Actian Corporation Search API
Vector similarity search operations
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OpenAPI Specification
openapi: 3.2.0
info:
title: Actian VectorAI DB Search API
description: Vector similarity search operations for Actian VectorAI DB.
version: 1.0.0
contact:
name: Actian Corporation
url: https://www.actian.com
servers:
- url: http://localhost:6573
description: Local development server (REST API)
- url: https://api.vectorai.actian.com
description: Production server
security:
- bearerAuth: []
tags:
- name: Search
description: Vector similarity search operations
paths:
/collections/{collection_name}/points/search:
post:
tags:
- Search
summary: Search vectors
description: Find the most similar vectors in a collection using approximate nearest neighbor search. Supports score thresholds, payload filtering, and tunable HNSW parameters.
operationId: search_points
parameters:
- name: collection_name
in: path
required: true
schema:
type: string
description: The name of the collection to search.
requestBody:
required: true
content:
application/json:
schema:
type: object
required:
- vector
properties:
vector:
type: array
items:
type: number
example:
- 0.1
- 0.2
- 0.3
- 0.4
description: The query vector to search for. The number of dimensions must match the collection's configured vector size.
limit:
type: integer
default: 10
example: 5
description: Maximum number of results to return. Defaults to 10.
offset:
type: integer
default: 0
example: 0
description: Number of results to skip for pagination. Use with `limit` to page through results.
score_threshold:
type: number
example: 0.7
description: Minimum similarity score for results. Only points with scores at or above this threshold are returned.
filter:
type: object
description: Optional filter to narrow search results based on payload metadata. See the Filters API for syntax.
with_payload:
type: boolean
default: true
example: true
description: Whether to include payload metadata in the response.
with_vector:
type: boolean
default: false
example: false
description: Whether to include vector data in the response. Set to `false` to reduce response size.
params:
type: object
description: Advanced search parameters for tuning search quality and performance.
properties:
hnsw_ef:
type: integer
example: 128
description: Controls the number of candidates HNSW evaluates during search. Higher values improve accuracy at the cost of latency.
exact:
type: boolean
example: false
description: If `true`, performs an exact brute-force search instead of approximate. Slower but guarantees finding true nearest neighbors.
examples:
basic_search:
summary: Basic search
value:
vector:
- 0.1
- 0.2
- 0.3
- 0.4
limit: 5
offset: 0
with_payload: true
with_vector: false
search_with_threshold:
summary: With score threshold
value:
vector:
- 0.1
- 0.2
- 0.3
- 0.4
limit: 10
offset: 0
score_threshold: 0.7
with_payload: true
with_vector: false
params:
hnsw_ef: 128
exact: false
search_with_filter:
summary: With payload filter
value:
vector:
- 0.1
- 0.2
- 0.3
- 0.4
limit: 10
offset: 0
filter:
must:
- key: category
match:
value: electronics
with_payload: true
with_vector: false
responses:
'200':
description: Search results
content:
application/json:
schema:
type: object
properties:
usage:
type: object
properties:
hardware:
type:
- object
- 'null'
description: Hardware resource counters for the operation, including CPU, payload I/O, payload index I/O, and vector I/O metrics.
time:
type: number
format: double
example: 5.53e-05
description: Time spent to process this request, in seconds.
status:
type: string
example: ok
description: Operation status.
result:
type: array
items:
type: object
properties:
id:
oneOf:
- type: integer
- type: string
example: 1
description: The point's unique identifier.
version:
type: integer
example: 0
description: The point's version number.
score:
type: number
example: 0.9843740463256836
description: Similarity score between the query vector and this point.
payload:
type:
- object
- 'null'
example: null
description: Payload metadata, or null if `with_payload` is false.
vector:
type:
- array
- 'null'
items:
type: number
example: null
description: Vector data, or null if `with_vector` is false.
