A comprehensive implementation of a Retrieval-Augmented Generation (RAG) pipeline for processing, embedding, and querying documents.
This project implements a complete RAG pipeline that processes various document formats, chunks them into manageable pieces, converts them into vector embeddings, and stores them in a vector database for efficient similarity search.
Data Ingestion → Data Parsing → Chunking → Embedding → Vector DB → Similarity Search
Accepts multiple document formats:
- 📄 PDF - Portable Document Format files
- 🌐 HTML - Web pages and HTML documents
- 📊 Excel - Spreadsheets and tabular data
- 🗄️ Database - Direct database connections
Extracts key components:
- Metadata - Document properties, author, creation date
- Content - Main text content
- Structure - Document hierarchy and organization
Processes raw documents into structured format:
- Cleans and normalizes text
- Extracts relevant information
- Preserves document structure
- Prepares data for chunking
Splits documents into smaller, manageable pieces:
- Creates multiple chunks (Chunk 1, Chunk 2, Chunk 3, Chunk 4, ...)
- Considers context size for optimal embedding
- Maintains semantic coherence within chunks
- Preserves important context boundaries
Key Consideration: hLu4s = context size - Chunk size must fit within the embedding model's context window
Converts text chunks into numerical vector representations:
- Transforms text into high-dimensional vectors
- Captures semantic meaning
- Enables similarity comparison
- Optimizes for context size constraints
Stores embeddings for efficient retrieval:
- Indexes vector embeddings
- Enables fast similarity search
- Supports scalable storage
- Facilitates quick retrieval of relevant chunks
Retrieves relevant documents based on queries:
- Compares query vectors with stored embeddings
- Returns most relevant chunks
- Ranks results by similarity score
- Provides context for LLM generation