openapi: 3.2.0 info: description: Provides the Oracle REST Data Services (ORDS) users the ability to perform Oracle Database management and monitoring operations through a user-friendly REST API. version: 2026.03.26 title: Oracle REST Data Services Vector Database/Inference………… contact: name: Oracle REST Data Services url: https://www.oracle.com/database/technologies/appdev/rest.html x-summary: Provides the Oracle REST Data Services (ORDS) users the ability to perform Oracle Database management and monitoring operations through a user-friendly REST API. tags: - name: Vector Database/Inference Operations description: The operations from the Vector Database/Inference Operations category. paths: /vecdb/embed: post: tags: - Vector Database/Inference Operations operationId: generate_embedding summary: Generate text embeddings description: Generate vector embeddings for one or more input texts using an ONNX model hosted in the database. security: - BasicAuth: [] - BearerAuth: [] - OAuth2: [] requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/VectorEmbedRequest' example: modelName: TEXT_MODEL inputs: - text: Apple is a popular fruit known for its sweetness and crisp texture. - text: The tech company Apple is known for its innovative products like the iPhone. - text: Many people enjoy eating apples as a healthy snack. - text: Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces. - text: An apple a day keeps the doctor away, as the saying goes. - text: Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership. responses: '200': description: Embeddings generated successfully. content: application/json: schema: $ref: '#/components/schemas/VectorEmbedResponse' example: data: - text: Apple is a popular fruit known for its sweetness and crisp texture. embedding: - -0.0013012911 - 0.008567206 - 0.010289432 - -0.031064631 - -0.082377188 - 0.047858458 '400': description: The request body included invalid parameters. content: application/problem+json: schema: $ref: '#/components/schemas/ORDSErrorResponse' x-internal-id: vecdb-embed-post x-filename-id: vecdb-embed-post /vecdb/rerank: post: tags: - Vector Database/Inference Operations operationId: rerank summary: Rerank documents by query relevance description: Score and order documents by relevance to a query using an ONNX reranker hosted in the database. security: - BasicAuth: [] - BearerAuth: [] - OAuth2: [] requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/VectorRerankRequest' example: query: What is the capital of the United States? documents: - Carson City is the capital city of the American state of Nevada. - The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan. - Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. - Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages. - Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states. modelName: reranker modelParams: provider: database responses: '200': description: Rerank scores for each input document in descending relevance. content: application/json: schema: $ref: '#/components/schemas/VectorRerankResponse' example: - index: 2 score: 8.034194946289062 - index: 0 score: 4.549003601074219 - index: 1 score: 1.1962472200393677 - index: 3 score: -5.338606834411621 - index: 4 score: -6.584031105041504 '400': description: The request body included invalid parameters. content: application/problem+json: schema: $ref: '#/components/schemas/ORDSErrorResponse' x-internal-id: vecdb-rerank-post x-filename-id: vecdb-rerank-post components: schemas: VectorDebugFlags: type: object description: Debug flags to be used to trace vector modules properties: VECTOR_INDEX: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH_BUILD: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH_MEM: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH_SEARCH: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH_APPCHNG: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_GRAPH_STATS: type: string enum: - low - medium - high VECTOR_INDEX_NEIGHBOR_PARTITIONS: type: string enum: - low - medium - high VECTOR_INDEX_FIXED_VIEW: type: string enum: - low - medium - high VECIDX_TRANS: type: string enum: - low - medium - high VECIDX_TRANS_COM: type: string enum: - low - medium - high VECIDX_TRANS_PJ: type: string enum: - low - medium - high VECIDX_TRANS_PJ_DWNGRD: type: string enum: - low - medium - high VECIDX_TRANS_PJ_GROW: type: string enum: - low - medium - high VECIDX_TRANS_SJ: type: string enum: - low - medium - high VECIDX_TRANS_SJ_BG: type: string enum: - low - medium - high VEC_INDEX_CALIBRATION: type: string enum: - low - medium - high VECTOR_TRACE: type: string enum: - low - medium - high VectorEmbedItem: type: object required: - text - embedding properties: text: type: string description: Echo of the input text. embedding: type: array description: Embedding vector for the input text. items: type: number format: double VectorRerankItem: type: object required: - index - score properties: index: type: integer format: int32 description: Zero-based index of the input document in the request payload. score: type: number format: double description: Relevance score for the document (higher is more relevant). VectorRerankRequest: type: object required: - query - documents - modelName properties: query: type: string description: Natural language query to use for reranking. documents: type: array description: Array of raw document strings to score. minItems: 1 items: type: string modelName: type: string description: The name of the ONNX reranking model hosted in the database. modelParams: type: object description: Model-specific parameters. additionalProperties: true properties: provider: type: string description: Optional model provider (e.g., 'database'). debugFlags: $ref: '#/components/schemas/VectorDebugFlags' ORDSErrorResponse: type: object required: - code - message - type - instance properties: code: type: string message: type: string type: type: string instance: type: string diagnosticTrace: type: string stackTrace: type: string VectorEmbedRequest: type: object required: - modelName - inputs properties: modelName: type: string description: The name of the text embedding model hosted in the database. inputs: type: array description: Array of input items to embed. minItems: 1 items: $ref: '#/components/schemas/VectorEmbedInputItem' debugFlags: $ref: '#/components/schemas/VectorDebugFlags' VectorEmbedInputItem: type: object required: - text properties: text: type: string description: Input text to embed. VectorEmbedResponse: type: object required: - data properties: data: type: array description: List of generated embeddings. items: $ref: '#/components/schemas/VectorEmbedItem' VectorRerankResponse: type: array description: List of rerank results sorted by descending relevance. items: $ref: '#/components/schemas/VectorRerankItem' securitySchemes: BasicAuth: type: http scheme: basic BearerAuth: type: http scheme: bearer OAuth2: type: oauth2 flows: implicit: authorizationUrl: /oauth/auth scopes: {} authorizationCode: authorizationUrl: /oauth/auth tokenUrl: /oauth/token scopes: {} clientCredentials: tokenUrl: /oauth/token scopes: {} externalDocs: description: Oracle REST Data Services product documentation. url: https://docs.oracle.com/en/database/oracle/oracle-rest-data-services/