Update agent.py

agent.py

@@ -10,11 +10,11 @@ from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingF
 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.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 supabase.client import Client, create_client
 from langchain_openai import ChatOpenAI
 
 from langchain.tools import Tool
@@ -231,6 +231,7 @@ execute_code_multilang_tool = Tool(
 ### ======================================== RETRIEVER TOOLS ======================================== ###
 
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+"""
 vector_store = Chroma(
     collection_name="gaia_dataset",
     embedding_function=embeddings,
@@ -242,6 +243,23 @@ create_retriever_tool = create_retriever_tool(
     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.",
+)
 
 
 # load the system prompt from the file