privateGPT.py

#!/usr/bin/env python3
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All
import os
import time


load_dotenv()


models_directory = os.environ.get('MODELS_DIRECTORY')
embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
persist_directory = os.environ.get('PERSIST_DIRECTORY')
llm_model_name = os.environ.get('LLM_MODEL_NAME')
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))


from constants import CHROMA_SETTINGS


def main():
    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name, cache_folder=models_directory)
    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})    
    
    # Prepare the LLM
    llm = GPT4All(model=f'./{models_directory}/{llm_model_name}', n_ctx=model_n_ctx, backend='gptj', n_batch=model_n_batch, verbose=False)
    
    # Create a QA chain        
    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
        if query.strip() == "":
            continue

        # Get the answer from the chain
        start = time.time()
        res = qa(query)
        answer, docs = res['result'], 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)


if __name__ == "__main__":
    main()