Lecture 17 - Complete RAG Pipeline: From Document to Vector Store | End-to-End Implementation
Автор: NeuroVed
Загружено: 2025-12-07
Просмотров: 34
Master the complete RAG (Retrieval Augmented Generation) pipeline from scratch! This hands-on tutorial takes you through every step - from loading documents to storing embeddings in a vector database and performing similarity search.
🎯 What You'll Learn
Complete RAG Pipeline: End-to-end implementation with real code
Document Loading: Using PyMuPDF for efficient PDF processing
Text Chunking: Recursive character text splitter in action
Embedding Generation: Practical examples with Ollama and OpenAI
Vector Databases: ChromaDB setup and configuration
Similarity Search: Retrieve relevant documents from your database
Memory vs In-Memory Stores: Understanding storage options
📋 Complete RAG Pipeline Steps
1. Document Loading
Load PDF using PyMuPDF loader
271-page book example (Panchatantra)
2. Chunking
Recursive character text splitter
Chunk size: 1000
Overlap: 200
3. Embedding Generation
Initialize embedding model (Ollama/OpenAI)
Convert chunks to vectors
Model examples: Granite Embedding (384d), Embedding Gemma (768d)
4. Vector Store
Store embeddings in ChromaDB
Configure collection names
Set persist directory
5. Similarity Search
Query the vector database
Retrieve relevant documents
Get similarity scores
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