# rag_chain.py
import os
import weaviate
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Weaviate
from langchain.chains import RetrievalQA

def build_rag_chain():
    client = weaviate.Client(
        url=os.getenv("WEAVIATE_URL"),
        auth_client_secret=weaviate.AuthApiKey(os.getenv("WEAVIATE_API_KEY")),
        additional_headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY")}
    )

    vectorstore = Weaviate(
        client=client,
        index_name="LangDocs",
        text_key="text",
        embedding=OpenAIEmbeddings()
    )

    retriever = vectorstore.as_retriever()
    llm = ChatOpenAI(temperature=0)

    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever)