Update agent.py

agent.py

@@ -8,13 +8,13 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_groq import ChatGroq
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
 from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.document_loaders import WikipediaLoader
-from langchain_community.document_loaders import ArxivLoader
-from langchain_community.vectorstores import SupabaseVectorStore
+# from langchain_community.document_loaders import WikipediaLoader
+# from langchain_community.document_loaders import ArxivLoader
+# from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
-from langchain.tools.retriever import create_retriever_tool
-from supabase.client import Client, create_client
+# from langchain.tools.retriever import create_retriever_tool
+# from supabase.client import Client, create_client
 
 load_dotenv()
 
@@ -69,19 +69,19 @@ def modulus(a: int, b: int) -> int:
     """
     return a % b
 
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia for a query and return maximum 2 results.
+# @tool
+# def wiki_search(query: str) -> str:
+#     """Search Wikipedia for a query and return maximum 2 results.
     
-    Args:
-        query: The search query."""
-    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"wiki_results": formatted_search_docs}
+#     Args:
+#         query: The search query."""
+#     search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+#     formatted_search_docs = "\n\n---\n\n".join(
+#         [
+#             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
+#             for doc in search_docs
+#         ])
+#     return {"wiki_results": formatted_search_docs}
 
 @tool
 def web_search(query: str) -> str:
@@ -97,19 +97,19 @@ def web_search(query: str) -> str:
         ])
     return {"web_results": formatted_search_docs}
 
-@tool
-def arvix_search(query: str) -> str:
-    """Search Arxiv for a query and return maximum 3 result.
+# @tool
+# def arvix_search(query: str) -> str:
+#     """Search Arxiv for a query and return maximum 3 result.
     
-    Args:
-        query: The search query."""
-    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
-    formatted_search_docs = "\n\n---\n\n".join(
-        [
-            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
-            for doc in search_docs
-        ])
-    return {"arvix_results": formatted_search_docs}
+#     Args:
+#         query: The search query."""
+#     search_docs = ArxivLoader(query=query, load_max_docs=3).load()
+#     formatted_search_docs = "\n\n---\n\n".join(
+#         [
+#             f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
+#             for doc in search_docs
+#         ])
+#     return {"arvix_results": formatted_search_docs}
 
 
 
@@ -120,22 +120,22 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
 # System message
 sys_msg = SystemMessage(content=system_prompt)
 
-# build a retriever
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
-supabase: Client = create_client(
-    os.environ.get("SUPABASE_URL"), 
-    os.environ.get("SUPABASE_SERVICE_KEY"))
-vector_store = SupabaseVectorStore(
-    client=supabase,
-    embedding= embeddings,
-    table_name="documents",
-    query_name="match_documents_langchain",
-)
-create_retriever_tool = create_retriever_tool(
-    retriever=vector_store.as_retriever(),
-    name="Question Search",
-    description="A tool to retrieve similar questions from a vector store.",
-)
+# # build a retriever
+# embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
+# supabase: Client = create_client(
+#     os.environ.get("SUPABASE_URL"), 
+#     os.environ.get("SUPABASE_SERVICE_KEY"))
+# vector_store = SupabaseVectorStore(
+#     client=supabase,
+#     embedding= embeddings,
+#     table_name="documents",
+#     query_name="match_documents_langchain",
+# )
+# create_retriever_tool = create_retriever_tool(
+#     retriever=vector_store.as_retriever(),
+#     name="Question Search",
+#     description="A tool to retrieve similar questions from a vector store.",
+# )
 
 
 
@@ -145,9 +145,9 @@ tools = [
     subtract,
     divide,
     modulus,
-    wiki_search,
+    # wiki_search,
     web_search,
-    arvix_search,
+    # arvix_search,
 ]
 
 # Build graph function
@@ -178,20 +178,20 @@ 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]}
+    # 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]}
 
     builder = StateGraph(MessagesState)
-    builder.add_node("retriever", retriever)
+    # 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_edge(START, "assistant")
+    # builder.add_edge("retriever", "assistant")
     builder.add_conditional_edges(
         "assistant",
         tools_condition,