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feat: Add missing Vector Store Files API surface (#2468)
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# What does this PR do? This adds the ability to list, retrieve, update, and delete Vector Store Files. It implements these new APIs for the faiss and sqlite-vec providers, since those are the two that also have the rest of the vector store files implementation. Closes #2445 ## Test Plan ### test_openai_vector_stores Integration Tests There are a number of new integration tests added, which I ran for each provider as outlined below. faiss (from ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` sqlite-vec (from starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` ### file_search verification tests I also ensured the file_search verification tests continue to work, both for faiss and sqlite-vec. faiss (ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` sqlite-vec (starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ben Browning
GitHub
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11 changed files with 1991 additions and 122 deletions
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"/v1/openai/v1/vector_stores/{vector_store_id}/files": {
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"get": {
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"responses": {
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"200": {
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"description": "A VectorStoreListFilesResponse containing the list of files.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/VectorStoreListFilesResponse"
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"tags": [
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"VectorIO"
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],
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"description": "List files in a vector store.",
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"parameters": [
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{
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"name": "vector_store_id",
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"in": "path",
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"description": "The ID of the vector store to list files from.",
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"name": "filter",
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"schema": {
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"/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}": {
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"get": {
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"responses": {
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"200": {
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"description": "A VectorStoreFileObject representing the file.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/VectorStoreFileObject"
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"400": {
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"429": {
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"$ref": "#/components/responses/TooManyRequests429"
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"500": {
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"tags": [
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"description": "Retrieves a vector store file.",
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"parameters": [
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{
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"name": "vector_store_id",
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"in": "path",
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"description": "The ID of the vector store containing the file to retrieve.",
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"post": {
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"200": {
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"description": "A VectorStoreFileObject representing the updated file.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/VectorStoreFileObject"
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"tags": [
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"description": "Updates a vector store file.",
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"parameters": [
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{
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"name": "vector_store_id",
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"in": "path",
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"description": "The ID of the vector store containing the file to update.",
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"requestBody": {
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"content": {
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"schema": {
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"$ref": "#/components/schemas/OpenaiUpdateVectorStoreFileRequest"
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"required": true
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"delete": {
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"responses": {
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"200": {
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"description": "A VectorStoreFileDeleteResponse indicating the deletion status.",
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"content": {
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"schema": {
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"$ref": "#/components/schemas/VectorStoreFileDeleteResponse"
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"tags": [
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"VectorIO"
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],
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"description": "Delete a vector store file.",
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"parameters": [
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{
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"name": "vector_store_id",
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"in": "path",
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"description": "The ID of the vector store containing the file to delete.",
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"required": true,
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"schema": {
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"type": "string"
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{
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"name": "file_id",
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"description": "The ID of the file to delete.",
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"/v1/openai/v1/embeddings": {
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"/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content": {
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"get": {
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"responses": {
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"200": {
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"description": "A list of InterleavedContent representing the file contents.",
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"content": {
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"application/json": {
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"schema": {
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"$ref": "#/components/schemas/VectorStoreFileContentsResponse"
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"400": {
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"default": {
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"$ref": "#/components/responses/DefaultError"
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"tags": [
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"VectorIO"
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],
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"description": "Retrieves the contents of a vector store file.",
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"parameters": [
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{
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"name": "vector_store_id",
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"in": "path",
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"description": "The ID of the vector store containing the file to retrieve.",
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"required": true,
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"schema": {
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"type": "string"
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}
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},
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{
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"name": "file_id",
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"in": "path",
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"description": "The ID of the file to retrieve.",
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"required": true,
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"/v1/openai/v1/vector_stores/{vector_store_id}/search": {
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"$ref": "#/components/schemas/VectorStoreFileLastError"
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"status": {
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"oneOf": [
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{
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"type": "string",
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"const": "completed"
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"type": "string",
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"const": "in_progress"
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{
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"type": "string",
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"const": "cancelled"
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"type": "string",
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"const": "failed"
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]
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"$ref": "#/components/schemas/VectorStoreFileStatus"
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"usage_bytes": {
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"title": "VectorStoreFileObject",
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"description": "OpenAI Vector Store File object."
