Update agent.py

agent.py

@@ -165,16 +165,13 @@ tools = [
     create_retriever_tool
 ]
 
-
-
-
 def build_graph(provider: str = "openai"):
     """Build the graph using OpenAI or Hugging Face"""
     # Validate provider
     if provider not in ["openai", "huggingface"]:
         raise ValueError("Invalid provider. Choose 'openai' or 'huggingface'.")
 
-    # Initialize LLM based on provider
+    # Initialize LLM
     if provider == "openai":
         from langchain_openai import ChatOpenAI
         llm = ChatOpenAI(model="gpt-4o", temperature=0)
@@ -192,27 +189,35 @@ def build_graph(provider: str = "openai"):
 
     # Define nodes
     def assistant(state: MessagesState):
-        """Assistant node"""
-        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+        """Assistant node - generates responses"""
+        # Get current messages
+        messages = state["messages"]
+        # Generate response using LLM
+        response = llm_with_tools.invoke(messages)
+        # Append new message to state
+        return {"messages": messages + [response]}
     
     def retriever(state: MessagesState):
         """Retriever node - provides context from vector store"""
-        query = state["messages"][-1].content
+        # Get current messages
+        messages = state["messages"]
+        # Last message is the user query
+        query = messages[-1].content
+        
+        # Retrieve similar documents
         similar_docs = vector_store.similarity_search(query, k=1)
         
         if not similar_docs:
-            return {"messages": [AIMessage(content="No relevant information found")]}
-            
-        similar_doc = similar_docs[0]
-        content = similar_doc.page_content
+            # Return original messages if no context found
+            return {"messages": messages}
         
-        # Extract answer if formatted, otherwise use full content
-        if "Final answer :" in content:
-            answer = content.split("Final answer :")[-1].strip()
-        else:
-            answer = content.strip()
-
-        return {"messages": [AIMessage(content=answer)]}
+        # Get context from first document
+        context = similar_docs[0].page_content
+        # Create system message with context
+        context_msg = SystemMessage(content=f"Reference context:\n{context}")
+        
+        # Append context to messages
+        return {"messages": messages + [context_msg]}
 
     # Build graph
     builder = StateGraph(MessagesState)
@@ -230,9 +235,76 @@ def build_graph(provider: str = "openai"):
         tools_condition,
         {"continue": "tools", "end": END}
     )
-    builder.add_edge("tools", "assistant")
+    builder.add_edge("tools", "retriever")  # Go back to retriever after tools
     
     return builder.compile()
+
+
+# def build_graph(provider: str = "openai"):
+#     """Build the graph using OpenAI or Hugging Face"""
+#     # Validate provider
+#     if provider not in ["openai", "huggingface"]:
+#         raise ValueError("Invalid provider. Choose 'openai' or 'huggingface'.")
+
+#     # Initialize LLM based on provider
+#     if provider == "openai":
+#         from langchain_openai import ChatOpenAI
+#         llm = ChatOpenAI(model="gpt-4o", temperature=0)
+#     else:  # huggingface
+#         from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+#         llm = ChatHuggingFace(
+#             llm=HuggingFaceEndpoint(
+#                 endpoint_url="https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct",
+#                 temperature=0,
+#             )
+#         )
+
+#     # Bind tools to LLM
+#     llm_with_tools = llm.bind_tools(tools)
+
+#     # Define nodes
+#     def assistant(state: MessagesState):
+#         """Assistant node"""
+#         return {"messages": [llm_with_tools.invoke(state["messages"])]}
+    
+#     def retriever(state: MessagesState):
+#         """Retriever node - provides context from vector store"""
+#         query = state["messages"][-1].content
+#         similar_docs = vector_store.similarity_search(query, k=1)
+        
+#         if not similar_docs:
+#             return {"messages": [AIMessage(content="No relevant information found")]}
+            
+#         similar_doc = similar_docs[0]
+#         content = similar_doc.page_content
+        
+#         # Extract answer if formatted, otherwise use full content
+#         if "Final answer :" in content:
+#             answer = content.split("Final answer :")[-1].strip()
+#         else:
+#             answer = content.strip()
+
+#         return {"messages": [AIMessage(content=answer)]}
+
+#     # Build graph
+#     builder = StateGraph(MessagesState)
+    
+#     # Add nodes
+#     builder.add_node("retriever", retriever)
+#     builder.add_node("assistant", assistant)
+#     builder.add_node("tools", ToolNode(tools))
+    
+#     # Set up edges
+#     builder.set_entry_point("retriever")
+#     builder.add_edge("retriever", "assistant")
+#     builder.add_conditional_edges(
+#         "assistant",
+#         tools_condition,
+#         {"continue": "tools", "end": END}
+#     )
+#     builder.add_edge("tools", "assistant")
+    
+#     return builder.compile()
 # def build_graph(provider: str = "google"):
 #     """Build the graph"""
 #     # Load environment variables from .env file