openapi: 3.0.3
info:
  title: Llama Platform Agent Data Data Sinks API
  version: 0.1.0
tags:
- name: Data Sinks
paths:
  /api/v1/data-sinks:
    get:
      tags:
      - Data Sinks
      summary: List Data Sinks
      description: List data sinks for a given project.
      operationId: list_data_sinks_api_v1_data_sinks_get
      security:
      - HTTPBearer: []
      parameters:
      - name: project_id
        in: query
        required: false
        schema:
          anyOf:
          - type: string
            format: uuid
          - type: 'null'
          title: Project Id
      - name: organization_id
        in: query
        required: false
        schema:
          anyOf:
          - type: string
            format: uuid
          - type: 'null'
          title: Organization Id
      - name: session
        in: cookie
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Session
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/DataSink'
                title: Response List Data Sinks Api V1 Data Sinks Get
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
    post:
      tags:
      - Data Sinks
      summary: Create Data Sink
      description: Create a new data sink.
      operationId: create_data_sink_api_v1_data_sinks_post
      security:
      - HTTPBearer: []
      parameters:
      - name: project_id
        in: query
        required: false
        schema:
          anyOf:
          - type: string
            format: uuid
          - type: 'null'
          title: Project Id
      - name: organization_id
        in: query
        required: false
        schema:
          anyOf:
          - type: string
            format: uuid
          - type: 'null'
          title: Organization Id
      - name: session
        in: cookie
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Session
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/DataSinkCreate'
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DataSink'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
  /api/v1/data-sinks/{data_sink_id}:
    get:
      tags:
      - Data Sinks
      summary: Get Data Sink
      description: Get a data sink by ID.
      operationId: get_data_sink_api_v1_data_sinks__data_sink_id__get
      security:
      - HTTPBearer: []
      parameters:
      - name: data_sink_id
        in: path
        required: true
        schema:
          type: string
          format: uuid
          title: Data Sink Id
      - name: session
        in: cookie
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Session
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DataSink'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
    put:
      tags:
      - Data Sinks
      summary: Update Data Sink
      description: Update a data sink by ID.
      operationId: update_data_sink_api_v1_data_sinks__data_sink_id__put
      security:
      - HTTPBearer: []
      parameters:
      - name: data_sink_id
        in: path
        required: true
        schema:
          type: string
          format: uuid
          title: Data Sink Id
      - name: session
        in: cookie
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Session
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/DataSinkUpdate'
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DataSink'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
    delete:
      tags:
      - Data Sinks
      summary: Delete Data Sink
      description: Delete a data sink by ID.
      operationId: delete_data_sink_api_v1_data_sinks__data_sink_id__delete
      security:
      - HTTPBearer: []
      parameters:
      - name: data_sink_id
        in: path
        required: true
        schema:
          type: string
          format: uuid
          title: Data Sink Id
      - name: session
        in: cookie
        required: false
        schema:
          anyOf:
          - type: string
          - type: 'null'
          title: Session
      responses:
        '204':
          description: Successful Response
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
components:
  schemas:
    CloudPostgresVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          title: Supports Nested Metadata Filters
          default: true
        database:
          type: string
          title: Database
        host:
          type: string
          title: Host
        password:
          type: string
          format: password
          title: Password
          writeOnly: true
        port:
          type: integer
          title: Port
        user:
          type: string
          title: User
        table_name:
          type: string
          title: Table Name
        schema_name:
          type: string
          title: Schema Name
        embed_dim:
          type: integer
          title: Embed Dim
        hybrid_search:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Hybrid Search
          default: true
        perform_setup:
          type: boolean
          title: Perform Setup
          default: true
        hnsw_settings:
          anyOf:
          - $ref: '#/components/schemas/PGVectorHNSWSettings'
          - type: 'null'
          description: HNSW settings for PGVector index. Set to null to disable HNSW indexing in favor of a brute force indexing/exact search strategy instead.
