Untitled

import argparse
# from dataclasses import dataclass
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from pathlib import Path
import os
import openai
import numpy as np

np.float_ = np.float64


CHROMA_PATH = "chroma"

PROMPT_TEMPLATE = """
Answer the question based only on the following context:

{context}

---

Answer the question based on the above context: {question}
"""
load_dotenv(Path(".env"))

# Set OpenAI API key
openai_api_key = os.getenv("OPENAI_API_KEY")

if openai_api_key is None:
    raise ValueError("OpenAI API key not found. Make sure it's set in APIKEY.env.")

openai.api_key = openai_api_key


def main():
    # Create CLI.
    parser = argparse.ArgumentParser()
    parser.add_argument("query_text", type=str, help="The query text.")
    args = parser.parse_args()
    query_text = args.query_text

    # Prepare the DB.
    embedding_function = OpenAIEmbeddings()
    db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)

    # Search the DB.
    results = db.similarity_search_with_relevance_scores(query_text, k=3)
    # if len(results) == 0 or results[0][1] < 0.7:
    #     print(f"Unable to find matching results.")
    #     return

    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
    prompt = prompt_template.format(context=context_text, question=query_text)
    print(prompt)

    model = ChatOpenAI()
    response_text = model.predict(prompt)

    sources = [doc.metadata.get("source", None) for doc, _score in results]
    formatted_response = f"Response: {response_text}\nSources: {sources}"
    print(formatted_response)


if __name__ == "__main__":
    main()