What Is RAG and Why It Matters
RAG = Retrieval-Augmented Generation. Combines searching a knowledge base with generating answers. Without RAG, AI only knows training data. With RAG, it answers about YOUR documents and data.
Document Processing and Chunking
Parse PDFs, Word docs, HTML. Split into chunks of 300-1000 tokens. Strategies: fixed-size, sentence, paragraph, semantic. Overlap 10-20% to preserve context.
Vector Databases and Embeddings
Embeddings convert text to numbers capturing meaning. Vector DBs store and search fast. Options: ChromaDB, Pinecone, Weaviate, FAISS. Flow: document → chunks → embeddings → vector DB → search → LLM.
Building a Complete RAG Pipeline
End-to-end: load documents → split → create embeddings → store → on query search relevant chunks → send chunks + query to LLM → return answer with sources. Using Python and LangChain.
Advanced RAG Techniques
Hybrid search (keyword + semantic). Re-ranking for relevance. Query transformation. Multi-step RAG. Self-RAG. Evaluation: faithfulness, answer relevancy, context relevancy.
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