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Build garageTry it → read → next · ~11 min

Tutorials · Chapter D (4/4) · ~11 min

Vector DB integration lab

Try it → see it → read → next

Wire embed → store → query → prompt — the production RAG retrieval layer.

On paths: Builder · Student

Try yourself

Playground

Vector DB + RAG lab

Store → query → generate. Same stack as production, smaller shelf.

  1. → Store vectors
  2. ○ Query by meaning
  3. ○ Pass chunks to LLM

Step 1 / 3: Store vectors

Recap

What you just did

You walked store → semantic query → chunks ready for the LLM.

Teach

How it works

# Conceptual flow
vectors = embed(chunks)
db.upsert(ids, vectors, metadata=text)
hits = db.query(embed(question), top_k=5)
answer = llm(prompt=context_from(hits))

Pick a DB (pgvector, Pinecone, Chroma…) based on ops comfort and scale.

Use it

When you'd use this

  • More than ~100 chunks
  • Need filters (team, date, product)
  • Multiple apps sharing one knowledge base

Watch out

Watch out

Re-embed when you change embedding models. Version your index.