        class_name:
          type: string
          title: Class Name
          default: CloudPostgresVectorStore
      type: object
      required:
      - database
      - host
      - password
      - port
      - user
      - table_name
      - schema_name
      - embed_dim
      title: CloudPostgresVectorStore
    ConfigurableDataSinkNames:
      type: string
      enum:
      - PINECONE
      - POSTGRES
      - QDRANT
      - AZUREAI_SEARCH
      - MONGODB_ATLAS
      - MILVUS
      - ASTRA_DB
      title: ConfigurableDataSinkNames
    DataSink:
      properties:
        id:
          type: string
          format: uuid
          title: Id
          description: Unique identifier
        created_at:
          anyOf:
          - type: string
            format: date-time
          - type: 'null'
          title: Created At
          description: Creation datetime
        updated_at:
          anyOf:
          - type: string
            format: date-time
          - type: 'null'
          title: Updated At
          description: Update datetime
        name:
          type: string
          title: Name
          description: The name of the data sink.
        sink_type:
          $ref: '#/components/schemas/ConfigurableDataSinkNames'
        component:
          anyOf:
          - additionalProperties: true
            type: object
          - $ref: '#/components/schemas/CloudPineconeVectorStore'
          - $ref: '#/components/schemas/CloudPostgresVectorStore'
          - $ref: '#/components/schemas/CloudQdrantVectorStore'
          - $ref: '#/components/schemas/CloudAzureAISearchVectorStore'
          - $ref: '#/components/schemas/CloudMongoDBAtlasVectorSearch'
          - $ref: '#/components/schemas/CloudMilvusVectorStore'
          - $ref: '#/components/schemas/CloudAstraDBVectorStore'
          title: DataSinkCreateComponent
          description: Component that implements the data sink
        project_id:
          type: string
          format: uuid
          title: Project Id
      type: object
      required:
      - id
      - name
      - sink_type
      - component
      - project_id
      title: DataSink
      description: Schema for a data sink.
    DataSinkCreate:
      properties:
        name:
          type: string
          title: Name
          description: The name of the data sink.
        sink_type:
          $ref: '#/components/schemas/ConfigurableDataSinkNames'
        component:
          anyOf:
          - additionalProperties: true
            type: object
          - $ref: '#/components/schemas/CloudPineconeVectorStore'
          - $ref: '#/components/schemas/CloudPostgresVectorStore'
          - $ref: '#/components/schemas/CloudQdrantVectorStore'
          - $ref: '#/components/schemas/CloudAzureAISearchVectorStore'
          - $ref: '#/components/schemas/CloudMongoDBAtlasVectorSearch'
          - $ref: '#/components/schemas/CloudMilvusVectorStore'
          - $ref: '#/components/schemas/CloudAstraDBVectorStore'
          title: DataSinkCreateComponent
          description: Component that implements the data sink
      type: object
      required:
      - name
      - sink_type
      - component
      title: DataSinkCreate
      description: Schema for creating a data sink.
    PGVectorHNSWSettings:
      properties:
        ef_construction:
          type: integer
          minimum: 1.0
          title: Ef Construction
          description: The number of edges to use during the construction phase.
          default: 64
        ef_search:
          type: integer
          minimum: 1.0
          title: Ef Search
          description: The number of edges to use during the search phase.
          default: 40
        m:
          type: integer
          minimum: 1.0
          title: M
          description: The number of bi-directional links created for each new element.
          default: 16
        vector_type:
          $ref: '#/components/schemas/PGVectorVectorType'
          description: The type of vector to use.
          default: vector
        distance_method:
          $ref: '#/components/schemas/PGVectorDistanceMethod'
          description: The distance method to use.
          default: cosine
      type: object
      title: PGVectorHNSWSettings
      description: HNSW settings for PGVector.
    PGVectorDistanceMethod:
      type: string
      enum:
      - l2
      - ip
      - cosine
      - l1
      - hamming
      - jaccard
      title: PGVectorDistanceMethod
      description: 'Distance methods for PGVector.

