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AI Engineering · Version 1.4.0 · Reviewed 2026-08-02

Vector Database Selection Advisor

Make AI behavior measurable and safer for recall requirements and metadata filtering with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Selects vector storage based on recall requirements, filtering needs, scale, and operational burden.

₹99 one-time

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What this skill helps you do

  • Recall requirements
  • Metadata filtering
  • Operational cost

How Vector Database Selection Advisor works

You provide

Schema, query plans, and the real access pattern

It inspects

Plan accuracy and lock behavior for recall requirements

It decides

A metadata filtering change weighed against write cost

You verify

Re-measured plan with buffer reads and timing compared

What it checks first

Vector Database Selection Advisor selects vector storage based on recall requirements, filtering needs, scale, and operational burden. Use it when the work involves Recall requirements, Metadata filtering, Operational cost.

  1. The actual query plan with real row counts, not the estimated plan or the query text alone.
  2. Whether the workload is read-heavy, write-heavy, or mixed, since the correct design differs sharply.
  3. Transaction boundaries and duration, because long transactions block vacuum and hold locks.
  4. Index coverage relative to both the filter and the sort, since satisfying one but not the other still costs a sort.
  5. Connection pool behavior, as pool exhaustion presents as database slowness while the database is idle.

Failure modes it recognizes

  • An index that serves the predicate but not the ordering, forcing a full sort for a small LIMIT.
  • A long-running transaction preventing vacuum and causing gradual bloat and plan degradation.
  • Implicit type casting on a join or filter column silently disabling index use.
  • Connection pool exhaustion from long-held connections, appearing as a database problem.
  • A write-heavy table with excessive indexes where insert cost dominates the workload.
  • Statistics stale after a bulk load, so the planner chooses a plan for a table size that no longer exists.

Answers it will reject

  • Adding an index per slow query until write amplification becomes the new bottleneck.
  • Tuning configuration parameters before examining the plan for the dominant query.
  • Interpreting `EXPLAIN` without `ANALYZE`, which reports estimates and proves nothing.
  • Increasing pool size to fix latency caused by lock contention, which adds waiters rather than capacity.

Decision rules it applies

  • Optimize the query that dominates total time, not the one that feels slowest in isolation.
  • Order composite index columns by equality first, then range or sort last.
  • Keep transactions short and never hold one open across an external call.
  • Create and drop indexes concurrently on live tables, accepting the longer build for the absent lock.

Evidence it asks for

  • `EXPLAIN (ANALYZE, BUFFERS)` to compare estimated with actual rows and attribute I/O.
  • Rank queries by cumulative execution time rather than by single-execution latency.
  • Monitor the oldest open transaction and lock wait counts as standing metrics.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to recall requirements.
  2. Trace concrete failure or abuse paths for metadata filtering; do not report checklist items without a mechanism.
  3. Prioritize operational cost findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Recall requirements assessment
  • Metadata filtering decision and action plan
  • Operational cost verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Do we need a dedicated vector database, or can we use the vector extension in the Postgres we already run?

Expected output

At your scale the deciding factor is filtering rather than raw vector search. If queries combine similarity with heavy metadata predicates, a database that filters before search wins; if you need approximate search across many millions of vectors, dedicated storage starts to pay...

Boundaries and compatibility

Ideal for

  • Recall requirements: produce a decision or artifact grounded in supplied evidence.
  • Metadata filtering: produce a decision or artifact grounded in supplied evidence.
  • Operational cost: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.