
+  useEffect(() => {
+    if (agentId) fetchAgent(agentId);
+  }, [agentId, token]);
+  return (
+    <div className="p-4 md:p-12">
+      <div className="flex items-center gap-3 px-4">
+        <button
+          className="rounded-full border p-3 text-sm text-gray-400 dark:border-0 dark:bg-[#28292D] dark:text-gray-500 dark:hover:bg-[#2E2F34]"
+          onClick={() => navigate('/agents')}
+        >
+          <img src={ArrowLeft} alt="left-arrow" className="h-3 w-3" />
+        </button>
+        <p className="mt-px text-sm font-semibold text-eerie-black dark:text-bright-gray">
+          Back to all agents
+        </p>
+      </div>
+      <div className="mt-5 flex w-full flex-wrap items-center justify-between gap-2 px-4">
+        <h1 className="m-0 text-[40px] font-bold text-[#212121] dark:text-white">
+          Agent Logs
+        </h1>
+      </div>
+      <div className="mt-6 flex flex-col gap-3 px-4">
+        {agent && (
+          <div className="flex flex-col gap-1">
+            <p className="text-[#28292E] dark:text-[#E0E0E0]">{agent.name}</p>
+            <p className="text-xs text-[#28292E] dark:text-[#E0E0E0]/40">
+              {agent.last_used_at
+                ? 'Last used at ' +
+                  new Date(agent.last_used_at).toLocaleString()
+                : 'No usage history'}
+            </p>
+          </div>
+        )}
+      </div>
+      {loadingAgent ? (
+        <div className="flex h-[345px] w-full items-center justify-center">
+          <Spinner />
+        </div>
+      ) : (
+        agent && <Analytics agentId={agent.id} />
+      )}
+      {loadingAgent ? (
+        <div className="flex h-[55vh] w-full items-center justify-center">
+          {' '}
+          <Spinner />
+        </div>
+      ) : (
+        agent && <Logs agentId={agent.id} tableHeader="Agent endpoint logs" />
+      )}
+    </div>
+  );
+}

frontend/src/agents/AgentPreview.tsx

@@ -0,0 +1,140 @@
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { useDispatch, useSelector } from 'react-redux';
+
+import MessageInput from '../components/MessageInput';
+import ConversationMessages from '../conversation/ConversationMessages';
+import { Query } from '../conversation/conversationModels';
+import {
+  addQuery,
+  fetchAnswer,
+  handleAbort,
+  resendQuery,
+  resetConversation,
+  selectQueries,
+  selectStatus,
+} from '../conversation/conversationSlice';
+import { selectSelectedAgent } from '../preferences/preferenceSlice';
+import { AppDispatch } from '../store';
+
+export default function AgentPreview() {
+  const dispatch = useDispatch<AppDispatch>();
+
+  const queries = useSelector(selectQueries);
+  const status = useSelector(selectStatus);
+  const selectedAgent = useSelector(selectSelectedAgent);
+
+  const [input, setInput] = useState('');
+  const [lastQueryReturnedErr, setLastQueryReturnedErr] = useState(false);
+
+  const fetchStream = useRef<any>(null);
+
+  const handleFetchAnswer = useCallback(
+    ({ question, index }: { question: string; index?: number }) => {
+      fetchStream.current = dispatch(
+        fetchAnswer({ question, indx: index, isPreview: true }),
+      );
+    },
+    [dispatch],
+  );
+
+  const handleQuestion = useCallback(
+    ({
+      question,
+      isRetry = false,
+      index = undefined,
+    }: {
+      question: string;
+      isRetry?: boolean;
+      index?: number;
+    }) => {
+      const trimmedQuestion = question.trim();
+      if (trimmedQuestion === '') return;
+
+      if (index !== undefined) {
+        if (!isRetry) dispatch(resendQuery({ index, prompt: trimmedQuestion }));
+        handleFetchAnswer({ question: trimmedQuestion, index });
+      } else {
+        if (!isRetry) {
+          const newQuery: Query = { prompt: trimmedQuestion };
+          dispatch(addQuery(newQuery));
+        }
+        handleFetchAnswer({ question: trimmedQuestion, index: undefined });
+      }
+    },
+    [dispatch, handleFetchAnswer],
+  );
+
+  const handleQuestionSubmission = (
+    question?: string,
+    updated?: boolean,
+    indx?: number,
+  ) => {
+    if (updated === true && question !== undefined && indx !== undefined) {
+      handleQuestion({
+        question,
+        index: indx,
+        isRetry: false,
+      });
+    } else if (question && status !== 'loading') {
+      const currentInput = question.trim();
+      if (lastQueryReturnedErr && queries.length > 0) {
+        const lastQueryIndex = queries.length - 1;
+        handleQuestion({
+          question: currentInput,
+          isRetry: true,
+          index: lastQueryIndex,
+        });
+      } else {
+        handleQuestion({
+          question: currentInput,
+          isRetry: false,
+          index: undefined,
+        });
+      }
+    }
+  };
+
+  useEffect(() => {
+    dispatch(resetConversation());
+    return () => {
+      if (fetchStream.current) fetchStream.current.abort();
+      handleAbort();
+      dispatch(resetConversation());
+    };
+  }, [dispatch]);
+
+  useEffect(() => {
+    if (queries.length > 0) {
+      const lastQuery = queries[queries.length - 1];
+      setLastQueryReturnedErr(!!lastQuery.error);
+    } else setLastQueryReturnedErr(false);
+  }, [queries]);
+  return (
+    <div>
+      <div className="flex h-full flex-col items-center justify-between gap-2 overflow-y-hidden dark:bg-raisin-black">
+        <div className="h-[512px] w-full overflow-y-auto">
+          <ConversationMessages
+            handleQuestion={handleQuestion}
+            handleQuestionSubmission={handleQuestionSubmission}
+            queries={queries}
+            status={status}
+            showHeroOnEmpty={false}
+          />
+        </div>
+        <div className="flex w-[95%] max-w-[1500px] flex-col items-center gap-4 pb-2 md:w-9/12 lg:w-8/12 xl:w-8/12 2xl:w-6/12">
+          <MessageInput
+            onSubmit={(text) => handleQuestionSubmission(text)}
+            loading={status === 'loading'}
+            showSourceButton={selectedAgent ? false : true}
+            showToolButton={selectedAgent ? false : true}
+            autoFocus={false}
+          />
+          <p className="w-full self-center bg-transparent pt-2 text-center text-xs text-gray-4000 dark:text-sonic-silver md:inline">
+            This is a preview of the agent. You can publish it to start using it
+            in conversations.
+          </p>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/agents/NewAgent.tsx

