from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, LlamaCpp
import os

# embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
# persist_directory = os.environ.get('PERSIST_DIRECTORY')

# model_type = os.environ.get('MODEL_TYPE')
# model_path = os.environ.get('MODEL_PATH')
# model_n_ctx = os.environ.get('MODEL_N_CTX')

from constants import CHROMA_SETTINGS

def main():
    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
    retriever = db.as_retriever()
    # Prepare the LLM
    callbacks = [StreamingStdOutCallbackHandler()]
    match model_type:
        case "LlamaCpp":
            llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx, callbacks=callbacks, verbose=False)
        case "GPT4All":
            llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj', callbacks=callbacks, verbose=False)
        case _default:
            print(f"Model {model_type} not supported!")
            exit;
    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
    # Interactive questions and answers
    while True:
        query = input("\nEnter a query: ")
        if query == "exit":
            break
        
        # Get the answer from the chain
        res = qa(query)    
        answer, docs = res['result'], res['source_documents']

        # Print the result
        print("\n\n> Question:")
        print(query)
        print("\n> Answer:")
        print(answer)
        
        # Print the relevant sources used for the answer
        for document in docs:
            print("\n> " + document.metadata["source"] + ":")
            print(document.page_content)

if __name__ == "__main__":
    main()