Basics of Embeddings and Vector Search (Developer Edition)
RAG's heart is embeddings and vector search. The mechanism converts text into meaning vectors and finds near ones.
Key Points
- Embedding: convert a sentence to a high-dimensional vector. Close-meaning sentences have close vectors
- Vector DB: fast nearest-neighbor search (similarity) over many vectors
- Chunk design: split size and overlap greatly affect accuracy
Implementation Tips
- Cut chunks by "semantic units" (too-mechanical splitting lowers accuracy)
- Carry metadata (source, date, permission) together
- Choose the embedding model by use, language, and cost
- Design the re-indexing flow for updates from the start
Caution
- Vector search is only "semantically near." Keyword matching may be separately needed (next chapter's hybrid)
- Confirm terms/access control for embedding/storing confidential data
Key Point
"Embedding quality × chunk design × retrieval" decides RAG accuracy. Crafting the retrieval side drives results more than generation.
