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feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794)
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Applies the same pattern from https://github.com/llamastack/llama-stack/pull/3777 to embeddings and vector_stores.create() endpoints. This should _not_ be a breaking change since (a) our tests were already using the `extra_body` parameter when passing in to the backend (b) but the backend probably wasn't extracting the parameters correctly. This PR will fix that. Updated APIs: `openai_embeddings(), openai_create_vector_store(), openai_create_vector_store_file_batch()`
This commit is contained in:
Ashwin Bharambe
GitHub
parent
3bb6ef351b
commit
ecc8a554d2
26 changed files with 451 additions and 426 deletions
54
docs/static/deprecated-llama-stack-spec.html
vendored
54
docs/static/deprecated-llama-stack-spec.html
vendored
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@ -1662,7 +1662,7 @@
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
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"$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
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}
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}
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},
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@ -2436,13 +2436,13 @@
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"VectorIO"
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],
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"summary": "Creates a vector store.",
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"description": "Creates a vector store.",
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"description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
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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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"$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
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"$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
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}
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}
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},
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@ -2622,7 +2622,7 @@
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"VectorIO"
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],
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"summary": "Create a vector store file batch.",
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"description": "Create a vector store file batch.",
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"description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
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"parameters": [
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{
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"name": "vector_store_id",
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@ -2638,7 +2638,7 @@
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
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"$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
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}
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}
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},
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@ -8174,7 +8174,7 @@
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"title": "OpenAICompletionChoice",
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"description": "A choice from an OpenAI-compatible completion response."
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},
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"OpenaiEmbeddingsRequest": {
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"OpenAIEmbeddingsRequestWithExtraBody": {
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"type": "object",
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"properties": {
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"model": {
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@ -8197,6 +8197,7 @@
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},
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"encoding_format": {
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"type": "string",
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"default": "float",
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"description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
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},
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"dimensions": {
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@ -8213,7 +8214,8 @@
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"model",
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"input"
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],
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"title": "OpenaiEmbeddingsRequest"
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"title": "OpenAIEmbeddingsRequestWithExtraBody",
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"description": "Request parameters for OpenAI-compatible embeddings endpoint."
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},
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"OpenAIEmbeddingData": {
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"type": "object",
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@ -12061,19 +12063,19 @@
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"title": "VectorStoreObject",
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"description": "OpenAI Vector Store object."
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},
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"OpenaiCreateVectorStoreRequest": {
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"OpenAICreateVectorStoreRequestWithExtraBody": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "A name for the vector store."
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"description": "(Optional) A name for the vector store"
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},
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"file_ids": {
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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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"description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
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"description": "List of file IDs to include in the vector store"
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},
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"expires_after": {
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"type": "object",
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@ -12099,7 +12101,7 @@
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}
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]
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},
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"description": "The expiration policy for a vector store."
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"description": "(Optional) Expiration policy for the vector store"
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},
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"chunking_strategy": {
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"type": "object",
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@ -12125,7 +12127,7 @@
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}
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]
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},
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"description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
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"description": "(Optional) Strategy for splitting files into chunks"
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},
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"metadata": {
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"type": "object",
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@ -12151,23 +12153,12 @@
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}
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]
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},
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"description": "Set of 16 key-value pairs that can be attached to an object."
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},
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"embedding_model": {
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"type": "string",
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"description": "The embedding model to use for this vector store."
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},
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"embedding_dimension": {
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"type": "integer",
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"description": "The dimension of the embedding vectors (default: 384)."
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},
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"provider_id": {
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"type": "string",
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"description": "The ID of the provider to use for this vector store."
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"description": "Set of key-value pairs that can be attached to the vector store"
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}
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},
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"additionalProperties": false,
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"title": "OpenaiCreateVectorStoreRequest"
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"title": "OpenAICreateVectorStoreRequestWithExtraBody",
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"description": "Request to create a vector store with extra_body support."
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},
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"OpenaiUpdateVectorStoreRequest": {
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"type": "object",
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@ -12337,7 +12328,7 @@
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"title": "VectorStoreChunkingStrategyStaticConfig",
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"description": "Configuration for static chunking strategy."
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},
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"OpenaiCreateVectorStoreFileBatchRequest": {
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"OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
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"type": "object",
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"properties": {
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"file_ids": {
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@ -12345,7 +12336,7 @@
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"items": {
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"type": "string"
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},
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"description": "A list of File IDs that the vector store should use."
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"description": "A list of File IDs that the vector store should use"
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},
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"attributes": {
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"type": "object",
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@ -12371,18 +12362,19 @@
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}
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]
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},
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"description": "(Optional) Key-value attributes to store with the files."
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"description": "(Optional) Key-value attributes to store with the files"
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},
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"chunking_strategy": {
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"$ref": "#/components/schemas/VectorStoreChunkingStrategy",
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"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
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"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
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}
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},
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"additionalProperties": false,
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"required": [
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"file_ids"
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],
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"title": "OpenaiCreateVectorStoreFileBatchRequest"
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"title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
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"description": "Request to create a vector store file batch with extra_body support."
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},
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"VectorStoreFileBatchObject": {
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"type": "object",
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docs/static/deprecated-llama-stack-spec.yaml
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@ -1203,7 +1203,7 @@ paths:
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/OpenaiEmbeddingsRequest'
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$ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
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required: true
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deprecated: true
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/v1/openai/v1/files:
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@ -1792,13 +1792,16 @@ paths:
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tags:
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- VectorIO
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summary: Creates a vector store.
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description: Creates a vector store.
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description: >-
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Creates a vector store.
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Generate an OpenAI-compatible vector store with the given parameters.
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parameters: []
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requestBody:
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
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$ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
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required: true
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deprecated: true
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/v1/openai/v1/vector_stores/{vector_store_id}:
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tags:
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- VectorIO
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summary: Create a vector store file batch.
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description: Create a vector store file batch.
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description: >-
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Create a vector store file batch.
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Generate an OpenAI-compatible vector store file batch for the given vector
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store.
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parameters:
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- name: vector_store_id
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in: path
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
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$ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
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required: true
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deprecated: true
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/v1/openai/v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
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title: OpenAICompletionChoice
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description: >-
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A choice from an OpenAI-compatible completion response.
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OpenaiEmbeddingsRequest:
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OpenAIEmbeddingsRequestWithExtraBody:
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type: object
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properties:
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model:
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@ -6054,6 +6061,7 @@ components:
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multiple inputs in a single request, pass an array of strings.
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encoding_format:
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type: string
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default: float
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description: >-
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(Optional) The format to return the embeddings in. Can be either "float"
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or "base64". Defaults to "float".
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@ -6071,7 +6079,9 @@ components:
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required:
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- model
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- input
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title: OpenaiEmbeddingsRequest
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title: OpenAIEmbeddingsRequestWithExtraBody
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description: >-
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Request parameters for OpenAI-compatible embeddings endpoint.
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OpenAIEmbeddingData:
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type: object
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properties:
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@ -9147,19 +9157,18 @@ components:
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- metadata
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title: VectorStoreObject
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description: OpenAI Vector Store object.
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OpenaiCreateVectorStoreRequest:
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"OpenAICreateVectorStoreRequestWithExtraBody":
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type: object
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properties:
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name:
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type: string
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description: A name for the vector store.
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description: (Optional) A name for the vector store
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file_ids:
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type: array
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items:
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type: string
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description: >-
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A list of File IDs that the vector store should use. Useful for tools
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like `file_search` that can access files.
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List of file IDs to include in the vector store
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expires_after:
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type: object
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additionalProperties:
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@ -9171,7 +9180,7 @@ components:
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- type: array
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- type: object
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description: >-
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The expiration policy for a vector store.
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(Optional) Expiration policy for the vector store
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chunking_strategy:
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type: object
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additionalProperties:
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@ -9183,8 +9192,7 @@ components:
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- type: array
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- type: object
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description: >-
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The chunking strategy used to chunk the file(s). If not set, will use
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the `auto` strategy.
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(Optional) Strategy for splitting files into chunks
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metadata:
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type: object
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additionalProperties:
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@ -9196,21 +9204,12 @@ components:
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- type: array
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- type: object
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description: >-
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Set of 16 key-value pairs that can be attached to an object.
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embedding_model:
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type: string
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description: >-
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The embedding model to use for this vector store.
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embedding_dimension:
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type: integer
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description: >-
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The dimension of the embedding vectors (default: 384).
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provider_id:
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type: string
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description: >-
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The ID of the provider to use for this vector store.
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Set of key-value pairs that can be attached to the vector store
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additionalProperties: false
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title: OpenaiCreateVectorStoreRequest
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title: >-
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OpenAICreateVectorStoreRequestWithExtraBody
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description: >-
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Request to create a vector store with extra_body support.
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OpenaiUpdateVectorStoreRequest:
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type: object
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properties:
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title: VectorStoreChunkingStrategyStaticConfig
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description: >-
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Configuration for static chunking strategy.
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OpenaiCreateVectorStoreFileBatchRequest:
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"OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
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type: object
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properties:
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file_ids:
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@ -9339,7 +9338,7 @@ components:
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items:
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type: string
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description: >-
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A list of File IDs that the vector store should use.
