So, my question is that whenever I ask question to my chatbot it gave me answer based on saved vector which is in chroma db.
I have tried this, but it did not work: -https://python.langchain.com/docs/how_to/indexing/

def my_chain(project_id, f_tag):
    # Load documents
    raw_documents = load_documents_from_db(project_id)
    if not raw_documents:
        raise ValueError(
            "No documents were loaded from the database. Please check the database connection and query.")

     # Load edges
    connections = load_edges_from_db(project_id)
    # if not connections:
    #     raise ValueError(
    #         "No connections were loaded from the database. Please check the database connection and query.")
    # print('edges',connections)  

    if not texts:
        raise ValueError(
            "No texts were created from the documents. Please check the document splitting logic.")

    # Use the document graph to enhance page content with node connections
    for doc in texts:
        node_id = doc.metadata.get('id')
        print("node_id", node_id)  

    # Initialize embeddings and vector store with IDs
    embeddings = OpenAIEmbeddings(
        model="text-embedding-3-small", openai_api_key=os.getenv("OPENAI_API_KEY"))

    vector_store = Chroma.from_documents(
        texts,
        embeddings,
        collection_name=str(f"tale_teller_{project_id}"),
        #ids=[doc.metadata['id'] for doc in texts]
    )

so, what I like to do is whenever user ask question related to content it should be based on latest content rather than saved vector in chroma DB.