@@ -0,0 +1,611 @@
+import React, { useEffect, useRef, useState } from 'react';
+import { useDispatch, useSelector } from 'react-redux';
+import { useNavigate, useParams } from 'react-router-dom';
+
+import userService from '../api/services/userService';
+import ArrowLeft from '../assets/arrow-left.svg';
+import SourceIcon from '../assets/source.svg';
+import Dropdown from '../components/Dropdown';
+import MultiSelectPopup, { OptionType } from '../components/MultiSelectPopup';
+import AgentDetailsModal from '../modals/AgentDetailsModal';
+import ConfirmationModal from '../modals/ConfirmationModal';
+import { ActiveState, Doc, Prompt } from '../models/misc';
+import {
+  selectSelectedAgent,
+  selectSourceDocs,
+  selectToken,
+  setSelectedAgent,
+} from '../preferences/preferenceSlice';
+import PromptsModal from '../preferences/PromptsModal';
+import { UserToolType } from '../settings/types';
+import AgentPreview from './AgentPreview';
+import { Agent } from './types';
+
+const embeddingsName =
+  import.meta.env.VITE_EMBEDDINGS_NAME ||
+  'huggingface_sentence-transformers/all-mpnet-base-v2';
+
+export default function NewAgent({ mode }: { mode: 'new' | 'edit' | 'draft' }) {
+  const navigate = useNavigate();
+  const dispatch = useDispatch();
+  const { agentId } = useParams();
+
+  const token = useSelector(selectToken);
+  const sourceDocs = useSelector(selectSourceDocs);
+  const selectedAgent = useSelector(selectSelectedAgent);
+
+  const [effectiveMode, setEffectiveMode] = useState(mode);
+  const [agent, setAgent] = useState<Agent>({
+    id: agentId || '',
+    name: '',
+    description: '',
+    image: '',
+    source: '',
+    chunks: '',
+    retriever: '',
+    prompt_id: '',
+    tools: [],
+    agent_type: '',
+    status: '',
+  });
+  const [prompts, setPrompts] = useState<
+    { name: string; id: string; type: string }[]
+  >([]);
+  const [userTools, setUserTools] = useState<OptionType[]>([]);
+  const [isSourcePopupOpen, setIsSourcePopupOpen] = useState(false);
+  const [isToolsPopupOpen, setIsToolsPopupOpen] = useState(false);
+  const [selectedSourceIds, setSelectedSourceIds] = useState<
+    Set<string | number>
+  >(new Set());
+  const [selectedToolIds, setSelectedToolIds] = useState<Set<string | number>>(
+    new Set(),
+  );
+  const [deleteConfirmation, setDeleteConfirmation] =
+    useState<ActiveState>('INACTIVE');
+  const [agentDetails, setAgentDetails] = useState<ActiveState>('INACTIVE');
+  const [addPromptModal, setAddPromptModal] = useState<ActiveState>('INACTIVE');
+
+  const sourceAnchorButtonRef = useRef<HTMLButtonElement>(null);
+  const toolAnchorButtonRef = useRef<HTMLButtonElement>(null);
+
+  const modeConfig = {
+    new: {
+      heading: 'New Agent',
+      buttonText: 'Create Agent',
+      showDelete: false,
+      showSaveDraft: true,
+      showLogs: false,
+      showAccessDetails: false,
+    },
+    edit: {
+      heading: 'Edit Agent',
+      buttonText: 'Save Changes',
+      showDelete: true,
+      showSaveDraft: false,
+      showLogs: true,
+      showAccessDetails: true,
+    },
+    draft: {
+      heading: 'New Agent (Draft)',
+      buttonText: 'Publish Draft',
+      showDelete: true,
+      showSaveDraft: true,
+      showLogs: false,
+      showAccessDetails: false,
+    },
+  };
+  const chunks = ['0', '2', '4', '6', '8', '10'];
+  const agentTypes = [
+    { label: 'Classic', value: 'classic' },
+    { label: 'ReAct', value: 'react' },
+  ];
+
+  const isPublishable = () => {
+    return (
+      agent.name && agent.description && agent.prompt_id && agent.agent_type
+    );
+  };
+
+  const handleCancel = () => {
+    if (selectedAgent) dispatch(setSelectedAgent(null));
+    navigate('/agents');
+  };
+
+  const handleDelete = async (agentId: string) => {
+    const response = await userService.deleteAgent(agentId, token);
+    if (!response.ok) throw new Error('Failed to delete agent');
+    navigate('/agents');
+  };
+
+  const handleSaveDraft = async () => {
+    const response =
+      effectiveMode === 'new'
+        ? await userService.createAgent({ ...agent, status: 'draft' }, token)
+        : await userService.updateAgent(
+            agent.id || '',
+            { ...agent, status: 'draft' },
+            token,
+          );
+    if (!response.ok) throw new Error('Failed to create agent draft');
+    const data = await response.json();
+    if (effectiveMode === 'new') {
+      setEffectiveMode('draft');
+      setAgent((prev) => ({ ...prev, id: data.id }));
+    }
+  };
+
+  const handlePublish = async () => {
+    const response =
+      effectiveMode === 'new'
+        ? await userService.createAgent(
+            { ...agent, status: 'published' },
+            token,
+          )
+        : await userService.updateAgent(
+            agent.id || '',
+            { ...agent, status: 'published' },
+            token,
+          );
+    if (!response.ok) throw new Error('Failed to publish agent');
+    const data = await response.json();
+    if (data.id) setAgent((prev) => ({ ...prev, id: data.id }));
+    if (data.key) setAgent((prev) => ({ ...prev, key: data.key }));
+    if (effectiveMode === 'new' || effectiveMode === 'draft') {
+      setEffectiveMode('edit');
+      setAgent((prev) => ({ ...prev, status: 'published' }));
+      setAgentDetails('ACTIVE');
+    }
+  };
+
+  useEffect(() => {
+    const getTools = async () => {
+      const response = await userService.getUserTools(token);
+      if (!response.ok) throw new Error('Failed to fetch tools');
+      const data = await response.json();
+      const tools: OptionType[] = data.tools.map((tool: UserToolType) => ({
+        id: tool.id,
+        label: tool.displayName,
+        icon: `/toolIcons/tool_${tool.name}.svg`,
+      }));
+      setUserTools(tools);
+    };
+    const getPrompts = async () => {
+      const response = await userService.getPrompts(token);
+      if (!response.ok) {
+        throw new Error('Failed to fetch prompts');
+      }
+      const data = await response.json();
+      setPrompts(data);
+    };
+    getTools();
+    getPrompts();
+  }, [token]);
+
+  useEffect(() => {
+    if ((mode === 'edit' || mode === 'draft') && agentId) {
+      const getAgent = async () => {
+        const response = await userService.getAgent(agentId, token);
+        if (!response.ok) {
+          navigate('/agents');
+          throw new Error('Failed to fetch agent');
+        }
+        const data = await response.json();
+        if (data.source) setSelectedSourceIds(new Set([data.source]));
+        else if (data.retriever)
+          setSelectedSourceIds(new Set([data.retriever]));
+        if (data.tools) setSelectedToolIds(new Set(data.tools));
+        if (data.status === 'draft') setEffectiveMode('draft');
+        setAgent(data);
+      };
+      getAgent();
+    }
+  }, [agentId, mode, token]);
+
+  useEffect(() => {
+    const selectedSource = Array.from(selectedSourceIds).map((id) =>
+      sourceDocs?.find(
+        (source) =>
+          source.id === id || source.retriever === id || source.name === id,
+      ),
+    );
+    if (selectedSource[0]?.model === embeddingsName) {
+      if (selectedSource[0] && 'id' in selectedSource[0]) {
+        setAgent((prev) => ({
+          ...prev,
+          source: selectedSource[0]?.id || 'default',
+          retriever: '',
+        }));
+      } else
+        setAgent((prev) => ({
+          ...prev,
+          source: '',
+          retriever: selectedSource[0]?.retriever || 'classic',
+        }));
+    }
+  }, [selectedSourceIds]);
+
+  useEffect(() => {
+    const selectedTool = Array.from(selectedToolIds).map((id) =>
+      userTools.find((tool) => tool.id === id),
+    );
+    setAgent((prev) => ({
+      ...prev,
+      tools: selectedTool
+        .map((tool) => tool?.id)
+        .filter((id): id is string => typeof id === 'string'),
+    }));
+  }, [selectedToolIds]);
+
+  useEffect(() => {
+    if (isPublishable()) dispatch(setSelectedAgent(agent));
+  }, [agent, dispatch]);
+  return (