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A list of File IDs that the vector store should use
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attributes:
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type: object
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additionalProperties:
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- type: array
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- type: object
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description: >-
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(Optional) Key-value attributes to store with the files.
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(Optional) Key-value attributes to store with the files
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chunking_strategy:
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$ref: '#/components/schemas/VectorStoreChunkingStrategy'
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description: >-
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(Optional) The chunking strategy used to chunk the file(s). Defaults to
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auto.
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auto
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additionalProperties: false
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required:
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- file_ids
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title: OpenaiCreateVectorStoreFileBatchRequest
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title: >-
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OpenAICreateVectorStoreFileBatchRequestWithExtraBody
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description: >-
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Request to create a vector store file batch with extra_body support.
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VectorStoreFileBatchObject:
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type: object
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properties:
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54
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
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"$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
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}
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}
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},
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@ -3170,13 +3170,13 @@
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"VectorIO"
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],
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"summary": "Creates a vector store.",
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"description": "Creates a vector store.",
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"description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
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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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"$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
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"$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
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}
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}
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},
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"VectorIO"
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],
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"summary": "Create a vector store file batch.",
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"description": "Create a vector store file batch.",
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"description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
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"parameters": [
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{
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"name": "vector_store_id",
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@ -3372,7 +3372,7 @@
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
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"$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
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}
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}
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},
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@ -6324,7 +6324,7 @@
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"title": "ConversationItemDeletedResource",
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"description": "Response for deleted conversation item."
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},
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"OpenaiEmbeddingsRequest": {
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"OpenAIEmbeddingsRequestWithExtraBody": {
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"type": "object",
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"properties": {
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"model": {
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|
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@ -6347,6 +6347,7 @@
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},
|
||||
"encoding_format": {
|
||||
"type": "string",
|
||||
"default": "float",
|
||||
"description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
|
||||
},
|
||||
"dimensions": {
|
||||
|
|
@ -6363,7 +6364,8 @@
|
|||
"model",
|
||||
"input"
|
||||
],
|
||||
"title": "OpenaiEmbeddingsRequest"
|
||||
"title": "OpenAIEmbeddingsRequestWithExtraBody",
|
||||
"description": "Request parameters for OpenAI-compatible embeddings endpoint."
|
||||
},
|
||||
"OpenAIEmbeddingData": {
|
||||
"type": "object",
|
||||
|
|
@ -12587,19 +12589,19 @@
|
|||
"title": "VectorStoreObject",
|
||||
"description": "OpenAI Vector Store object."
|
||||
},
|
||||
"OpenaiCreateVectorStoreRequest": {
|
||||
"OpenAICreateVectorStoreRequestWithExtraBody": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "A name for the vector store."
|
||||
"description": "(Optional) A name for the vector store"
|
||||
},
|
||||
"file_ids": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
|
||||
"description": "List of file IDs to include in the vector store"
|
||||
},
|
||||
"expires_after": {
|
||||
"type": "object",
|
||||
|
|
@ -12625,7 +12627,7 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "The expiration policy for a vector store."
|
||||
"description": "(Optional) Expiration policy for the vector store"
|
||||
},
|
||||
"chunking_strategy": {
|
||||
"type": "object",
|
||||
|
|
@ -12651,7 +12653,7 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
|
||||
"description": "(Optional) Strategy for splitting files into chunks"
|
||||
},
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
|
|
@ -12677,23 +12679,12 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "Set of 16 key-value pairs that can be attached to an object."
|
||||
},
|
||||
"embedding_model": {
|
||||
"type": "string",
|
||||
"description": "The embedding model to use for this vector store."
|
||||
},
|
||||
"embedding_dimension": {
|
||||
"type": "integer",
|
||||
"description": "The dimension of the embedding vectors (default: 384)."
|
||||
},
|
||||
"provider_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the provider to use for this vector store."
|
||||
"description": "Set of key-value pairs that can be attached to the vector store"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"title": "OpenaiCreateVectorStoreRequest"
|
||||
"title": "OpenAICreateVectorStoreRequestWithExtraBody",
|
||||
"description": "Request to create a vector store with extra_body support."
|
||||
},
|
||||
"OpenaiUpdateVectorStoreRequest": {
|
||||
"type": "object",
|
||||
|
|
@ -12863,7 +12854,7 @@
|
|||
"title": "VectorStoreChunkingStrategyStaticConfig",
|
||||
"description": "Configuration for static chunking strategy."
|
||||
},
|
||||
"OpenaiCreateVectorStoreFileBatchRequest": {
|
||||
"OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"file_ids": {
|
||||
|
|
@ -12871,7 +12862,7 @@
|
|||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "A list of File IDs that the vector store should use."
|
||||
"description": "A list of File IDs that the vector store should use"
|
||||
},
|
||||
"attributes": {
|
||||
"type": "object",
|
||||
|
|
@ -12897,18 +12888,19 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "(Optional) Key-value attributes to store with the files."
|
||||
"description": "(Optional) Key-value attributes to store with the files"
|
||||
},
|
||||
"chunking_strategy": {
|
||||
"$ref": "#/components/schemas/VectorStoreChunkingStrategy",
|
||||
"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
|
||||
"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"file_ids"
|
||||
],
|
||||
"title": "OpenaiCreateVectorStoreFileBatchRequest"
|
||||
"title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
|
||||
"description": "Request to create a vector store file batch with extra_body support."
|
||||
},
|
||||
"VectorStoreFileBatchObject": {
|
||||
"type": "object",
|
||||
|
|
|
|||
68
docs/static/llama-stack-spec.yaml
vendored
68
docs/static/llama-stack-spec.yaml
vendored
|
|
@ -617,7 +617,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiEmbeddingsRequest'
|
||||
$ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/files:
|
||||
|
|
@ -2413,13 +2413,16 @@ paths:
|
|||
tags:
|
||||
- VectorIO
|
||||
summary: Creates a vector store.
|
||||
description: Creates a vector store.
|
||||
description: >-
|
||||
Creates a vector store.
|
||||
|
||||
Generate an OpenAI-compatible vector store with the given parameters.
|
||||
parameters: []
|
||||
requestBody:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
|
||||
$ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}:
|
||||
|
|
@ -2545,7 +2548,11 @@ paths:
|
|||
tags:
|
||||
- VectorIO
|
||||
summary: Create a vector store file batch.
|
||||
description: Create a vector store file batch.
|
||||
description: >-
|
||||
Create a vector store file batch.
|
||||
|
||||
Generate an OpenAI-compatible vector store file batch for the given vector
|
||||
store.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
|
|
@ -2558,7 +2565,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
|
||||
$ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
|
||||
|
|
@ -4797,7 +4804,7 @@ components:
|
|||
- deleted
|
||||
title: ConversationItemDeletedResource
|
||||
description: Response for deleted conversation item.
|
||||
OpenaiEmbeddingsRequest:
|
||||
OpenAIEmbeddingsRequestWithExtraBody:
|
||||
type: object
|
||||
properties:
|
||||
model:
|
||||
|
|
@ -4816,6 +4823,7 @@ components:
|
|||
multiple inputs in a single request, pass an array of strings.
|
||||
encoding_format:
|
||||
type: string
|
||||
default: float
|
||||
description: >-
|
||||
(Optional) The format to return the embeddings in. Can be either "float"
|
||||
or "base64". Defaults to "float".
|
||||
|
|
@ -4833,7 +4841,9 @@ components:
|
|||
required:
|
||||
- model
|
||||
- input
|
||||
title: OpenaiEmbeddingsRequest
|
||||
title: OpenAIEmbeddingsRequestWithExtraBody
|
||||
description: >-
|
||||
Request parameters for OpenAI-compatible embeddings endpoint.
|
||||
OpenAIEmbeddingData:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9612,19 +9622,18 @@ components:
|
|||
- metadata
|
||||
title: VectorStoreObject
|
||||
description: OpenAI Vector Store object.
|
||||
OpenaiCreateVectorStoreRequest:
|
||||
"OpenAICreateVectorStoreRequestWithExtraBody":
|
||||
type: object
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
description: A name for the vector store.
|
||||
description: (Optional) A name for the vector store
|
||||
file_ids:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: >-
|
||||
A list of File IDs that the vector store should use. Useful for tools
|
||||
like `file_search` that can access files.
|
||||
List of file IDs to include in the vector store
|
||||
expires_after:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -9636,7 +9645,7 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
The expiration policy for a vector store.
|
||||
(Optional) Expiration policy for the vector store
|
||||
chunking_strategy:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -9648,8 +9657,7 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
The chunking strategy used to chunk the file(s). If not set, will use
|
||||
the `auto` strategy.
|
||||
(Optional) Strategy for splitting files into chunks
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -9661,21 +9669,12 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
Set of 16 key-value pairs that can be attached to an object.
|
||||
embedding_model:
|
||||
type: string
|
||||
description: >-
|
||||
The embedding model to use for this vector store.
|
||||
embedding_dimension:
|
||||
type: integer
|
||||
description: >-
|
||||
The dimension of the embedding vectors (default: 384).
|
||||
provider_id:
|
||||
type: string
|
||||
description: >-
|
||||
The ID of the provider to use for this vector store.
|
||||
Set of key-value pairs that can be attached to the vector store
|
||||
additionalProperties: false
|
||||
title: OpenaiCreateVectorStoreRequest
|
||||
title: >-
|
||||
OpenAICreateVectorStoreRequestWithExtraBody
|
||||
description: >-
|
||||
Request to create a vector store with extra_body support.
|
||||
OpenaiUpdateVectorStoreRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9796,7 +9795,7 @@ components:
|
|||
title: VectorStoreChunkingStrategyStaticConfig
|
||||
description: >-
|
||||
Configuration for static chunking strategy.
|
||||
OpenaiCreateVectorStoreFileBatchRequest:
|
||||
"OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
|
||||
type: object
|
||||
properties:
|
||||
file_ids:
|
||||
|
|
@ -9804,7 +9803,7 @@ components:
|
|||
items:
|
||||
type: string
|
||||
description: >-
|
||||
A list of File IDs that the vector store should use.
|
||||
A list of File IDs that the vector store should use
|
||||
attributes:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -9816,16 +9815,19 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
(Optional) Key-value attributes to store with the files.
|
||||
(Optional) Key-value attributes to store with the files
|
||||
chunking_strategy:
|
||||
$ref: '#/components/schemas/VectorStoreChunkingStrategy'
|
||||
description: >-
|
||||
(Optional) The chunking strategy used to chunk the file(s). Defaults to
|
||||
auto.
|
||||
auto
|
||||
additionalProperties: false
|
||||
required:
|
||||
- file_ids
|
||||
title: OpenaiCreateVectorStoreFileBatchRequest
|
||||
title: >-
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody
|
||||
description: >-
|
||||
Request to create a vector store file batch with extra_body support.
|
||||
VectorStoreFileBatchObject:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
54
docs/static/stainless-llama-stack-spec.html
vendored
54
docs/static/stainless-llama-stack-spec.html
vendored
|
|
@ -765,7 +765,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/OpenaiEmbeddingsRequest"
|
||||
"$ref": "#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody"
|
||||
}
|
||||
}
|
||||
},
|
||||
|
|
@ -3170,13 +3170,13 @@
|
|||
"VectorIO"
|
||||
],
|
||||
"summary": "Creates a vector store.",
|
||||
"description": "Creates a vector store.",
|
||||
"description": "Creates a vector store.\nGenerate an OpenAI-compatible vector store with the given parameters.",
|
||||
"parameters": [],
|
||||
"requestBody": {
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/OpenaiCreateVectorStoreRequest"
|
||||
"$ref": "#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody"
|
||||
}
|
||||
}
|
||||
},
|
||||
|
|
@ -3356,7 +3356,7 @@
|
|||
"VectorIO"
|
||||
],
|
||||
"summary": "Create a vector store file batch.",
|
||||
"description": "Create a vector store file batch.",
|
||||
"description": "Create a vector store file batch.\nGenerate an OpenAI-compatible vector store file batch for the given vector store.",
|
||||
"parameters": [
|
||||
{
|
||||
"name": "vector_store_id",
|
||||
|
|
@ -3372,7 +3372,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest"
|
||||
"$ref": "#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody"
|
||||
}
|
||||
}
|
||||
},
|
||||
|
|
@ -8333,7 +8333,7 @@
|
|||
"title": "ConversationItemDeletedResource",
|
||||
"description": "Response for deleted conversation item."
|
||||
},
|
||||
"OpenaiEmbeddingsRequest": {
|
||||
"OpenAIEmbeddingsRequestWithExtraBody": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"model": {
|
||||
|
|
@ -8356,6 +8356,7 @@
|
|||
},
|
||||
"encoding_format": {
|
||||
"type": "string",
|
||||
"default": "float",
|
||||
"description": "(Optional) The format to return the embeddings in. Can be either \"float\" or \"base64\". Defaults to \"float\"."
|
||||
},
|
||||
"dimensions": {
|
||||
|
|
@ -8372,7 +8373,8 @@
|
|||
"model",
|
||||
"input"
|
||||
],
|
||||
"title": "OpenaiEmbeddingsRequest"
|
||||
"title": "OpenAIEmbeddingsRequestWithExtraBody",
|
||||
"description": "Request parameters for OpenAI-compatible embeddings endpoint."
|
||||
},
|
||||
"OpenAIEmbeddingData": {
|
||||
"type": "object",
|
||||
|
|
@ -14596,19 +14598,19 @@
|
|||
"title": "VectorStoreObject",
|
||||
"description": "OpenAI Vector Store object."
|
||||
},
|
||||
"OpenaiCreateVectorStoreRequest": {
|
||||
"OpenAICreateVectorStoreRequestWithExtraBody": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "A name for the vector store."
|
||||
"description": "(Optional) A name for the vector store"
|
||||
},
|
||||
"file_ids": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files."
|
||||
"description": "List of file IDs to include in the vector store"
|
||||
},
|
||||
"expires_after": {
|
||||
"type": "object",
|
||||
|
|
@ -14634,7 +14636,7 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "The expiration policy for a vector store."
|
||||
"description": "(Optional) Expiration policy for the vector store"
|
||||
},
|
||||
"chunking_strategy": {
|
||||
"type": "object",
|
||||
|
|
@ -14660,7 +14662,7 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy."
|
||||
"description": "(Optional) Strategy for splitting files into chunks"
|
||||
},
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
|
|
@ -14686,23 +14688,12 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "Set of 16 key-value pairs that can be attached to an object."
|
||||
},
|
||||
"embedding_model": {
|
||||
"type": "string",
|
||||
"description": "The embedding model to use for this vector store."
|
||||
},
|
||||
"embedding_dimension": {
|
||||
"type": "integer",
|
||||
"description": "The dimension of the embedding vectors (default: 384)."
|
||||
},
|
||||
"provider_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the provider to use for this vector store."
|
||||
"description": "Set of key-value pairs that can be attached to the vector store"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"title": "OpenaiCreateVectorStoreRequest"
|
||||
"title": "OpenAICreateVectorStoreRequestWithExtraBody",
|
||||
"description": "Request to create a vector store with extra_body support."
|
||||
},
|
||||
"OpenaiUpdateVectorStoreRequest": {
|
||||
"type": "object",
|
||||
|
|
@ -14872,7 +14863,7 @@
|
|||
"title": "VectorStoreChunkingStrategyStaticConfig",
|
||||
"description": "Configuration for static chunking strategy."
|
||||
},
|
||||
"OpenaiCreateVectorStoreFileBatchRequest": {
|
||||
"OpenAICreateVectorStoreFileBatchRequestWithExtraBody": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"file_ids": {
|
||||
|
|
@ -14880,7 +14871,7 @@
|
|||
"items": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": "A list of File IDs that the vector store should use."
|
||||
"description": "A list of File IDs that the vector store should use"
|
||||
},
|
||||
"attributes": {
|
||||
"type": "object",
|
||||
|
|
@ -14906,18 +14897,19 @@
|
|||
}
|
||||
]
|
||||
},
|
||||
"description": "(Optional) Key-value attributes to store with the files."
|
||||
"description": "(Optional) Key-value attributes to store with the files"
|
||||
},
|
||||
"chunking_strategy": {
|
||||
"$ref": "#/components/schemas/VectorStoreChunkingStrategy",
|
||||
"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto."
|
||||
"description": "(Optional) The chunking strategy used to chunk the file(s). Defaults to auto"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"file_ids"
|
||||
],
|
||||
"title": "OpenaiCreateVectorStoreFileBatchRequest"
|
||||
"title": "OpenAICreateVectorStoreFileBatchRequestWithExtraBody",
|
||||
"description": "Request to create a vector store file batch with extra_body support."
|
||||
},
|
||||
"VectorStoreFileBatchObject": {
|
||||
"type": "object",
|
||||
|
|
|
|||
68
docs/static/stainless-llama-stack-spec.yaml
vendored
68
docs/static/stainless-llama-stack-spec.yaml
vendored
|
|
@ -620,7 +620,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiEmbeddingsRequest'
|
||||
$ref: '#/components/schemas/OpenAIEmbeddingsRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/files:
|
||||
|
|
@ -2416,13 +2416,16 @@ paths:
|
|||
tags:
|
||||
- VectorIO
|
||||
summary: Creates a vector store.
|
||||
description: Creates a vector store.
|
||||
description: >-
|
||||
Creates a vector store.
|
||||
|
||||
Generate an OpenAI-compatible vector store with the given parameters.
|
||||
parameters: []
|
||||
requestBody:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiCreateVectorStoreRequest'
|
||||
$ref: '#/components/schemas/OpenAICreateVectorStoreRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}:
|
||||
|
|
@ -2548,7 +2551,11 @@ paths:
|
|||
tags:
|
||||
- VectorIO
|
||||
summary: Create a vector store file batch.
|
||||
description: Create a vector store file batch.
|
||||
description: >-
|
||||
Create a vector store file batch.
