Generative Q&A with ChromDB and HuggingFace Transformers

This Python notebook demonstrates how to build a simple generative question-answering (Q&A) system using ChromDB and HuggingFace Transformers. The notebook provides step-by-step instructions and code snippets to help you understand and implement the Q&A model.

Prerequisites

To run this notebook, you need the following prerequisites:

1. Python 3.7 or above
2. ChromDB installed (Refer to ChromDB's documentation for installation instructions)
3. Necessary Python libraries: torch, transformers, nltk, numpy

Setup

1. Install the required Python libraries by running the following command:

pip install torch transformers nltk numpy

2. Import the necessary libraries in your Python notebook:

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from nltk.tokenize import sent_tokenize
import numpy as np

3. Load the pre-trained GPT-2 model and tokenizer:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# model_id = 'EleutherAI/gpt-neo-125M'
model_id = 'gpt2'
tokenizer = AutoTokenizer.from_pretrained(model_id)
lm_model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = pipeline(
    "text-generation", model=lm_model, tokenizer=tokenizer, max_new_tokens=256, device_map="auto", handle_long_generation="hole"
)

2. Ask a question and get the generated answer:

question = 'Why is society ignoring the potentially devastating consequences of AI development'

# Retrieve context from vector database
results = talks_collection.query(
    query_texts=question,
    n_results=5
)

context = results['documents'][0][0]

prompt_template = f"Answer the given question only using the context provided. Do not Hallucinate.\n\nContext: {context}\n\nQuestion: {question}\n\n\
Answer:"

lm_response = pipe(prompt_template)
print(lm_response[0]["generated_text"])

Customizing and Improving the Q&A Model

To customize and improve the Q&A model, you can experiment with the following techniques:

1. Fine-tuning: Fine-tune the pre-trained GPT-2 model on a specific dataset to make it more domain-specific and improve its performance on certain types of questions.

2. Data preprocessing: Perform additional data preprocessing steps such as sentence tokenization, removing irrelevant text, or applying specific filters to improve the quality of the generated answers.

3. Model selection: Experiment with different pre-trained language models available in HuggingFace Transformers to find the one that best suits your needs.

4. Hyperparameter tuning: Adjust the hyperparameters of the GPT-2 model, such as the maximum sequence length, temperature, or repetition penalty, to control the quality and diversity of the generated answers.

Conclusion

This notebook provides a basic implementation of a generative Q&A system using ChromDB and HuggingFace Transformers. By following the instructions and experimenting with different approaches, you can build and customize your own Q&A model for various applications.

Remember to refer to the ChromDB and HuggingFace Transformers documentation for more detailed information and advanced techniques.