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},
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"VectorStoreFileStatus": {
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"oneOf": [
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"type": "string",
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"const": "completed"
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|
||||
"const": "in_progress"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "cancelled"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "failed"
|
||||
}
|
||||
]
|
||||
},
|
||||
"OpenAIJSONSchema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -12969,6 +13267,35 @@
|
|||
],
|
||||
"title": "OpenaiCreateVectorStoreRequest"
|
||||
},
|
||||
"VectorStoreFileCounts": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"completed": {
|
||||
"type": "integer"
|
||||
},
|
||||
"cancelled": {
|
||||
"type": "integer"
|
||||
},
|
||||
"failed": {
|
||||
"type": "integer"
|
||||
},
|
||||
"in_progress": {
|
||||
"type": "integer"
|
||||
},
|
||||
"total": {
|
||||
"type": "integer"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"completed",
|
||||
"cancelled",
|
||||
"failed",
|
||||
"in_progress",
|
||||
"total"
|
||||
],
|
||||
"title": "VectorStoreFileCounts"
|
||||
},
|
||||
"VectorStoreObject": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -12990,10 +13317,7 @@
|
|||
"default": 0
|
||||
},
|
||||
"file_counts": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"type": "integer"
|
||||
}
|
||||
"$ref": "#/components/schemas/VectorStoreFileCounts"
|
||||
},
|
||||
"status": {
|
||||
"type": "string",
|
||||
|
|
@ -13120,6 +13444,30 @@
|
|||
"title": "VectorStoreDeleteResponse",
|
||||
"description": "Response from deleting a vector store."
|
||||
},
|
||||
"VectorStoreFileDeleteResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"object": {
|
||||
"type": "string",
|
||||
"default": "vector_store.file.deleted"
|
||||
},
|
||||
"deleted": {
|
||||
"type": "boolean",
|
||||
"default": true
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"id",
|
||||
"object",
|
||||
"deleted"
|
||||
],
|
||||
"title": "VectorStoreFileDeleteResponse",
|
||||
"description": "Response from deleting a vector store file."
|
||||
},
|
||||
"OpenaiEmbeddingsRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -13348,6 +13696,39 @@
|
|||
"title": "OpenAIFileObject",
|
||||
"description": "OpenAI File object as defined in the OpenAI Files API."
|
||||
},
|
||||
"VectorStoreListFilesResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"object": {
|
||||
"type": "string",
|
||||
"default": "list"
|
||||
},
|
||||
"data": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/VectorStoreFileObject"
|
||||
}
|
||||
},
|
||||
"first_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"last_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"has_more": {
|
||||
"type": "boolean",
|
||||
"default": false
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"object",
|
||||
"data",
|
||||
"has_more"
|
||||
],
|
||||
"title": "VectorStoreListFilesResponse",
|
||||
"description": "Response from listing vector stores."
|
||||
},
|
||||
"OpenAIModel": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -13429,6 +13810,75 @@
|
|||
"type": "object",
|
||||
"title": "Response"
|
||||
},
|
||||
"VectorStoreContent": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "text"
|
||||
},
|
||||
"text": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"type",
|
||||
"text"
|
||||
],
|
||||
"title": "VectorStoreContent"
|
||||
},
|
||||
"VectorStoreFileContentsResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"file_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"filename": {
|
||||
"type": "string"
|
||||
},
|
||||
"attributes": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "array"
|
||||
},
|
||||
{
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"content": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/VectorStoreContent"
|
||||
}
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"file_id",
|
||||
"filename",
|
||||
"attributes",
|
||||
"content"
|
||||
],
|
||||
"title": "VectorStoreFileContentsResponse",
|
||||
"description": "Response from retrieving the contents of a vector store file."
|
||||
},
|
||||
"OpenaiSearchVectorStoreRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -13501,24 +13951,6 @@
|
|||
],
|
||||
"title": "OpenaiSearchVectorStoreRequest"
|
||||
},
|
||||
"VectorStoreContent": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "text"
|
||||
},
|
||||
"text": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"type",
|
||||
"text"
|
||||
],
|
||||
"title": "VectorStoreContent"
|
||||
},
|
||||
"VectorStoreSearchResponse": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -13661,6 +14093,42 @@
|
|||
"additionalProperties": false,
|
||||
"title": "OpenaiUpdateVectorStoreRequest"
|
||||
},
|
||||
"OpenaiUpdateVectorStoreFileRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"attributes": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "array"
|
||||
},
|
||||
{
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
},
|
||||
"description": "The updated key-value attributes to store with the file."