|
||||
|
||||
Generate an OpenAI-compatible vector store file batch for the given vector
|
||||
store.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
|
|
@ -2561,7 +2568,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiCreateVectorStoreFileBatchRequest'
|
||||
$ref: '#/components/schemas/OpenAICreateVectorStoreFileBatchRequestWithExtraBody'
|
||||
required: true
|
||||
deprecated: false
|
||||
/v1/vector_stores/{vector_store_id}/file_batches/{batch_id}:
|
||||
|
|
@ -6242,7 +6249,7 @@ components:
|
|||
- deleted
|
||||
title: ConversationItemDeletedResource
|
||||
description: Response for deleted conversation item.
|
||||
OpenaiEmbeddingsRequest:
|
||||
OpenAIEmbeddingsRequestWithExtraBody:
|
||||
type: object
|
||||
properties:
|
||||
model:
|
||||
|
|
@ -6261,6 +6268,7 @@ components:
|
|||
multiple inputs in a single request, pass an array of strings.
|
||||
encoding_format:
|
||||
type: string
|
||||
default: float
|
||||
description: >-
|
||||
(Optional) The format to return the embeddings in. Can be either "float"
|
||||
or "base64". Defaults to "float".
|
||||
|
|
@ -6278,7 +6286,9 @@ components:
|
|||
required:
|
||||
- model
|
||||
- input
|
||||
title: OpenaiEmbeddingsRequest
|
||||
title: OpenAIEmbeddingsRequestWithExtraBody
|
||||
description: >-
|
||||
Request parameters for OpenAI-compatible embeddings endpoint.
|
||||
OpenAIEmbeddingData:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -11057,19 +11067,18 @@ components:
|
|||
- metadata
|
||||
title: VectorStoreObject
|
||||
description: OpenAI Vector Store object.
|
||||
OpenaiCreateVectorStoreRequest:
|
||||
"OpenAICreateVectorStoreRequestWithExtraBody":
|
||||
type: object
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
description: A name for the vector store.
|
||||
description: (Optional) A name for the vector store
|
||||
file_ids:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: >-
|
||||
A list of File IDs that the vector store should use. Useful for tools
|
||||
like `file_search` that can access files.
|
||||
List of file IDs to include in the vector store
|
||||
expires_after:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -11081,7 +11090,7 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
The expiration policy for a vector store.
|
||||
(Optional) Expiration policy for the vector store
|
||||
chunking_strategy:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -11093,8 +11102,7 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
The chunking strategy used to chunk the file(s). If not set, will use
|
||||
the `auto` strategy.
|
||||
(Optional) Strategy for splitting files into chunks
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -11106,21 +11114,12 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
Set of 16 key-value pairs that can be attached to an object.
|
||||
embedding_model:
|
||||
type: string
|
||||
description: >-
|
||||
The embedding model to use for this vector store.
|
||||
embedding_dimension:
|
||||
type: integer
|
||||
description: >-
|
||||
The dimension of the embedding vectors (default: 384).
|
||||
provider_id:
|
||||
type: string
|
||||
description: >-
|
||||
The ID of the provider to use for this vector store.
|
||||
Set of key-value pairs that can be attached to the vector store
|
||||
additionalProperties: false
|
||||
title: OpenaiCreateVectorStoreRequest
|
||||
title: >-
|
||||
OpenAICreateVectorStoreRequestWithExtraBody
|
||||
description: >-
|
||||
Request to create a vector store with extra_body support.
|
||||
OpenaiUpdateVectorStoreRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -11241,7 +11240,7 @@ components:
|
|||
title: VectorStoreChunkingStrategyStaticConfig
|
||||
description: >-
|
||||
Configuration for static chunking strategy.
|
||||
OpenaiCreateVectorStoreFileBatchRequest:
|
||||
"OpenAICreateVectorStoreFileBatchRequestWithExtraBody":
|
||||
type: object
|
||||
properties:
|
||||
file_ids:
|
||||
|
|
@ -11249,7 +11248,7 @@ components:
|
|||
items:
|
||||
type: string
|
||||
description: >-
|
||||
A list of File IDs that the vector store should use.
|
||||
A list of File IDs that the vector store should use
|
||||
attributes:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -11261,16 +11260,19 @@ components:
|
|||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
(Optional) Key-value attributes to store with the files.
|
||||
(Optional) Key-value attributes to store with the files
|
||||
chunking_strategy:
|
||||
$ref: '#/components/schemas/VectorStoreChunkingStrategy'
|
||||
description: >-
|
||||
(Optional) The chunking strategy used to chunk the file(s). Defaults to
|
||||
auto.
|
||||
auto
|
||||
additionalProperties: false
|
||||
required:
|
||||
- file_ids
|
||||
title: OpenaiCreateVectorStoreFileBatchRequest
|
||||
title: >-
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody
|
||||
description: >-
|
||||
Request to create a vector store file batch with extra_body support.
|
||||
VectorStoreFileBatchObject:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
|
|
@ -1140,6 +1140,25 @@ class OpenAIChatCompletionRequestWithExtraBody(BaseModel, extra="allow"):
|
|||
user: str | None = None
|
||||
|
||||
|
||||
# extra_body can be accessed via .model_extra
|
||||
@json_schema_type
|
||||
class OpenAIEmbeddingsRequestWithExtraBody(BaseModel, extra="allow"):
|
||||
"""Request parameters for OpenAI-compatible embeddings endpoint.
|
||||
|
||||
:param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
|
||||
:param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings.
|
||||
:param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float".
|
||||
:param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
|
||||
:param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
||||
"""
|
||||
|
||||
model: str
|
||||
input: str | list[str]
|
||||
encoding_format: str | None = "float"
|
||||
dimensions: int | None = None
|
||||
user: str | None = None
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
@trace_protocol
|
||||
class InferenceProvider(Protocol):
|
||||
|
|
@ -1200,21 +1219,11 @@ class InferenceProvider(Protocol):
|
|||
@webmethod(route="/embeddings", method="POST", level=LLAMA_STACK_API_V1)
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: Annotated[OpenAIEmbeddingsRequestWithExtraBody, Body(...)],
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
"""Create embeddings.
|
||||
|
||||
Generate OpenAI-compatible embeddings for the given input using the specified model.
|
||||
|
||||
:param model: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
|
||||
:param input: Input text to embed, encoded as a string or array of strings. To embed multiple inputs in a single request, pass an array of strings.
|
||||
:param encoding_format: (Optional) The format to return the embeddings in. Can be either "float" or "base64". Defaults to "float".
|
||||
:param dimensions: (Optional) The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
|
||||
:param user: (Optional) A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
|
||||
:returns: An OpenAIEmbeddingsResponse containing the embeddings.
|
||||
"""
|
||||
...
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@
|
|||
import uuid
|
||||
from typing import Annotated, Any, Literal, Protocol, runtime_checkable
|
||||
|
||||
from fastapi import Body
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.apis.inference import InterleavedContent
|
||||
|
|
@ -466,6 +467,40 @@ class VectorStoreFilesListInBatchResponse(BaseModel):
|
|||
has_more: bool = False
|
||||
|
||||
|
||||
# extra_body can be accessed via .model_extra
|
||||
@json_schema_type
|
||||
class OpenAICreateVectorStoreRequestWithExtraBody(BaseModel, extra="allow"):
|
||||
"""Request to create a vector store with extra_body support.
|
||||
|
||||
:param name: (Optional) A name for the vector store
|
||||
:param file_ids: List of file IDs to include in the vector store
|
||||
:param expires_after: (Optional) Expiration policy for the vector store
|
||||
:param chunking_strategy: (Optional) Strategy for splitting files into chunks
|
||||
:param metadata: Set of key-value pairs that can be attached to the vector store
|
||||
"""
|
||||
|
||||
name: str | None = None
|
||||
file_ids: list[str] | None = None
|
||||
expires_after: dict[str, Any] | None = None
|
||||
chunking_strategy: dict[str, Any] | None = None
|
||||
metadata: dict[str, Any] | None = None
|
||||
|
||||
|
||||
# extra_body can be accessed via .model_extra
|
||||
@json_schema_type
|
||||
class OpenAICreateVectorStoreFileBatchRequestWithExtraBody(BaseModel, extra="allow"):
|
||||
"""Request to create a vector store file batch with extra_body support.
|
||||
|
||||
:param file_ids: A list of File IDs that the vector store should use
|
||||
:param attributes: (Optional) Key-value attributes to store with the files
|
||||
:param chunking_strategy: (Optional) The chunking strategy used to chunk the file(s). Defaults to auto
|
||||
"""
|
||||
|
||||
file_ids: list[str]
|
||||
attributes: dict[str, Any] | None = None
|
||||
chunking_strategy: VectorStoreChunkingStrategy | None = None
|
||||
|
||||
|
||||
class VectorDBStore(Protocol):
|
||||
def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
|
||||
|
||||
|
|
@ -516,25 +551,11 @@ class VectorIO(Protocol):
|
|||
@webmethod(route="/vector_stores", method="POST", level=LLAMA_STACK_API_V1)
|
||||
async def openai_create_vector_store(
|
||||
self,
|
||||
name: str | None = None,
|
||||
file_ids: list[str] | None = None,
|
||||
expires_after: dict[str, Any] | None = None,
|
||||
chunking_strategy: dict[str, Any] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
embedding_model: str | None = None,
|
||||
embedding_dimension: int | None = 384,
|
||||
provider_id: str | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreObject:
|
||||
"""Creates a vector store.