example:
usage:
hardware: null
time: 5.53e-05
status: ok
result:
- id: 1
version: 0
score: 1
payload: null
vector: null
- id: 3
version: 0
score: 0.9843740463256836
payload: null
vector: null
- id: 2
version: 0
score: 0.9688639640808105
payload: null
vector: null
4XX:
description: Error response
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
x-codeSamples:
- lang: Python
label: Basic search
source: "from actian_vectorai_client import VectorAIClient\nimport random\n\nwith VectorAIClient(\"localhost:6574\") as client:\n # Basic vector search\n query = [random.gauss(0, 1) for _ in range(64)]\n results = client.points.search(\"my_collection\", vector=query, limit=5)\n \n for r in results:\n print(f\"ID: {r.id}, Score: {r.score:.4f}\")\n print(f\"Payload: {r.payload.get('category')}\")\n"
- lang: Python
label: Search with score threshold
source: "from actian_vectorai_client import VectorAIClient\nimport random\n\nwith VectorAIClient(\"localhost:6574\") as client:\n # Search with minimum score\n query = [random.gauss(0, 1) for _ in range(64)]\n results = client.points.search(\n \"my_collection\",\n vector=query,\n limit=10,\n score_threshold=0.7\n )\n print(f\"Found {len(results)} results above threshold\")\n"
- lang: Python
label: Search with custom HNSW
source: "from actian_vectorai_client import VectorAIClient, SearchParams\nimport random\n\nwith VectorAIClient(\"localhost:6574\") as client:\n # Search with custom HNSW parameters\n query = [random.gauss(0, 1) for _ in range(64)]\n results = client.points.search(\n \"my_collection\",\n vector=query,\n limit=5,\n params=SearchParams(hnsw_ef=256, exact=False)\n )\n print(f\"Found {len(results)} results\")\n"
- lang: Python
label: Exact search
source: "from actian_vectorai_client import VectorAIClient, SearchParams\nimport random\n\nwith VectorAIClient(\"localhost:6574\") as client:\n # Exact (brute-force) search\n query = [random.gauss(0, 1) for _ in range(64)]\n results = client.points.search(\n \"my_collection\",\n vector=query,\n limit=5,\n params=SearchParams(exact=True)\n )\n for r in results:\n print(f\"ID: {r.id}, Score: {r.score:.4f}\")\n"
- lang: JavaScript
label: Basic search
source: "import { VectorAIClient } from '@actian/vectorai-client';\n\nconst client = new VectorAIClient('localhost:6574');\n\nconst results = await client.points.search('my_collection', [0.1, 0.2, 0.3, 0.4], {\n limit: 5,\n withPayload: true,\n});\nfor (const r of results) {\n console.log(`id=${r.id} score=${r.score.toFixed(4)} payload=${JSON.stringify(r.payload)}`);\n}\n\nclient.close();\n"
- lang: JavaScript
label: Filtered search
source: "import { VectorAIClient, Field } from '@actian/vectorai-client';\n\nconst client = new VectorAIClient('localhost:6574');\n\nconst filter = Field.of('category').eq('electronics');\nconst results = await client.points.search('my_collection', [0.1, 0.2, 0.3, 0.4], {\n limit: 5,\n filter,\n withPayload: true,\n});\nconsole.log(`Found ${results.length} results`);\n\nclient.close();\n"
- lang: cURL
label: Search
source: "curl -X POST \"http://localhost:6573/collections/my_collection/points/search\" \\\n -H \"Content-Type: application/json\" \\\n -H 'Authorization: Bearer <admin-jwt-or-access-token>' \\\n -d '{\n \"vector\": [0.1, 0.2, 0.3, 0.4],\n \"limit\": 5\n }'\n"
/collections/{collection_name}/points/search/batch:
post:
tags:
- Search
summary: Batch search
description: Execute multiple search queries in a single request. This is more efficient than sending individual search requests when you have several queries to run.
operationId: search_batch
parameters:
- name: collection_name
in: path
required: true
schema:
type: string
description: The name of the collection to search.
requestBody:
required: true
content:
application/json:
schema:
type: object
required:
- searches
properties:
searches:
type: array
description: Array of search request objects to execute.
items:
type: object
required:
- vector
properties:
vector:
type: array
items:
type: number
example:
- 0.1
- 0.2
- 0.3
- 0.4
description: The query vector for this search.
limit:
type: integer
example: 3
description: Maximum number of results for this search.
offset:
type: integer
example: 0
description: Number of results to skip for pagination.
score_threshold:
type: number
example: 0.7
description: Minimum similarity score for results.
filter:
type: object
description: Optional filter conditions for this search.