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"attributes"
|
||||
],
|
||||
"title": "OpenaiUpdateVectorStoreFileRequest"
|
||||
},
|
||||
"DPOAlignmentConfig": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
|
|||
371
docs/_static/llama-stack-spec.yaml
vendored
371
docs/_static/llama-stack-spec.yaml
vendored
|
|
@ -2264,6 +2264,61 @@ paths:
|
|||
$ref: '#/components/schemas/LogEventRequest'
|
||||
required: true
|
||||
/v1/openai/v1/vector_stores/{vector_store_id}/files:
|
||||
get:
|
||||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreListFilesResponse containing the list of files.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreListFilesResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
$ref: >-
|
||||
#/components/responses/TooManyRequests429
|
||||
'500':
|
||||
$ref: >-
|
||||
#/components/responses/InternalServerError500
|
||||
default:
|
||||
$ref: '#/components/responses/DefaultError'
|
||||
tags:
|
||||
- VectorIO
|
||||
description: List files in a vector store.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
description: >-
|
||||
The ID of the vector store to list files from.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: limit
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
type: integer
|
||||
- name: order
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
type: string
|
||||
- name: after
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
type: string
|
||||
- name: before
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
type: string
|
||||
- name: filter
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreFileStatus'
|
||||
post:
|
||||
responses:
|
||||
'200':
|
||||
|
|
@ -2572,6 +2627,121 @@ paths:
|
|||
required: true
|
||||
schema:
|
||||
type: string
|
||||
/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}:
|
||||
get:
|
||||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileObject representing the file.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreFileObject'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
$ref: >-
|
||||
#/components/responses/TooManyRequests429
|
||||
'500':
|
||||
$ref: >-
|
||||
#/components/responses/InternalServerError500
|
||||
default:
|
||||
$ref: '#/components/responses/DefaultError'
|
||||
tags:
|
||||
- VectorIO
|
||||
description: Retrieves a vector store file.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
description: >-
|
||||
The ID of the vector store containing the file to retrieve.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: file_id
|
||||
in: path
|
||||
description: The ID of the file to retrieve.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
post:
|
||||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileObject representing the updated file.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreFileObject'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
$ref: >-
|
||||
#/components/responses/TooManyRequests429
|
||||
'500':
|
||||
$ref: >-
|
||||
#/components/responses/InternalServerError500
|
||||
default:
|
||||
$ref: '#/components/responses/DefaultError'
|
||||
tags:
|
||||
- VectorIO
|
||||
description: Updates a vector store file.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
description: >-
|
||||
The ID of the vector store containing the file to update.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: file_id
|
||||
in: path
|
||||
description: The ID of the file to update.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
requestBody:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/OpenaiUpdateVectorStoreFileRequest'
|
||||
required: true
|
||||
delete:
|
||||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A VectorStoreFileDeleteResponse indicating the deletion status.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreFileDeleteResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
$ref: >-
|
||||
#/components/responses/TooManyRequests429
|
||||
'500':
|
||||
$ref: >-
|
||||
#/components/responses/InternalServerError500
|
||||
default:
|
||||
$ref: '#/components/responses/DefaultError'
|
||||
tags:
|
||||
- VectorIO
|
||||
description: Delete a vector store file.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
description: >-
|
||||
The ID of the vector store containing the file to delete.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: file_id
|
||||
in: path
|
||||
description: The ID of the file to delete.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
/v1/openai/v1/embeddings:
|
||||
post:
|
||||
responses:
|
||||
|
|
@ -2762,6 +2932,44 @@ paths:
|
|||
required: true
|
||||
schema:
|
||||
type: string
|
||||
/v1/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content:
|
||||
get:
|
||||
responses:
|
||||
'200':
|
||||
description: >-
|
||||
A list of InterleavedContent representing the file contents.
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/VectorStoreFileContentsResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
$ref: >-
|
||||
#/components/responses/TooManyRequests429
|
||||
'500':
|
||||
$ref: >-
|
||||
#/components/responses/InternalServerError500
|
||||
default:
|
||||
$ref: '#/components/responses/DefaultError'
|
||||
tags:
|
||||
- VectorIO
|
||||
description: >-
|
||||
Retrieves the contents of a vector store file.
|
||||
parameters:
|
||||
- name: vector_store_id
|
||||
in: path
|
||||
description: >-
|
||||
The ID of the vector store containing the file to retrieve.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
- name: file_id
|
||||
in: path
|
||||
description: The ID of the file to retrieve.
