fix: webhooks

* feat: context compression

* fix: ruff

README.md

@@ -26,13 +26,6 @@
 
 </div>
 
-<div align="center">
-  <br>
-🎃 <a href="https://github.com/arc53/DocsGPT/blob/main/HACKTOBERFEST.md"> Hacktoberfest Prizes, Rules & Q&A </a> 🎃
-  <br>
-  <br>
-</div>
-
 
 <div align="center">
   <br>

application/api/answer/services/stream_processor.py

@@ -103,11 +103,10 @@ class StreamProcessor:
 
     def initialize(self):
         """Initialize all required components for processing"""
-        self._validate_and_set_model()
         self._configure_agent()
+        self._validate_and_set_model()
         self._configure_source()
         self._configure_retriever()
-        self._configure_agent()
         self._load_conversation_history()
         self._process_attachments()
 
@@ -230,7 +229,12 @@ class StreamProcessor:
                 )
             self.model_id = requested_model
         else:
-            self.model_id = get_default_model_id()
+            # Check if agent has a default model configured
+            agent_default_model = self.agent_config.get("default_model_id", "")
+            if agent_default_model and validate_model_id(agent_default_model):
+                self.model_id = agent_default_model
+            else:
+                self.model_id = get_default_model_id()
 
     def _get_agent_key(self, agent_id: Optional[str], user_id: Optional[str]) -> tuple:
         """Get API key for agent with access control"""
@@ -303,6 +307,10 @@ class StreamProcessor:
             data["sources"] = sources_list
         else:
             data["sources"] = []
+
+        # Preserve model configuration from agent
+        data["default_model_id"] = data.get("default_model_id", "")
+
         return data
 
     def _configure_source(self):
@@ -355,6 +363,7 @@ class StreamProcessor:
                     "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
                     "user_api_key": api_key,
                     "json_schema": data_key.get("json_schema"),
+                    "default_model_id": data_key.get("default_model_id", ""),
                 }
             )
             self.initial_user_id = data_key.get("user")
@@ -379,6 +388,7 @@ class StreamProcessor:
                     "agent_type": data_key.get("agent_type", settings.AGENT_NAME),
                     "user_api_key": self.agent_key,
                     "json_schema": data_key.get("json_schema"),
+                    "default_model_id": data_key.get("default_model_id", ""),
                 }
             )
             self.decoded_token = (
@@ -405,6 +415,7 @@ class StreamProcessor:
                     "agent_type": settings.AGENT_NAME,
                     "user_api_key": None,
                     "json_schema": None,
+                    "default_model_id": "",
                 }
             )
 

application/core/model_configs.py

@@ -37,7 +37,7 @@ OPENAI_MODELS = [
             supports_tools=True,
             supports_structured_output=True,
             supported_attachment_types=OPENAI_ATTACHMENTS,
-            context_window=400000,
+            context_window=200000,
         ),
     ),
     AvailableModel(
@@ -49,7 +49,7 @@ OPENAI_MODELS = [
             supports_tools=True,
             supports_structured_output=True,
             supported_attachment_types=OPENAI_ATTACHMENTS,
-            context_window=400000,
+            context_window=200000,
         ),
     )
 ]
@@ -133,7 +133,7 @@ GOOGLE_MODELS = [
             supports_tools=True,
             supports_structured_output=True,
             supported_attachment_types=GOOGLE_ATTACHMENTS,
-            context_window=20000,  # Set low for testing compression
+            context_window=2000000,
         ),
     ),
 ]

application/worker.py

@@ -146,6 +146,14 @@ def upload_index(full_path, file_data):
 
 def run_agent_logic(agent_config, input_data):
     try:
+        from application.core.model_utils import (
+            get_api_key_for_provider,
+            get_default_model_id,
+            get_provider_from_model_id,
+            validate_model_id,
+        )
+        from application.utils import calculate_doc_token_budget
+
         source = agent_config.get("source")
         retriever = agent_config.get("retriever", "classic")
         if isinstance(source, DBRef):
@@ -160,31 +168,62 @@ def run_agent_logic(agent_config, input_data):
         user_api_key = agent_config["key"]
         agent_type = agent_config.get("agent_type", "classic")
         decoded_token = {"sub": agent_config.get("user")}
+        json_schema = agent_config.get("json_schema")
         prompt = get_prompt(prompt_id, db["prompts"])
-        agent = AgentCreator.create_agent(
-            agent_type,
-            endpoint="webhook",
-            llm_name=settings.LLM_PROVIDER,
-            model_id=settings.LLM_NAME,
-            api_key=settings.API_KEY,
-            user_api_key=user_api_key,
-            prompt=prompt,
-            chat_history=[],
-            decoded_token=decoded_token,
-            attachments=[],
+
+        # Determine model_id: check agent's default_model_id, fallback to system default
+        agent_default_model = agent_config.get("default_model_id", "")
+        if agent_default_model and validate_model_id(agent_default_model):
+            model_id = agent_default_model
+        else:
+            model_id = get_default_model_id()
+
+        # Get provider and API key for the selected model
+        provider = get_provider_from_model_id(model_id) if model_id else settings.LLM_PROVIDER
+        system_api_key = get_api_key_for_provider(provider or settings.LLM_PROVIDER)
+
+        # Calculate proper doc_token_limit based on model's context window
+        history_token_limit = 2000  # Default for webhooks
+        doc_token_limit = calculate_doc_token_budget(
+            model_id=model_id, history_token_limit=history_token_limit
         )
+
         retriever = RetrieverCreator.create_retriever(
             retriever,
             source=source,
             chat_history=[],
             prompt=prompt,
             chunks=chunks,
-            token_limit=settings.DEFAULT_MAX_HISTORY,
-            model_id=settings.LLM_NAME,
+            doc_token_limit=doc_token_limit,
+            model_id=model_id,
             user_api_key=user_api_key,
             decoded_token=decoded_token,
         )
-        answer = agent.gen(query=input_data, retriever=retriever)
+
+        # Pre-fetch documents using the retriever
+        retrieved_docs = []
+        try:
+            docs = retriever.search(input_data)
+            if docs:
+                retrieved_docs = docs
+        except Exception as e:
+            logging.warning(f"Failed to retrieve documents: {e}")
+
+        agent = AgentCreator.create_agent(
+            agent_type,
+            endpoint="webhook",
+            llm_name=provider or settings.LLM_PROVIDER,
+            model_id=model_id,
+            api_key=system_api_key,
+            user_api_key=user_api_key,
+            prompt=prompt,
+            chat_history=[],
+            retrieved_docs=retrieved_docs,
+            decoded_token=decoded_token,
+            attachments=[],
+            json_schema=json_schema,
+        )
+        answer = agent.gen(query=input_data)
         response_full = ""
         thought = ""
         source_log_docs = []