|
||||
|
||||
:param name: A name for the vector store.
|
||||
:param file_ids: A list of File IDs that the vector store should use. Useful for tools like `file_search` that can access files.
|
||||
:param expires_after: The expiration policy for a vector store.
|
||||
:param chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto` strategy.
|
||||
:param metadata: Set of 16 key-value pairs that can be attached to an object.
|
||||
:param embedding_model: The embedding model to use for this vector store.
|
||||
:param embedding_dimension: The dimension of the embedding vectors (default: 384).
|
||||
:param provider_id: The ID of the provider to use for this vector store.
|
||||
Generate an OpenAI-compatible vector store with the given parameters.
|
||||
:returns: A VectorStoreObject representing the created vector store.
|
||||
"""
|
||||
...
|
||||
|
|
@ -827,16 +848,12 @@ class VectorIO(Protocol):
|
|||
async def openai_create_vector_store_file_batch(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_ids: list[str],
|
||||
attributes: dict[str, Any] | None = None,
|
||||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreFileBatchObject:
|
||||
"""Create a vector store file batch.
|
||||
|
||||
Generate an OpenAI-compatible vector store file batch for the given vector store.
|
||||
:param vector_store_id: The ID of the vector store to create the file batch for.
|
||||
:param file_ids: A list of File IDs that the vector store should use.
|
||||
:param attributes: (Optional) Key-value attributes to store with the files.
|
||||
:param chunking_strategy: (Optional) The chunking strategy used to chunk the file(s). Defaults to auto.
|
||||
:returns: A VectorStoreFileBatchObject representing the created file batch.
|
||||
"""
|
||||
...
|
||||
|
|
|
|||
|
|
@ -513,6 +513,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
|
|||
# Strip NOT_GIVENs to use the defaults in signature
|
||||
body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
|
||||
|
||||
# Check if there's an unwrapped body parameter among multiple parameters
|
||||
# (e.g., path param + body param like: vector_store_id: str, params: Annotated[Model, Body(...)])
|
||||
unwrapped_body_param = None
|
||||
for param in params_list:
|
||||
if is_unwrapped_body_param(param.annotation):
|
||||
unwrapped_body_param = param
|
||||
break
|
||||
|
||||
# Convert parameters to Pydantic models where needed
|
||||
converted_body = {}
|
||||
for param_name, param in sig.parameters.items():
|
||||
|
|
@ -522,5 +530,11 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
|
|||
converted_body[param_name] = value
|
||||
else:
|
||||
converted_body[param_name] = convert_to_pydantic(param.annotation, value)
|
||||
elif unwrapped_body_param and param.name == unwrapped_body_param.name:
|
||||
# This is the unwrapped body param - construct it from remaining body keys
|
||||
base_type = get_args(param.annotation)[0]
|
||||
# Extract only the keys that aren't already used by other params
|
||||
remaining_keys = {k: v for k, v in body.items() if k not in converted_body}
|
||||
converted_body[param.name] = base_type(**remaining_keys)
|
||||
|
||||
return converted_body
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ from llama_stack.apis.inference import (
|
|||
OpenAICompletion,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAICompletionWithInputMessages,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
OpenAIMessageParam,
|
||||
Order,
|
||||
|
|
@ -279,26 +280,18 @@ class InferenceRouter(Inference):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: Annotated[OpenAIEmbeddingsRequestWithExtraBody, Body(...)],
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
logger.debug(
|
||||
f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}",
|
||||
)
|
||||
model_obj = await self._get_model(model, ModelType.embedding)
|
||||
params = dict(
|
||||
model=model_obj.identifier,
|
||||
input=input,
|
||||
encoding_format=encoding_format,
|
||||
dimensions=dimensions,
|
||||
user=user,
|
||||
f"InferenceRouter.openai_embeddings: model={params.model}, input_type={type(params.input)}, encoding_format={params.encoding_format}, dimensions={params.dimensions}",
|
||||
)
|
||||
model_obj = await self._get_model(params.model, ModelType.embedding)
|
||||
|
||||
# Update model to use resolved identifier
|
||||
params.model = model_obj.identifier
|
||||
|
||||
provider = await self.routing_table.get_provider_impl(model_obj.identifier)
|
||||
return await provider.openai_embeddings(**params)
|
||||
return await provider.openai_embeddings(params)
|
||||
|
||||
async def list_chat_completions(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -6,12 +6,16 @@
|
|||
|
||||
import asyncio
|
||||
import uuid
|
||||
from typing import Any
|
||||
from typing import Annotated, Any
|
||||
|
||||
from fastapi import Body
|
||||
|
||||
from llama_stack.apis.common.content_types import InterleavedContent
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
|
||||
OpenAICreateVectorStoreRequestWithExtraBody,
|
||||
QueryChunksResponse,
|
||||
SearchRankingOptions,
|
||||
VectorIO,
|
||||
|
|
@ -120,18 +124,19 @@ class VectorIORouter(VectorIO):
|
|||
# OpenAI Vector Stores API endpoints
|
||||
async def openai_create_vector_store(
|
||||
self,
|
||||
name: str,
|
||||
file_ids: list[str] | None = None,
|
||||
expires_after: dict[str, Any] | None = None,
|
||||
chunking_strategy: dict[str, Any] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
embedding_model: str | None = None,
|
||||
embedding_dimension: int | None = None,
|
||||
provider_id: str | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreObject:
|
||||
logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
|
||||
# Extract llama-stack-specific parameters from extra_body
|
||||
extra = params.model_extra or {}
|
||||
embedding_model = extra.get("embedding_model")
|
||||
embedding_dimension = extra.get("embedding_dimension", 384)
|
||||
provider_id = extra.get("provider_id")
|
||||
|
||||
logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={provider_id}")
|
||||
|
||||
# If no embedding model is provided, use the first available one
|
||||
# TODO: this branch will soon be deleted so you _must_ provide the embedding_model when
|
||||
# creating a vector store
|
||||
if embedding_model is None:
|
||||
embedding_model_info = await self._get_first_embedding_model()
|
||||
if embedding_model_info is None:
|
||||
|
|
@ -146,20 +151,19 @@ class VectorIORouter(VectorIO):
|
|||
embedding_dimension=embedding_dimension,
|
||||
provider_id=provider_id,
|
||||
provider_vector_db_id=vector_db_id,
|
||||
vector_db_name=name,
|
||||
vector_db_name=params.name,
|
||||
)
|
||||
provider = await self.routing_table.get_provider_impl(registered_vector_db.identifier)
|
||||
return await provider.openai_create_vector_store(
|
||||
name=name,
|
||||
file_ids=file_ids,
|
||||
expires_after=expires_after,
|
||||
chunking_strategy=chunking_strategy,
|
||||
metadata=metadata,
|
||||
embedding_model=embedding_model,
|
||||
embedding_dimension=embedding_dimension,
|
||||
provider_id=registered_vector_db.provider_id,
|
||||
provider_vector_db_id=registered_vector_db.provider_resource_id,
|
||||
)
|
||||
|
||||
# Update model_extra with registered values so provider uses the already-registered vector_db
|
||||
if params.model_extra is None:
|
||||
params.model_extra = {}
|
||||
params.model_extra["provider_vector_db_id"] = registered_vector_db.provider_resource_id
|
||||
params.model_extra["provider_id"] = registered_vector_db.provider_id
|
||||
params.model_extra["embedding_model"] = embedding_model
|
||||
params.model_extra["embedding_dimension"] = embedding_dimension
|
||||
|
||||
return await provider.openai_create_vector_store(params)
|
||||
|
||||
async def openai_list_vector_stores(
|
||||
self,
|
||||
|
|
@ -219,7 +223,8 @@ class VectorIORouter(VectorIO):
|
|||
vector_store_id: str,
|
||||
) -> VectorStoreObject:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store: {vector_store_id}")
|
||||
return await self.routing_table.openai_retrieve_vector_store(vector_store_id)
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_retrieve_vector_store(vector_store_id)
|
||||
|
||||
async def openai_update_vector_store(
|
||||
self,
|
||||
|
|
@ -229,7 +234,8 @@ class VectorIORouter(VectorIO):
|
|||
metadata: dict[str, Any] | None = None,
|
||||
) -> VectorStoreObject:
|
||||
logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
|
||||
return await self.routing_table.openai_update_vector_store(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_update_vector_store(
|
||||
vector_store_id=vector_store_id,
|
||||
name=name,
|
||||
expires_after=expires_after,
|
||||
|
|
@ -241,7 +247,8 @@ class VectorIORouter(VectorIO):
|
|||
vector_store_id: str,
|
||||
) -> VectorStoreDeleteResponse:
|
||||
logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
|
||||
return await self.routing_table.openai_delete_vector_store(vector_store_id)
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_delete_vector_store(vector_store_id)
|
||||
|
||||
async def openai_search_vector_store(
|
||||
self,
|
||||
|
|
@ -254,7 +261,8 @@ class VectorIORouter(VectorIO):
|
|||
search_mode: str | None = "vector",
|
||||
) -> VectorStoreSearchResponsePage:
|
||||
logger.debug(f"VectorIORouter.openai_search_vector_store: {vector_store_id}")
|
||||
return await self.routing_table.openai_search_vector_store(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_search_vector_store(
|
||||
vector_store_id=vector_store_id,
|
||||
