Upload agent.py

agent.py

@@ -156,13 +156,10 @@ def build_graph(provider: str = "groq"):
     """Build the graph"""
     # Load environment variables from .env file
     if provider == "google":
-        # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
     elif provider == "groq":
-        # Groq https://console.groq.com/docs/models
         llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
     elif provider == "huggingface":
-        # TODO: Add huggingface endpoint
         llm = ChatHuggingFace(
             llm=HuggingFaceEndpoint(
                 url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
@@ -181,14 +178,21 @@ def build_graph(provider: str = "groq"):
     
     def retriever(state: MessagesState):
         """Retriever node"""
+        print("DEBUG: Starting retriever function")
+        print(f"DEBUG: Incoming state messages count: {len(state['messages'])}")
+        print(f"DEBUG: Content of first message: {state['messages'][0].content}")
+        
         similar_question = vector_store.similarity_search(state["messages"][0].content)
+        print(f"DEBUG: Found {len(similar_question)} similar questions")
         
         if similar_question:
             example_msg = HumanMessage(
                 content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
             )
+            print(f"DEBUG: Example message content preview: {example_msg.content[:100]}...")  # print first 100 chars
             return {"messages": [sys_msg] + state["messages"] + [example_msg]}
         else:
+            print("DEBUG: No similar question found")
             return {"messages": [sys_msg] + state["messages"]}