app.py

# Install necessary packages
#!pip install streamlit
#!pip install wikipedia
#!pip install langchain_community
#!pip install sentence-transformers
#!pip install chromadb
#!pip install huggingface_hub
#!pip install transformers

import streamlit as st
from langchain_community.document_loaders import WikipediaLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter, SentenceTransformersTokenTextSplitter
import chromadb
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
from huggingface_hub import login, InferenceClient
from sentence_transformers import CrossEncoder
import numpy as np
import random
import string

# User variables
topic = st.sidebar.text_input("Enter the Wikipedia topic:", "Japanese History")
#query = st.sidebar.text_input("Enter your first query:", "First query")
model_name = 'mistralai/Mistral-7B-Instruct-v0.3'
HF_TOKEN = st.sidebar.text_input("Enter your Hugging Face token:", "", type="password")

# Initialize session state for error message
if 'error_message' not in st.session_state:
    st.session_state.error_message = ""

# Function to validate token
def validate_token(token):
    try:
        # Attempt to log in with the provided token
        login(token=token)
        # Check if the token is valid by trying to access some data
        HfApi().whoami()
        return True
    except Exception as e:
        return False

# Validate the token and display appropriate message
if HF_TOKEN:
    if validate_token(HF_TOKEN):
        st.session_state.error_message = ""  # Clear error message if the token is valid
        st.sidebar.success("Token is valid!")
    else:
        st.session_state.error_message = "Invalid token. Please try again."
        st.sidebar.error(st.session_state.error_message)
elif st.session_state.error_message:
    st.sidebar.error(st.session_state.error_message)

# Memory for chat history
if "history" not in st.session_state:
    st.session_state.history = []

# Function to generate a random string for collection name
def generate_random_string(max_length=60):
    if max_length > 60:
        raise ValueError("The maximum length cannot exceed 60 characters.")
    length = random.randint(1, max_length)
    characters = string.ascii_letters + string.digits
    return ''.join(random.choice(characters) for _ in range(length))

collection_name = generate_random_string()

# Function for query expansion
def augment_multiple_query(query):
    client = InferenceClient(model_name, token=HF_TOKEN)
    content = client.chat_completion(
        messages=[
            {
                "role": "system",
                "content": f"""You are a helpful expert in {topic}. Your users are asking questions about {topic}.
                Suggest up to five additional related questions to help them find the information they need for the provided question.
                Suggest only short questions without compound sentences. Suggest a variety of questions that cover different aspects of the topic.
                Make sure they are complete questions, and that they are related to the original question."""
            },
            {
                "role": "user",
                "content": query
            }
        ],
        max_tokens=500,
    )
    return content.choices[0].message.content.split("\n")

# Function to handle RAG-based question answering
def rag_advanced(user_query):
    # Document Loading
    docs = WikipediaLoader(query=topic).load()

    # Text Splitting
    character_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n", ". ", " ", ""], chunk_size=1000, chunk_overlap=0)
    concat_texts = "".join([doc.page_content for doc in docs])
    character_split_texts = character_splitter.split_text(concat_texts)

    token_splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0, tokens_per_chunk=256)
    token_split_texts = [text for text in character_split_texts for text in token_splitter.split_text(text)]

    # Embedding and Document Storage
    embedding_function = SentenceTransformerEmbeddingFunction()
    chroma_client = chromadb.Client()
    chroma_collection = chroma_client.create_collection(collection_name, embedding_function=embedding_function)

    ids = [str(i) for i in range(len(token_split_texts))]
    chroma_collection.add(ids=ids, documents=token_split_texts)

    # Document Retrieval
    augmented_queries = augment_multiple_query(user_query)
    joint_query = [user_query] + augmented_queries

    results = chroma_collection.query(query_texts=joint_query, n_results=5, include=['documents', 'embeddings'])
    retrieved_documents = results['documents']

    unique_documents = list(set(doc for docs in retrieved_documents for doc in docs))

    # Re-Ranking
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
    pairs = [[user_query, doc] for doc in unique_documents]
    scores = cross_encoder.predict(pairs)

    top_indices = np.argsort(scores)[::-1][:5]
    top_documents = [unique_documents[idx] for idx in top_indices]

    # LLM Reference
    client = InferenceClient(model_name, token=HF_TOKEN)
    response = ""
    for message in client.chat_completion(
        messages=[
            {
                "role": "system",
                "content": f"""You are a helpful expert in {topic}. Your users are asking questions about {topic}.
                You will be shown the user's questions, and the relevant information from the documents related to {topic}.
                Answer the user's question using only this information."""
            },
            {
                "role": "user",
                "content": f"Questions: {user_query}. \n Information: {top_documents}"
            }
        ],
        max_tokens=500,
        stream=True,
    ):
        response += message.choices[0].delta.content

    return response

# Streamlit UI
st.title("Wikipedia RAG Chatbot")
st.markdown("Choose a topic. Don't forget to put your 🤗 token!")
st.link_button("Get Token Here", "https://huggingface.co/settings/tokens")

# Input box for the user to type their message
user_input = st.text_input("You: ", "")

# Generate response and update conversation history
if user_input:
    response = rag_advanced(user_input)
    st.session_state.history.append({"user": user_input, "bot": response})

# Display the conversation history
for chat in st.session_state.history:
    st.write(f"You: {chat['user']}")
    st.write(f"Bot: {chat['bot']}")


st.markdown("-----------------")
st.markdown("What is this app?")
st.markdown("""This is a simple RAG application using Wikipedia API.  
The model for chat is Mistral-7B-Instruct-v0.3.  
Main libraries: Langchain (text splitting), Chromadb (vector store)  
This RAG uses query expansion and re-ranking to improve the quality.  
Feel free to check the files or DM me for any questions. Thank you."""
st.markdown("[Current agenda] Creating fallback for fuzzy keywords for wikipedia search")