Configuration

Declaring a collection

A collection definition names its backend and its fields:

kind: collection
metadata:
  name: product-docs
  tenant: acme
spec:
  backend: pgvector
  fields:
    - name: title
      type: string
      searchable: true
      embedded: true
    - name: body
      type: text
      searchable: true
      embedded: true
    - name: category
      type: string
      facet: true
    - name: updated_at
      type: timestamp
      facet: true
    - name: search_blob
      type: computed
      from: [title, summary, tags]
      embedded: true
  embedding:
    model_ref: gateway:acme-embed-default
    dimensions: 1024

Note model_ref is a gateway alias, not a provider model name. Embedding calls go through the AI Gateway like every other model call, so they land in the same budgets and cost accounting — embedding a large corpus is a real expense and this is where you see it.

Choosing a backend

Backend	Choose it when
meilisearch	Lexical and faceted search; typo tolerance matters
pgvector	Semantic search alongside relational data you already have in Postgres
milvus	Vector volumes beyond what pgvector serves comfortably

Start on pgvector if your data already lives in Postgres — co-locating vectors with the records they describe removes a synchronisation problem that is easy to underestimate. Move to milvus when vector count, not convenience, becomes the binding constraint.

Indexing

Index one document:

POST /index/pgvector/product-docs
X-Kis-Tenant: acme
Content-Type: application/json

{
  "docid": "01JB7CY1Q6R0GK09RZAVDQGF11",
  "doctype": "product-docs",
  "contenttype": "text",
  "document": {
    "title": "Configuring intermittent reconnects",
    "body": "When a client sees ERR_CONN_4021 …",
    "category": "networking",
    "updated_at": "2026-07-01T10:00:00Z"
  }
}

Index in bulk:

POST /bulk/pgvector/product-docs
X-Kis-Tenant: acme

{ "documents": [ { "docid": "…", "document": { … } }, … ] }

docid is yours to choose and is the idempotency key — re-indexing the same docid replaces the document rather than duplicating it. Use a stable identifier from your own system, not a generated one, or every reindex doubles your corpus.

Searching

POST /search/pgvector/product-docs
X-Kis-Tenant: acme

{
  "query": "connection keeps dropping",
  "mode": "hybrid",
  "limit": 5,
  "filter": { "category": "networking" }
}

Parameter	Meaning
query	The search text
mode	lexical, semantic, hybrid or entity
limit	Maximum results
filter	Facet constraints, applied during the search
offset	For pagination

Deleting

DELETE /index/pgvector/product-docs/01JB7CY1Q6R0GK09RZAVDQGF11

Bulk deletion takes a list of docids, and deleting a collection removes its index entirely.

Introspection

GET /list/backends      backends available in this deployment
GET /list/documents     collections visible to the tenant

Useful in deployment checks — a collection missing from /list/documents after a deploy means its definition did not load, which is a quieter failure than an indexing error.

Next

• Operations — reindexing, throughput and monitoring