Generative QA - RAG(Gemini-pro)

This repository implements a Generative Question Answering (QA) using the Retrieval-Augmented Generation (RAG) approach. It leverages Haystack, an open-source AI framework, and Google's powerful Gemini-Pro generative model. The Pipeline is designed to generate answers to questions based on custom data extracted from two books.

Overview

The pipeline follows these main steps:

1. Preprocessing: The input documents are preprocessed to extract relevant information.
2. Embedding: The preprocessed documents are converted into embeddings, which are then stored in a vector database. In this project, we utilize Chroma, an open-source embedding database.
3. RAG Pipeline: A Retriever-Augmented-Generation (RAG) pipeline is created to generate answers to questions. For the answer generation step, we leverage Google's gemini-pro model as the generator.

Prerequisites

• Hugging Face Token
• Gemini API Key

Limitations

It has been observed that splitting documents into very small chunks can sometimes lead to the model generating incomplete or inaccurate answers. To potentially improve answer quality, consider increasing the split_length parameter when splitting documents. Additionally, introducing split_overlap when splitting documents might help the model retain context across splits, leading to more comprehensive answers.

Future Enhancements:

• Interactive Interface: Develop a user-friendly interface for more convenient questioning and exploration of generated answers.
• Performance Optimization: Consider optimizations for better quality of answer generation.