models/llamaCustom.py

import os
import pickle
from json import dumps, loads
from typing import Any, List, Mapping, Optional

import numpy as np
import openai
import pandas as pd
import streamlit as st
from dotenv import load_dotenv
from huggingface_hub import HfFileSystem, Repository
from llama_index import (
    Document,
    GPTVectorStoreIndex,
    LLMPredictor,
    PromptHelper,
    ServiceContext,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage,
)
from llama_index.llms import CompletionResponse, CustomLLM, LLMMetadata

# from langchain.llms.base import LLM
from llama_index.prompts import Prompt
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, pipeline

# from utils.customLLM import CustomLLM

load_dotenv()
# openai.api_key = os.getenv("OPENAI_API_KEY")

fs = HfFileSystem()

# define prompt helper
# set maximum input size
CONTEXT_WINDOW = 2048
# set number of output tokens
NUM_OUTPUT = 525
# set maximum chunk overlap
CHUNK_OVERLAP_RATIO = 0.2

prompt_helper = PromptHelper(
    context_window=CONTEXT_WINDOW,
    num_output=NUM_OUTPUT,
    chunk_overlap_ratio=CHUNK_OVERLAP_RATIO,
)

text_qa_template_str = (
    "Context information is below.\n"
    "---------------------\n"
    "{context_str}\n"
    "---------------------\n"
    "Using both the context information and also using your own knowledge, "
    "answer the question: {query_str}\n"
    "If the question is relevant, you can answer by providing the name of the chapter, the article and the title to the answer. In addition, you can add the page number of the document when you found the answer.\n"
    "If the context isn't helpful, you can also answer the question on your own.\n"
)
text_qa_template = Prompt(text_qa_template_str)

refine_template_str = (
    "The original question is as follows: {query_str}\n"
    "We have provided an existing answer: {existing_answer}\n"
    "We have the opportunity to refine the existing answer "
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{context_msg}\n"
    "------------\n"
    "Using both the new context and your own knowledege, update or repeat the existing answer.\n"
)
refine_template = Prompt(refine_template_str)


@st.cache_resource
def load_model(mode_name: str):
    # llm_model_name = "bigscience/bloom-560m"
    tokenizer = AutoTokenizer.from_pretrained(mode_name)
    model = AutoModelForCausalLM.from_pretrained(mode_name, config="T5Config")

    pipe = pipeline(
        task="text-generation",
        model=model,
        tokenizer=tokenizer,
        # device=0, # GPU device number
        # max_length=512,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
    )

    return pipe


class OurLLM(CustomLLM):
    def __init__(self, model_name: str, model_pipeline):
        self.model_name = model_name
        self.pipeline = model_pipeline

    @property
    def metadata(self) -> LLMMetadata:
        """Get LLM metadata."""
        return LLMMetadata(
            context_window=CONTEXT_WINDOW,
            num_output=NUM_OUTPUT,
            model_name=self.model_name,
        )

    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        prompt_length = len(prompt)
        response = self.pipeline(prompt, max_new_tokens=NUM_OUTPUT)[0]["generated_text"]

        # only return newly generated tokens
        text = response[prompt_length:]
        return CompletionResponse(text=text)

    def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        raise NotImplementedError()

    # def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
    #     prompt_length = len(prompt)
    #     response = self.pipeline(prompt, max_new_tokens=525)[0]["generated_text"]

    #     # only return newly generated tokens
    #     return response[prompt_length:]

    # @property
    # def _identifying_params(self) -> Mapping[str, Any]:
    #     return {"name_of_model": self.model_name}

    # @property
    # def _llm_type(self) -> str:
    #     return "custom"


@st.cache_resource
class LlamaCustom:
    # define llm
    # llm_predictor = LLMPredictor(llm=OurLLM())
    # service_context = ServiceContext.from_defaults(
    #     llm_predictor=llm_predictor, prompt_helper=prompt_helper
    # )

    def __init__(self, model_name: str) -> None:
        pipe = load_model(mode_name=model_name)
        llm = OurLLM(model_name=model_name, model_pipeline=pipe)
        self.service_context = ServiceContext.from_defaults(
            llm=llm, prompt_helper=prompt_helper
        )
        self.vector_index = self.initialize_index(model_name=model_name)

    def initialize_index(self, model_name: str):
        index_name = model_name.split("/")[-1]

        file_path = f"./vectorStores/{index_name}"

        if os.path.exists(path=file_path):
            # rebuild storage context
            storage_context = StorageContext.from_defaults(persist_dir=file_path)

            # local load index access
            index = load_index_from_storage(storage_context)

            # huggingface repo load access
            # with fs.open(file_path, "r") as file:
            #     index = pickle.loads(file.readlines())
            return index
        else:
            # documents = prepare_data(r"./assets/regItems.json")
            documents = SimpleDirectoryReader(input_dir="./assets/pdf").load_data()

            index = GPTVectorStoreIndex.from_documents(
                documents, service_context=self.service_context
            )

            # local write access
            index.storage_context.persist(file_path)

            # huggingface repo write access
            # with fs.open(file_path, "w") as file:
            #     file.write(pickle.dumps(index))
            return index

    def get_response(self, query_str):
        print("query_str: ", query_str)
        # query_engine = self.vector_index.as_query_engine()
        query_engine = self.vector_index.as_query_engine(
            text_qa_template=text_qa_template, refine_template=refine_template
        )
        response = query_engine.query(query_str)
        print("metadata: ", response.metadata)
        return str(response)