Turbocharge Your RAG: Quickstart Guide
Lab Overview​
Get started with Retrieval Augmented Generation (RAG) in LangChain, learning how to enhance your AI applications with external knowledge.
Lab Materials​
Key Topics​
- RAG fundamentals
- Document processing
- Vector stores
- Retrieval strategies
- Response generation
Features​
- Document ingestion
- Text chunking
- Vector embeddings
- Semantic search
- Context-aware responses
Technical Components​
- Document loaders
- Text splitters
- Embedding models
- Vector databases
- LLM integration
Implementation Steps​
- Document preparation
- Text chunking and processing
- Embedding generation
- Vector store setup
- Query pipeline creation
- Response generation
Prerequisites​
- Google Colab account
- OpenAI API key
- Basic understanding of:
- LLMs
- Vector embeddings
- Python programming