app.py

print("eeeh")
import asyncio
import json
from websockets.server import serve
import os
from langchain_chroma import Chroma
from langchain_community.embeddings import *
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface.llms import HuggingFaceEndpoint
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import DirectoryLoader
from langchain import hub
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain.chains import create_history_aware_retriever
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from multiprocessing import Process

print()
print("-------")
print("started")
print("-------")

async def echo(websocket):
    async for message in websocket:
        data = json.loads(message)
        if not "message" in message:
            return
        if not "token" in message:
            return
        m = data["message"]
        token = data["token"]
        docs = retriever.get_relevant_documents(m)
        userData[token]["docs"] = str(docs)
        response = conversational_rag_chain.invoke(
            {"input": m},
            config={
                "configurable": {"session_id": token}
            },
        )["answer"]
        await websocket.send(json.dumps({"response": response}))

async def main():
    async with serve(echo, "0.0.0.0", 7860):
        await asyncio.Future()
def g():
    if not os.path.isdir('database'):
        os.system("unzip database.zip")
    
    loader = DirectoryLoader('./database', glob="./*.txt", loader_cls=TextLoader)
    
    documents = loader.load()
    
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    splits = text_splitter.split_documents(documents)
    
    print()
    print("-------")
    print("TextSplitter, DirectoryLoader")
    print("-------")
    
    persist_directory = 'db'
    
    # embedding = HuggingFaceInferenceAPIEmbeddings(api_key=os.environ["HUGGINGFACE_API_KEY"], model=)
    model_name = "BAAI/bge-large-en"
    model_kwargs = {'device': 'cpu'}
    encode_kwargs = {'normalize_embeddings': True}
    embedding = HuggingFaceBgeEmbeddings(
        model_name=model_name,
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs,
        show_progress=True,
    )
    
    print()
    print("-------")
    print("Embeddings")
    print("-------")
    
    vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)
    
    def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)
    
    retriever = vectorstore.as_retriever()
    
    prompt = hub.pull("rlm/rag-prompt")
    llm = HuggingFaceEndpoint(repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1")
    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    
    print()
    print("-------")
    print("Retriever, Prompt, LLM, Rag_Chain")
    print("-------")
    
    ### Contextualize question ###
    contextualize_q_system_prompt = """Given a chat history and the latest user question \
    which might reference context in the chat history, formulate a standalone question \
    which can be understood without the chat history. Do NOT answer the question, \
    just reformulate it if needed and otherwise return it as is."""
    contextualize_q_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", contextualize_q_system_prompt),
            MessagesPlaceholder("chat_history"),
            ("human", "{input}"),
        ]
    )
    history_aware_retriever = create_history_aware_retriever(
        llm, retriever, contextualize_q_prompt
    )
    
    
    ### Answer question ###
    qa_system_prompt = """You are an assistant for question-answering tasks. \
    Use the following pieces of retrieved context to answer the question. \
    If you don't know the answer, just say that you don't know. \
    Use three sentences maximum and keep the answer concise.\
    
    {context}"""
    qa_prompt = ChatPromptTemplate.from_messages(
        [
            ("system", qa_system_prompt),
            MessagesPlaceholder("chat_history"),
            ("human", "{input}"),
        ]
    )
    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
    
    rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
    
    
    ### Statefully manage chat history ###
    store = {}
    
    
    def get_session_history(session_id: str) -> BaseChatMessageHistory:
        if session_id not in store:
            store[session_id] = ChatMessageHistory()
        return store[session_id]
    
    
    conversational_rag_chain = RunnableWithMessageHistory(
        rag_chain,
        get_session_history,
        input_messages_key="input",
        history_messages_key="chat_history",
        output_messages_key="answer",
    )

def f():
    asyncio.run(main())
Process(f).start()
Process(g).start()
"""
websocket
streamlit app ~> backend
{"token": "random", "message": "what is something"} ~> backend
backend ~> {"response": "something is something"}
streamlit app ~> display response
"""