app/engine.py

import os
from dotenv import load_dotenv
from langchain_mistralai import ChatMistralAI
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_pinecone import PineconeVectorStore
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate

load_dotenv()

def get_rag_chain():
    index_name = os.getenv("PINECONE_INDEX_NAME", "locus-rag")
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
    
    llm = ChatMistralAI(
        model="mistral-large-latest", 
        temperature=0,
        max_retries=3
    )
    
    from langchain_classic.retrievers.multi_query import MultiQueryRetriever
    
    base_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
    retriever = MultiQueryRetriever.from_llm(
        retriever=base_retriever, 
        llm=llm
    )
    
    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, say that you "
        "don't know. Use three sentences maximum and keep the "
        "answer concise."
        "\n\n"
        "{context}"
    )
    
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            ("human", "{input}"),
        ]
    )
    
    question_answer_chain = create_stuff_documents_chain(llm, prompt)
    rag_chain = create_retrieval_chain(retriever, question_answer_chain)
    
    return rag_chain

if __name__ == "__main__":
    chain = get_rag_chain()
    response = chain.invoke({"input": "What is LOCUS?"})
    print(response["answer"])