Upload 2 files

agent.py

@@ -7,7 +7,7 @@ from langgraph.prebuilt import ToolNode
 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.tools.tavily_search import TavilySearch
 from langchain_community.document_loaders import WikipediaLoader
 from langchain_community.document_loaders import ArxivLoader
 from langchain_community.vectorstores import SupabaseVectorStore
@@ -143,7 +143,7 @@ vector_store = SupabaseVectorStore(
     table_name="documents",
     query_name="match_documents_langchain",
 )
-create_retriever_tool = create_retriever_tool(
+retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),
     name="Question Search",
     description="A tool to retrieve similar questions from a vector store.",
@@ -160,7 +160,8 @@ tools = [
     wiki_search,
     web_search,
     arvix_search,
-    wolfram_alpha_query
+    wolfram_alpha_query,
+    retriever_tool
 ]
 
 # Build graph function
@@ -169,10 +170,10 @@ def build_graph(provider: str = "openai"):
     # Load environment variables from .env file
     if provider == "openai":
         from langchain.chat_models import ChatOpenAI
-    llm = ChatOpenAI(model_name="gpt-4", temperature=0)
-        elif provider == "anthropic":
-    from langchain.chat_models import ChatAnthropic
-    llm = ChatAnthropic(model="claude-v1", temperature=0)
+        llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+    elif provider == "anthropic":
+        from langchain.chat_models import ChatAnthropic
+        llm = ChatAnthropic(model="claude-v1", temperature=0)
     if provider == "google":
         # Google Gemini
         llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)