+    <div className="p-4 md:p-12">
+      <div className="flex items-center gap-3 px-4">
+        <button
+          className="rounded-full border p-3 text-sm text-gray-400 dark:border-0 dark:bg-[#28292D] dark:text-gray-500 dark:hover:bg-[#2E2F34]"
+          onClick={handleCancel}
+        >
+          <img src={ArrowLeft} alt="left-arrow" className="h-3 w-3" />
+        </button>
+        <p className="mt-px text-sm font-semibold text-eerie-black dark:text-bright-gray">
+          Back to all agents
+        </p>
+      </div>
+      <div className="mt-5 flex w-full flex-wrap items-center justify-between gap-2 px-4">
+        <h1 className="m-0 text-[40px] font-bold text-[#212121] dark:text-white">
+          {modeConfig[effectiveMode].heading}
+        </h1>
+        <div className="flex flex-wrap items-center gap-1">
+          <button
+            className="mr-4 rounded-3xl py-2 text-sm font-medium text-purple-30 dark:bg-transparent dark:text-light-gray"
+            onClick={handleCancel}
+          >
+            Cancel
+          </button>
+          {modeConfig[effectiveMode].showDelete && agent.id && (
+            <button
+              className="group flex items-center gap-2 rounded-3xl border border-solid border-red-2000 px-5 py-2 text-sm font-medium text-red-2000 transition-colors hover:bg-red-2000 hover:text-white"
+              onClick={() => setDeleteConfirmation('ACTIVE')}
+            >
+              <span className="block h-4 w-4 bg-[url('/src/assets/red-trash.svg')] bg-contain bg-center bg-no-repeat transition-all group-hover:bg-[url('/src/assets/white-trash.svg')]" />
+              Delete
+            </button>
+          )}
+          {modeConfig[effectiveMode].showSaveDraft && (
+            <button
+              className="hover:bg-vi</button>olets-are-blue rounded-3xl border border-solid border-violets-are-blue px-5 py-2 text-sm font-medium text-violets-are-blue transition-colors hover:bg-violets-are-blue hover:text-white"
+              onClick={handleSaveDraft}
+            >
+              Save Draft
+            </button>
+          )}
+          {modeConfig[effectiveMode].showAccessDetails && (
+            <button
+              className="group flex items-center gap-2 rounded-3xl border border-solid border-violets-are-blue px-5 py-2 text-sm font-medium text-violets-are-blue transition-colors hover:bg-violets-are-blue hover:text-white"
+              onClick={() => navigate(`/agents/logs/${agent.id}`)}
+            >
+              <span className="block h-5 w-5 bg-[url('/src/assets/monitoring-purple.svg')] bg-contain bg-center bg-no-repeat transition-all group-hover:bg-[url('/src/assets/monitoring-white.svg')]" />
+              Logs
+            </button>
+          )}
+          {modeConfig[effectiveMode].showAccessDetails && (
+            <button
+              className="hover:bg-vi</button>olets-are-blue rounded-3xl border border-solid border-violets-are-blue px-5 py-2 text-sm font-medium text-violets-are-blue transition-colors hover:bg-violets-are-blue hover:text-white"
+              onClick={() => setAgentDetails('ACTIVE')}
+            >
+              Access Details
+            </button>
+          )}
+          <button
+            disabled={!isPublishable()}
+            className={`${!isPublishable() && 'cursor-not-allowed opacity-30'} rounded-3xl bg-purple-30 px-5 py-2 text-sm font-medium text-white hover:bg-violets-are-blue`}
+            onClick={handlePublish}
+          >
+            Publish
+          </button>
+        </div>
+      </div>
+      <div className="mt-5 flex w-full grid-cols-5 flex-col gap-10 min-[1180px]:grid min-[1180px]:gap-5">
+        <div className="col-span-2 flex flex-col gap-5">
+          <div className="rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+            <h2 className="text-lg font-semibold">Meta</h2>
+            <input
+              className="mt-3 w-full rounded-3xl border border-silver bg-white px-5 py-3 text-sm text-jet outline-none placeholder:text-gray-400 dark:border-[#7E7E7E] dark:bg-[#222327] dark:text-bright-gray placeholder:dark:text-silver"
+              type="text"
+              value={agent.name}
+              placeholder="Agent name"
+              onChange={(e) => setAgent({ ...agent, name: e.target.value })}
+            />
+            <textarea
+              className="mt-3 h-32 w-full rounded-3xl border border-silver bg-white px-5 py-4 text-sm text-jet outline-none placeholder:text-gray-400 dark:border-[#7E7E7E] dark:bg-[#222327] dark:text-bright-gray placeholder:dark:text-silver"
+              placeholder="Describe your agent"
+              value={agent.description}
+              onChange={(e) =>
+                setAgent({ ...agent, description: e.target.value })
+              }
+            />
+          </div>
+          <div className="rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+            <h2 className="text-lg font-semibold">Source</h2>
+            <div className="mt-3">
+              <div className="flex flex-wrap items-center gap-1">
+                <button
+                  ref={sourceAnchorButtonRef}
+                  onClick={() => setIsSourcePopupOpen(!isSourcePopupOpen)}
+                  className="w-full truncate rounded-3xl border border-silver bg-white px-5 py-3 text-left text-sm text-gray-400 dark:border-[#7E7E7E] dark:bg-[#222327] dark:text-silver"
+                >
+                  {selectedSourceIds.size > 0
+                    ? Array.from(selectedSourceIds)
+                        .map(
+                          (id) =>
+                            sourceDocs?.find(
+                              (source) =>
+                                source.id === id ||
+                                source.name === id ||
+                                source.retriever === id,
+                            )?.name,
+                        )
+                        .filter(Boolean)
+                        .join(', ')
+                    : 'Select source'}
+                </button>
+                <MultiSelectPopup
+                  isOpen={isSourcePopupOpen}
+                  onClose={() => setIsSourcePopupOpen(false)}
+                  anchorRef={sourceAnchorButtonRef}
+                  options={
+                    sourceDocs?.map((doc: Doc) => ({
+                      id: doc.id || doc.retriever || doc.name,
+                      label: doc.name,
+                      icon: <img src={SourceIcon} alt="" />,
+                    })) || []
+                  }
+                  selectedIds={selectedSourceIds}
+                  onSelectionChange={(newSelectedIds: Set<string | number>) => {
+                    setSelectedSourceIds(newSelectedIds);
+                    setIsSourcePopupOpen(false);
+                  }}
+                  title="Select Source"
+                  searchPlaceholder="Search sources..."
+                  noOptionsMessage="No source available"
+                  singleSelect={true}
+                />
+              </div>
+              <div className="mt-3">
+                <Dropdown
+                  options={chunks}
+                  selectedValue={agent.chunks ? agent.chunks : null}
+                  onSelect={(value: string) =>
+                    setAgent({ ...agent, chunks: value })
+                  }
+                  size="w-full"
+                  rounded="3xl"
+                  buttonDarkBackgroundColor="[#222327]"
+                  border="border"
+                  darkBorderColor="[#7E7E7E]"
+                  placeholder="Chunks per query"
+                  placeholderTextColor="gray-400"
+                  darkPlaceholderTextColor="silver"
+                  contentSize="text-sm"
+                />
+              </div>
+            </div>
+          </div>
+          <div className="rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+            <h2 className="text-lg font-semibold">Prompt</h2>
+            <div className="mt-3 flex flex-wrap items-center gap-1">
