Remove chat_completion tool from shared and app modules

lfunderburk · lfunderburk · commit 96db02b31de9 · 2025-12-17T14:36:44.000-08:00
The chat_completion tool and its related code have been removed from shared/tools.py and are no longer re-exported in the yelp-navigator app tools modules (v1, v2, v3). This simplifies the toolset and eliminates unused or deprecated functionality.
diff --git a/epilogue/context-engineering/shared/chat_utils.py b/epilogue/context-engineering/shared/chat_utils.py
@@ -6,15 +6,14 @@
 from typing import Dict, Any, Optional, Tuple
 
 
-def handle_general_chat(state: Dict[str, Any], track_errors: bool = False) -> Tuple[str, Optional[Dict[str, Any]]]:
+def handle_general_chat(track_errors: bool = False) -> Tuple[str, Optional[Dict[str, Any]]]:
     """
     Handle non-business chat queries with a welcoming message that redirects to business searches.
     
     It always returns a friendly welcome message that explains what the chatbot
     is designed for (business search and analysis) and prompts the user to ask about businesses.
     
     Args:
-        state: Current agent state (not used, but kept for consistency)
         track_errors: If True, returns error tracking dict; if False, returns None
     
     Returns:
diff --git a/epilogue/context-engineering/shared/tools.py b/epilogue/context-engineering/shared/tools.py
@@ -1,163 +1,10 @@
 """Shared tools for interacting with Hayhooks API endpoints."""
 import requests
-from typing import Dict, Any, List
+from typing import Dict, Any
 from langchain_core.tools import tool
-from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
-from .config import get_llm
 
 BASE_URL = "http://localhost:1416"
 
-MODEL = "gpt-oss:20b" #"gpt-4o-mini" #change to your preferred model 
-
-# OpenAI Chat Completion Tool
-@tool
-def chat_completion(messages: List[Dict[str, str]], model: str = MODEL, stream: bool = False, base_url: str = BASE_URL) -> Dict[str, Any]:
-    """Send a general chat completion request using OpenAI's chat completion API.
-    
-    Args:
-        messages: List of message dicts, e.g. [{"role": "user", "content": "Hello!"}]
-        model: Model name to use (please review shared/config.py for supported models)
-        stream: Whether to stream the response (default: False)
-        base_url: Base URL for the API endpoint (deprecated, kept for compatibility)
-        
-    Returns:
-        Dictionary containing the chat completion response in OpenAI format:
-        {
-            "success": True/False,
-            "response": {
-                "id": "string",
-                "object": "chat.completion",
-                "created": int,
-                "model": "string",
-                "choices": [
-                    {
-                        "index": 0,
-                        "message": {
-                            "role": "assistant",
-                            "content": "string"
-                        },
-                        "finish_reason": "stop"
-                    }
-                ]
-            },
-            "error": "error message" (only if success is False)
-        }
-    """
-    max_attempts = 3
-    last_error = None
-    
-    for attempt in range(max_attempts):
-        try:
-            # Get the LLM instance with error handling
-            try:
-                llm = get_llm(model=model)
-            except Exception as llm_error:
-                if attempt == max_attempts - 1:
-                    return {
-                        "success": False, 
-                        "error": f"Failed to initialize model '{model}': {str(llm_error)}. Please check your API keys and model configuration."
-                    }
-                # Wait before retry (exponential backoff)
-                import time
-                time.sleep(2 ** attempt)
-                continue
-            
-            # Add system instruction to always end with a summary
-            system_instruction = SystemMessage(content=(
-                "You are a helpful assistant for a Yelp business search and analysis system. "
-                "After answering the user's question, ALWAYS end your response with a brief summary that includes:\n"
-                "1. What this agentic system does (searches for businesses, analyzes reviews, and provides recommendations)\n"
-                "2. What information it needs from users (location, business type/category, and specific preferences or requirements)"
-            ))
-            
-            # Convert message dicts to LangChain message objects
-            langchain_messages = [system_instruction]
-            for msg in messages:
-                role = msg.get("role", "user")
-                content = msg.get("content", "")
-                
-                if role == "system":
-                    langchain_messages.append(SystemMessage(content=content))
-                elif role == "assistant":
-                    langchain_messages.append(AIMessage(content=content))
-                else:  # user or any other role
-                    langchain_messages.append(HumanMessage(content=content))
-            
-            # Invoke the LLM with timeout protection
-            try:
-                response = llm.invoke(langchain_messages)
-            except Exception as invoke_error:
-                last_error = invoke_error
-                if "rate limit" in str(invoke_error).lower() or "quota" in str(invoke_error).lower():
-                    # Rate limit error - wait longer
-                    if attempt < max_attempts - 1:
-                        import time
-                        wait_time = min(30, 5 * (2 ** attempt))
-                        print(f"Rate limit hit, waiting {wait_time}s before retry...")
-                        time.sleep(wait_time)
-                        continue
-                elif "timeout" in str(invoke_error).lower():
-                    # Timeout error - retry with shorter timeout
-                    if attempt < max_attempts - 1:
-                        import time
-                        time.sleep(2)
-                        continue
-                raise  # Re-raise if not rate limit or last attempt
-            
-            # Format response in OpenAI-compatible format
-            return {
-                "success": True,
-                "response": {
-                    "id": getattr(response, "id", "chatcmpl-" + str(hash(response.content))),
-                    "object": "chat.completion",
-                    "created": int(getattr(response, "response_metadata", {}).get("created", 0)),
-                    "model": model,
-                    "choices": [
-                        {
-                            "index": 0,
-                            "message": {
-                                "role": "assistant",
-                                "content": response.content
-                            },
-                            "finish_reason": "stop"
-                        }
-                    ]
-                },
-                "metadata": {
-                    "attempts": attempt + 1,
-                    "model_used": model
-                }
-            }
-            
-        except Exception as e:
-            last_error = e
-            if attempt == max_attempts - 1:
-                error_type = type(e).__name__
-                error_details = str(e)
-                
-                # Provide helpful error messages based on error type
-                if "api_key" in error_details.lower() or "authentication" in error_details.lower():
-                    error_msg = f"Authentication failed for model '{model}': Check your API keys in .env file"
-                elif "not found" in error_details.lower() or "does not exist" in error_details.lower():
-                    error_msg = f"Model '{model}' not found. Available models depend on your provider."
-                elif "connection" in error_details.lower() or "network" in error_details.lower():
-                    error_msg = f"Network error connecting to {model}: Check your internet connection"
-                else:
-                    error_msg = f"Error in chat completion ({error_type}): {error_details}"
-                
-                return {
-                    "success": False, 
-                    "error": error_msg,
-                    "error_type": error_type,
-                    "attempts": max_attempts
-                }
-    
-    # Should not reach here, but just in case
-    return {
-        "success": False, 
-        "error": f"Failed after {max_attempts} attempts: {str(last_error)}",
-        "attempts": max_attempts
-    }
 
