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Merge remote-tracking branch 'origin/main' into openai_v1
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commit
35546386a2
52 changed files with 580 additions and 802 deletions
103
docs/static/llama-stack-spec.yaml
vendored
103
docs/static/llama-stack-spec.yaml
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@ -861,41 +861,6 @@ paths:
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required: true
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schema:
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type: string
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/v1/inference/embeddings:
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post:
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responses:
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'200':
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description: >-
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An array of embeddings, one for each content. Each embedding is a list
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of floats. The dimensionality of the embedding is model-specific; you
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can check model metadata using /models/{model_id}.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/EmbeddingsResponse'
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'400':
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$ref: '#/components/responses/BadRequest400'
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'429':
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$ref: >-
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#/components/responses/TooManyRequests429
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'500':
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$ref: >-
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#/components/responses/InternalServerError500
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default:
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$ref: '#/components/responses/DefaultError'
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tags:
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- Inference
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summary: >-
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Generate embeddings for content pieces using the specified model.
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description: >-
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Generate embeddings for content pieces using the specified model.
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parameters: []
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requestBody:
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/EmbeddingsRequest'
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required: true
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/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
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post:
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responses:
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@ -5040,7 +5005,7 @@ paths:
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schema:
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$ref: '#/components/schemas/QueryTracesRequest'
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required: true
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/v1/inference/rerank:
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/v1alpha/inference/rerank:
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post:
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responses:
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'200':
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@ -8937,72 +8902,6 @@ components:
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title: OpenAIDeleteResponseObject
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description: >-
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Response object confirming deletion of an OpenAI response.
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EmbeddingsRequest:
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type: object
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properties:
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model_id:
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type: string
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description: >-
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The identifier of the model to use. The model must be an embedding model
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registered with Llama Stack and available via the /models endpoint.
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contents:
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oneOf:
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- type: array
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items:
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type: string
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- type: array
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items:
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$ref: '#/components/schemas/InterleavedContentItem'
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description: >-
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List of contents to generate embeddings for. Each content can be a string
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or an InterleavedContentItem (and hence can be multimodal). The behavior
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depends on the model and provider. Some models may only support text.
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text_truncation:
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type: string
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enum:
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- none
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- start
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- end
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description: >-
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(Optional) Config for how to truncate text for embedding when text is
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longer than the model's max sequence length.
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output_dimension:
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type: integer
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description: >-
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(Optional) Output dimensionality for the embeddings. Only supported by
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Matryoshka models.
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task_type:
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type: string
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enum:
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- query
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- document
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description: >-
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(Optional) How is the embedding being used? This is only supported by
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asymmetric embedding models.
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additionalProperties: false
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required:
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- model_id
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- contents
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title: EmbeddingsRequest
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EmbeddingsResponse:
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type: object
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properties:
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embeddings:
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type: array
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items:
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type: array
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items:
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type: number
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description: >-
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List of embedding vectors, one per input content. Each embedding is a
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list of floats. The dimensionality of the embedding is model-specific;
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you can check model metadata using /models/{model_id}
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additionalProperties: false
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required:
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- embeddings
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title: EmbeddingsResponse
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description: >-
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Response containing generated embeddings.
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AgentCandidate:
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type: object
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properties:
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