|
||||
required: true
|
||||
schema:
|
||||
type: string
|
||||
/v1/openai/v1/vector_stores/{vector_store_id}/search:
|
||||
post:
|
||||
responses:
|
||||
|
|
@ -8458,15 +8666,7 @@ components:
|
|||
last_error:
|
||||
$ref: '#/components/schemas/VectorStoreFileLastError'
|
||||
status:
|
||||
oneOf:
|
||||
- type: string
|
||||
const: completed
|
||||
- type: string
|
||||
const: in_progress
|
||||
- type: string
|
||||
const: cancelled
|
||||
- type: string
|
||||
const: failed
|
||||
$ref: '#/components/schemas/VectorStoreFileStatus'
|
||||
usage_bytes:
|
||||
type: integer
|
||||
default: 0
|
||||
|
|
@ -8484,6 +8684,16 @@ components:
|
|||
- vector_store_id
|
||||
title: VectorStoreFileObject
|
||||
description: OpenAI Vector Store File object.
|
||||
VectorStoreFileStatus:
|
||||
oneOf:
|
||||
- type: string
|
||||
const: completed
|
||||
- type: string
|
||||
const: in_progress
|
||||
- type: string
|
||||
const: cancelled
|
||||
- type: string
|
||||
const: failed
|
||||
OpenAIJSONSchema:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9031,6 +9241,27 @@ components:
|
|||
required:
|
||||
- name
|
||||
title: OpenaiCreateVectorStoreRequest
|
||||
VectorStoreFileCounts:
|
||||
type: object
|
||||
properties:
|
||||
completed:
|
||||
type: integer
|
||||
cancelled:
|
||||
type: integer
|
||||
failed:
|
||||
type: integer
|
||||
in_progress:
|
||||
type: integer
|
||||
total:
|
||||
type: integer
|
||||
additionalProperties: false
|
||||
required:
|
||||
- completed
|
||||
- cancelled
|
||||
- failed
|
||||
- in_progress
|
||||
- total
|
||||
title: VectorStoreFileCounts
|
||||
VectorStoreObject:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9047,9 +9278,7 @@ components:
|
|||
type: integer
|
||||
default: 0
|
||||
file_counts:
|
||||
type: object
|
||||
additionalProperties:
|
||||
type: integer
|
||||
$ref: '#/components/schemas/VectorStoreFileCounts'
|
||||
status:
|
||||
type: string
|
||||
default: completed
|
||||
|
|
@ -9129,6 +9358,25 @@ components:
|
|||
- deleted
|
||||
title: VectorStoreDeleteResponse
|
||||
description: Response from deleting a vector store.
|
||||
VectorStoreFileDeleteResponse:
|
||||
type: object
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
object:
|
||||
type: string
|
||||
default: vector_store.file.deleted
|
||||
deleted:
|
||||
type: boolean
|
||||
default: true
|
||||
additionalProperties: false
|
||||
required:
|
||||
- id
|
||||
- object
|
||||
- deleted
|
||||
title: VectorStoreFileDeleteResponse
|
||||
description: >-
|
||||
Response from deleting a vector store file.
|
||||
OpenaiEmbeddingsRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9320,6 +9568,30 @@ components:
|
|||
title: OpenAIFileObject
|
||||
description: >-
|
||||
OpenAI File object as defined in the OpenAI Files API.
|
||||
VectorStoreListFilesResponse:
|
||||
type: object
|
||||
properties:
|
||||
object:
|
||||
type: string
|
||||
default: list
|
||||
data:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/VectorStoreFileObject'
|
||||
first_id:
|
||||
type: string
|
||||
last_id:
|
||||
type: string
|
||||
has_more:
|
||||
type: boolean
|
||||
default: false
|
||||
additionalProperties: false
|
||||
required:
|
||||
- object
|
||||
- data
|
||||
- has_more
|
||||
title: VectorStoreListFilesResponse
|
||||
description: Response from listing vector stores.
|
||||
OpenAIModel:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9379,6 +9651,49 @@ components:
|
|||
Response:
|
||||
type: object
|
||||
title: Response
|
||||
VectorStoreContent:
|
||||
type: object
|
||||
properties:
|
||||
type:
|
||||
type: string
|
||||
const: text
|
||||
text:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
- text
|
||||
title: VectorStoreContent
|
||||
VectorStoreFileContentsResponse:
|
||||
type: object
|
||||
properties:
|
||||
file_id:
|
||||
type: string
|
||||
filename:
|
||||
type: string
|
||||
attributes:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
content:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/VectorStoreContent'
|
||||
additionalProperties: false
|
||||
required:
|
||||
- file_id
|
||||
- filename
|
||||
- attributes
|
||||
- content
|
||||
title: VectorStoreFileContentsResponse
|
||||
description: >-
|
||||
Response from retrieving the contents of a vector store file.
