Typesense

opencollection: 1.0.0
info:
  name: Typesense Vector Search API
  version: '30.1'
request:
  auth:
    type: apikey
    key: X-TYPESENSE-API-KEY
    value: '{{X-TYPESENSE-API-KEY}}'
    placement: header
items:
- info:
    name: Vector Collections
    type: folder
  items:
  - info:
      name: Create A Collection With Vector Fields
      type: http
    http:
      method: POST
      url: '{protocol}://{hostname}:{port}/collections'
      body:
        type: json
        data: '{}'
    docs: Creates a collection that includes vector embedding fields for semantic search. Vector fields are defined with a
      type of float[] and a num_dim property specifying the number of dimensions. The embed property can be used to configure
      automatic embedding generation from source fields using built-in models or external APIs like OpenAI.
- info:
    name: Vector Search
    type: folder
  items:
  - info:
      name: Perform Vector Or Hybrid Search
      type: http
    http:
      method: GET
      url: '{protocol}://{hostname}:{port}/collections/:collectionName/documents/search'
      params:
      - name: collectionName
        value: ''
        type: path
        description: Name of the collection.
      - name: q
        value: ''
        type: query
        description: Text search query. Use * for pure vector search without keyword matching.
      - name: query_by
        value: ''
        type: query
        description: Comma-separated list of fields to search against for keyword matching.
      - name: vector_query
        value: ''
        type: query
        description: Vector query in the format field_name:([v1,v2,...],k:num) or field_name:([], id:doc_id) for finding similar
          documents. The k parameter controls how many nearest neighbors to return.
      - name: filter_by
        value: ''
        type: query
        description: Filter conditions to narrow search results.
      - name: sort_by
        value: ''
        type: query
        description: Sort conditions. Use _vector_distance for sorting by vector similarity.
      - name: include_fields
        value: ''
        type: query
        description: Comma-separated list of fields to include in results.
      - name: exclude_fields
        value: ''
        type: query
        description: Comma-separated list of fields to exclude from results.
      - name: page
        value: ''
        type: query
        description: Page number for pagination.
      - name: per_page
        value: ''
        type: query
        description: Number of results per page.
      - name: prefix
        value: ''
        type: query
        description: Whether prefix search is enabled for keyword matching.
    docs: Searches for documents using vector similarity, keyword matching, or a hybrid combination of both. When vector_query
      is specified, Typesense performs nearest-neighbor search using the specified vector field. When combined with a text
      query (q parameter), results are scored using a hybrid approach that blends keyword relevance and vector similarity.
  - info:
      name: Multi-search With Vector Queries
      type: http
    http:
      method: POST
      url: '{protocol}://{hostname}:{port}/multi_search'
      body:
        type: json
        data: '{}'
    docs: Sends multiple search requests in a single HTTP request, where one or more requests can include vector queries for
      semantic or hybrid search across different collections.
- info:
    name: NL Search Models
    type: folder
  items:
  - info:
      name: List All NL Search Models
      type: http
    http:
      method: GET
      url: '{protocol}://{hostname}:{port}/nl_search_models'
    docs: Retrieves all natural language search models configured in the Typesense instance. NL search models are used for
      query understanding and semantic matching.
  - info:
      name: Create An NL Search Model
      type: http
    http:
      method: POST
      url: '{protocol}://{hostname}:{port}/nl_search_models'
      body:
        type: json
        data: '{}'
    docs: Creates a new natural language search model configuration for semantic query understanding.
  - info:
      name: Retrieve An NL Search Model
      type: http
    http:
      method: GET
      url: '{protocol}://{hostname}:{port}/nl_search_models/:modelId'
      params:
      - name: modelId
        value: ''
        type: path
        description: ID of the NL search model.
    docs: Retrieves a specific NL search model by ID.
  - info:
      name: Update An NL Search Model
      type: http
    http:
      method: PUT
      url: '{protocol}://{hostname}:{port}/nl_search_models/:modelId'
      params:
      - name: modelId
        value: ''
        type: path
        description: ID of the NL search model.
      body:
        type: json
        data: '{}'
    docs: Updates an existing NL search model configuration.
  - info:
      name: Delete An NL Search Model
      type: http
    http:
      method: DELETE
      url: '{protocol}://{hostname}:{port}/nl_search_models/:modelId'
      params:
      - name: modelId
        value: ''
        type: path
        description: ID of the NL search model.
    docs: Deletes an NL search model by ID.
bundled: true