couchbase-fts

name	couchbase-fts
description	Design, build, and tune Couchbase Full Text Search (FTS) and vector search indexes. Use whenever the user asks about FTS indexes, Search service, text search, full-text search, fuzzy search, phrase search, wildcard search, regex search, geo search, geo-distance, geo-bounding-box, facets, scoring, boosting, analyzers, tokenizers, custom analyzers, language analyzers, type mappings, dynamic mappings, child mappings, FTS index design, FTS synonyms (8.x), vector search, kNN search, vector index, embedding search, hybrid search (FTS + vector), cb_fts_search, admin_fts_*, BLEVE, or 'how do I search text in Couchbase.' Distinct from couchbase-sqlpp-tuning (SQL++ index design) and couchbase-mcp (operating the tools). Use proactively when the user has a search use case — text relevance, semantic similarity, geo-proximity, or faceted navigation.
license	MIT

Couchbase Full Text Search & Vector Search

A skill for designing and operating Couchbase FTS and vector search indexes. Distinct from:

• couchbase-sqlpp-tuning — SQL++ / GSI index design (not Search service)
• couchbase-data-modeling — document shape decisions that affect what FTS can index
• couchbase-mcp — calling the actual cb_fts_search, admin_fts_*, and admin_vector_index_* tools

If the conversation is "I need to search text / find similar documents / search by location," this is the right skill.

When this skill applies

• "How do I do full-text search in Couchbase?"
• "How do I build an FTS index?"
• "Why isn't my FTS query matching what I expect?"
• "How do I add fuzzy / wildcard / phrase search?"
• "How do I search by geo-distance?"
• "How do I implement vector / semantic / kNN search?"
• "How do I combine text search with vector search (hybrid)?"
• "What analyzer should I use for [language]?"
• "How do I boost certain fields?"
• "What are FTS synonyms and when should I use them?"
• "How do I size FTS / vector index memory?"

Pick the right reference

Question	Read
"How do I design an FTS index — mappings, analyzers, type fields?"	references/index-design.md
"How do I write FTS queries — match, phrase, fuzzy, wildcard, conjunction, geo?"	references/query-types.md
"Analyzers, tokenizers, token filters — how do they work, which to pick?"	references/analyzers.md
"Vector search — kNN, hybrid search, index design for embeddings?"	references/vector-search.md
"FTS synonyms (8.x) — what they are, how to create, when to use?"	references/synonyms.md
"FTS is slow / results are wrong / index not updating — how to debug?"	references/troubleshooting.md

Three core principles

Principle 1 — The index definition determines everything. FTS doesn't infer what to index from the document. If a field isn't mapped (or dynamic mapping is off), FTS ignores it. Get the index definition right before debugging queries. The most common FTS failure mode is "the field isn't indexed."

Principle 2 — Analyzer choice is irreversible at query time. The analyzer applied at index time must be consistent with what you apply at query time. If you index with the en (English stemming) analyzer and query with standard, "running" and "run" won't match. Pick the analyzer once, document it, use it consistently.

Principle 3 — FTS and GSI solve different problems. FTS is for relevance-ranked text search, fuzzy matching, linguistic analysis, geo search, and vector kNN. GSI is for exact-match, range, and structured queries with SQL++ syntax. They're complementary — use both. A common pattern: GSI for filtering (WHERE status = 'active'), FTS for text relevance (SEARCH(description, "fast delivery")).

The five-question FTS design pass

Before building an index:

1. What fields need to be searchable? List them explicitly. Enabling dynamic mapping on large documents indexes everything, including fields you'll never search — wastes RAM and slows indexing.
2. What languages? Use language-specific analyzers (en, de, fr, etc.) for stemming and stop-word handling. Multi-language content needs multiple type mappings or a custom analyzer.
3. Do you need relevance ranking? If you just need "does this doc contain X" (filter), a simple match query is fine. If you need "most relevant first," you need to think about field weighting and boost values.
4. Geo, facets, or highlighting? These require specific field types (geopoint for geo, text with include_term_vectors: true for highlighting). Plan for them in the index definition, not after the fact.
5. Vector search? Vector indexes are separate from FTS indexes (8.x uses admin_vector_index_* tools; earlier versions use an FTS vector type). Decide whether you need pure vector, pure text, or hybrid before designing the index.

Quick tool map

Task	Tool
Run a search query	cb_fts_search
List FTS indexes	admin_fts_list_indexes
Create/update an FTS index	admin_fts_upsert_index
Delete an FTS index	admin_fts_delete_index
Get index stats (doc count, indexing rate)	admin_fts_get_index_stats
Pause/resume index ingestion	admin_fts_pause_index_ingestion, admin_fts_resume_index_ingestion
FTS synonym sets (8.x)	cb_fts_synonym_upsert, cb_fts_synonym_list, cb_fts_synonym_delete
Vector index create/drop/list (8.x)	admin_vector_index_create, admin_vector_index_drop, admin_vector_index_list
FTS service memory quota	admin_stats_fts (read), cluster settings (write)

Version notes

• 7.x: FTS indexes, standard analyzers, geo search, facets, dynamic/static mappings. Vector search available as experimental in late 7.x releases.
• 8.0+: admin_vector_index_* tools for dedicated vector indexes; FTS synonym sets via cb_fts_synonym_*; hybrid search combining FTS and vector scores. Note the algorithm differs by index type: the FTS/Search Vector Index (SVI) uses HNSW, while the dedicated Index-Service vector indexes — Composite (CVI) and Hyperscale (HVI) — use a different family (HVI is built on a Vamana/DiskANN-derived hybrid combined with IVF). See couchbase-ai-applications for index-type selection.
• Capella: FTS service is available on all tiers. Vector indexes require a minimum compute tier (check current Capella docs for limits).

Related skills

• couchbase-data-modeling — document field types and structure that FTS will index
• couchbase-sizing — FTS and vector index memory budgeting
• couchbase-mcp — the actual tools for creating and querying FTS indexes
• couchbase-sqlpp-tuning — complementary GSI index design for the SQL++ side of hybrid queries