        Docs:

        https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options'
    CloudMongoDBAtlasVectorSearch:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          title: Supports Nested Metadata Filters
          default: false
        mongodb_uri:
          type: string
          format: password
          title: Mongodb Uri
          writeOnly: true
        db_name:
          type: string
          title: Db Name
        collection_name:
          type: string
          title: Collection Name
        vector_index_name:
          anyOf:
          - type: string
          - type: 'null'
          title: Vector Index Name
        fulltext_index_name:
          anyOf:
          - type: string
          - type: 'null'
          title: Fulltext Index Name
        embedding_dimension:
          anyOf:
          - type: integer
          - type: 'null'
          title: Embedding Dimension
        class_name:
          type: string
          title: Class Name
          default: CloudMongoDBAtlasVectorSearch
      type: object
      required:
      - mongodb_uri
      - db_name
      - collection_name
      title: CloudMongoDBAtlasVectorSearch
      description: "Cloud MongoDB Atlas Vector Store.\n\nThis class is used to store the configuration for a MongoDB Atlas vector store,\nso that it can be created and used in LlamaCloud.\n\nArgs:\n    mongodb_uri (str): URI for connecting to MongoDB Atlas\n    db_name (str): name of the MongoDB database\n    collection_name (str): name of the MongoDB collection\n    vector_index_name (str): name of the MongoDB Atlas vector index\n    fulltext_index_name (str): name of the MongoDB Atlas full-text index"
    DataSinkUpdate:
      properties:
        name:
          anyOf:
          - type: string
          - type: 'null'
          title: Name
          description: The name of the data sink.
        sink_type:
          $ref: '#/components/schemas/ConfigurableDataSinkNames'
        component:
          anyOf:
          - additionalProperties: true
            type: object
          - $ref: '#/components/schemas/CloudPineconeVectorStore'
          - $ref: '#/components/schemas/CloudPostgresVectorStore'
          - $ref: '#/components/schemas/CloudQdrantVectorStore'
          - $ref: '#/components/schemas/CloudAzureAISearchVectorStore'
          - $ref: '#/components/schemas/CloudMongoDBAtlasVectorSearch'
          - $ref: '#/components/schemas/CloudMilvusVectorStore'
          - $ref: '#/components/schemas/CloudAstraDBVectorStore'
          - type: 'null'
          title: DataSinkUpdateComponent
          description: Component that implements the data sink
      type: object
      required:
      - sink_type
      title: DataSinkUpdate
      description: Schema for updating a data sink.
    ValidationError:
      properties:
        loc:
          items:
            anyOf:
            - type: string
            - type: integer
          type: array
          title: Location
        msg:
          type: string
          title: Message
        type:
          type: string
          title: Error Type
        input:
          title: Input
        ctx:
          type: object
          title: Context
      type: object
      required:
      - loc
      - msg
      - type
      title: ValidationError
    CloudAzureAISearchVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          const: true
          title: Supports Nested Metadata Filters
          default: true
        search_service_api_key:
          type: string
          format: password
          title: Search Service Api Key
          writeOnly: true
        search_service_endpoint:
          type: string
          title: Search Service Endpoint
        search_service_api_version:
          anyOf:
          - type: string
          - type: 'null'
          title: Search Service Api Version
        index_name:
          anyOf:
          - type: string
          - type: 'null'
          title: Index Name
        filterable_metadata_field_keys:
          anyOf:
          - additionalProperties: true
            type: object
          - type: 'null'
          title: Filterable Metadata Field Keys
        embedding_dimension:
          anyOf:
          - type: integer
          - type: 'null'
          title: Embedding Dimension
        client_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Client Id
        client_secret:
          anyOf:
          - type: string
            format: password
            writeOnly: true
          - type: 'null'
          title: Client Secret
        tenant_id:
          anyOf:
          - type: string
          - type: 'null'
          title: Tenant Id
        class_name:
          type: string
          title: Class Name
          default: CloudAzureAISearchVectorStore
      type: object
      required:
      - search_service_api_key
      - search_service_endpoint
      title: CloudAzureAISearchVectorStore
      description: Cloud Azure AI Search Vector Store.
    CloudMilvusVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          title: Supports Nested Metadata Filters
          default: false
        uri:
          type: string
          title: Uri
        collection_name:
          anyOf:
          - type: string
          - type: 'null'
          title: Collection Name
        token:
          anyOf:
          - type: string
            format: password
            writeOnly: true
          - type: 'null'
          title: Token
        embedding_dimension:
          anyOf:
          - type: integer
          - type: 'null'
          title: Embedding Dimension
        class_name:
          type: string
          title: Class Name
          default: CloudMilvusVectorStore
      type: object
      required:
      - uri
      title: CloudMilvusVectorStore
      description: Cloud Milvus Vector Store.
    CloudQdrantVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          const: true
          title: Supports Nested Metadata Filters
          default: true
        collection_name:
          type: string
          title: Collection Name
        url:
          type: string
          title: Url
        api_key:
          type: string
          format: password
          title: Api Key
          writeOnly: true
        max_retries:
          type: integer
          title: Max Retries
          default: 3
        client_kwargs:
          additionalProperties: true
          type: object
          title: Client Kwargs
        class_name:
          type: string
          title: Class Name
          default: CloudQdrantVectorStore
      type: object
      required:
      - collection_name
      - url
      - api_key
      title: CloudQdrantVectorStore
      description: "Cloud Qdrant Vector Store.\n\nThis class is used to store the configuration for a Qdrant vector store, so that it can be\ncreated and used in LlamaCloud.\n\nArgs:\n    collection_name (str): name of the Qdrant collection\n    url (str): url of the Qdrant instance\n    api_key (str): API key for authenticating with Qdrant\n    max_retries (int): maximum number of retries in case of a failure. Defaults to 3\n    client_kwargs (dict): additional kwargs to pass to the Qdrant client"
    PGVectorVectorType:
      type: string
      enum:
      - vector
      - half_vec
      - bit
      - sparse_vec
      title: PGVectorVectorType
      description: 'Vector storage formats for PGVector.

        Docs:

        https://github.com/pgvector/pgvector?tab=readme-ov-file#query-options'
    CloudPineconeVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          const: true
          title: Supports Nested Metadata Filters
          default: true
        api_key:
          type: string
          format: password
          title: Api Key
          description: The API key for authenticating with Pinecone
          writeOnly: true
        index_name:
          type: string
          title: Index Name
        namespace:
          anyOf:
          - type: string
          - type: 'null'
          title: Namespace
        insert_kwargs:
          anyOf:
          - additionalProperties: true
            type: object
          - type: 'null'
          title: Insert Kwargs
        class_name:
          type: string
          title: Class Name
          default: CloudPineconeVectorStore
      type: object
      required:
      - api_key
      - index_name
      title: CloudPineconeVectorStore
      description: "Cloud Pinecone Vector Store.\n\nThis class is used to store the configuration for a Pinecone vector store, so that it can be\ncreated and used in LlamaCloud.\n\nArgs:\n    api_key (str): API key for authenticating with Pinecone\n    index_name (str): name of the Pinecone index\n    namespace (optional[str]): namespace to use in the Pinecone index\n    insert_kwargs (optional[dict]): additional kwargs to pass during insertion"
    CloudAstraDBVectorStore:
      properties:
        supports_nested_metadata_filters:
          type: boolean
          const: true
          title: Supports Nested Metadata Filters
          default: true
        token:
          type: string
          format: password
          title: Token
          description: The Astra DB Application Token to use
          writeOnly: true
        api_endpoint:
          type: string
          title: Api Endpoint
          description: The Astra DB JSON API endpoint for your database
        collection_name:
          type: string
          title: Collection Name
          description: Collection name to use. If not existing, it will be created
        embedding_dimension:
          type: integer
          title: Embedding Dimension
          description: Length of the embedding vectors in use
        keyspace:
          anyOf:
          - type: string
          - type: 'null'
          title: Keyspace
          description: The keyspace to use. If not provided, 'default_keyspace'
        class_name:
          type: string
          title: Class Name
          default: CloudAstraDBVectorStore
      type: object
      required:
      - token
      - api_endpoint
      - collection_name
      - embedding_dimension
      title: CloudAstraDBVectorStore
      description: "Cloud AstraDB Vector Store.\n\nThis class is used to store the configuration for an AstraDB vector store, so that it can be\ncreated and used in LlamaCloud.\n\nArgs:\n    token (str): The Astra DB Application Token to use.\n    api_endpoint (str): The Astra DB JSON API endpoint for your database.\n    collection_name (str): Collection name to use. If not existing, it will be created.\n    embedding_dimension (int): Length of the embedding vectors in use.\n    keyspace (optional[str]): The keyspace to use. If not provided, 'default_keyspace'"
    HTTPValidationError:
      properties:
        detail:
          items:
            $ref: '#/components/schemas/ValidationError'
          type: array
          title: Detail
      type: object
      title: HTTPValidationError
  securitySchemes:
    HTTPBearer:
      type: http
      scheme: bearer