Semantic Search

POST /api/v1/ai/search

Embeds the supplied query text (or uses a pre-computed vector) and returns the most relevant records by similarity score. The property referenced by propertyName must have a ready embedding index.

RushDB performs exact semantic search: candidates are narrowed with label and where filters first, then ranked by cosine or euclidean similarity.

---

Request Body

Field	Type	Required	Description
propertyName	string	yes	The indexed property to search against (e.g. "description")
labels	string or array of strings	yes	Label(s) to search within (min 1)
query	string	conditionally	Free-text query to embed. Required for managed indexes; not allowed for external indexes.
queryVector	array of numbers	conditionally	Pre-computed query vector. Required for external indexes. Also accepted for managed indexes (bypasses server embedding).
similarityFunction	string	no	"cosine" or "euclidean". Required when multiple indexes target the same (label, propertyName).
dimensions	number	no	Disambiguates when multiple indexes match. Inferred from queryVector.length when queryVector is supplied.
where	object	no	Standard RushDB filter expression applied before similarity scoring.
skip	number	no	Pagination offset (default 0)
limit	number	no	Maximum results to return (default 20)

---

Result shape

Results are flat records with __score injected alongside your fields, ordered by __score descending (closest match first):

{
  "data": [
    {
      "__id": "rec_abc123",
      "__label": "Product",
      "__score": 0.921,
      "description": "Same-day shipping with hassle-free returns policy",
      "status": "active"
    },
    {
      "__id": "rec_def456",
      "__label": "Product",
      "__score": 0.887,
      "description": "Free returns within 30 days, express shipping available",
      "status": "featured"
    }
  ],
  "success": true
}

---

Managed search (query text)

For a managed index, pass query — a natural-language string. The server embeds it using the same model that was used when building the index, then ranks candidates by similarity.

curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{
    "propertyName": "description",
    "query": "fast delivery and easy returns",
    "labels": ["Product"],
    "limit": 5
  }'

---

External search (query vector)

For an external index, pass queryVector — a pre-computed embedding produced by your own model. No text is sent to an embedding model.

curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{
    "propertyName": "body",
    "queryVector": [0.1, 0.2, 0.3],
    "labels": ["Article"],
    "limit": 10
  }'

• query is not allowed with external indexes — the server has no model to embed it.
• queryVector is not required for managed indexes but is accepted (bypasses server embedding).
• When queryVector is supplied, dimensions can be omitted — the server infers it from queryVector.length.

---

Filtering with where

The where clause acts as a prefilter — only records satisfying the filter are candidates for similarity ranking. All filter operators supported by POST /api/v1/records/search are available here.

curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{
    "propertyName": "description",
    "query": "wireless headphones",
    "labels": ["Product"],
    "where": {
      "category": { "$eq": "electronics" },
      "inStock": { "$eq": true },
      "price": { "$lt": 100 }
    },
    "limit": 20
  }'

---

Multi-label search

Pass an array of labels to search across multiple entity types simultaneously:

curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{
    "propertyName": "body",
    "query": "machine learning trends",
    "labels": ["Article", "Post", "Comment"],
    "limit": 10
  }'

Each result carries __label so you can tell the entity types apart:

{
  "data": [
    { "__id": "rec_1", "__label": "Article", "__score": 0.93, "title": "...", "body": "..." },
    { "__id": "rec_2", "__label": "Post",    "__score": 0.87, "body": "..." },
    { "__id": "rec_3", "__label": "Comment", "__score": 0.82, "body": "..." }
  ],
  "success": true
}

All listed labels must have an embedding index on the same propertyName, or the request returns 404 for the missing labels.

---

Disambiguation

When two indexes exist for the same (label, propertyName), specify similarityFunction to select the target index:

# Two indexes: Product:embedding/cosine and Product:embedding/euclidean
curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{
    "labels": ["Product"],
    "propertyName": "embedding",
    "queryVector": [0.1, 0.9, 0.4],
    "similarityFunction": "cosine"
  }'

Omitting similarityFunction when multiple indexes match returns 422 Unprocessable Entity.

---

Pagination

# Page 1
curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{"propertyName":"description","query":"...", "labels":["Product"],"limit":20,"skip":0}'

# Page 2
curl -X POST https://api.rushdb.com/api/v1/ai/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $RUSHDB_API_KEY" \
  -d '{"propertyName":"description","query":"...", "labels":["Product"],"limit":20,"skip":20}'

---

Error reference

HTTP	Cause
404 Not Found	No enabled embedding index found for (label, propertyName)
422 Unprocessable Entity	Multiple indexes match and similarityFunction was not specified
422 Unprocessable Entity	query text supplied for an external index (server cannot embed it)
422 Unprocessable Entity	queryVector length does not match index dimensions
503 Service Unavailable	Embedding model unavailable (managed indexes only)