+              <div className="min-w-20 flex-grow basis-full sm:basis-0">
+                <Dropdown
+                  options={prompts.map((prompt) => ({
+                    label: prompt.name,
+                    value: prompt.id,
+                  }))}
+                  selectedValue={
+                    agent.prompt_id
+                      ? prompts.filter(
+                          (prompt) => prompt.id === agent.prompt_id,
+                        )[0]?.name || null
+                      : null
+                  }
+                  onSelect={(option: { label: string; value: string }) =>
+                    setAgent({ ...agent, prompt_id: option.value })
+                  }
+                  size="w-full"
+                  rounded="3xl"
+                  buttonDarkBackgroundColor="[#222327]"
+                  border="border"
+                  darkBorderColor="[#7E7E7E]"
+                  placeholder="Select a prompt"
+                  placeholderTextColor="gray-400"
+                  darkPlaceholderTextColor="silver"
+                  contentSize="text-sm"
+                />
+              </div>
+              <button
+                className="w-20 flex-shrink-0 basis-full rounded-3xl border-2 border-solid border-violets-are-blue px-5 py-[11px] text-sm text-violets-are-blue transition-colors hover:bg-violets-are-blue hover:text-white sm:basis-auto"
+                onClick={() => setAddPromptModal('ACTIVE')}
+              >
+                Add
+              </button>
+            </div>
+          </div>
+          <div className="rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+            <h2 className="text-lg font-semibold">Tools</h2>
+            <div className="mt-3 flex flex-wrap items-center gap-1">
+              <button
+                ref={toolAnchorButtonRef}
+                onClick={() => setIsToolsPopupOpen(!isToolsPopupOpen)}
+                className="w-full truncate rounded-3xl border border-silver bg-white px-5 py-3 text-left text-sm text-gray-400 dark:border-[#7E7E7E] dark:bg-[#222327] dark:text-silver"
+              >
+                {selectedToolIds.size > 0
+                  ? Array.from(selectedToolIds)
+                      .map(
+                        (id) => userTools.find((tool) => tool.id === id)?.label,
+                      )
+                      .filter(Boolean)
+                      .join(', ')
+                  : 'Select tools'}
+              </button>
+              <MultiSelectPopup
+                isOpen={isToolsPopupOpen}
+                onClose={() => setIsToolsPopupOpen(false)}
+                anchorRef={toolAnchorButtonRef}
+                options={userTools}
+                selectedIds={selectedToolIds}
+                onSelectionChange={(newSelectedIds: Set<string | number>) =>
+                  setSelectedToolIds(newSelectedIds)
+                }
+                title="Select Tools"
+                searchPlaceholder="Search tools..."
+                noOptionsMessage="No tools available"
+              />
+            </div>
+          </div>
+          <div className="rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+            <h2 className="text-lg font-semibold">Agent type</h2>
+            <div className="mt-3">
+              <Dropdown
+                options={agentTypes}
+                selectedValue={
+                  agent.agent_type
+                    ? agentTypes.find((type) => type.value === agent.agent_type)
+                        ?.label || null
+                    : null
+                }
+                onSelect={(option: { label: string; value: string }) =>
+                  setAgent({ ...agent, agent_type: option.value })
+                }
+                size="w-full"
+                rounded="3xl"
+                buttonDarkBackgroundColor="[#222327]"
+                border="border"
+                darkBorderColor="[#7E7E7E]"
+                placeholder="Select type"
+                placeholderTextColor="gray-400"
+                darkPlaceholderTextColor="silver"
+                contentSize="text-sm"
+              />
+            </div>
+          </div>
+        </div>
+        <div className="col-span-3 flex flex-col gap-3 rounded-[30px] bg-[#F6F6F6] px-6 py-3 dark:bg-[#383838] dark:text-[#E0E0E0]">
+          <h2 className="text-lg font-semibold">Preview</h2>
+          <AgentPreviewArea />
+        </div>
+      </div>
+      <ConfirmationModal
+        message="Are you sure you want to delete this agent?"
+        modalState={deleteConfirmation}
+        setModalState={setDeleteConfirmation}
+        submitLabel="Delete"
+        handleSubmit={() => {
+          handleDelete(agent.id || '');
+          setDeleteConfirmation('INACTIVE');
+        }}
+        cancelLabel="Cancel"
+        variant="danger"
+      />
+      <AgentDetailsModal
+        agent={agent}
+        mode={effectiveMode}
+        modalState={agentDetails}
+        setModalState={setAgentDetails}
+      />
+      <AddPromptModal
+        prompts={prompts}
+        setPrompts={setPrompts}
+        isOpen={addPromptModal}
+        onClose={() => setAddPromptModal('INACTIVE')}
+        onSelect={(name: string, id: string, type: string) => {
+          setAgent({ ...agent, prompt_id: id });
+        }}
+      />
+    </div>
+  );
+}
+
+function AgentPreviewArea() {
+  const selectedAgent = useSelector(selectSelectedAgent);
+  return (
+    <div className="h-full w-full rounded-[30px] border border-[#F6F6F6] bg-white dark:border-[#7E7E7E] dark:bg-[#222327] max-[1180px]:h-[48rem]">
+      {selectedAgent?.status === 'published' ? (
+        <div className="flex h-full w-full flex-col justify-end overflow-auto rounded-[30px]">
+          <AgentPreview />
+        </div>
+      ) : (
+        <div className="flex h-full w-full flex-col items-center justify-center gap-2">
+          <span className="block h-12 w-12 bg-[url('/src/assets/science-spark.svg')] bg-contain bg-center bg-no-repeat transition-all dark:bg-[url('/src/assets/science-spark-dark.svg')]" />{' '}
+          <p className="text-xs text-[#18181B] dark:text-[#949494]">
+            Published agents can be previewed here
+          </p>
+        </div>
+      )}
+    </div>
+  );
+}
+
+function AddPromptModal({
+  prompts,
+  setPrompts,
+  isOpen,
+  onClose,
+  onSelect,
+}: {
+  prompts: Prompt[];
+  setPrompts?: React.Dispatch<React.SetStateAction<Prompt[]>>;
+  isOpen: ActiveState;
+  onClose: () => void;
+  onSelect?: (name: string, id: string, type: string) => void;
+}) {
+  const token = useSelector(selectToken);
+
+  const [newPromptName, setNewPromptName] = useState('');
+  const [newPromptContent, setNewPromptContent] = useState('');
+
+  const handleAddPrompt = async () => {
+    try {
+      const response = await userService.createPrompt(
+        {
+          name: newPromptName,
+          content: newPromptContent,
+        },
+        token,
+      );
+      if (!response.ok) {
+        throw new Error('Failed to add prompt');
+      }
+      const newPrompt = await response.json();
+      if (setPrompts) {
+        setPrompts([
+          ...prompts,
+          { name: newPromptName, id: newPrompt.id, type: 'private' },
+        ]);
+      }
+      onClose();
+      setNewPromptName('');
+      setNewPromptContent('');
+      onSelect?.(newPromptName, newPrompt.id, newPromptContent);
+    } catch (error) {
+      console.error(error);
+    }
+  };
+  return (
+    <PromptsModal
+      modalState={isOpen}
+      setModalState={onClose}
+      type="ADD"
+      existingPrompts={prompts}
+      newPromptName={newPromptName}
+      setNewPromptName={setNewPromptName}
+      newPromptContent={newPromptContent}
+      setNewPromptContent={setNewPromptContent}
+      editPromptName={''}
+      setEditPromptName={() => undefined}
+      editPromptContent={''}
+      setEditPromptContent={() => undefined}
+      currentPromptEdit={{ id: '', name: '', type: '' }}
+      handleAddPrompt={handleAddPrompt}
+    />
+  );
+}

