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ADR-0002: Vector Store Selection

Status​

Accepted - Q2 2025

Context​

ShieldCraft AI needs semantic search across alerts, artifacts, and knowledge objects. Early workloads prioritize developer velocity and modest scale; later stages may demand hybrid query (BM25 + ANN), multi-tenant isolation, and managed high availability.

Evaluation Criteria​

  • Integration effort and developer productivity
  • Hybrid search support (keyword + vector)
  • Scale characteristics and operational overhead
  • Cost efficiency for low/medium traffic
  • Managed service availability on AWS

Decision​

Start with PGVector on PostgreSQL for speed and simplicity. Define a small vector store interface so we can swap implementations without invasive refactors. Keep Amazon OpenSearch Service as the scale/enterprise path when:

  • corpus size, QPS, or latency SLOs demand it, or
  • hybrid search and per-tenant isolation benefits outweigh operational cost.

Codify the choice in ai_core/vector_store.py, keeping the interface aligned with the tier expectations from ADR-0001 and the config contracts introduced in ADR-0003.

Rationale​

  • PGVector
    • Simple to stand up and operate; good developer ergonomics
    • Cost-efficient for early and medium stages
    • Sufficient for embeddings search, reranking, and prototypes
  • OpenSearch
    • Strong hybrid search, facets, aggregations, and security plugins
    • Managed scaling and integrations with AWS ecosystem
    • Heavier operational profile and cost floor

Alternatives Considered​

  • PGVector only
    • Pro: Minimum surface area
    • Con: Limits hybrid/search features at scale
  • OpenSearch only
    • Pro: Powerful features from the start
    • Con: Higher cost/ops; slower onboarding
  • Managed third-party vector DB
    • Pro: Feature-rich ANN
    • Con: Extra vendor; networking and cost complexity

Migration Path​

  1. Implement a thin repository interface (insert/query/delete) in ai_core/vector_store.py
  2. Provide pgvector and opensearch backends behind a factory
  3. Add data export/import scripts to reindex embeddings
  4. Gate selection via config/env, default to PGVector
  5. Fold rollout telemetry into the evaluation loop from ADR-0006 to detect swap thresholds early

Risks & Mitigations​

  • Risk: Query latency spikes on PGVector at larger scale
    • Mitigation: Add indexes, batching, and move hot paths to OpenSearch when thresholds are crossed
  • Risk: Feature gaps (filters, aggregations)
    • Mitigation: Hybrid approach: use Postgres + a lightweight keyword index, or move to OpenSearch

Success Metrics​

  • P95 query latency within target SLO at current scale
  • Swap backend without API changes to callers
  • Controlled infra cost trajectory from dev to prod

References​