app.py

import os

# For type hints
from typing import List
from langchain_core.vectorstores import VectorStoreRetriever
from langchain_openai import ChatOpenAI
from chainlit.types import AskFileResponse
from langchain_openai.embeddings import OpenAIEmbeddings

# Libraries to be used
from langchain_community.document_loaders.text import TextLoader
from langchain_community.document_loaders.pdf import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_wrappers.langchain_chat_models import MyChatOpenAI
from langchain_wrappers.langchain_embedding_models import MyOpenAIEmbeddings
from langchain_qdrant import QdrantVectorStore
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
import chainlit as cl
from dotenv import load_dotenv

# Cache
from langchain.globals import set_llm_cache, get_llm_cache 
from langchain_community.cache import InMemoryCache
set_llm_cache(InMemoryCache())

system_template = """\
Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer.\

Context:
{context}    
"""
human_template = """\
Question:
{question}
"""
system_msg = ('system', system_template)
user_msg = ('human', human_template)

text_splitter = RecursiveCharacterTextSplitter()


load_dotenv()

### RAG chain
def Get_RAG_pipeline(retriever: VectorStoreRetriever, llm: ChatOpenAI):
    
    retriever = retriever.with_config({'run_name': 'RAG: Retriever'})
    prompt = ChatPromptTemplate([system_msg, user_msg]).with_config({'run_name': 'RAG Step2: Prompt (Augmented)'})
    llm = llm.with_config({'run_name': 'RAG Step3: LLM (Generation)'})
    
    def get_context(relevant_docs: List):
        context = ""
        for doc in relevant_docs:
            context += doc.page_content + "\n"
        return context
    

    RAG_chain =  RunnableParallel(
        relevant_docs = retriever,
        question = lambda x: x 
    ).with_config({'run_name':'RAG Step1-1: Get relevant docs (Retrieval)'}) | RunnablePassthrough.assign(
        context = lambda x: get_context(x['relevant_docs'])
    ).with_config({'run_name':'RAG Step1-2: Get context (Retrieval)'}) | prompt | llm
    RAG_chain = RAG_chain.with_config({'run_name':'RAG pipeline'})

    return RAG_chain


def process_text_file(file: AskFileResponse):
    import tempfile

    if file.name.endswith('.pdf'):
        print("PDF file detected")
        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".pdf") as temp_file:
            temp_file_path = temp_file.name
        with open(temp_file_path, "wb") as f:
            f.write(file.content)
        document_loader = PyPDFLoader(temp_file_path)
    elif file.name.endswith('.txt'):
        with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
            temp_file_path = temp_file.name
        with open(temp_file_path, "wb") as f:
            f.write(file.content)
        document_loader = TextLoader(temp_file_path, autodetect_encoding=True)
       
    documents = document_loader.load()
    splitted_documents = [x.page_content for x in text_splitter.transform_documents(documents)]
   
    return splitted_documents


@cl.on_chat_start
async def on_chat_start():
    files = None

    # Wait for the user to upload a file
    while files == None:
        files = await cl.AskFileMessage(
            content="Please upload a Text File file to begin!",
            accept=["text/plain", "application/pdf"],  
            max_size_mb=5,
            timeout=180,
        ).send()


    file = files[0] 
    msg = cl.Message(
        content=f"Processing `{file.name}`...", disable_human_feedback=True
    )
    await msg.send()

    # load the file
    texts = process_text_file(file)

    print(f"Processing {len(texts)} text chunks")

    # Create a dict vector store
    vector_db = await QdrantVectorStore.afrom_texts(
        texts, MyOpenAIEmbeddings.from_model('small'), location=":memory:", collection_name="texts"
    )
    

    # Create a chain
    RAG_chain = Get_RAG_pipeline(
        retriever=vector_db.as_retriever(search_kwargs = {'k':3}),
        llm=MyChatOpenAI.from_model()
    )
    
    # Let the user know that the system is ready
    msg.content = f"Processing `{file.name}` done ({len(texts)} chunks in total). You can now ask questions!"
    await msg.update()

    cl.user_session.set("chain", RAG_chain)




@cl.on_message
async def main(message):
    os.environ['LANGSMITH_PROJECT'] = os.getenv('LANGCHAIN_PROJECT')

    chain = cl.user_session.get("chain")

    msg = cl.Message(content="")
    async for stream_resp in chain.astream(message.content):
        await msg.stream_token(stream_resp.content)

    await msg.send()