frontend/src/agents/SharedAgent.tsx

@@ -0,0 +1,196 @@
+import { useCallback, useEffect, useRef, useState } from 'react';
+import { useTranslation } from 'react-i18next';
+import { useDispatch, useSelector } from 'react-redux';
+import { useParams } from 'react-router-dom';
+
+import userService from '../api/services/userService';
+import NoFilesDarkIcon from '../assets/no-files-dark.svg';
+import NoFilesIcon from '../assets/no-files.svg';
+import Robot from '../assets/robot.svg';
+import MessageInput from '../components/MessageInput';
+import Spinner from '../components/Spinner';
+import ConversationMessages from '../conversation/ConversationMessages';
+import { Query } from '../conversation/conversationModels';
+import {
+  addQuery,
+  fetchAnswer,
+  resendQuery,
+  selectQueries,
+  selectStatus,
+} from '../conversation/conversationSlice';
+import { useDarkTheme } from '../hooks';
+import { selectToken, setSelectedAgent } from '../preferences/preferenceSlice';
+import { AppDispatch } from '../store';
+import SharedAgentCard from './SharedAgentCard';
+import { Agent } from './types';
+
+export default function SharedAgent() {
+  const { t } = useTranslation();
+  const { agentId } = useParams();
+  const dispatch = useDispatch<AppDispatch>();
+  const [isDarkTheme] = useDarkTheme();
+
+  const token = useSelector(selectToken);
+  const queries = useSelector(selectQueries);
+  const status = useSelector(selectStatus);
+
+  const [sharedAgent, setSharedAgent] = useState<Agent>();
+  const [isLoading, setIsLoading] = useState(true);
+  const [input, setInput] = useState('');
+  const [lastQueryReturnedErr, setLastQueryReturnedErr] = useState(false);
+
+  const fetchStream = useRef<any>(null);
+
+  const getSharedAgent = async () => {
+    try {
+      setIsLoading(true);
+      const response = await userService.getSharedAgent(agentId ?? '', token);
+      if (!response.ok) throw new Error('Failed to fetch Shared Agent');
+      const agent: Agent = await response.json();
+      setSharedAgent(agent);
+    } catch (error) {
+      console.error('Error: ', error);
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  const handleFetchAnswer = useCallback(
+    ({ question, index }: { question: string; index?: number }) => {
+      fetchStream.current = dispatch(
+        fetchAnswer({ question, indx: index, isPreview: false }),
+      );
+    },
+    [dispatch],
+  );
+
+  const handleQuestion = useCallback(
+    ({
+      question,
+      isRetry = false,
+      index = undefined,
+    }: {
+      question: string;
+      isRetry?: boolean;
+      index?: number;
+    }) => {
+      const trimmedQuestion = question.trim();
+      if (trimmedQuestion === '') return;
+
+      if (index !== undefined) {
+        if (!isRetry) dispatch(resendQuery({ index, prompt: trimmedQuestion }));
+        handleFetchAnswer({ question: trimmedQuestion, index });
+      } else {
+        if (!isRetry) {
+          const newQuery: Query = { prompt: trimmedQuestion };
+          dispatch(addQuery(newQuery));
+        }
+        handleFetchAnswer({ question: trimmedQuestion, index: undefined });
+      }
+    },
+    [dispatch, handleFetchAnswer],
+  );
+
+  const handleQuestionSubmission = (
+    question?: string,
+    updated?: boolean,
+    indx?: number,
+  ) => {
+    if (updated === true && question !== undefined && indx !== undefined) {
+      handleQuestion({
+        question,
+        index: indx,
+        isRetry: false,
+      });
+    } else if (question && status !== 'loading') {
+      const currentInput = question.trim();
+      if (lastQueryReturnedErr && queries.length > 0) {
+        const lastQueryIndex = queries.length - 1;
+        handleQuestion({
+          question: currentInput,
+          isRetry: true,
+          index: lastQueryIndex,
+        });
+      } else {
+        handleQuestion({
+          question: currentInput,
+          isRetry: false,
+          index: undefined,
+        });
+      }
+      setInput('');
+    }
+  };
+
+  useEffect(() => {
+    if (agentId) getSharedAgent();
+  }, [agentId, token]);
+
+  useEffect(() => {
+    if (sharedAgent) dispatch(setSelectedAgent(sharedAgent));
+  }, [sharedAgent, dispatch]);
+
+  if (isLoading)
+    return (
+      <div className="flex h-full w-full items-center justify-center">
+        <Spinner />
+      </div>
+    );
+  if (!sharedAgent)
+    return (
+      <div className="flex h-full w-full items-center justify-center">
+        <div className="flex w-full flex-col items-center justify-center gap-4">
+          <img
+            src={isDarkTheme ? NoFilesDarkIcon : NoFilesIcon}
+            alt="No agent found"
+            className="mx-auto mb-6 h-32 w-32"
+          />
+          <p className="text-center text-lg text-[#71717A] dark:text-[#949494]">
+            No agent found. Please ensure the agent is shared.
+          </p>
+        </div>
+      </div>
+    );
+  return (
+    <div className="relative h-full w-full">
+      <div className="absolute left-4 top-5 hidden items-center gap-3 sm:flex">
+        <img
+          src={sharedAgent.image ?? Robot}
+          alt="agent-logo"
+          className="h-6 w-6"
+        />
+        <h2 className="text-lg font-semibold text-[#212121] dark:text-[#E0E0E0]">
+          {sharedAgent.name}
+        </h2>
+      </div>
+      <div className="flex h-full w-full flex-col items-center justify-between sm:pt-12">
+        <div className="flex w-full flex-col items-center overflow-y-auto">
+          <ConversationMessages
+            handleQuestion={handleQuestion}
+            handleQuestionSubmission={handleQuestionSubmission}
+            queries={queries}
+            status={status}
+            showHeroOnEmpty={false}
+            headerContent={
+              <div className="flex w-full items-center justify-center py-4">
+                <SharedAgentCard agent={sharedAgent} />
+              </div>
+            }
+          />
+        </div>
+        <div className="flex w-[95%] max-w-[1500px] flex-col items-center pb-2 md:w-9/12 lg:w-8/12 xl:w-8/12 2xl:w-6/12">
+          <MessageInput
+            onSubmit={(text) => handleQuestionSubmission(text)}
+            loading={status === 'loading'}
+            showSourceButton={sharedAgent ? false : true}
+            showToolButton={sharedAgent ? false : true}
+            autoFocus={false}
+          />
+          <p className="hidden w-[100vw] self-center bg-transparent py-2 text-center text-xs text-gray-4000 dark:text-sonic-silver md:inline md:w-full">
+            {t('tagline')}
+          </p>
+        </div>
+      </div>
+    </div>
+  );
+}

frontend/src/agents/SharedAgentCard.tsx

@@ -0,0 +1,69 @@
+import Robot from '../assets/robot.svg';
+import { Agent } from './types';
+
+export default function SharedAgentCard({ agent }: { agent: Agent }) {
+  return (
+    <div className="flex w-full max-w-[720px] flex-col rounded-3xl border border-dark-gray p-6 shadow-sm dark:border-grey sm:w-fit sm:min-w-[480px]">
+      <div className="flex items-center gap-3">
+        <div className="flex h-12 w-12 items-center justify-center overflow-hidden rounded-full p-1">
+          <img src={Robot} className="h-full w-full object-contain" />
+        </div>
+        <div className="flex max-h-[92px] w-[80%] flex-col gap-px">
+          <h2 className="text-base font-semibold text-[#212121] dark:text-[#E0E0E0] sm:text-lg">
+            {agent.name}
+          </h2>
+          <p className="overflow-y-auto text-wrap break-all text-xs text-[#71717A] dark:text-[#949494] sm:text-sm">
+            {agent.description}
+          </p>
+        </div>
+      </div>
+      {agent.shared_metadata && (
+        <div className="mt-4 flex items-center gap-8">
+          {agent.shared_metadata?.shared_by && (
+            <p className="text-xs font-light text-[#212121] dark:text-[#E0E0E0] sm:text-sm">
+              by {agent.shared_metadata.shared_by}
+            </p>
+          )}
+          {agent.shared_metadata?.shared_at && (
+            <p className="text-xs font-light text-[#71717A] dark:text-[#949494] sm:text-sm">
+              Shared on{' '}
+              {new Date(agent.shared_metadata.shared_at).toLocaleString(
+                'en-US',
+                {
+                  month: 'long',
+                  day: 'numeric',
+                  year: 'numeric',
+                  hour: '2-digit',
+                  minute: '2-digit',
+                  hour12: true,
+                },
+              )}
+            </p>
+          )}
+        </div>
+      )}
+      {agent.tool_details && agent.tool_details.length > 0 && (
+        <div className="mt-8">
+          <p className="text-sm font-semibold text-[#212121] dark:text-[#E0E0E0] sm:text-base">
+            Connected Tools
+          </p>
+          <div className="mt-2 flex flex-wrap gap-2">
+            {agent.tool_details.map((tool, index) => (
+              <span
+                key={index}
+                className="flex items-center gap-1 rounded-full bg-bright-gray px-3 py-1 text-xs font-light text-[#212121] dark:bg-dark-charcoal dark:text-[#E0E0E0]"
+              >
+                <img
+                  src={`/toolIcons/tool_${tool.name}.svg`}
+                  alt={`${tool.name} icon`}
+                  className="h-3 w-3"
+                />{' '}
+                {tool.name}
+              </span>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/agents/SharedAgentGate.tsx