query=query,
|
||||
filters=filters,
|
||||
|
|
@ -272,7 +280,8 @@ class VectorIORouter(VectorIO):
|
|||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
logger.debug(f"VectorIORouter.openai_attach_file_to_vector_store: {vector_store_id}, {file_id}")
|
||||
return await self.routing_table.openai_attach_file_to_vector_store(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_attach_file_to_vector_store(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
attributes=attributes,
|
||||
|
|
@ -289,7 +298,8 @@ class VectorIORouter(VectorIO):
|
|||
filter: VectorStoreFileStatus | None = None,
|
||||
) -> list[VectorStoreFileObject]:
|
||||
logger.debug(f"VectorIORouter.openai_list_files_in_vector_store: {vector_store_id}")
|
||||
return await self.routing_table.openai_list_files_in_vector_store(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_list_files_in_vector_store(
|
||||
vector_store_id=vector_store_id,
|
||||
limit=limit,
|
||||
order=order,
|
||||
|
|
@ -304,7 +314,8 @@ class VectorIORouter(VectorIO):
|
|||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file: {vector_store_id}, {file_id}")
|
||||
return await self.routing_table.openai_retrieve_vector_store_file(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_retrieve_vector_store_file(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
)
|
||||
|
|
@ -315,7 +326,8 @@ class VectorIORouter(VectorIO):
|
|||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
|
||||
return await self.routing_table.openai_retrieve_vector_store_file_contents(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_retrieve_vector_store_file_contents(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
)
|
||||
|
|
@ -327,7 +339,8 @@ class VectorIORouter(VectorIO):
|
|||
attributes: dict[str, Any],
|
||||
) -> VectorStoreFileObject:
|
||||
logger.debug(f"VectorIORouter.openai_update_vector_store_file: {vector_store_id}, {file_id}")
|
||||
return await self.routing_table.openai_update_vector_store_file(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_update_vector_store_file(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
attributes=attributes,
|
||||
|
|
@ -339,7 +352,8 @@ class VectorIORouter(VectorIO):
|
|||
file_id: str,
|
||||
) -> VectorStoreFileDeleteResponse:
|
||||
logger.debug(f"VectorIORouter.openai_delete_vector_store_file: {vector_store_id}, {file_id}")
|
||||
return await self.routing_table.openai_delete_vector_store_file(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_delete_vector_store_file(
|
||||
vector_store_id=vector_store_id,
|
||||
file_id=file_id,
|
||||
)
|
||||
|
|
@ -370,17 +384,13 @@ class VectorIORouter(VectorIO):
|
|||
async def openai_create_vector_store_file_batch(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_ids: list[str],
|
||||
attributes: dict[str, Any] | None = None,
|
||||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreFileBatchObject:
|
||||
logger.debug(f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(file_ids)} files")
|
||||
return await self.routing_table.openai_create_vector_store_file_batch(
|
||||
vector_store_id=vector_store_id,
|
||||
file_ids=file_ids,
|
||||
attributes=attributes,
|
||||
chunking_strategy=chunking_strategy,
|
||||
logger.debug(
|
||||
f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(params.file_ids)} files"
|
||||
)
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_create_vector_store_file_batch(vector_store_id, params)
|
||||
|
||||
async def openai_retrieve_vector_store_file_batch(
|
||||
self,
|
||||
|
|
@ -388,7 +398,8 @@ class VectorIORouter(VectorIO):
|
|||
vector_store_id: str,
|
||||
) -> VectorStoreFileBatchObject:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_batch: {batch_id}, {vector_store_id}")
|
||||
return await self.routing_table.openai_retrieve_vector_store_file_batch(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_retrieve_vector_store_file_batch(
|
||||
batch_id=batch_id,
|
||||
vector_store_id=vector_store_id,
|
||||
)
|
||||
|
|
@ -404,7 +415,8 @@ class VectorIORouter(VectorIO):
|
|||
order: str | None = "desc",
|
||||
) -> VectorStoreFilesListInBatchResponse:
|
||||
logger.debug(f"VectorIORouter.openai_list_files_in_vector_store_file_batch: {batch_id}, {vector_store_id}")
|
||||
return await self.routing_table.openai_list_files_in_vector_store_file_batch(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_list_files_in_vector_store_file_batch(
|
||||
batch_id=batch_id,
|
||||
vector_store_id=vector_store_id,
|
||||
after=after,
|
||||
|
|
@ -420,7 +432,8 @@ class VectorIORouter(VectorIO):
|
|||
vector_store_id: str,
|
||||
) -> VectorStoreFileBatchObject:
|
||||
logger.debug(f"VectorIORouter.openai_cancel_vector_store_file_batch: {batch_id}, {vector_store_id}")
|
||||
return await self.routing_table.openai_cancel_vector_store_file_batch(
|
||||
provider = await self.routing_table.get_provider_impl(vector_store_id)
|
||||
return await provider.openai_cancel_vector_store_file_batch(
|
||||
batch_id=batch_id,
|
||||
vector_store_id=vector_store_id,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ from llama_stack.apis.inference import (
|
|||
OpenAIChatCompletionRequestWithExtraBody,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIDeveloperMessageParam,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIMessageParam,
|
||||
OpenAISystemMessageParam,
|
||||
OpenAIToolMessageParam,
|
||||
|
|
@ -640,7 +641,9 @@ class ReferenceBatchesImpl(Batches):
|
|||
},
|
||||
}
|
||||
else: # /v1/embeddings
|
||||
embeddings_response = await self.inference_api.openai_embeddings(**request.body)
|
||||
embeddings_response = await self.inference_api.openai_embeddings(
|
||||
OpenAIEmbeddingsRequestWithExtraBody(**request.body)
|
||||
)
|
||||
assert hasattr(embeddings_response, "model_dump_json"), (
|
||||
"Embeddings response must have model_dump_json method"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from llama_stack.apis.inference import (
|
|||
Inference,
|
||||
OpenAIChatCompletionRequestWithExtraBody,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import (
|
||||
|
|
@ -124,11 +125,7 @@ class BedrockInferenceAdapter(
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,10 @@
|
|||
|
||||
from urllib.parse import urljoin
|
||||
|
||||
from llama_stack.apis.inference import OpenAIEmbeddingsResponse
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
||||
|
||||
from .config import CerebrasImplConfig
|
||||
|
|
@ -20,10 +23,6 @@ class CerebrasInferenceAdapter(OpenAIMixin):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@
|
|||
from llama_stack.apis.inference.inference import (
|
||||
OpenAICompletion,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.log import get_logger
|
||||
|
|
@ -40,10 +41,6 @@ class LlamaCompatInferenceAdapter(OpenAIMixin):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from openai import NOT_GIVEN
|
|||
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
OpenAIEmbeddingUsage,
|
||||
)
|
||||
|
|
@ -78,11 +79,7 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
"""
|
||||
OpenAI-compatible embeddings for NVIDIA NIM.
|
||||
|
|
@ -99,11 +96,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin):
|
|||
)
|
||||
|
||||
response = await self.client.embeddings.create(
|
||||
model=await self._get_provider_model_id(model),
|
||||
input=input,
|
||||
encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
|
||||
dimensions=dimensions if dimensions is not None else NOT_GIVEN,
|
||||
user=user if user is not None else NOT_GIVEN,
|
||||
model=await self._get_provider_model_id(params.model),
|
||||
input=params.input,
|
||||
encoding_format=params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
|
||||
dimensions=params.dimensions if params.dimensions is not None else NOT_GIVEN,
|
||||
user=params.user if params.user is not None else NOT_GIVEN,
|
||||
extra_body=extra_body,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from llama_stack.apis.inference import (
|
|||
OpenAIChatCompletionRequestWithExtraBody,
|
||||
OpenAICompletion,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.apis.models import Model
|
||||
|
|
@ -69,11 +70,7 @@ class PassthroughInferenceAdapter(Inference):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
|
|
|||
|
|
@ -10,7 +10,10 @@ from collections.abc import Iterable
|
|||
from huggingface_hub import AsyncInferenceClient, HfApi
|
||||
from pydantic import SecretStr
|
||||
|
||||
from llama_stack.apis.inference import OpenAIEmbeddingsResponse
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
||||
|
||||
|
|
@ -40,11 +43,7 @@ class _HfAdapter(OpenAIMixin):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ from together import AsyncTogether
|
|||
from together.constants import BASE_URL
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
|
||||
|
|
@ -62,11 +63,7 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
"""
|
||||
Together's OpenAI-compatible embeddings endpoint is not compatible with
|
||||
|
|
@ -78,25 +75,27 @@ class TogetherInferenceAdapter(OpenAIMixin, NeedsRequestProviderData):
|
|||
- does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
|
||||
"""
|
||||
# Together support ticket #13332 -> will not fix
|
||||
if user is not None:
|
||||
if params.user is not None:
|
||||
raise ValueError("Together's embeddings endpoint does not support user param.")
|
||||
# Together support ticket #13333 -> escalated
|
||||
if dimensions is not None:
|
||||
if params.dimensions is not None:
|
||||
raise ValueError("Together's embeddings endpoint does not support dimensions param.")
