privateGPT.py

#!/usr/bin/env python3
from constants import CHROMA_SETTINGS
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, LlamaCpp, OpenAI

import os
import argparse
import time

load_dotenv()

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')
model_n_batch = int(os.environ.get('MODEL_N_BATCH', 8))
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS', 4))


def main():
    # Parse the command line arguments
    args = parse_arguments()
    # embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
    embeddings = OpenAIEmbeddings()

    db = Chroma(persist_directory=persist_directory,
                embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
    # activate/deactivate the streaming StdOut callback for LLMs
    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
    # Prepare the LLM
    match model_type:
        case "LlamaCpp":
            llm = LlamaCpp(model_path=model_path, n_ctx=model_n_ctx,
                           n_batch=model_n_batch, callbacks=callbacks, verbose=False)
        case "GPT4All":
            llm = GPT4All(model=model_path, n_ctx=model_n_ctx, backend='gptj',
                          n_batch=model_n_batch, callbacks=callbacks, verbose=False)
        case "OpenAI":
            llm = OpenAI(model_name=model_path)
        case _default:
            # raise exception if model_type is not supported
            raise Exception(
                f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All")

    qa = RetrievalQA.from_chain_type(
        llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=not args.hide_source)
    # Interactive questions and answers
    while True:
        query = input("\nEnter a query: ")
        if query == "exit":
            break
        if query.strip() == "":
            continue

        # Get the answer from the chain
        start = time.time()
        res = qa(query)
        answer, docs = res['result'], [
        ] if args.hide_source else res['source_documents']
        end = time.time()

        # Print the result
        print("\n\n> Question:")
        print(query)
        print(f"\n> Answer (took {round(end - start, 2)} s.):")
        print(answer)

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


def parse_arguments():
    parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
                                                 'using the power of LLMs.')
    parser.add_argument("--hide-source", "-S", action='store_true',
                        help='Use this flag to disable printing of source documents used for answers.')

    parser.add_argument("--mute-stream", "-M",
                        action='store_true',
                        help='Use this flag to disable the streaming StdOut callback for LLMs.')

    return parser.parse_args()


if __name__ == "__main__":
    main()