Update app.py

app.py

@@ -250,14 +250,15 @@ def should_continue(state: AgentState) -> str:
     return "reason"
 
 
+# ====== NEW IMPORTS ======
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import Chroma
+from langchain_community.vectorstores import FAISS  # Use FAISS instead of Chroma
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_core.documents import Document
 
 # ====== DOCUMENT PROCESSING SETUP ======
 def create_vector_store():
-    """Create vector store with predefined documents"""
+    """Create vector store with predefined documents using FAISS"""
     # Define the documents
     documents = [
         Document(page_content="The capital of France is Paris.", metadata={"source": "geography"}),
@@ -275,13 +276,14 @@ def create_vector_store():
     )
     chunks = text_splitter.split_documents(documents)
     
-    # Create in-memory vector store
-    return Chroma.from_documents(
+    # Create FAISS vector store
+    return FAISS.from_documents(
         documents=chunks,
         embedding=embeddings
     )
 
 
+
 def reasoning_node(state: AgentState) -> AgentState:
     """
     Node for the agent to analyze the question, determine next steps,
@@ -353,23 +355,6 @@ def reasoning_node(state: AgentState) -> AgentState:
     rag_context = "\n\n[Relevant Knowledge]\n"
     rag_context += "\n---\n".join([doc.page_content for doc in relevant_docs])
     
-    # ====== RAG RETRIEVAL ======
-    # Initialize vector store if not present
-    if "vector_store" not in state["context"]:
-        state["context"]["vector_store"] = create_vector_store()
-    
-    vector_store = state["context"]["vector_store"]
-    
-    # Perform retrieval
-    relevant_docs = vector_store.similarity_search(
-        state["question"], 
-        k=3  # Retrieve top 3 most relevant chunks
-    )
-    
-    # Format context for LLM
-    rag_context = "\n\n[Relevant Knowledge]\n"
-    rag_context += "\n---\n".join([doc.page_content for doc in relevant_docs])
-    
     # ====== MODIFIED PROMPT ======
     # Add RAG context to system prompt
     system_prompt = (
@@ -563,7 +548,7 @@ class BasicAgent:
         self.workflow = create_agent_workflow(self.tools)
     
     def __call__(self, question: str) -> str:
-        print(f"\n--- Agent received question: {question[:80]}{'...' if len(question) > 80 else ''} ---")
+        print(f"\n--- Agent received question: {question[:50]}{'...' if len(question) > 50 else ''} ---")
 
         state = {
             "question": question,