|
||||
|
||||
response = await self.client.embeddings.create(
|
||||
model=await self._get_provider_model_id(model),
|
||||
input=input,
|
||||
encoding_format=encoding_format,
|
||||
model=await self._get_provider_model_id(params.model),
|
||||
input=params.input,
|
||||
encoding_format=params.encoding_format,
|
||||
)
|
||||
|
||||
response.model = model # return the user the same model id they provided, avoid exposing the provider model id
|
||||
response.model = (
|
||||
params.model
|
||||
) # return the user the same model id they provided, avoid exposing the provider model id
|
||||
|
||||
# Together support ticket #13330 -> escalated
|
||||
# - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
|
||||
if not hasattr(response, "usage") or response.usage is None:
|
||||
logger.warning(
|
||||
f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
|
||||
f"Together's embedding endpoint for {params.model} did not return usage information, substituting -1s."
|
||||
)
|
||||
response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
|
||||
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ if TYPE_CHECKING:
|
|||
from llama_stack.apis.inference import (
|
||||
ModelStore,
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
OpenAIEmbeddingUsage,
|
||||
)
|
||||
|
|
@ -32,26 +33,22 @@ class SentenceTransformerEmbeddingMixin:
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
# Convert input to list format if it's a single string
|
||||
input_list = [input] if isinstance(input, str) else input
|
||||
input_list = [params.input] if isinstance(params.input, str) else params.input
|
||||
if not input_list:
|
||||
raise ValueError("Empty list not supported")
|
||||
|
||||
# Get the model and generate embeddings
|
||||
model_obj = await self.model_store.get_model(model)
|
||||
model_obj = await self.model_store.get_model(params.model)
|
||||
embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
|
||||
embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
|
||||
|
||||
# Convert embeddings to the requested format
|
||||
data = []
|
||||
for i, embedding in enumerate(embeddings):
|
||||
if encoding_format == "base64":
|
||||
if params.encoding_format == "base64":
|
||||
# Convert float array to base64 string
|
||||
float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
|
||||
embedding_value = base64.b64encode(float_bytes).decode("ascii")
|
||||
|
|
@ -70,7 +67,7 @@ class SentenceTransformerEmbeddingMixin:
|
|||
usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
|
||||
return OpenAIEmbeddingsResponse(
|
||||
data=data,
|
||||
model=model,
|
||||
model=params.model,
|
||||
usage=usage,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ from llama_stack.apis.inference import (
|
|||
OpenAICompletion,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
OpenAIEmbeddingUsage,
|
||||
ToolChoice,
|
||||
|
|
@ -189,16 +190,12 @@ class LiteLLMOpenAIMixin(
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
model_obj = await self.model_store.get_model(model)
|
||||
model_obj = await self.model_store.get_model(params.model)
|
||||
|
||||
# Convert input to list if it's a string
|
||||
input_list = [input] if isinstance(input, str) else input
|
||||
input_list = [params.input] if isinstance(params.input, str) else params.input
|
||||
|
||||
# Call litellm embedding function
|
||||
# litellm.drop_params = True
|
||||
|
|
@ -207,11 +204,11 @@ class LiteLLMOpenAIMixin(
|
|||
input=input_list,
|
||||
api_key=self.get_api_key(),
|
||||
api_base=self.api_base,
|
||||
dimensions=dimensions,
|
||||
dimensions=params.dimensions,
|
||||
)
|
||||
|
||||
# Convert response to OpenAI format
|
||||
data = b64_encode_openai_embeddings_response(response.data, encoding_format)
|
||||
data = b64_encode_openai_embeddings_response(response.data, params.encoding_format)
|
||||
|
||||
usage = OpenAIEmbeddingUsage(
|
||||
prompt_tokens=response["usage"]["prompt_tokens"],
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ from llama_stack.apis.inference import (
|
|||
OpenAICompletion,
|
||||
OpenAICompletionRequestWithExtraBody,
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
OpenAIEmbeddingsResponse,
|
||||
OpenAIEmbeddingUsage,
|
||||
OpenAIMessageParam,
|
||||
|
|
@ -316,23 +317,27 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
|
|||
|
||||
async def openai_embeddings(
|
||||
self,
|
||||
model: str,
|
||||
input: str | list[str],
|
||||
encoding_format: str | None = "float",
|
||||
dimensions: int | None = None,
|
||||
user: str | None = None,
|
||||
params: OpenAIEmbeddingsRequestWithExtraBody,
|
||||
) -> OpenAIEmbeddingsResponse:
|
||||
"""
|
||||
Direct OpenAI embeddings API call.
|
||||
"""
|
||||
# Prepare request parameters
|
||||
request_params = {
|
||||
"model": await self._get_provider_model_id(params.model),
|
||||
"input": params.input,
|
||||
"encoding_format": params.encoding_format if params.encoding_format is not None else NOT_GIVEN,
|
||||
"dimensions": params.dimensions if params.dimensions is not None else NOT_GIVEN,
|
||||
"user": params.user if params.user is not None else NOT_GIVEN,
|
||||
}
|
||||
|
||||
# Add extra_body if present
|
||||
extra_body = params.model_extra
|
||||
if extra_body:
|
||||
request_params["extra_body"] = extra_body
|
||||
|
||||
# Call OpenAI embeddings API with properly typed parameters
|
||||
response = await self.client.embeddings.create(
|
||||
model=await self._get_provider_model_id(model),
|
||||
input=input,
|
||||
encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
|
||||
dimensions=dimensions if dimensions is not None else NOT_GIVEN,
|
||||
user=user if user is not None else NOT_GIVEN,
|
||||
)
|
||||
response = await self.client.embeddings.create(**request_params)
|
||||
|
||||
data = []
|
||||
for i, embedding_data in enumerate(response.data):
|
||||
|
|
@ -350,7 +355,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
|
|||
|
||||
return OpenAIEmbeddingsResponse(
|
||||
data=data,
|
||||
model=model,
|
||||
model=params.model,
|
||||
usage=usage,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,9 @@ import mimetypes
|
|||
import time
|
||||
import uuid
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any
|
||||
from typing import Annotated, Any
|
||||
|
||||
from fastapi import Body
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
|
|
@ -19,6 +20,8 @@ from llama_stack.apis.files import Files, OpenAIFileObject
|
|||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
|
||||
OpenAICreateVectorStoreRequestWithExtraBody,
|
||||
QueryChunksResponse,
|
||||
SearchRankingOptions,
|
||||
VectorStoreChunkingStrategy,
|
||||
|
|
@ -340,18 +343,18 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
async def openai_create_vector_store(
|
||||
self,
|
||||
name: str | None = None,
|
||||
file_ids: list[str] | None = None,
|
||||
expires_after: dict[str, Any] | None = None,
|
||||
chunking_strategy: dict[str, Any] | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
embedding_model: str | None = None,
|
||||
embedding_dimension: int | None = 384,
|
||||
provider_id: str | None = None,
|
||||
provider_vector_db_id: str | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreObject:
|
||||
"""Creates a vector store."""
