rag_101/rag.py

import time
from typing import List, Optional, Union

from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from retriever import (
    create_parent_retriever,
    load_embedding_model,
    load_pdf,
    load_reranker_model,
    retrieve_context,
)


def main(
    file: str = "2401.08406v3.pdf",
    llm_name="mistral",
):
    docs = load_pdf(files=file)

    embedding_model = load_embedding_model()
    retriever = create_parent_retriever(docs, embedding_model)
    reranker_model = load_reranker_model()

    llm = ChatOllama(model=llm_name)
    prompt_template = ChatPromptTemplate.from_template(
        (
            "Please answer the following question based on the provided `context` that follows the question.\n"
            "If you do not know the answer then just say 'I do not know'\n"
            "question: {question}\n"
            "context: ```{context}```\n"
        )
    )
    chain = prompt_template | llm | StrOutputParser()

    while True:
        query = input("Ask question: ")
        context = retrieve_context(
            query, retriever=retriever, reranker_model=reranker_model
        )[0]
        print("LLM Response: ", end="")
        for e in chain.stream({"context": context[0].page_content, "question": query}):
            print(e, end="")
        print()
        time.sleep(0.1)


if __name__ == "__main__":
    from jsonargparse import CLI

    CLI(main)