make my vectorestore more effectire:
import os
import gradio as gr
from langchain_ollama import ChatOllama
from langchain_unstructured import UnstructuredLoader
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.retrievers import ArxivRetriever
from langchain_community.tools import DuckDuckGoSearchRun

# For LangSmith tracing
from langsmith import traceable
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "<TRUFFLEHOG_REDACTED_LANGSMITH>"  # Replace with your actual API key
os.environ["LANGCHAIN_PROJECT"] = "l"  # Optional: "default" is used if not set

# Initialize Llama model
llm = ChatOllama(
    model="llama3.1",
    temperature=0,
    base_url="http://localhost:11434",
    streaming=True
)

# Set up embeddings and vector store
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
persist_directory = "./chroma_db"
vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings)

# Initialize knowledge base retriever
knowledge_base = vectorstore.as_retriever(search_kwargs={"k": 3})

# Function to update the knowledge base
@traceable
def update_knowledge_base(uploaded_files, website_urls):
    docs = []
    # Load documents from uploaded files
    if uploaded_files:
        for uploaded_file in uploaded_files:
            loader = UnstructuredLoader(file_path=uploaded_file.name)
            docs.extend(loader.load())
    # Load documents from URLs
    if website_urls:
        urls = website_urls.split(',')
        for url in urls:
            url = url.strip()
            if url:
                loader = UnstructuredLoader(web_url=url)
                docs.extend(loader.load())
    # Split documents and add to vector store
    if docs:
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
        split_docs = text_splitter.split_documents(docs)
        vectorstore.add_documents(split_docs)
    return "Knowledge base updated."

# Function to process uploaded files in the chat and include their content
def process_uploaded_files(files):
    content = ""
    if files:
        for file in files:
            if file.name.endswith(".pdf") or file.name.endswith(".txt"):
                loader = UnstructuredLoader(file_path=file.name)
                docs = loader.load()
                content += "\n".join([doc.page_content for doc in docs])
    return content

# Chat with Llama (normal chat) function
@traceable
def chat_with_llama(message, history, uploaded_files):
    # Process uploaded files and include their content
    file_content = process_uploaded_files(uploaded_files)
    # Convert history to list of messages
    messages = [SystemMessage(content="You are a helpful assistant.")]
    if history:
        for user_msg, assistant_msg in history:
            messages.append(HumanMessage(content=user_msg))
            messages.append(AIMessage(content=assistant_msg))
    # Append file content to user message if any
    if file_content:
        message += f"\n\nHere is some additional content:\n{file_content}"
    messages.append(HumanMessage(content=message))
    # Stream the response from the model
    try:
        response_stream = llm.stream(messages)
        partial_response = ""
        for chunk in response_stream:
            partial_response += chunk.content
            yield partial_response
    except Exception as e:
        yield f"Error: {str(e)}"

# Function to handle knowledge base Q&A
@traceable
def knowledge_base_chat(message, history):
    # Search in the knowledge base
    relevant_docs = knowledge_base.get_relevant_documents(message)
    if not relevant_docs:
        yield "No relevant information found in the knowledge base."
        return
    context = "\n\n".join([doc.page_content for doc in relevant_docs])
    prompt = f"Context:\n{context}\n\nQuestion:\n{message}\n\nAnswer:"
    messages = [HumanMessage(content=prompt)]
    # Stream the response from the model
    try:
        response_stream = llm.stream(messages)
        partial_response = ""
        for chunk in response_stream:
            partial_response += chunk.content
            yield partial_response
    except Exception as e:
        yield f"Error: {str(e)}"

# Function for the Internet-Enabled LLM
@traceable
def internet_access_llm(message, history, use_wikipedia):
    # Generate a query from the user input
    query = message
    # Search the internet
    search_tool = DuckDuckGoSearchRun()
    search_results = search_tool.invoke(query)
    # Optionally search Wikipedia
    if use_wikipedia:
        wikipedia_tool = DuckDuckGoSearchRun(backend="wikipedia")
        wiki_results = wikipedia_tool.invoke(query)
        search_results += "\n\n" + wiki_results
    # Combine search results and prepare the prompt
    prompt = f"Search Results:\n{search_results}\n\nQuestion:\n{message}\n\nAnswer:"
    messages = [HumanMessage(content=prompt)]
    # Stream the response from the model
    try:
        response_stream = llm.stream(messages)
        partial_response = ""
        for chunk in response_stream:
            partial_response += chunk.content
            yield partial_response
    except Exception as e:
        yield f"Error: {str(e)}"

# Function to retrieve arXiv papers and update knowledge base
@traceable
def arxiv_research(query, max_papers):
    retriever = ArxivRetriever(load_max_docs=int(max_papers), get_full_documents=True)
    docs = retriever.get_relevant_documents(query)
    # Add documents to the knowledge base
    if docs:
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
        split_docs = text_splitter.split_documents(docs)
        vectorstore.add_documents(split_docs)
    return f"Added {len(docs)} papers to the knowledge base."

# Function to handle arXiv Knowledge Base Q&A
@traceable
def ask_arxiv_knowledge_base(message, history):
    # Search in the knowledge base
    relevant_docs = knowledge_base.get_relevant_documents(message)
    if not relevant_docs:
        yield "No relevant information found in the arXiv knowledge base."
        return
    context = "\n\n".join([doc.page_content for doc in relevant_docs])
    prompt = f"Context:\n{context}\n\nQuestion:\n{message}\n\nAnswer:"
    messages = [HumanMessage(content=prompt)]
    # Stream the response from the model
    try:
        response_stream = llm.stream(messages)
        partial_response = ""
        for chunk in response_stream:
            partial_response += chunk.content
            yield partial_response
    except Exception as e:
        yield f"Error: {str(e)}"

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Multi-Tab Gradio App with Llama")
    # Chat with Llama Tab
    with gr.Tab("Chat with Llama"):
        gr.Markdown("## Chat Interface")
        with gr.Row():
            upload_files = gr.File(
                label="Upload PDFs or TXT files",
                file_types=[".pdf", ".txt"],
                file_count="multiple",
                visible=True
            )
        gr.ChatInterface(
            fn=chat_with_llama,
            title="Chat with Llama",
            additional_inputs=[upload_files]
        )
    # Knowledge Base Tab
    with gr.Tab("Knowledge Base"):
        gr.Markdown("## Knowledge Base Management and Chat")
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("### Update Knowledge Base")
                kb_upload_files = gr.File(
                    label="Upload Files",
                    file_types=["text", "pdf", "docx"],
                    file_count="multiple"
                )
                kb_website_urls = gr.Textbox(
                    label="Website URLs",
                    placeholder="Enter comma-separated URLs"
                )
                kb_update_button = gr.Button("Update Knowledge Base")
                kb_update_status = gr.Textbox(label="Update Status", interactive=False)
                kb_update_button.click(
                    update_knowledge_base,
                    inputs=[kb_upload_files, kb_website_urls],
                    outputs=kb_update_status
                )
            with gr.Column(scale=2):
                gr.Markdown("### Chat with Knowledge Base")
                gr.ChatInterface(
                    fn=knowledge_base_chat,
                    title="Knowledge Base Chat"
                )
    # LLM with Internet Access Tab
    with gr.Tab("LLM with Internet Access"):
        gr.Markdown("## Internet-Enabled LLM")
        use_wikipedia = gr.Checkbox(label="Use Wikipedia")
        gr.ChatInterface(
            fn=internet_access_llm,
            title="LLM with Internet Access",
            additional_inputs=[use_wikipedia]
        )
    # arXiv Research Papers Tab
    with gr.Tab("arXiv Research Papers"):
        gr.Markdown("## Retrieve arXiv Papers")
        arxiv_query = gr.Textbox(
            label="Search Query",
            placeholder="Enter your search query for arXiv papers"
        )
        max_papers = gr.Slider(
            label="Number of Papers",
            minimum=1,
            maximum=10,
            step=1,
            value=5
        )
        arxiv_button = gr.Button("Retrieve Papers")
        arxiv_result = gr.Textbox(label="Result", interactive=False)
        arxiv_button.click(
            arxiv_research,
            inputs=[arxiv_query, max_papers],
            outputs=arxiv_result
        )
        gr.Markdown("## Chat with arXiv Knowledge Base")
        gr.ChatInterface(
            fn=ask_arxiv_knowledge_base,
            title="arXiv Knowledge Base Chat"
        )

demo.launch()




here is some documentation:
Get started

This walkthrough showcases basic functionality related to vector stores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the text embedding model interfaces before diving into this.

There are many great vector store options, here are a few that are free, open-source, and run entirely on your local machine. Review all integrations for many great hosted offerings.

    Chroma
    Pinecone
    FAISS
    Lance

This walkthrough uses the chroma vector database, which runs on your local machine as a library.

pip install langchain-chroma

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')

from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_chroma import Chroma

# Load the document, split it into chunks, embed each chunk and load it into the vector store.
raw_documents = TextLoader('../../../state_of_the_union.txt').load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
db = Chroma.from_documents(documents, OpenAIEmbeddings())

API Reference:

    TextLoader
    OpenAIEmbeddings
    CharacterTextSplitter

Similarity search

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)

    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

    Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

Similarity search by vector

It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.

embedding_vector = OpenAIEmbeddings().embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector)
print(docs[0].page_content)

The query is the same, and so the result is also the same.

    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

    Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

Asynchronous operations

Vector stores are usually run as a separate service that requires some IO operations, and therefore they might be called asynchronously. That gives performance benefits as you don't waste time waiting for responses from external services. That might also be important if you work with an asynchronous framework, such as FastAPI.

LangChain supports async operation on vector stores. All the methods might be called using their async counterparts, with the prefix a, meaning async.

Qdrant is a vector store, which supports all the async operations, thus it will be used in this walkthrough.

pip install qdrant-client

from langchain_community.vectorstores import Qdrant

API Reference:

    Qdrant

Create a vector store asynchronously

db = await Qdrant.afrom_documents(documents, embeddings, "http://localhost:6333")

Similarity search

query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query)
print(docs[0].page_content)

    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

    Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

    One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

    And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

Similarity search by vector

embedding_vector = embeddings.embed_query(query)
docs = await db.asimilarity_search_by_vector(embedding_vector)

Maximum marginal relevance search (MMR)

Maximal marginal relevance optimizes for similarity to query and diversity among selected documents. It is also supported in async API.

query = "What did the president say about Ketanji Brown Jackson"
found_docs = await qdrant.amax_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
    print(f"{i + 1}.", doc.page_content, "\n")