Update agent.py

agent.py

@@ -11,6 +11,7 @@ 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.embeddings import HuggingFaceEmbeddings
 from langchain_core.messages import SystemMessage, HumanMessage
 from langchain_core.tools import tool
 from langchain.tools.retriever import create_retriever_tool
@@ -118,19 +119,30 @@ with open("system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read().strip()
 sys_msg = SystemMessage(content=system_prompt)
 
-# Vector Store Retrieval
+
+# Load environment variables
+url = os.environ["SUPABASE_URL"]
+key = os.environ["SUPABASE_SERVICE_KEY"]
+client = create_client(url, key)
+
+# Create embedding model
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-supabase: Client = create_client(
-    os.environ["SUPABASE_URL"],
-    os.environ["SUPABASE_SERVICE_KEY"]
-)
+
+# Sample documents to insert
+docs = [
+    {"content": "Newton's First Law states that an object in motion stays in motion unless acted upon."},
+    {"content": "LangChain enables developers to build context-aware agents using LLMs and tools."},
+    {"content": "Supabase is an open-source alternative to Firebase built on PostgreSQL."}
+]
 vector_store = SupabaseVectorStore(
-    client=supabase,
+    client=client,
     embedding=embeddings,
     table_name="documents",
     query_name="match_documents_langchain"
 )
-
+texts = [doc["content"] for doc in docs]
+vectorstore.add_texts(texts)
+print("✅ Documents successfully embedded and stored.")
 
 retriever_tool = create_retriever_tool(
     retriever=vector_store.as_retriever(),