@@ -0,0 +1,7 @@
+import { Navigate, useParams } from 'react-router-dom';
+
+export default function SharedAgentGate() {
+  const { agentId } = useParams();
+
+  return <Navigate to={`/agents/shared/${agentId}`} replace />;
+}

frontend/src/agents/index.tsx

@@ -0,0 +1,470 @@
+import { SyntheticEvent, useEffect, useRef, useState } from 'react';
+import { useDispatch, useSelector } from 'react-redux';
+import { Route, Routes, useNavigate } from 'react-router-dom';
+
+import userService from '../api/services/userService';
+import Edit from '../assets/edit.svg';
+import Link from '../assets/link-gray.svg';
+import Monitoring from '../assets/monitoring.svg';
+import Pin from '../assets/pin.svg';
+import Trash from '../assets/red-trash.svg';
+import Robot from '../assets/robot.svg';
+import ThreeDots from '../assets/three-dots.svg';
+import UnPin from '../assets/unpin.svg';
+import ContextMenu, { MenuOption } from '../components/ContextMenu';
+import Spinner from '../components/Spinner';
+import {
+  setConversation,
+  updateConversationId,
+} from '../conversation/conversationSlice';
+import ConfirmationModal from '../modals/ConfirmationModal';
+import { ActiveState } from '../models/misc';
+import {
+  selectAgents,
+  selectSelectedAgent,
+  selectSharedAgents,
+  selectToken,
+  setAgents,
+  setSelectedAgent,
+  setSharedAgents,
+} from '../preferences/preferenceSlice';
+import AgentLogs from './AgentLogs';
+import NewAgent from './NewAgent';
+import SharedAgent from './SharedAgent';
+import { Agent } from './types';
+
+export default function Agents() {
+  return (
+    <Routes>
+      <Route path="/" element={<AgentsList />} />
+      <Route path="/new" element={<NewAgent mode="new" />} />
+      <Route path="/edit/:agentId" element={<NewAgent mode="edit" />} />
+      <Route path="/logs/:agentId" element={<AgentLogs />} />
+      <Route path="/shared/:agentId" element={<SharedAgent />} />
+    </Routes>
+  );
+}
+
+const sectionConfig = {
+  user: {
+    title: 'By me',
+    description: 'Agents created or published by you',
+    showNewAgentButton: true,
+    emptyStateDescription: 'You don’t have any created agents yet',
+  },
+  shared: {
+    title: 'Shared with me',
+    description: 'Agents imported by using a public link',
+    showNewAgentButton: false,
+    emptyStateDescription: 'No shared agents found',
+  },
+};
+
+function AgentsList() {
+  const dispatch = useDispatch();
+  const token = useSelector(selectToken);
+  const agents = useSelector(selectAgents);
+  const sharedAgents = useSelector(selectSharedAgents);
+  const selectedAgent = useSelector(selectSelectedAgent);
+
+  const [loadingUserAgents, setLoadingUserAgents] = useState<boolean>(true);
+  const [loadingSharedAgents, setLoadingSharedAgents] = useState<boolean>(true);
+
+  const getAgents = async () => {
+    try {
+      setLoadingUserAgents(true);
+      const response = await userService.getAgents(token);
+      if (!response.ok) throw new Error('Failed to fetch agents');
+      const data = await response.json();
+      dispatch(setAgents(data));
+      setLoadingUserAgents(false);
+    } catch (error) {
+      console.error('Error:', error);
+      setLoadingUserAgents(false);
+    }
+  };
+
+  const getSharedAgents = async () => {
+    try {
+      setLoadingSharedAgents(true);
+      const response = await userService.getSharedAgents(token);
+      if (!response.ok) throw new Error('Failed to fetch shared agents');
+      const data = await response.json();
+      dispatch(setSharedAgents(data));
+      setLoadingSharedAgents(false);
+    } catch (error) {
+      console.error('Error:', error);
+      setLoadingSharedAgents(false);
+    }
+  };
+
+  useEffect(() => {
+    getAgents();
+    getSharedAgents();
+    dispatch(setConversation([]));
+    dispatch(
+      updateConversationId({
+        query: { conversationId: null },
+      }),
+    );
+    if (selectedAgent) dispatch(setSelectedAgent(null));
+  }, [token]);
+  return (
+    <div className="p-4 md:p-12">
+      <h1 className="mb-0 text-[40px] font-bold text-[#212121] dark:text-[#E0E0E0]">
+        Agents
+      </h1>
+      <p className="mt-5 text-[15px] text-[#71717A] dark:text-[#949494]">
+        Discover and create custom versions of DocsGPT that combine
+        instructions, extra knowledge, and any combination of skills
+      </p>
+      {/* Premade agents section */}
+      {/* <div className="mt-6">
+        <h2 className="text-[18px] font-semibold text-[#18181B] dark:text-[#E0E0E0]">
+          Premade by DocsGPT
+        </h2>
+        <div className="mt-4 flex w-full flex-wrap gap-4">
+          {Array.from({ length: 5 }, (_, index) => (
+            <div
+              key={index}
+              className="relative flex h-44 w-48 flex-col justify-between rounded-[1.2rem] bg-[#F6F6F6] px-6 py-5 dark:bg-[#383838]"
+            >
+              <button onClick={() => {}} className="absolute right-4 top-4">
+                <img
+                  src={Copy}
+                  alt={'use-agent'}
+                  className="h-[19px] w-[19px]"
+                />
+              </button>
+              <div className="w-full">
+                <div className="flex w-full items-center px-1">
+                  <img
+                    src={Robot}
+                    alt="agent-logo"
+                    className="h-7 w-7 rounded-full"
+                  />
+                </div>
+                <div className="mt-2">
+                  <p
+                    title={''}
+                    className="truncate px-1 text-[13px] font-semibold capitalize leading-relaxed text-raisin-black-light dark:text-bright-gray"
+                  >
+                    {}
+                  </p>
+                  <p className="mt-1 h-20 overflow-auto px-1 text-[12px] leading-relaxed text-old-silver dark:text-sonic-silver-light">
+                    {}
+                  </p>
+                </div>
+              </div>
+              <div className="absolute bottom-4 right-4"></div>
+            </div>
+          ))}
+        </div>
+      </div> */}
+      <AgentSection
+        agents={agents ?? []}
+        updateAgents={(updatedAgents) => {
+          dispatch(setAgents(updatedAgents));
+        }}
+        loading={loadingUserAgents}
+        section="user"
+      />
+      <AgentSection
+        agents={sharedAgents ?? []}
+        updateAgents={(updatedAgents) => {
+          dispatch(setSharedAgents(updatedAgents));
+        }}
+        loading={loadingSharedAgents}
+        section="shared"
+      />
+    </div>
+  );
+}
+
+function AgentSection({
+  agents,
+  updateAgents,
+  loading,
+  section,
+}: {
+  agents: Agent[];
+  updateAgents?: (agents: Agent[]) => void;
+  loading: boolean;
+  section: keyof typeof sectionConfig;
+}) {
+  const navigate = useNavigate();
+  return (
+    <div className="mt-8 flex flex-col gap-4">
+      <div className="flex w-full items-center justify-between">
+        <div className="flex flex-col gap-2">
+          <h2 className="text-[18px] font-semibold text-[#18181B] dark:text-[#E0E0E0]">
+            {sectionConfig[section].title}
+          </h2>
+          <p className="text-[13px] text-[#71717A]">
+            {sectionConfig[section].description}
+          </p>
+        </div>
+        {sectionConfig[section].showNewAgentButton && (
+          <button
+            className="rounded-full bg-purple-30 px-4 py-2 text-sm text-white hover:bg-violets-are-blue"
+            onClick={() => navigate('/agents/new')}
+          >
+            New Agent
