run_localGPT_API.py

import logging
import os
import shutil
import subprocess

import torch
from flask import Flask, jsonify, request
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings

# from langchain.embeddings import HuggingFaceEmbeddings
from run_localGPT import load_model
from prompt_template_utils import get_prompt_template

# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from werkzeug.utils import secure_filename

from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME

if torch.backends.mps.is_available():
    DEVICE_TYPE = "mps"
elif torch.cuda.is_available():
    DEVICE_TYPE = "cuda"
else:
    DEVICE_TYPE = "cpu"

SHOW_SOURCES = True
logging.info(f"Running on: {DEVICE_TYPE}")
logging.info(f"Display Source Documents set to: {SHOW_SOURCES}")

EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE})

# uncomment the following line if you used HuggingFaceEmbeddings in the ingest.py
# EMBEDDINGS = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
# if os.path.exists(PERSIST_DIRECTORY):
#     try:
#         shutil.rmtree(PERSIST_DIRECTORY)
#     except OSError as e:
#         print(f"Error: {e.filename} - {e.strerror}.")
# else:
#     print("The directory does not exist")

# run_langest_commands = ["python", "ingest.py"]
# if DEVICE_TYPE == "cpu":
#     run_langest_commands.append("--device_type")
#     run_langest_commands.append(DEVICE_TYPE)

# result = subprocess.run(run_langest_commands, capture_output=True)
# if result.returncode != 0:
#     raise FileNotFoundError(
#         "No files were found inside SOURCE_DOCUMENTS, please put a starter file inside before starting the API!"
#     )

# load the vectorstore
DB = Chroma(
    persist_directory=PERSIST_DIRECTORY,
    embedding_function=EMBEDDINGS,
    client_settings=CHROMA_SETTINGS,
)

RETRIEVER = DB.as_retriever()

LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME)
prompt, memory = get_prompt_template(promptTemplate_type="llama", history=False)

QA = RetrievalQA.from_chain_type(
    llm=LLM,
    chain_type="stuff",
    retriever=RETRIEVER,
    return_source_documents=SHOW_SOURCES,
    chain_type_kwargs={
        "prompt": prompt,
    },
)

app = Flask(__name__)


@app.route("/api/delete_source", methods=["GET"])
def delete_source_route():
    folder_name = "SOURCE_DOCUMENTS"

    if os.path.exists(folder_name):
        shutil.rmtree(folder_name)

    os.makedirs(folder_name)

    return jsonify({"message": f"Folder '{folder_name}' successfully deleted and recreated."})


@app.route("/api/save_document", methods=["GET", "POST"])
def save_document_route():
    if "document" not in request.files:
        return "No document part", 400
    file = request.files["document"]
    if file.filename == "":
        return "No selected file", 400
    if file:
        filename = secure_filename(file.filename)
        folder_path = "SOURCE_DOCUMENTS"
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)
        file_path = os.path.join(folder_path, filename)
        file.save(file_path)
        return "File saved successfully", 200


@app.route("/api/run_ingest", methods=["GET"])
def run_ingest_route():
    global DB
    global RETRIEVER
    global QA
    try:
        if os.path.exists(PERSIST_DIRECTORY):
            try:
                shutil.rmtree(PERSIST_DIRECTORY)
            except OSError as e:
                print(f"Error: {e.filename} - {e.strerror}.")
        else:
            print("The directory does not exist")

        run_langest_commands = ["python", "ingest.py"]
        if DEVICE_TYPE == "cpu":
            run_langest_commands.append("--device_type")
            run_langest_commands.append(DEVICE_TYPE)

        result = subprocess.run(run_langest_commands, capture_output=True)
        if result.returncode != 0:
            return "Script execution failed: {}".format(result.stderr.decode("utf-8")), 500
        # load the vectorstore
        DB = Chroma(
            persist_directory=PERSIST_DIRECTORY,
            embedding_function=EMBEDDINGS,
            client_settings=CHROMA_SETTINGS,
        )
        RETRIEVER = DB.as_retriever()
        prompt, memory = get_prompt_template(promptTemplate_type="llama", history=False)

        QA = RetrievalQA.from_chain_type(
            llm=LLM,
            chain_type="stuff",
            retriever=RETRIEVER,
            return_source_documents=SHOW_SOURCES,
            chain_type_kwargs={
                "prompt": prompt,
            },
        )
        return "Script executed successfully: {}".format(result.stdout.decode("utf-8")), 200
    except Exception as e:
        return f"Error occurred: {str(e)}", 500


@app.route("/api/prompt_route", methods=["GET", "POST"])
def prompt_route():
    global QA
    user_prompt = request.form.get("user_prompt")
    if user_prompt:
        # print(f'User Prompt: {user_prompt}')
        # Get the answer from the chain
        res = QA(user_prompt)
        answer, docs = res["result"], res["source_documents"]

        prompt_response_dict = {
            "Prompt": user_prompt,
            "Answer": answer,
        }

        prompt_response_dict["Sources"] = []
        for document in docs:
            prompt_response_dict["Sources"].append(
                (os.path.basename(str(document.metadata["source"])), str(document.page_content))
            )

        return jsonify(prompt_response_dict), 200
    else:
        return "No user prompt received", 400


if __name__ == "__main__":
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s", level=logging.INFO
    )
    app.run(debug=False, port=5110)