> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Configure Semantic Set

> Configure a semantic set.

    Updates the embedder and/or language model used for a specific set.
    This allows customizing the models used for feature extraction and
    embedding on a per-set basis.

    If not configured, sets inherit the default embedder and language model.



## OpenAPI

````yaml /openapi.json post /api/v2/memories/semantic/set/configure
openapi: 3.1.0
info:
  title: MemMachine API
  version: 0.3.10
  description: >-
    Architectural Memory Systems for AI Agents. Specialized in episodic and
    semantic memory management with strict namespace isolation.
servers:
  - url: http://localhost:8080
security: []
tags:
  - name: Configuration
    description: System overview and memory subsystem configuration.
  - name: Episodic Configuration
    description: Per-project episodic memory subsystem configuration.
  - name: Memories
    description: Core operations for episodic and semantic memory ingestion and retrieval.
  - name: Projects
    description: Lifecycle management for isolated memory namespaces.
  - name: Resources
    description: Embedder, language model, and reranker lifecycle management.
  - name: 'Semantic Memory: Categories'
    description: Category, template, and tag management for semantic sets.
  - name: 'Semantic Memory: Features'
    description: Add, retrieve, and update individual semantic features.
  - name: 'Semantic Memory: Sets'
    description: Set type and set ID lifecycle, listing, and configuration.
  - name: System
    description: Infrastructure, health, and observability.
paths:
  /api/v2/memories/semantic/set/configure:
    post:
      tags:
        - 'Semantic Memory: Sets'
      summary: Configure Semantic Set
      description: |-
        Configure a semantic set.

            Updates the embedder and/or language model used for a specific set.
            This allows customizing the models used for feature extraction and
            embedding on a per-set basis.

            If not configured, sets inherit the default embedder and language model.
      operationId: configure_semantic_set
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ConfigureSemanticSetSpec'
        required: true
      responses:
        '204':
          description: Successful Response
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
components:
  schemas:
    ConfigureSemanticSetSpec:
      properties:
        org_id:
          type: string
          title: Org Id
          description: |2-

                The unique identifier of the organization.

                - Must not contain slashes (`/`).
                - Must contain only letters, numbers, underscores, hyphens, colon, and Unicode
                  characters (e.g., Chinese/Japanese/Korean). No slashes or other symbols
                  are allowed.

                This value determines the namespace the project belongs to.
                
          default: universal
          examples:
            - MemVerge
            - AI_Labs
        project_id:
          type: string
          title: Project Id
          description: |2-

                The identifier of the project.

                - Must be unique within the organization.
                - Must not contain slashes (`/`).
                - Must contain only letters, numbers, underscores, hyphens, colon, and Unicode
                  characters (e.g., Chinese/Japanese/Korean). No slashes or other symbols
                  are allowed.

                This ID is used in API paths and resource locations.
                
          default: universal
          examples:
            - memmachine
            - research123
            - qa_pipeline
        set_id:
          type: string
          title: Set Id
          description: Identifier of the semantic set.
        embedder_name:
          anyOf:
            - type: string
            - type: 'null'
          title: Embedder Name
          description: |2-

                Optional embedder name override for this semantic set. If not specified,
                the default embedder is used.
        llm_name:
          anyOf:
            - type: string
            - type: 'null'
          title: Llm Name
          description: |2-

                Optional language model name override for this semantic set. If not
                specified, the default language model is used.
      type: object
      required:
        - set_id
      title: ConfigureSemanticSetSpec
      description: Specification model for configuring a semantic set.
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
              - type: string
              - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
      type: object
      required:
        - loc
        - msg
        - type
      title: ValidationError

````