> ## 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.

# Get Semantic Memory Config

> Get semantic memory configuration.

    Returns the current semantic memory configuration including database,
    LLM model, embedding model, and enabled status.



## OpenAPI

````yaml /openapi.json get /api/v2/config/memory/semantic
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/config/memory/semantic:
    get:
      tags:
        - Configuration
      summary: Get Semantic Memory Config
      description: |-
        Get semantic memory configuration.

            Returns the current semantic memory configuration including database,
            LLM model, embedding model, and enabled status.
      operationId: get_semantic_memory_config
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/SemanticMemoryConfigResponse'
components:
  schemas:
    SemanticMemoryConfigResponse:
      properties:
        enabled:
          type: boolean
          title: Enabled
          description: |2-

                Whether semantic memory is enabled. When set to true, the required
                fields (database, llm_model, embedding_model) must also be configured.
                Set to false to disable semantic memory entirely.
          default: false
        database:
          anyOf:
            - type: string
            - type: 'null'
          title: Database
          description: |2-

                The ID of the database to use for semantic memory storage.
                Must reference a database configured in the resources section. Used by
                legacy semantic storage backends.
        storage_backend:
          anyOf:
            - type: string
            - type: 'null'
          title: Storage Backend
          description: |2-

                The storage backend used for semantic memory.
        feature_store:
          anyOf:
            - type: string
            - type: 'null'
          title: Feature Store
          description: |2-

                The ID of the relational database resource used to store semantic feature
                data when the semantic storage backend is `vector_store`.
        vector_collection:
          anyOf:
            - type: string
            - type: 'null'
          title: Vector Collection
          description: |2-

                The ID of the vector store resource used to store semantic feature
                embeddings when the semantic storage backend is `vector_store`.
        llm_model:
          anyOf:
            - type: string
            - type: 'null'
          title: Llm Model
          description: |2-

                The ID of the language model to use for semantic memory extraction.
                Must reference a language model configured in the resources section.
        embedding_model:
          anyOf:
            - type: string
            - type: 'null'
          title: Embedding Model
          description: |2-

                The ID of the embedder to use for semantic memory vector search.
                Must reference an embedder configured in the resources section.
      type: object
      title: SemanticMemoryConfigResponse
      description: Response model for semantic memory configuration.

````