|
||||
OpenaiSearchVectorStoreRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9426,19 +9741,6 @@ components:
|
|||
required:
|
||||
- query
|
||||
title: OpenaiSearchVectorStoreRequest
|
||||
VectorStoreContent:
|
||||
type: object
|
||||
properties:
|
||||
type:
|
||||
type: string
|
||||
const: text
|
||||
text:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
- text
|
||||
title: VectorStoreContent
|
||||
VectorStoreSearchResponse:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -9524,6 +9826,25 @@ components:
|
|||
Set of 16 key-value pairs that can be attached to an object.
|
||||
additionalProperties: false
|
||||
title: OpenaiUpdateVectorStoreRequest
|
||||
OpenaiUpdateVectorStoreFileRequest:
|
||||
type: object
|
||||
properties:
|
||||
attributes:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: >-
|
||||
The updated key-value attributes to store with the file.
|
||||
additionalProperties: false
|
||||
required:
|
||||
- attributes
|
||||
title: OpenaiUpdateVectorStoreFileRequest
|
||||
DPOAlignmentConfig:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
|
|
@ -38,6 +38,15 @@ class QueryChunksResponse(BaseModel):
|
|||
scores: list[float]
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileCounts(BaseModel):
|
||||
completed: int
|
||||
cancelled: int
|
||||
failed: int
|
||||
in_progress: int
|
||||
total: int
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreObject(BaseModel):
|
||||
"""OpenAI Vector Store object."""
|
||||
|
|
@ -47,7 +56,7 @@ class VectorStoreObject(BaseModel):
|
|||
created_at: int
|
||||
name: str | None = None
|
||||
usage_bytes: int = 0
|
||||
file_counts: dict[str, int] = Field(default_factory=dict)
|
||||
file_counts: VectorStoreFileCounts
|
||||
status: str = "completed"
|
||||
expires_after: dict[str, Any] | None = None
|
||||
expires_at: int | None = None
|
||||
|
|
@ -168,6 +177,10 @@ class VectorStoreFileLastError(BaseModel):
|
|||
message: str
|
||||
|
||||
|
||||
VectorStoreFileStatus = Literal["completed"] | Literal["in_progress"] | Literal["cancelled"] | Literal["failed"]
|
||||
register_schema(VectorStoreFileStatus, name="VectorStoreFileStatus")
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileObject(BaseModel):
|
||||
"""OpenAI Vector Store File object."""
|
||||
|
|
@ -178,11 +191,41 @@ class VectorStoreFileObject(BaseModel):
|
|||
chunking_strategy: VectorStoreChunkingStrategy
|
||||
created_at: int
|
||||
last_error: VectorStoreFileLastError | None = None
|
||||
status: Literal["completed"] | Literal["in_progress"] | Literal["cancelled"] | Literal["failed"]
|
||||
status: VectorStoreFileStatus
|
||||
usage_bytes: int = 0
|
||||
vector_store_id: str
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreListFilesResponse(BaseModel):
|
||||
"""Response from listing vector stores."""
|
||||
|
||||
object: str = "list"
|
||||
data: list[VectorStoreFileObject]
|
||||
first_id: str | None = None
|
||||
last_id: str | None = None
|
||||
has_more: bool = False
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileContentsResponse(BaseModel):
|
||||
"""Response from retrieving the contents of a vector store file."""
|
||||
|
||||
file_id: str
|
||||
filename: str
|
||||
attributes: dict[str, Any]
|
||||
content: list[VectorStoreContent]
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class VectorStoreFileDeleteResponse(BaseModel):
|
||||
"""Response from deleting a vector store file."""
|
||||
|
||||
id: str
|
||||
object: str = "vector_store.file.deleted"
|
||||
deleted: bool = True
|
||||
|
||||
|
||||
class VectorDBStore(Protocol):
|
||||
def get_vector_db(self, vector_db_id: str) -> VectorDB | None: ...