|
||||
created_at = int(time.time())
|
||||
|
||||
# Extract llama-stack-specific parameters from extra_body
|
||||
extra = params.model_extra or {}
|
||||
provider_vector_db_id = extra.get("provider_vector_db_id")
|
||||
embedding_model = extra.get("embedding_model")
|
||||
embedding_dimension = extra.get("embedding_dimension", 384)
|
||||
provider_id = extra.get("provider_id")
|
||||
|
||||
# Derive the canonical vector_db_id (allow override, else generate)
|
||||
vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
|
||||
|
||||
|
|
@ -372,7 +375,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
embedding_model=embedding_model,
|
||||
provider_id=provider_id,
|
||||
provider_resource_id=vector_db_id,
|
||||
vector_db_name=name,
|
||||
vector_db_name=params.name,
|
||||
)
|
||||
await self.register_vector_db(vector_db)
|
||||
|
||||
|
|
@ -391,19 +394,19 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
"id": vector_db_id,
|
||||
"object": "vector_store",
|
||||
"created_at": created_at,
|
||||
"name": name,
|
||||
"name": params.name,
|
||||
"usage_bytes": 0,
|
||||
"file_counts": file_counts.model_dump(),
|
||||
"status": status,
|
||||
"expires_after": expires_after,
|
||||
"expires_after": params.expires_after,
|
||||
"expires_at": None,
|
||||
"last_active_at": created_at,
|
||||
"file_ids": [],
|
||||
"chunking_strategy": chunking_strategy,
|
||||
"chunking_strategy": params.chunking_strategy,
|
||||
}
|
||||
|
||||
# Add provider information to metadata if provided
|
||||
metadata = metadata or {}
|
||||
metadata = params.metadata or {}
|
||||
if provider_id:
|
||||
metadata["provider_id"] = provider_id
|
||||
if provider_vector_db_id:
|
||||
|
|
@ -417,7 +420,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
self.openai_vector_stores[vector_db_id] = store_info
|
||||
|
||||
# Now that our vector store is created, attach any files that were provided
|
||||
file_ids = file_ids or []
|
||||
file_ids = params.file_ids or []
|
||||
tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
|
|
@ -976,15 +979,13 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
async def openai_create_vector_store_file_batch(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_ids: list[str],
|
||||
attributes: dict[str, Any] | None = None,
|
||||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
|
||||
) -> VectorStoreFileBatchObject:
|
||||
"""Create a vector store file batch."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise VectorStoreNotFoundError(vector_store_id)
|
||||
|
||||
chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
|
||||
chunking_strategy = params.chunking_strategy or VectorStoreChunkingStrategyAuto()
|
||||
|
||||
created_at = int(time.time())
|
||||
batch_id = generate_object_id("vector_store_file_batch", lambda: f"batch_{uuid.uuid4()}")
|
||||
|
|
@ -996,8 +997,8 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
completed=0,
|
||||
cancelled=0,
|
||||
failed=0,
|
||||
in_progress=len(file_ids),
|
||||
total=len(file_ids),
|
||||
in_progress=len(params.file_ids),
|
||||
total=len(params.file_ids),
|
||||
)
|
||||
|
||||
# Create batch object immediately with in_progress status
|
||||
|
|
@ -1011,8 +1012,8 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
|
||||
batch_info = {
|
||||
**batch_object.model_dump(),
|
||||
"file_ids": file_ids,
|
||||
"attributes": attributes,
|
||||
"file_ids": params.file_ids,
|
||||
"attributes": params.attributes,
|
||||
"chunking_strategy": chunking_strategy.model_dump(),
|
||||
"expires_at": expires_at,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ from llama_stack.apis.common.content_types import (
|
|||
URL,
|
||||
InterleavedContent,
|
||||
)
|
||||
from llama_stack.apis.inference import OpenAIEmbeddingsRequestWithExtraBody
|
||||
from llama_stack.apis.tools import RAGDocument
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
|
||||
|
|
@ -274,10 +275,11 @@ class VectorDBWithIndex:
|
|||
_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
|
||||
|
||||
if chunks_to_embed:
|
||||
resp = await self.inference_api.openai_embeddings(
|
||||
self.vector_db.embedding_model,
|
||||
[c.content for c in chunks_to_embed],
|
||||
params = OpenAIEmbeddingsRequestWithExtraBody(
|
||||
model=self.vector_db.embedding_model,
|
||||
input=[c.content for c in chunks_to_embed],
|
||||
)
|
||||
resp = await self.inference_api.openai_embeddings(params)
|
||||
for c, data in zip(chunks_to_embed, resp.data, strict=False):
|
||||
c.embedding = data.embedding
|
||||
|
||||
|
|
@ -316,7 +318,11 @@ class VectorDBWithIndex:
|
|||
if mode == "keyword":
|
||||
return await self.index.query_keyword(query_string, k, score_threshold)
|
||||
|
||||
embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string])
|
||||
params = OpenAIEmbeddingsRequestWithExtraBody(
|
||||
model=self.vector_db.embedding_model,
|
||||
input=[query_string],
|
||||
)
|
||||
embeddings_response = await self.inference_api.openai_embeddings(params)
|
||||
query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32)
|
||||
if mode == "hybrid":
|
||||
return await self.index.query_hybrid(
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
|||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
|
||||
QueryChunksResponse,
|
||||
VectorStoreChunkingStrategyAuto,
|
||||
VectorStoreFileObject,
|
||||
|
|
@ -326,8 +327,7 @@ async def test_create_vector_store_file_batch(vector_io_adapter):
|
|||
vector_io_adapter._process_file_batch_async = AsyncMock()
|
||||
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
assert batch.vector_store_id == store_id
|
||||
|
|
@ -354,8 +354,7 @@ async def test_retrieve_vector_store_file_batch(vector_io_adapter):
|
|||
|
||||
# Create batch first
|
||||
created_batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Retrieve batch
|
||||
|
|
@ -388,8 +387,7 @@ async def test_cancel_vector_store_file_batch(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Cancel batch
|
||||
|
|
@ -434,8 +432,7 @@ async def test_list_files_in_vector_store_file_batch(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# List files
|
||||
|
|
@ -455,7 +452,7 @@ async def test_file_batch_validation_errors(vector_io_adapter):
|
|||
with pytest.raises(VectorStoreNotFoundError):
|
||||
await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id="nonexistent",
|
||||
file_ids=["file_1"],
|
||||
params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"]),
|
||||
)
|
||||
|
||||
# Setup store for remaining tests
|
||||
|
|
@ -472,8 +469,7 @@ async def test_file_batch_validation_errors(vector_io_adapter):
|
|||
# Test wrong vector store for batch
|
||||
vector_io_adapter.openai_attach_file_to_vector_store = AsyncMock()
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=["file_1"],
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"])
|
||||
)
|
||||
|
||||
# Create wrong_store so it exists but the batch doesn't belong to it
|
||||
|
|
@ -520,8 +516,7 @@ async def test_file_batch_pagination(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Test pagination with limit
|
||||
|
|
@ -593,8 +588,7 @@ async def test_file_batch_status_filtering(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Test filtering by completed status
|
||||
|
|
@ -636,8 +630,7 @@ async def test_cancel_completed_batch_fails(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Manually update status to completed
|
||||
|
|
@ -671,8 +664,7 @@ async def test_file_batch_persistence_across_restarts(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
batch_id = batch.id
|
||||
|
||||
|
|
@ -727,8 +719,7 @@ async def test_cancelled_batch_persists_in_storage(vector_io_adapter):
|
|||
|
||||
# Create batch
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
batch_id = batch.id
|
||||
|
||||
|
|
@ -775,10 +766,10 @@ async def test_only_in_progress_batches_resumed(vector_io_adapter):
|
|||
|
||||
# Create multiple batches
|
||||
batch1 = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id, file_ids=["file_1"]
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_1"])
|
||||
)
|
||||
batch2 = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id, file_ids=["file_2"]
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_2"])
|
||||
)
|
||||
|
||||
# Complete one batch (should persist with completed status)
|
||||
|
|
@ -791,7 +782,7 @@ async def test_only_in_progress_batches_resumed(vector_io_adapter):
|
|||
|
||||
# Create a third batch that stays in progress
|
||||
batch3 = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id, file_ids=["file_3"]
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=["file_3"])
|
||||
)
|
||||
|
||||
# Simulate restart - clear memory and reload from persistence
|
||||
|
|
@ -952,8 +943,7 @@ async def test_max_concurrent_files_per_batch(vector_io_adapter):
|
|||
file_ids = [f"file_{i}" for i in range(8)] # 8 files, but limit should be 5
|
||||
|
||||
batch = await vector_io_adapter.openai_create_vector_store_file_batch(
|
||||
vector_store_id=store_id,
|
||||
file_ids=file_ids,
|
||||
vector_store_id=store_id, params=OpenAICreateVectorStoreFileBatchRequestWithExtraBody(file_ids=file_ids)
|
||||
)
|
||||
|
||||
# Give time for the semaphore logic to start processing files
|
||||
|
|
|
|||
|
|
@ -13,7 +13,10 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference.inference import OpenAIEmbeddingData
|
||||
from llama_stack.apis.inference.inference import (
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
)
|
||||
from llama_stack.apis.tools import RAGDocument
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
|
|
@ -226,9 +229,14 @@ class TestVectorDBWithIndex:
|
|||
|
||||
await vector_db_with_index.insert_chunks(chunks)
|
||||
|
||||
mock_inference_api.openai_embeddings.assert_called_once_with(
|
||||
"test-model without embeddings", ["Test 1", "Test 2"]
|
||||
)
|
||||
# Verify openai_embeddings was called with correct params
|
||||
mock_inference_api.openai_embeddings.assert_called_once()
|
||||
call_args = mock_inference_api.openai_embeddings.call_args[0]
|
||||
assert len(call_args) == 1
|
||||
params = call_args[0]
|
||||
assert isinstance(params, OpenAIEmbeddingsRequestWithExtraBody)
|
||||
assert params.model == "test-model without embeddings"
|
||||
assert params.input == ["Test 1", "Test 2"]
|
||||
mock_index.add_chunks.assert_called_once()
|
||||
args = mock_index.add_chunks.call_args[0]
|
||||
assert args[0] == chunks
|
||||
|
|
@ -321,9 +329,14 @@ class TestVectorDBWithIndex:
|
|||
|
||||
await vector_db_with_index.insert_chunks(chunks)
|
||||
|
||||
mock_inference_api.openai_embeddings.assert_called_once_with(
|
||||
"test-model with partial embeddings", ["Test 1", "Test 3"]
|
||||
)
|
||||
# Verify openai_embeddings was called with correct params
|
||||
mock_inference_api.openai_embeddings.assert_called_once()
|
||||
call_args = mock_inference_api.openai_embeddings.call_args[0]
|
||||
assert len(call_args) == 1
|
||||
params = call_args[0]
|
||||
assert isinstance(params, OpenAIEmbeddingsRequestWithExtraBody)
|
||||
assert params.model == "test-model with partial embeddings"
|
||||
assert params.input == ["Test 1", "Test 3"]
|
||||
mock_index.add_chunks.assert_called_once()
|
||||
args = mock_index.add_chunks.call_args[0]
|
||||
assert len(args[0]) == 3
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue