added llm

app.py

@@ -203,12 +203,23 @@ def log_message(message: BaseMessage, prefix: str = ""):
 
 class BasicAgent:
     def __init__(self):
-        self.llm = ChatGoogleGenerativeAI(
+        # Initialize primary LLM (Gemini)
+        self.primary_llm = ChatGoogleGenerativeAI(
             model="gemini-2.5-flash-preview-05-20",
             max_tokens=8192,
             temperature=0
         )
         
+        # Initialize fallback LLM (if available)
+        self.fallback_llm = None
+        if os.getenv("OPENAI_API_KEY"):
+            from langchain_openai import ChatOpenAI
+            self.fallback_llm = ChatOpenAI(
+                model="gpt-3.5-turbo",
+                temperature=0,
+                max_tokens=4096
+            )
+        
         # Create tool executor
         self.tools = [
             get_file, analyse_excel, add_numbers, transcribe_audio,
@@ -262,11 +273,11 @@ class BasicAgent:
         # Set recursion limit through environment variable
         os.environ["LANGRAPH_RECURSION_LIMIT"] = "50"
         
-        logger.info("BasicAgent initialized.")
+        logger.info("BasicAgent initialized with fallback LLM support.")
     
     @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60))
     def call_model(self, state: AgentState) -> AgentState:
-        """Call the model to generate a response with retry logic."""
+        """Call the model to generate a response with retry logic and fallback support."""
         try:
             messages = state["messages"]
             logger.info("\n=== Model Input ===")
@@ -274,9 +285,9 @@ class BasicAgent:
             for msg in messages:
                 log_message(msg, "  ")
             
-            # Add tools to the model invocation
+            # Try primary LLM first
             try:
-                response = self.llm.invoke(
+                response = self.primary_llm.invoke(
                     [self.sys_msg] + messages,
                     tools=[{"type": "function", "function": {
                         "name": "google_search",
@@ -297,12 +308,39 @@ class BasicAgent:
                 error_str = str(e)
                 if "429" in error_str:
                     if "GenerateRequestsPerDayPerProjectPerModel-FreeTier" in error_str:
-                        logger.warning("Daily quota limit reached, providing fallback response")
-                        # For daily quota limits, provide a fallback response and end the conversation
-                        return {
-                            "messages": [AIMessage(content="I've reached my daily limit for processing requests. Please try again tomorrow or contact support for assistance.")], 
-                            "next": END
-                        }
+                        logger.warning("Daily quota limit reached for primary LLM, trying fallback")
+                        if self.fallback_llm:
+                            try:
+                                response = self.fallback_llm.invoke(
+                                    [self.sys_msg] + messages,
+                                    tools=[{"type": "function", "function": {
+                                        "name": "google_search",
+                                        "description": "Search for information on the web",
+                                        "parameters": {
+                                            "type": "object",
+                                            "properties": {
+                                                "query": {
+                                                    "type": "string",
+                                                    "description": "The search query"
+                                                }
+                                            },
+                                            "required": ["query"]
+                                        }
+                                    }}]
+                                )
+                                logger.info("Successfully used fallback LLM")
+                            except Exception as fallback_error:
+                                logger.error(f"Fallback LLM also failed: {str(fallback_error)}")
+                                return {
+                                    "messages": [AIMessage(content="All LLM services are currently unavailable. Please try again later.")], 
+                                    "next": END
+                                }
+                        else:
+                            logger.warning("No fallback LLM available")
+                            return {
+                                "messages": [AIMessage(content="I've reached my daily limit for processing requests. Please try again tomorrow or contact support for assistance.")], 
+                                "next": END
+                            }
                     else:
                         # For other rate limits, wait and retry
                         wait_time = 60
@@ -348,8 +386,32 @@ class BasicAgent:
             error_str = str(e)
             if "429" in error_str:
                 if "GenerateRequestsPerDayPerProjectPerModel-FreeTier" in error_str:
-                    logger.warning("Daily quota limit reached, providing fallback response")
-                    return {"messages": [AIMessage(content="I've reached my daily limit for processing requests. Please try again tomorrow or contact support for assistance.")], "next": END}
+                    logger.warning("Daily quota limit reached, trying fallback")
+                    if self.fallback_llm:
+                        try:
+                            response = self.fallback_llm.invoke(
+                                [self.sys_msg] + messages,
+                                tools=[{"type": "function", "function": {
+                                    "name": "google_search",
+                                    "description": "Search for information on the web",
+                                    "parameters": {
+                                        "type": "object",
+                                        "properties": {
+                                            "query": {
+                                                "type": "string",
+                                                "description": "The search query"
+                                            }
+                                        },
+                                        "required": ["query"]
+                                    }
+                                }}]
+                            )
+                            return {"messages": [response], "next": "tools"}
+                        except Exception as fallback_error:
+                            logger.error(f"Fallback LLM also failed: {str(fallback_error)}")
+                            return {"messages": [AIMessage(content="All LLM services are currently unavailable. Please try again later.")], "next": END}
+                    else:
+                        return {"messages": [AIMessage(content="I've reached my daily limit for processing requests. Please try again tomorrow or contact support for assistance.")], "next": END}
                 else:
                     logger.warning("Rate limit hit, waiting before retry...")
                     time.sleep(60)  # Wait for 60 seconds before retry

requirements.txt

@@ -1,15 +1,12 @@
-langchain>=0.3,<0.4
-langchain-core>=0.3,<0.4
-langchain-community>=0.3,<0.4
-langchain-experimental>=0.3,<0.4
-langchain-google-genai>=2,<3
-langgraph==0.2.20
-gradio>=4.44.1
-requests
-openpyxl
-pytube
-google
-google-generativeai>=0.3.1
-duckduckgo-search
-pandas
-tenacity>=8.0.1
+langchain==0.1.12
+langchain-google-genai==0.0.11
+langchain-core==0.1.32
+langgraph==0.0.27
+gradio==4.19.2
+pandas==2.2.1
+openpyxl==3.1.2
+tenacity==8.2.3
+google-generativeai==0.3.2
+langchain-openai==0.0.8
+python-dotenv==1.0.1
+requests==2.31.0