+          </button>
+        )}
+      </div>
+      <div>
+        {loading ? (
+          <div className="flex h-72 w-full items-center justify-center">
+            <Spinner />
+          </div>
+        ) : agents && agents.length > 0 ? (
+          <div className="grid grid-cols-1 gap-4 sm:flex sm:flex-wrap">
+            {agents.map((agent, idx) => (
+              <AgentCard
+                key={agent.id}
+                agent={agent}
+                agents={agents}
+                updateAgents={updateAgents}
+                section={section}
+              />
+            ))}
+          </div>
+        ) : (
+          <div className="flex h-72 w-full flex-col items-center justify-center gap-3 text-base text-[#18181B] dark:text-[#E0E0E0]">
+            <p>{sectionConfig[section].emptyStateDescription}</p>
+            {sectionConfig[section].showNewAgentButton && (
+              <button
+                className="ml-2 rounded-full bg-purple-30 px-4 py-2 text-sm text-white hover:bg-violets-are-blue"
+                onClick={() => navigate('/agents/new')}
+              >
+                New Agent
+              </button>
+            )}
+          </div>
+        )}
+      </div>
+    </div>
+  );
+}
+
+function AgentCard({
+  agent,
+  agents,
+  updateAgents,
+  section,
+}: {
+  agent: Agent;
+  agents: Agent[];
+  updateAgents?: (agents: Agent[]) => void;
+  section: keyof typeof sectionConfig;
+}) {
+  const navigate = useNavigate();
+  const dispatch = useDispatch();
+  const token = useSelector(selectToken);
+
+  const [isMenuOpen, setIsMenuOpen] = useState<boolean>(false);
+  const [deleteConfirmation, setDeleteConfirmation] =
+    useState<ActiveState>('INACTIVE');
+
+  const menuRef = useRef<HTMLDivElement>(null);
+
+  const togglePin = async () => {
+    try {
+      const response = await userService.togglePinAgent(agent.id ?? '', token);
+      if (!response.ok) throw new Error('Failed to pin agent');
+      const updatedAgents = agents.map((prevAgent) => {
+        if (prevAgent.id === agent.id)
+          return { ...prevAgent, pinned: !prevAgent.pinned };
+        return prevAgent;
+      });
+      updateAgents?.(updatedAgents);
+    } catch (error) {
+      console.error('Error:', error);
+    }
+  };
+
+  const handleHideSharedAgent = async () => {
+    try {
+      const response = await userService.removeSharedAgent(
+        agent.id ?? '',
+        token,
+      );
+      if (!response.ok) throw new Error('Failed to hide shared agent');
+      const updatedAgents = agents.filter(
+        (prevAgent) => prevAgent.id !== agent.id,
+      );
+      updateAgents?.(updatedAgents);
+    } catch (error) {
+      console.error('Error:', error);
+    }
+  };
+
+  const menuOptionsConfig: Record<string, MenuOption[]> = {
+    user: [
+      {
+        icon: Monitoring,
+        label: 'Logs',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          navigate(`/agents/logs/${agent.id}`);
+        },
+        variant: 'primary',
+        iconWidth: 14,
+        iconHeight: 14,
+      },
+      {
+        icon: Edit,
+        label: 'Edit',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          navigate(`/agents/edit/${agent.id}`);
+        },
+        variant: 'primary',
+        iconWidth: 14,
+        iconHeight: 14,
+      },
+      {
+        icon: agent.pinned ? UnPin : Pin,
+        label: agent.pinned ? 'Unpin' : 'Pin agent',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          togglePin();
+        },
+        variant: 'primary',
+        iconWidth: 18,
+        iconHeight: 18,
+      },
+      {
+        icon: Trash,
+        label: 'Delete',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          setDeleteConfirmation('ACTIVE');
+        },
+        variant: 'danger',
+        iconWidth: 13,
+        iconHeight: 13,
+      },
+    ],
+    shared: [
+      {
+        icon: Link,
+        label: 'Open',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          navigate(`/agents/shared/${agent.shared_token}`);
+        },
+        variant: 'primary',
+        iconWidth: 12,
+        iconHeight: 12,
+      },
+      {
+        icon: agent.pinned ? UnPin : Pin,
+        label: agent.pinned ? 'Unpin' : 'Pin agent',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          togglePin();
+        },
+        variant: 'primary',
+        iconWidth: 18,
+        iconHeight: 18,
+      },
+      {
+        icon: Trash,
+        label: 'Remove',
+        onClick: (e: SyntheticEvent) => {
+          e.stopPropagation();
+          handleHideSharedAgent();
+        },
+        variant: 'danger',
+        iconWidth: 13,
+        iconHeight: 13,
+      },
+    ],
+  };
+
+  const menuOptions = menuOptionsConfig[section] || [];
+
+  const handleClick = () => {
+    if (section === 'user') {
+      if (agent.status === 'published') {
+        dispatch(setSelectedAgent(agent));
+        navigate(`/`);
+      }
+    }
+    if (section === 'shared') {
+      navigate(`/agents/shared/${agent.shared_token}`);
+    }
+  };
+
+  const handleDelete = async (agentId: string) => {
+    const response = await userService.deleteAgent(agentId, token);
+    if (!response.ok) throw new Error('Failed to delete agent');
+    const data = await response.json();
+    dispatch(setAgents(agents.filter((prevAgent) => prevAgent.id !== data.id)));
+  };
+  return (
+    <div
+      className={`relative flex h-44 w-full flex-col justify-between rounded-[1.2rem] bg-[#F6F6F6] px-6 py-5 hover:bg-[#ECECEC] dark:bg-[#383838] hover:dark:bg-[#383838]/80 md:w-48 ${agent.status === 'published' && 'cursor-pointer'}`}
+      onClick={(e) => {
+        e.stopPropagation();
+        handleClick();
+      }}
+    >
+      <div
+        ref={menuRef}
+        onClick={(e) => {
+          e.stopPropagation();
+          setIsMenuOpen(true);
+        }}
+        className="absolute right-4 top-4 z-10 cursor-pointer"
+      >
+        <img src={ThreeDots} alt={'use-agent'} className="h-[19px] w-[19px]" />
+        <ContextMenu
+          isOpen={isMenuOpen}
+          setIsOpen={setIsMenuOpen}
+          options={menuOptions}
+          anchorRef={menuRef}
+          position="top-right"
+          offset={{ x: 0, y: 0 }}
+        />
+      </div>
+      <div className="w-full">
+        <div className="flex w-full items-center gap-1 px-1">
+          <img
+            src={agent.image ?? Robot}
+            alt={`${agent.name}`}
+            className="h-7 w-7 rounded-full"
+          />
+          {agent.status === 'draft' && (
+            <p className="text-xs text-black opacity-50 dark:text-[#E0E0E0]">{`(Draft)`}</p>
+          )}
+        </div>
+        <div className="mt-2">
+          <p
+            title={agent.name}
+            className="truncate px-1 text-[13px] font-semibold capitalize leading-relaxed text-[#020617] dark:text-[#E0E0E0]"
+          >
+            {agent.name}
+          </p>
+          <p className="mt-1 h-20 overflow-auto px-1 text-[12px] leading-relaxed text-[#64748B] dark:text-sonic-silver-light">
+            {agent.description}
+          </p>
+        </div>
+      </div>
+      <ConfirmationModal
+        message="Are you sure you want to delete this agent?"
+        modalState={deleteConfirmation}
+        setModalState={setDeleteConfirmation}
+        submitLabel="Delete"
+        handleSubmit={() => {
+          handleDelete(agent.id || '');
+          setDeleteConfirmation('INACTIVE');
+        }}
+        cancelLabel="Cancel"
+        variant="danger"
+      />
+    </div>
+  );
+}

frontend/src/agents/types/index.ts

@@ -0,0 +1,29 @@
+export type ToolSummary = {
+  id: string;
+  name: string;
+  display_name: string;
+};
+
+export type Agent = {
+  id?: string;
+  name: string;
+  description: string;
+  image: string;
+  source: string;
+  chunks: string;
+  retriever: string;
+  prompt_id: string;
+  tools: string[];
+  tool_details?: ToolSummary[];
+  agent_type: string;
+  status: string;
+  key?: string;
+  incoming_webhook_token?: string;
+  pinned?: boolean;
+  shared?: boolean;
+  shared_token?: string;
+  shared_metadata?: any;
+  created_at?: string;
+  updated_at?: string;
+  last_used_at?: string;
+};

frontend/src/api/client.ts

@@ -1,4 +1,5 @@
-const baseURL = import.meta.env.VITE_API_HOST || 'https://docsapi.arc53.com';
+export const baseURL =
+  import.meta.env.VITE_API_HOST || 'https://docsapi.arc53.com';
 
 const defaultHeaders = {
   'Content-Type': 'application/json',

frontend/src/api/endpoints.ts

@@ -8,6 +8,18 @@ const endpoints = {
     API_KEYS: '/api/get_api_keys',
     CREATE_API_KEY: '/api/create_api_key',
     DELETE_API_KEY: '/api/delete_api_key',
+    AGENT: (id: string) => `/api/get_agent?id=${id}`,
+    AGENTS: '/api/get_agents',
+    CREATE_AGENT: '/api/create_agent',
+    UPDATE_AGENT: (agent_id: string) => `/api/update_agent/${agent_id}`,
+    DELETE_AGENT: (id: string) => `/api/delete_agent?id=${id}`,
+    PINNED_AGENTS: '/api/pinned_agents',
+    TOGGLE_PIN_AGENT: (id: string) => `/api/pin_agent?id=${id}`,
+    SHARED_AGENT: (id: string) => `/api/shared_agent?token=${id}`,
+    SHARED_AGENTS: '/api/shared_agents',
+    SHARE_AGENT: `/api/share_agent`,
+    REMOVE_SHARED_AGENT: (id: string) => `/api/remove_shared_agent?id=${id}`,
+    AGENT_WEBHOOK: (id: string) => `/api/agent_webhook?id=${id}`,
     PROMPTS: '/api/get_prompts',
     CREATE_PROMPT: '/api/create_prompt',
     DELETE_PROMPT: '/api/delete_prompt',
@@ -32,6 +44,7 @@ const endpoints = {
     DELETE_CHUNK: (docId: string, chunkId: string) =>
       `/api/delete_chunk?id=${docId}&chunk_id=${chunkId}`,
     UPDATE_CHUNK: '/api/update_chunk',
+    STORE_ATTACHMENT: '/api/store_attachment',
   },
   CONVERSATION: {
     ANSWER: '/api/answer',

frontend/src/api/services/userService.ts

@@ -17,6 +17,34 @@ const userService = {
     apiClient.post(endpoints.USER.CREATE_API_KEY, data, token),
   deleteAPIKey: (data: any, token: string | null): Promise<any> =>
     apiClient.post(endpoints.USER.DELETE_API_KEY, data, token),
+  getAgent: (id: string, token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.AGENT(id), token),
+  getAgents: (token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.AGENTS, token),
+  createAgent: (data: any, token: string | null): Promise<any> =>
+    apiClient.post(endpoints.USER.CREATE_AGENT, data, token),
+  updateAgent: (
+    agent_id: string,
+    data: any,
+    token: string | null,
+  ): Promise<any> =>
+    apiClient.put(endpoints.USER.UPDATE_AGENT(agent_id), data, token),
+  deleteAgent: (id: string, token: string | null): Promise<any> =>
+    apiClient.delete(endpoints.USER.DELETE_AGENT(id), token),
+  getPinnedAgents: (token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.PINNED_AGENTS, token),
+  togglePinAgent: (id: string, token: string | null): Promise<any> =>
+    apiClient.post(endpoints.USER.TOGGLE_PIN_AGENT(id), {}, token),
+  getSharedAgent: (id: string, token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.SHARED_AGENT(id), token),
+  getSharedAgents: (token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.SHARED_AGENTS, token),
+  shareAgent: (data: any, token: string | null): Promise<any> =>
+    apiClient.put(endpoints.USER.SHARE_AGENT, data, token),
+  removeSharedAgent: (id: string, token: string | null): Promise<any> =>
+    apiClient.delete(endpoints.USER.REMOVE_SHARED_AGENT(id), token),
+  getAgentWebhook: (id: string, token: string | null): Promise<any> =>
+    apiClient.get(endpoints.USER.AGENT_WEBHOOK(id), token),
   getPrompts: (token: string | null): Promise<any> =>
     apiClient.get(endpoints.USER.PROMPTS, token),
   createPrompt: (data: any, token: string | null): Promise<any> =>

frontend/src/assets/clip.svg

@@ -0,0 +1,3 @@
+<svg width="8" height="14" viewBox="0 0 8 14" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M3.08485 4.07133L3.08485 9.20109C3.08485 9.49263 3.20066 9.77222 3.40681 9.97837C3.61295 10.1845 3.89255 10.3003 4.18408 10.3003C4.47562 10.3003 4.75521 10.1845 4.96136 9.97837C5.1675 9.77222 5.28332 9.49263 5.28332 9.20109L5.28332 3.70492C5.28332 3.12185 5.05169 2.56266 4.6394 2.15037C4.22711 1.73808 3.66792 1.50645 3.08485 1.50645C2.50178 1.50645 1.94259 1.73808 1.5303 2.15037C1.118 2.56266 0.88638 3.12185 0.88638 3.70492L0.886379 9.20109C0.886379 10.0757 1.23381 10.9145 1.85225 11.5329C2.47069 12.1514 3.30948 12.4988 4.18408 12.4988C5.05869 12.4988 5.89747 12.1514 6.51591 11.5329C7.13435 10.9145 7.48178 10.0757 7.48178 9.20109L7.48178 4.07133" stroke="#5D5D5D" stroke-width="1.03637" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

frontend/src/assets/cloud.svg

@@ -0,0 +1,11 @@
+<svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M2 16C2.53043 16 3.03914 16.2107 3.41421 16.5858C3.78929 16.9609 4 17.4696 4 18C4 18.5304 3.78929 19.0391 3.41421 19.4142C3.03914 19.7893 2.53043 20 2 20C1.46957 20 0.960859 19.7893 0.585786 19.4142C0.210714 19.0391 0 18.5304 0 18C0 17.4696 0.210714 16.9609 0.585786 16.5858C0.960859 16.2107 1.46957 16 2 16ZM7.5 13C8.16304 13 8.79893 13.2634 9.26777 13.7322C9.73661 14.2011 10 14.837 10 15.5C10 16.163 9.73661 16.7989 9.26777 17.2678C8.79893 17.7366 8.16304 18 7.5 18C6.83696 18 6.20107 17.7366 5.73223 17.2678C5.26339 16.7989 5 16.163 5 15.5C5 14.837 5.26339 14.2011 5.73223 13.7322C6.20107 13.2634 6.83696 13 7.5 13ZM10 0C11.272 0.000250351 12.5033 0.448355 13.4779 1.26572C14.4525 2.08308 15.1082 3.2175 15.33 4.47H15.412C16.4105 4.47 17.3682 4.86667 18.0743 5.57274C18.7803 6.27882 19.177 7.23646 19.177 8.235C19.177 9.23354 18.7803 10.1912 18.0743 10.8973C17.3682 11.6033 16.4105 12 15.412 12H4.588C3.58946 12 2.63182 11.6033 1.92574 10.8973C1.21967 10.1912 0.823 9.23354 0.823 8.235C0.823 7.23646 1.21967 6.27882 1.92574 5.57274C2.63182 4.86667 3.58946 4.47 4.588 4.47H4.67C4.89179 3.2175 5.54749 2.08308 6.52211 1.26572C7.49673 0.448355 8.72801 0.000250351 10 0Z" fill="url(#paint0_linear_6161_11984)"/>
+<defs>
+<linearGradient id="paint0_linear_6161_11984" x1="0" y1="10" x2="19.177" y2="10" gradientUnits="userSpaceOnUse">
+<stop stop-color="#70FDF7"/>
+<stop offset="0.325" stop-color="#747696"/>
+<stop offset="0.68" stop-color="#BD5372"/>
+<stop offset="1" stop-color="#F5A06C"/>
+</linearGradient>
+</defs>
+</svg>