|
||||
|
||||
|
|
@ -358,3 +401,78 @@ class VectorIO(Protocol):
|
|||
:returns: A VectorStoreFileObject representing the attached file.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files", method="GET")
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
limit: int | None = 20,
|
||||
order: str | None = "desc",
|
||||
after: str | None = None,
|
||||
before: str | None = None,
|
||||
filter: VectorStoreFileStatus | None = None,
|
||||
) -> VectorStoreListFilesResponse:
|
||||
"""List files in a vector store.
|
||||
|
||||
:param vector_store_id: The ID of the vector store to list files from.
|
||||
:returns: A VectorStoreListFilesResponse containing the list of files.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="GET")
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
"""Retrieves a vector store file.
|
||||
|
||||
:param vector_store_id: The ID of the vector store containing the file to retrieve.
|
||||
:param file_id: The ID of the file to retrieve.
|
||||
:returns: A VectorStoreFileObject representing the file.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}/content", method="GET")
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
"""Retrieves the contents of a vector store file.
|
||||
|
||||
:param vector_store_id: The ID of the vector store containing the file to retrieve.
|
||||
:param file_id: The ID of the file to retrieve.
|
||||
:returns: A list of InterleavedContent representing the file contents.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="POST")
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any],
|
||||
) -> VectorStoreFileObject:
|
||||
"""Updates a vector store file.
|
||||
|
||||
:param vector_store_id: The ID of the vector store containing the file to update.
|
||||
:param file_id: The ID of the file to update.
|
||||
:param attributes: The updated key-value attributes to store with the file.
|
||||
:returns: A VectorStoreFileObject representing the updated file.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/openai/v1/vector_stores/{vector_store_id}/files/{file_id}", method="DELETE")
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileDeleteResponse:
|
||||
"""Delete a vector store file.
|
||||
|
||||
:param vector_store_id: The ID of the vector store containing the file to delete.
|
||||
:param file_id: The ID of the file to delete.
|
||||
:returns: A VectorStoreFileDeleteResponse indicating the deletion status.
|
||||
"""
|
||||
...
|
||||
|
|
|
|||
|
|
@ -21,7 +21,13 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreObject,
|
||||
VectorStoreSearchResponsePage,
|
||||
)
|
||||
from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
|
||||
from llama_stack.apis.vector_io.vector_io import (
|
||||
VectorStoreChunkingStrategy,
|
||||
VectorStoreFileContentsResponse,
|
||||
VectorStoreFileDeleteResponse,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreFileStatus,
|
||||
)
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
|
||||
|
||||
|
|
@ -279,6 +285,81 @@ class VectorIORouter(VectorIO):
|
|||
chunking_strategy=chunking_strategy,
|
||||
)
|
||||
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
limit: int | None = 20,
|
||||
order: str | None = "desc",
|
||||
after: str | None = None,
|
||||
before: str | None = None,
|
||||
filter: VectorStoreFileStatus | None = None,
|
||||
) -> list[VectorStoreFileObject]:
|
||||
logger.debug(f"VectorIORouter.openai_list_files_in_vector_store: {vector_store_id}")
|
||||
# Route based on vector store ID
|
||||
provider = 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,
|
||||
after=after,
|
||||
before=before,
|
||||
filter=filter,
|
||||
)
|
||||
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file: {vector_store_id}, {file_id}")
|
||||
# Route based on vector store ID
|
||||
provider = 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,
|
||||
)
|
||||
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
|
||||
# Route based on vector store ID
|
||||
provider = 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,
|
||||
)
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any],
|
||||
) -> VectorStoreFileObject:
|
||||
logger.debug(f"VectorIORouter.openai_update_vector_store_file: {vector_store_id}, {file_id}")
|
||||
# Route based on vector store ID
|
||||
provider = 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,
|
||||
)
|
||||
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileDeleteResponse:
|
||||
logger.debug(f"VectorIORouter.openai_delete_vector_store_file: {vector_store_id}, {file_id}")
|
||||
# Route based on vector store ID
|
||||
provider = 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,
|
||||
)
|
||||
|
||||
async def health(self) -> dict[str, HealthResponse]:
|
||||
health_statuses = {}
|
||||
timeout = 1 # increasing the timeout to 1 second for health checks
|
||||
|
|
|
|||
|
|
@ -45,6 +45,8 @@ VERSION = "v3"
|
|||
VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
|
||||
FAISS_INDEX_PREFIX = f"faiss_index:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:{VERSION}::"
|
||||
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
|
||||
|
||||
|
||||
class FaissIndex(EmbeddingIndex):
|
||||
|
|
@ -283,3 +285,39 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
|
|||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
|
||||
await self.kvstore.delete(key)
|
||||
|
||||
async def _save_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Save vector store file metadata to kvstore."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=key, value=json.dumps(file_info))
|
||||
content_key = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=content_key, value=json.dumps(file_contents))
|
||||
|
||||
async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
|
||||
"""Load vector store file metadata from kvstore."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
stored_data = await self.kvstore.get(key)
|
||||
return json.loads(stored_data) if stored_data else {}
|
||||
|
||||
async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
|
||||
"""Load vector store file contents from kvstore."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}"
|
||||
stored_data = await self.kvstore.get(key)
|
||||
return json.loads(stored_data) if stored_data else []
|
||||
|
||||
async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
|
||||
"""Update vector store file metadata in kvstore."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.set(key=key, value=json.dumps(file_info))
|
||||
|
||||
async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
|
||||
"""Delete vector store file metadata from kvstore."""
|
||||
assert self.kvstore is not None
|
||||
key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
|
||||
await self.kvstore.delete(key)
|
||||
|
|
|
|||
|
|
@ -461,6 +461,23 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
|
|||
metadata TEXT
|
||||
);
|
||||
""")
|
||||
# Create a table to persist OpenAI vector store files.
|
||||
cur.execute("""
|
||||
CREATE TABLE IF NOT EXISTS openai_vector_store_files (
|
||||
store_id TEXT,
|
||||
file_id TEXT,
|
||||
metadata TEXT,
|
||||
PRIMARY KEY (store_id, file_id)
|
||||
);
|
||||
""")
|
||||
cur.execute("""
|
||||
CREATE TABLE IF NOT EXISTS openai_vector_store_files_contents (
|
||||
store_id TEXT,
|
||||
file_id TEXT,
|
||||
contents TEXT,
|
||||
PRIMARY KEY (store_id, file_id)
|
||||
);
|
||||
""")
|
||||
connection.commit()
|
||||
# Load any existing vector DB registrations.
|
||||
cur.execute("SELECT metadata FROM vector_dbs")
|
||||
|
|
@ -615,6 +632,118 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
|
|||
|
||||
await asyncio.to_thread(_delete)
|
||||
|
||||
async def _save_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Save vector store file metadata to SQLite database."""
|
||||
|
||||
def _store():
|
||||
connection = _create_sqlite_connection(self.config.db_path)
|
||||
cur = connection.cursor()
|
||||
try:
|
||||
cur.execute(
|
||||
"INSERT OR REPLACE INTO openai_vector_store_files (store_id, file_id, metadata) VALUES (?, ?, ?)",
|
||||
(store_id, file_id, json.dumps(file_info)),
|
||||
)
|
||||
cur.execute(
|
||||
"INSERT OR REPLACE INTO openai_vector_store_files_contents (store_id, file_id, contents) VALUES (?, ?, ?)",
|
||||
(store_id, file_id, json.dumps(file_contents)),
|
||||
)
|
||||
connection.commit()
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving openai vector store file {store_id} {file_id}: {e}")
|
||||
raise
|
||||
finally:
|
||||
cur.close()
|
||||
connection.close()
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(_store)
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving openai vector store file {store_id} {file_id}: {e}")
|
||||
raise
|
||||
|
||||
async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
|
||||
"""Load vector store file metadata from SQLite database."""
|
||||
|
||||
def _load():
|
||||
connection = _create_sqlite_connection(self.config.db_path)
|
||||
cur = connection.cursor()
|
||||
try:
|
||||
cur.execute(
|
||||
"SELECT metadata FROM openai_vector_store_files WHERE store_id = ? AND file_id = ?",
|
||||
(store_id, file_id),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row is None:
|
||||
return None
|
||||
(metadata,) = row
|
||||
return metadata
|
||||
finally:
|
||||
cur.close()
|
||||
connection.close()
|
||||
|
||||
stored_data = await asyncio.to_thread(_load)
|
||||
return json.loads(stored_data) if stored_data else {}
|
||||
|
||||
async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
|
||||
"""Load vector store file contents from SQLite database."""