 @tool
 def search_businesses(query: str) -> Dict[str, Any]:
diff --git a/epilogue/context-engineering/yelp-navigator-v1/app/tools.py b/epilogue/context-engineering/yelp-navigator-v1/app/tools.py
@@ -1,14 +1,12 @@
 """Re-export shared tools for backwards compatibility."""
 from shared.tools import (
-    chat_completion,
     search_businesses,
     get_business_details,
     analyze_reviews_sentiment,
     BASE_URL
 )
 
 __all__ = [
-    'chat_completion',
     'search_businesses',
     'get_business_details',
     'analyze_reviews_sentiment',
diff --git a/epilogue/context-engineering/yelp-navigator-v2/app/tools.py b/epilogue/context-engineering/yelp-navigator-v2/app/tools.py
@@ -1,14 +1,12 @@
 """Re-export shared tools for backwards compatibility."""
 from shared.tools import (
-    chat_completion,
     search_businesses,
     get_business_details,
     analyze_reviews_sentiment,
     BASE_URL
 )
 
 __all__ = [
-    'chat_completion',
     'search_businesses',
     'get_business_details',
     'analyze_reviews_sentiment',
diff --git a/epilogue/context-engineering/yelp-navigator-v3/app/tools.py b/epilogue/context-engineering/yelp-navigator-v3/app/tools.py
@@ -1,14 +1,12 @@
-"""V3 Tools - re-exports shared tools for backwards compatibility."""
+"""Re-export shared tools for backwards compatibility."""
 from shared.tools import (
-    chat_completion,
     search_businesses,
     get_business_details,
     analyze_reviews_sentiment,
     BASE_URL
 )
 
 __all__ = [
-    'chat_completion',
     'search_businesses',
     'get_business_details',
     'analyze_reviews_sentiment',
