app_api.py

from flask import Flask, request, jsonify
import os
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from pathlib import Path
import chromadb
from unidecode import unidecode
import re

app = Flask(__name__)

# Configuration variables
PDF_PATH = "https://huggingface.co/spaces/CCCDev/PDFChat/resolve/main/Data-privacy-policy.pdf"  # Replace with your static PDF path
CHUNK_SIZE = 512
CHUNK_OVERLAP = 24
LLM_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
TEMPERATURE = 0.1
MAX_TOKENS = 512
TOP_K = 20

# Load PDF document and create doc splits
def load_doc(pdf_path, chunk_size, chunk_overlap):
    loader = PyPDFLoader(pdf_path)
    pages = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

# Create vector database
def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
    )
    return vectordb

# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        temperature=temperature,
        max_new_tokens=max_tokens,
        top_k=top_k,
    )

    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    retriever = vector_db.as_retriever()
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff",
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    return qa_chain

# Generate collection name for vector database
def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = collection_name.replace(" ", "-")
    collection_name = unidecode(collection_name)
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    collection_name = collection_name[:50]
    if len(collection_name) < 3:
        collection_name = collection_name + 'xyz'
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    return collection_name

# Initialize database and QA chain
doc_splits = load_doc(PDF_PATH, CHUNK_SIZE, CHUNK_OVERLAP)
collection_name = create_collection_name(PDF_PATH)
vector_db = create_db(doc_splits, collection_name)
qa_chain = initialize_llmchain(LLM_MODEL, TEMPERATURE, MAX_TOKENS, TOP_K, vector_db)

@app.route('/chat', methods=['POST'])
def chat():
    data = request.json
    message = data.get('message', '')
    history = data.get('history', [])

    formatted_chat_history = []
    for user_message, bot_message in history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")

    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]

    result = {
        "answer": response_answer,
        "sources": [
            {"content": doc.page_content.strip(), "page": doc.metadata["page"] + 1}
            for doc in response_sources
        ]
    }
    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)