ADR-001: Use Typesense as Unified DFL Search Engine

Status

Superseded 2026-07-31 by Azure AI Search. See Search-and-Retrieval.

Context

DEMI needed full-text and faceted search over DFL documents against a 10-second response target.

eagle-api already ran Typesense 30.1, deployed by docker-compose and Helm, with five collections (documents, projects, activities, notifications, document_chunks), a real-time MongoDB change-stream sync, PDF extraction and page-based chunking, a proxy that injected an allowed_roles filter, zero-downtime re-index by alias swap, and a frontend adapter in eagle-public. Reusing it meant no second service to deploy or operate.

Decision

DFL search used Typesense as its only search backend.

• The existing document_chunks collection took DFL metadata fields and facets: documentType, topics, orcsCode, visibility, holdState.
• The existing allowed_roles model carried DFL visibility.
• Change-stream sync indexed DFL documents as MongoDB created them.
• Typesense's built-in ts/e5-small embeddings on the content field supplied semantic search with no external API and no per-query cost, queried alongside keywords in one hybrid request.

Consequences

Typesense could not hold this corpus. Its indexed fields are RAM-only with no on-disk mode, clustering is replication rather than sharding, and its own sizing rule put the corpus at 17-26 GB of RAM against the 8 GiB ceiling of Container Apps Consumption. A larger container was not a lever.

The ts/e5-small embeddings that most of this decision rested on were never enabled, so the semantic half was never exercised. Typesense code and infrastructure were removed on 2026-07-31, and retrieval moved to lexical BM25 on Azure AI Search — see Search-and-Retrieval.