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chore(api): remove deprecated embeddings impls (#3301)
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# What does this PR do? remove deprecated embeddings implementations
This commit is contained in:
Matthew Farrellee
GitHub
parent
aab22dc759
commit
975ead1d6a
19 changed files with 3 additions and 632 deletions
118
docs/static/llama-stack-spec.html
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118
docs/static/llama-stack-spec.html
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@ -1035,50 +1035,6 @@
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]
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}
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},
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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": "An array of embeddings, one for each content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you 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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"$ref": "#/components/schemas/EmbeddingsResponse"
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}
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}
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}
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},
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"400": {
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"$ref": "#/components/responses/BadRequest400"
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},
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"429": {
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"$ref": "#/components/responses/TooManyRequests429"
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},
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"500": {
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"$ref": "#/components/responses/InternalServerError500"
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},
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"default": {
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"$ref": "#/components/responses/DefaultError"
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}
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},
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"tags": [
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"Inference"
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],
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"summary": "Generate embeddings for content pieces using the specified model.",
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"description": "Generate embeddings for content pieces using the specified model.",
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"parameters": [],
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"requestBody": {
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/EmbeddingsRequest"
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}
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}
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},
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"required": true
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}
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}
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},
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"/v1alpha/eval/benchmarks/{benchmark_id}/evaluations": {
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"post": {
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"responses": {
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@ -10547,80 +10503,6 @@
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"title": "OpenAIDeleteResponseObject",
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"description": "Response object confirming deletion of an OpenAI response."
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},
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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": "The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint."
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},
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"contents": {
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"oneOf": [
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{
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"type": "array",
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"items": {
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"type": "string"
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}
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},
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{
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"type": "array",
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"items": {
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"$ref": "#/components/schemas/InterleavedContentItem"
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}
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}
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],
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"description": "List of contents to generate embeddings for. Each content can be a string or an InterleavedContentItem (and hence can be multimodal). The behavior depends on the model and provider. Some models may only support text."
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},
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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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],
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"description": "(Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length."
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},
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"output_dimension": {
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"type": "integer",
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"description": "(Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models."
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},
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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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],
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"description": "(Optional) How is the embedding being used? This is only supported by asymmetric embedding models."
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}
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},
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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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],
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"title": "EmbeddingsRequest"
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},
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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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}
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},
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"description": "List of embedding vectors, one per input content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}"
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}
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},
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"additionalProperties": false,
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"required": [
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"embeddings"
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],
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"title": "EmbeddingsResponse",
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"description": "Response containing generated embeddings."
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},
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"AgentCandidate": {
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"type": "object",
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"properties": {
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101
docs/static/llama-stack-spec.yaml
vendored
101
docs/static/llama-stack-spec.yaml
vendored
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@ -720,41 +720,6 @@ 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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$ref: '#/components/schemas/EmbeddingsRequest'
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required: true
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/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
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post:
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responses:
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@ -7795,72 +7760,6 @@ components:
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title: OpenAIDeleteResponseObject
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description: >-
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Response object confirming deletion of an OpenAI response.
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EmbeddingsRequest:
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type: object
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properties:
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model_id:
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type: string
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description: >-
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The identifier of the model to use. The model must be an embedding model
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registered with Llama Stack and available via the /models endpoint.
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contents:
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oneOf:
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- type: array
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items:
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type: string
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- type: array
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items:
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$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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|
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