app.py

import os
import torch
from torch import cuda, bfloat16
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
from langchain.llms import HuggingFacePipeline
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
import gradio as gr
from langchain.embeddings import HuggingFaceEmbeddings


# Load the Hugging Face token from environment
HF_TOKEN = os.environ.get("HF_TOKEN", None)

# Define stopping criteria
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stop_ids in stop_token_ids:
            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
                return True
        return False

# Load the LLaMA model and tokenizer
# model_id = 'meta-llama/Meta-Llama-3-8B-Instruct'
model_id= "meta-llama/Llama-2-7b-chat-hf"
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

# Set quantization configuration
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", token=HF_TOKEN, quantization_config=bnb_config)

# Define stopping criteria
stop_list = ['\nHuman:', '\n```\n']
stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
stopping_criteria = StoppingCriteriaList([StopOnTokens()])

# Create text generation pipeline
generate_text = pipeline(
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,
    task='text-generation',
    stopping_criteria=stopping_criteria,
    temperature=0.1,
    max_new_tokens=512,
    repetition_penalty=1.1
)

llm = HuggingFacePipeline(pipeline=generate_text)

# Load the stored FAISS index
try:
    vectorstore = FAISS.load_local('faiss_index', HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"}))
    print("Loaded embedding successfully")
except ImportError as e:
    print("FAISS could not be imported. Make sure FAISS is installed correctly.")
    raise e

# Set up the Conversational Retrieval Chain
chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)

chat_history = []

def format_prompt(query):
    prompt = f"""
    You are a knowledgeable assistant with access to a comprehensive database. 
    I need you to answer my question and provide related information in a specific format.
    Here's what I need:
    1. A brief, general response to my question based on related answers retrieved.
    2. A JSON-formatted output containing:
       - "question": The original question.
       - "answer": The detailed answer.
       - "related_questions": A list of related questions and their answers, each as a dictionary with the keys:
         - "question": The related question.
         - "answer": The related answer.
    Here's my question:
    {query}
    Include a brief final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
    """
    return prompt

def qa_infer(query):
    formatted_prompt = format_prompt(query)
    result = chain({"question": formatted_prompt, "chat_history": chat_history})
    for doc in result['source_documents']:
        print("-"*50)
        print("Retrieved Document:", doc.page_content)
    print("#"*100)
    print(result['answer'])
    return result['answer']

EXAMPLES = ["How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM", 
            "Can BQ25896 support I2C interface?", 
            "Does TDA2 vout support bt656 8-bit mode?"]

demo = gr.Interface(fn=qa_infer, inputs="text", allow_flagging='never', examples=EXAMPLES, cache_examples=False, outputs="text")
demo.launch()

# import os
# import torch
# from torch import cuda, bfloat16
# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
# from langchain.llms import HuggingFacePipeline
# from langchain.vectorstores import FAISS
# from langchain.chains import ConversationalRetrievalChain
# import gradio as gr
# from langchain.embeddings import HuggingFaceEmbeddings

# # Load the Hugging Face token from environment
# HF_TOKEN = os.environ.get("HF_TOKEN", None)

# # Define stopping criteria
# class StopOnTokens(StoppingCriteria):
#     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
#         for stop_ids in stop_token_ids:
#             if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
#                 return True
#         return False

# # Load the LLaMA model and tokenizer
# model_id = 'meta-llama/Meta-Llama-3-8B-Instruct'
# device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

# # Set quantization configuration
# bnb_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_quant_type='nf4',
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_compute_dtype=bfloat16
# )

# tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
# model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", token=HF_TOKEN, quantization_config=bnb_config)

# # Define stopping criteria
# stop_list = ['\nHuman:', '\n```\n']
# stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
# stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
# stopping_criteria = StoppingCriteriaList([StopOnTokens()])

# # Create text generation pipeline
# generate_text = pipeline(
#     model=model,
#     tokenizer=tokenizer,
#     return_full_text=True,
#     task='text-generation',
#     stopping_criteria=stopping_criteria,
#     temperature=0.1,
#     max_new_tokens=512,
#     repetition_penalty=1.1
# )

# llm = HuggingFacePipeline(pipeline=generate_text)

# # Load the stored FAISS index
# try:
#     embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"})
#     vectorstore = FAISS.load_local('faiss_index', embeddings)
#     print("Loaded embedding successfully")
# except ImportError as e:
#     print("FAISS could not be imported. Make sure FAISS is installed correctly.")
#     raise e

# # Set up the Conversational Retrieval Chain
# chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)

# chat_history = []

# def format_prompt(query):
#     prompt = f"""
#     You are a knowledgeable assistant with access to a comprehensive database. 
#     I need you to answer my question and provide related information in a specific format.
#     Here's what I need:
#     1. A brief, general response to my question based on related answers retrieved.
#     2. A JSON-formatted output containing:
#        - "question": The original question.
#        - "answer": The detailed answer.
#        - "related_questions": A list of related questions and their answers, each as a dictionary with the keys:
#          - "question": The related question.
#          - "answer": The related answer.
#     Here's my question:
#     {query}
#     Include a brief final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
#     """
#     return prompt

# def qa_infer(query):
#     formatted_prompt = format_prompt(query)
#     result = chain({"question": formatted_prompt, "chat_history": chat_history})
#     return result['answer']

# EXAMPLES = ["How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM", 
#             "Can BQ25896 support I2C interface?", 
#             "Does TDA2 vout support bt656 8-bit mode?"]

# demo = gr.Interface(fn=qa_infer, inputs="text", allow_flagging='never', examples=EXAMPLES, cache_examples=False, outputs="text")
# demo.launch()