RAG System on "Leave No Context Behind" Research Paper
Production-grade Retrieval-Augmented Generation system for building context-aware AI applications with custom knowledge bases.
Overview
Designed a Retrieval-Augmented Generation (RAG) system specifically for the "Leave No Context Behind" research paper using LangChain and Google Gemini 1.5 Pro. The system processes documents using PyPDFLoader, chunks them into manageable segments, generates embeddings, and stores them in ChromaDB for efficient retrieval. Enhanced Large Language Model performance by integrating external data sources to improve context-awareness and generate more accurate, contextually relevant responses. The RAG architecture combines information retrieval with generative AI, allowing the model to access and utilize specific document content when generating responses.
Key Highlights
LangChain framework integration for building the RAG pipeline
Google Gemini 1.5 Pro as the large language model for response generation
PyPDFLoader for efficient document processing and text extraction
Document chunking strategy to break down large documents into manageable segments
Vector embeddings generation for semantic search capabilities
ChromaDB vector database for efficient storage and retrieval of embeddings
Enhanced context-awareness by integrating external research paper data
Improved response accuracy and relevance through retrieval-augmented generation
Semantic search to find relevant document chunks based on user queries
Tech Stack
Project Links
🏗️ System Architecture
System Components
PDF Loader
PyPDFLoader for document ingestion
Document Chunker
Splits documents into manageable segments
Embedding Generator
Creates vector embeddings for chunks
Vector Store
ChromaDB for storing and indexing embeddings
Retriever
Semantic search over document chunks
LLM
Google Gemini 1.5 Pro for generation
Response Generator
Combines retrieval with generation
Data Flow
Load research paper PDF
Raw PDF textSplit into chunks
Document segmentsGenerate and store embeddings
Vector embeddingsUser query
Question about paperSearch for relevant chunks
Query embeddingReturn matching chunks
Relevant contextProvide context
Retrieved chunksGenerate answer
Context-aware responseArchitecture Flow
PDF Loader
Document Chunker
Embedding Generator
Vector Store
Retriever
LLM
Response Generator