app_RAG.py

import torch
import transformers
from transformers import RagRetriever, RagSequenceForGeneration, AutoModelForCausalLM, pipeline
import gradio as gr

device = 'cuda' if torch.cuda.is_available() else 'cpu'

dataset_path = "./5k_index_data/my_knowledge_dataset"
index_path = "./5k_index_data/my_knowledge_dataset_hnsw_index.faiss"

retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
                                            passages_path = dataset_path,
                                            index_path = index_path,
                                            n_docs = 5)
rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
rag_model.retriever.init_retrieval()
rag_model.to(device)

pipe = pipeline(
"text-generation",
model="google/gemma-2-2b-it",
model_kwargs={"torch_dtype": torch.bfloat16},
device=device,  
)

def strip_title(title):
    if title.startswith('"'):
        title = title[1:]
    if title.endswith('"'):
        title = title[:-1]
    
    return title
    

def retrieved_info(query, rag_model = rag_model):
    # Tokenize Query
    retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        [query],
        return_tensors = 'pt',
        padding = True,
        truncation = True,
    )['input_ids'].to(device)

    # Retrieve Documents
    question_encoder_output = rag_model.rag.question_encoder(retriever_input_ids)
    question_encoder_pool_output = question_encoder_output[0]

    result = rag_model.retriever(
        retriever_input_ids,
        question_encoder_pool_output.cpu().detach().to(torch.float32).numpy(),
        prefix = rag_model.rag.generator.config.prefix,
        n_docs = rag_model.config.n_docs,
        return_tensors = 'pt',
    )

    # Preparing query and retrieved docs for model
    all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
    retrieved_context = []
    for docs in all_docs:
        titles = [strip_title(title) for title in docs['title']]
        texts = docs['text']
        for title, text in zip(titles, texts):
            retrieved_context.append(f'{title}: {text}')

    
    # Generating answer using gemma model

    messages = [
        {"role": "user", "content": f"{query}"},
        {"role": "system" , "content": f"Context: {retrieved_context}. Use the links and information from the Context to answer the query in brief. Provide links in the answer."}
    ]

    outputs = pipe(messages, max_new_tokens=256)
    assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
    
    return assistant_response


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens ,
    temperature,
    top_p,
):
    if message:  # If there's a user query
        response = retrieved_info(message)  # Get the answer from your local FAISS and Q&A model
        return response

    # In case no message, return an empty string
    return ""


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
# Custom title and description
title = "🧠 Welcome to Your AI Knowledge Assistant"
description = """
HI!!, I am your loyal assistant, y functionality is based on RAG model, I retrieves relevant information and provide answers based on that. Ask me any question, and let me assist you.
My capabilities are limited because I am still in development phase. I will do my best to assist you. SOOO LET'S BEGGINNNN......
"""

demo = gr.ChatInterface(
    respond,
    type = 'messages',
    additional_inputs=[
        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    title=title,
    description=description,
    submit_btn = True,
    textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
    examples=[["Future of AI"], ["App Development"]],
    theme="compact",
)

if __name__ == "__main__":
    demo.launch(share = True )