Vector DB integration lab
Frame the versioned pgvector support-runbook index experiment
Page 1 advances one concrete versioned pgvector support-runbook index: explain the decision, run the code, inspect failure, measure evidence, and keep only what is ready to ship.
1Try it yourself
Playground
Vector DB + RAG lab
Store → query → generate. Same stack as production, smaller shelf.
Before you start
Why this matters
Without running code, predict the output of this page's example and name the intermediate value that would prove your prediction. Then write one sentence answering: “What could look successful while actually being wrong?” For this stage, focus on dimension mismatch or cross-tenant retrieval. Keep the prediction nearby; comparing it with the real output is the first debugging exercise, not a quiz about syntax.
2Learn the idea
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Build focus
A lab needs a falsifiable claim before code. The claim here is that you can return five tenant-scoped chunks for a support question without changing the answer API. Record the tiny dataset, expected behavior, and one reason the result could be misleading. The first artifact is an experiment brief, not a screenshot. It names the user decision, the baseline you must beat, and the non-goals you will not pretend to solve on this page.
The in-memory list from a mini RAG demo cannot filter by tenant, cannot version embeddings, and cannot survive process restarts. pgvector keeps the answer contract identical—still “top chunks for a question”—while making those operational properties real. Your first job is to write acceptance criteria that mention expected chunk IDs, not vibes about better answers.
The artifact's user-facing goal is specific: return five tenant-scoped chunks for a support question without changing the answer API. Its accepted input is chunk IDs, tenant_id, body text, embedding model id, and index_version. Those statements are intentionally narrower than “build an AI system.” Narrow scope lets us inspect every input and expected result, and it prevents a toy result from being presented as a production claim. System shape for this chapter: an ingestion worker chunks documents, an embedding adapter creates fixed-size vectors, PostgreSQL with pgvector stores vectors plus tenant metadata, and a query service embeds one question, applies an authorization filter, and returns five chunks to the answer layer. Keep model calls behind adapters, keep authorization and validation in deterministic code, and carry stable IDs and versions through every response. That separation lets you decide whether a bad result came from input handling, retrieval, inference, validation, or deployment. This page's job is the experiment brief step: a lab needs a falsifiable claim before code. Setup baseline for the chapter (run once per machine, not secrets in git):
docker run --name rag-pg -e POSTGRES_PASSWORD=rag -p 5432:5432 -d pgvector/pgvector:pg16
python -m venv .venv && source .venv/bin/activate
pip install psycopg[binary] pgvector sentence-transformers
If hardware or a hosted provider differs, preserve the interface and expected behavior. Do not present provider syntax as universal—when a vendor adapter is unavoidable, keep it behind a thin boundary and test with a fake first. The deliverable is not “it ran once”; it is a reproducible artifact another developer can inspect, including expected output and one deliberate failure related to dimension mismatch or cross-tenant retrieval. Operationally, write down the owner of this stage, the command you ran, the observed output, and the next page's dependency on that output. If you cannot point to a file, fixture, metric, or config key, the stage is not done. Prefer small, reviewable increments: one contract, one path, one metric, one failure, one gate. When tradeoffs appear—latency versus quality, hit rate versus false hits, local privacy versus cloud quality—record both numbers instead of moving the threshold until the report looks green. The chapter ships only when evidence for recall@5 ≥ 0.90, zero cross-tenant hits, and p95 query latency under 150 ms and a rehearsed recovery path exist beside pgvector index mini-v1/mini-v2 with HNSW after bulk load and env-based version switch.
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Run the example
Save this as lesson.py and run python3 lesson.py. Prefer the standard library or the pinned packages from the setup block so the example stays reproducible.
acceptance={"question":"What does ERR-502 mean?","expected_chunk":"runbook-7","recall_at_5":0.90,"p95_ms":150,"index_version":"mini-v1","embedding_model":"all-MiniLM-L6-v2"}
print(sorted(acceptance))
print(acceptance["expected_chunk"])
Expected output: sorted acceptance keys and runbook-7. Exact floating-point formatting may vary slightly, but the asserted behavior must not. Read the output as evidence about this stage, not merely proof that the interpreter started.
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Debug the stage
Print the planned interfaces and the one fixture that would falsify the brief. If tenant, version, timeout, or refusal behavior is missing from the brief, stop before installing packages. At the experiment brief stage, save the smallest failing fixture beside the expected result. Change one cause at a time and rerun the exact command printed above; that makes the repair reviewable and keeps this chapter's progressive artifact reproducible.
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Evaluate before continuing
Preserve the acceptance brief beside the fixture. Connecting tools is not the same as meeting recall@5 ≥ 0.90, zero cross-tenant hits, and p95 query latency under 150 ms. For this experiment brief page, preserve the fixture and result as evidence for the next page. Label observations separately from conclusions: a passing assertion establishes the behavior it names, while broader usefulness requires the chapter's full evaluation set and stated operating limits. Primary metrics for the chapter remain recall@5 ≥ 0.90, zero cross-tenant hits, and p95 query latency under 150 ms.
Continue learning · glossary & guides
- [ ] What exact claim can this tiny fixture disprove?
- [ ] Which baseline prevents a decorative success claim?
- [ ] What result would make me stop before implementation?
- [ ] Can I explain how return five tenant-scoped chunks for a support question without changing the answer API?
Glossary: vector database · Glossary: vector index · Cheatsheet: RAG quality