Overview
This project introduces an Intelligent Document Assistant designed to interactively retrieve and generate accurate, context-aware responses from large textual documents. Utilizing advanced Retrieval-Augmented Generation (RAG) techniques, the system allows users to upload extensive documents and efficiently query specific information through natural language interactions.
Technical Highlights
- Document Parsing & Embeddings: Parsed and preprocessed documents into manageable text chunks, creating semantic embeddings using a custom PyTorch embedding model.
- Semantic Retrieval: Integrated ChromaDB vector database for efficient semantic similarity-based retrieval of relevant document chunks.
- Context-Aware Generation: Employed generative language models, such as ChatGPT, combined with precisely engineered prompts to provide accurate, contextual responses.
Challenges and Solutions
- Efficient Document Handling: Used RecursiveCharacterTextSplitter to effectively chunk large documents while preserving semantic context.
- Precision and Recall Optimization: Ensured high performance through rigorous evaluation metrics (Precision, Recall, Mean Reciprocal Rank), validating retrieval and generation accuracy.
Technologies and Tools
- Python, PyTorch, PyPDF2
- ChromaDB, Streamlit, Hugging Face Transformers
- RecursiveCharacterTextSplitter
My Role
Led embedding model development, fine-tuned semantic retrieval mechanisms, and contributed to prompt engineering, ensuring coherent, contextually accurate chatbot responses.
Key Results
The Intelligent Document Assistant effectively retrieved contextually relevant information, significantly improving user engagement and efficiency in information access from extensive documents.