Update agent.py

agent.py

@@ -21,7 +21,6 @@ load_dotenv()
 @tool
 def multiply(a: int, b: int) -> int:
     """Multiply two numbers.
-
     Args:
         a: first int
         b: second int
@@ -152,7 +151,7 @@ tools = [
 ]
 
 # Build graph function
-def build_graph(provider: str = "groq"):
+def build_graph(provider: str = "google"):
     """Build the graph"""
     # Load environment variables from .env file
     if provider == "google":
@@ -179,36 +178,46 @@ def build_graph(provider: str = "groq"):
         """Assistant node"""
         return {"messages": [llm_with_tools.invoke(state["messages"])]}
     
+    # def retriever(state: MessagesState):
+      #  """Retriever node"""
+       # similar_question = vector_store.similarity_search(state["messages"][0].content)
+        #example_msg = HumanMessage(
+         #   content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
+       # )
+       # return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+
+    from langchain_core.messages import AIMessage
+
     def retriever(state: MessagesState):
-        """Retriever node"""
-        similar_question = vector_store.similarity_search(state["messages"][0].content)
-        example_msg = HumanMessage(
-            content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
-        )
-        return {"messages": [sys_msg] + state["messages"] + [example_msg]}
+        query = state["messages"][-1].content
+        similar_doc = vector_store.similarity_search(query, k=1)[0]
+
+        content = similar_doc.page_content
+        if "Final answer :" in content:
+            answer = content.split("Final answer :")[-1].strip()
+        else:
+            answer = content.strip()
+
+        return {"messages": [AIMessage(content=answer)]}
+
+   # builder = StateGraph(MessagesState)
+    #builder.add_node("retriever", retriever)
+    #builder.add_node("assistant", assistant)
+    #builder.add_node("tools", ToolNode(tools))
+    #builder.add_edge(START, "retriever")
+    #builder.add_edge("retriever", "assistant")
+    #builder.add_conditional_edges(
+     #   "assistant",
+      #  tools_condition,
+    #)
+    #builder.add_edge("tools", "assistant")
 
     builder = StateGraph(MessagesState)
     builder.add_node("retriever", retriever)
-    builder.add_node("assistant", assistant)
-    builder.add_node("tools", ToolNode(tools))
-    builder.add_edge(START, "retriever")
-    builder.add_edge("retriever", "assistant")
-    builder.add_conditional_edges(
-        "assistant",
-        tools_condition,
-    )
-    builder.add_edge("tools", "assistant")
+
+    # Retriever ist Start und Endpunkt
+    builder.set_entry_point("retriever")
+    builder.set_finish_point("retriever")
 
     # Compile graph
-    return builder.compile()
-
-# test
-if __name__ == "__main__":
-    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
-    # Build the graph
-    graph = build_graph(provider="groq")
-    # Run the graph
-    messages = [HumanMessage(content=question)]
-    messages = graph.invoke({"messages": messages})
-    for m in messages["messages"]:
-        m.pretty_print()
+    return builder.compile()