with_payload:
type: boolean
example: true
description: Whether to include payload metadata in the response.
with_vector:
type: boolean
example: false
description: Whether to include vector data in the response.
examples:
batch_search:
summary: Multiple queries
value:
searches:
- vector:
- 0.1
- 0.2
- 0.3
- 0.4
limit: 3
with_payload: true
with_vector: false
- vector:
- 0.5
- 0.6
- 0.7
- 0.8
limit: 3
with_payload: true
with_vector: false
- vector:
- 0.9
- 0.8
- 0.7
- 0.6
limit: 3
with_payload: true
with_vector: false
responses:
'200':
description: Batch search results
content:
application/json:
schema:
type: object
properties:
usage:
type: object
properties:
hardware:
type:
- object
- 'null'
description: Hardware resource counters for the operation, including CPU, payload I/O, payload index I/O, and vector I/O metrics.
time:
type: number
format: double
example: 7.06e-05
description: Time spent to process this request, in seconds.
status:
type: string
example: ok
description: Operation status.
result:
type: array
description: Array of result arrays, one per search query.
items:
type: array
items:
type: object
properties:
id:
oneOf:
- type: integer
- type: string
example: 1
description: The point's unique identifier.
version:
type: integer
example: 0
description: The point's version number.
score:
type: number
example: 0.9843740463256836
description: Similarity score between the query vector and this point.
payload:
type:
- object
- 'null'
example: null
description: Payload metadata, or null if `with_payload` is false.
vector:
type:
- array
- 'null'
items:
type: number
example: null
description: Vector data, or null if `with_vector` is false.
example:
usage:
hardware: null
time: 7.06e-05
status: ok
result:
- - id: 1
version: 0
score: 1
payload: null
vector: null
- id: 3
version: 0
score: 0.9843740463256836
payload: null
vector: null
- id: 2
version: 0
score: 0.9688639640808105
payload: null
vector: null
- - id: 2
version: 0
score: 1
payload: null
vector: null
- id: 3
version: 0
score: 0.9973233938217163
payload: null
vector: null
4XX:
description: Error response
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
x-codeSamples:
- lang: Python
label: Batch search
source: "from actian_vectorai_client import VectorAIClient\nimport random\n\nwith VectorAIClient(\"localhost:6574\") as client:\n # Execute multiple searches at once\n queries = [\n {\"vector\": [random.gauss(0, 1) for _ in range(64)], \"limit\": 3}\n for _ in range(3)\n ]\n batch_results = client.points.search_batch(\"my_collection\", queries)\n \n for i, results in enumerate(batch_results):\n ids = [r.id for r in results]\n print(f\"Query {i + 1}: top IDs = {ids}\")\n"
- lang: JavaScript
label: Batch search
source: "import { VectorAIClient } from '@actian/vectorai-client';\n\nconst client = new VectorAIClient('localhost:6574');\n\nconst batchResults = await client.points.searchBatch('my_collection', [\n { vector: [0.1, 0.2, 0.3, 0.4], limit: 3 },\n { vector: [0.5, 0.6, 0.7, 0.8], limit: 3 },\n]);\nconsole.log(`Batch search: ${batchResults.length} result sets`);\nfor (let i = 0; i < batchResults.length; i++) {\n console.log(` Query ${i}: ${batchResults[i].length} results`);\n}\n\nclient.close();\n"
- lang: cURL
label: Batch search
source: "curl -X POST \"http://localhost:6573/collections/my_collection/points/search/batch\" \\\n -H \"Content-Type: application/json\" \\\n -H 'Authorization: Bearer <admin-jwt-or-access-token>' \\\n -d '{\n \"searches\": [\n {\"vector\": [0.1, 0.2, 0.3, 0.4], \"limit\": 3},\n {\"vector\": [0.5, 0.6, 0.7, 0.8], \"limit\": 3}\n ]\n }'\n"
components:
schemas:
ErrorResponse:
type: object
properties:
status:
type: object
properties:
error:
type: string
time:
type: number
format: double
description: Time spent to process this request, in seconds.
securitySchemes:
bearerAuth:
type: http
scheme: bearer
bearerFormat: JWT
description: Admin JWT or access token for authenticating requests to VectorAI DB.