|
||||
|
||||
def _load():
|
||||
connection = _create_sqlite_connection(self.config.db_path)
|
||||
cur = connection.cursor()
|
||||
try:
|
||||
cur.execute(
|
||||
"SELECT contents FROM openai_vector_store_files_contents WHERE store_id = ? AND file_id = ?",
|
||||
(store_id, file_id),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row is None:
|
||||
return None
|
||||
(contents,) = row
|
||||
return contents
|
||||
finally:
|
||||
cur.close()
|
||||
connection.close()
|
||||
|
||||
stored_contents = await asyncio.to_thread(_load)
|
||||
return json.loads(stored_contents) if stored_contents else []
|
||||
|
||||
async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
|
||||
"""Update vector store file metadata in SQLite database."""
|
||||
|
||||
def _update():
|
||||
connection = _create_sqlite_connection(self.config.db_path)
|
||||
cur = connection.cursor()
|
||||
try:
|
||||
cur.execute(
|
||||
"UPDATE openai_vector_store_files SET metadata = ? WHERE store_id = ? AND file_id = ?",
|
||||
(json.dumps(file_info), store_id, file_id),
|
||||
)
|
||||
connection.commit()
|
||||
finally:
|
||||
cur.close()
|
||||
connection.close()
|
||||
|
||||
await asyncio.to_thread(_update)
|
||||
|
||||
async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
|
||||
"""Delete vector store file metadata from SQLite database."""
|
||||
|
||||
def _delete():
|
||||
connection = _create_sqlite_connection(self.config.db_path)
|
||||
cur = connection.cursor()
|
||||
try:
|
||||
cur.execute(
|
||||
"DELETE FROM openai_vector_store_files WHERE store_id = ? AND file_id = ?", (store_id, file_id)
|
||||
)
|
||||
connection.commit()
|
||||
finally:
|
||||
cur.close()
|
||||
connection.close()
|
||||
|
||||
await asyncio.to_thread(_delete)
|
||||
|
||||
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||
if vector_db_id not in self.cache:
|
||||
raise ValueError(f"Vector DB {vector_db_id} not found. Found: {list(self.cache.keys())}")
|
||||
|
|
|
|||
|
|
@ -24,7 +24,12 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreObject,
|
||||
VectorStoreSearchResponsePage,
|
||||
)
|
||||
from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
|
||||
from llama_stack.apis.vector_io.vector_io import (
|
||||
VectorStoreChunkingStrategy,
|
||||
VectorStoreFileContentsResponse,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreListFilesResponse,
|
||||
)
|
||||
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
|
|
@ -263,3 +268,38 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
|
|||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
) -> VectorStoreListFilesResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any] | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
|
||||
|
|
|
|||
|
|
@ -26,7 +26,12 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreObject,
|
||||
VectorStoreSearchResponsePage,
|
||||
)
|
||||
from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
|
||||
from llama_stack.apis.vector_io.vector_io import (
|
||||
VectorStoreChunkingStrategy,
|
||||
VectorStoreFileContentsResponse,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreListFilesResponse,
|
||||
)
|
||||
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
|
|
@ -262,6 +267,41 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
|
|||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
) -> VectorStoreListFilesResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any] | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Milvus")
|
||||
|
||||
|
||||
def generate_chunk_id(document_id: str, chunk_text: str) -> str:
|
||||
"""Generate a unique chunk ID using a hash of document ID and chunk text."""
|
||||
|
|
|
|||
|
|
@ -24,7 +24,12 @@ from llama_stack.apis.vector_io import (
|
|||
VectorStoreObject,
|
||||
VectorStoreSearchResponsePage,
|
||||
)
|
||||
from llama_stack.apis.vector_io.vector_io import VectorStoreChunkingStrategy, VectorStoreFileObject
|
||||
from llama_stack.apis.vector_io.vector_io import (
|
||||
VectorStoreChunkingStrategy,
|
||||
VectorStoreFileContentsResponse,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreListFilesResponse,
|
||||
)
|
||||
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
|
|
@ -263,3 +268,38 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
|
|||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
) -> VectorStoreListFilesResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any] | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import mimetypes
|
||||
import time
|
||||
|
|
@ -12,6 +13,7 @@ from abc import ABC, abstractmethod
|
|||
from typing import Any
|
||||
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.files.files import OpenAIFileObject
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import (
|
||||
QueryChunksResponse,
|
||||
|
|
@ -28,8 +30,13 @@ from llama_stack.apis.vector_io.vector_io import (
|
|||
VectorStoreChunkingStrategy,
|
||||
VectorStoreChunkingStrategyAuto,
|
||||
VectorStoreChunkingStrategyStatic,
|
||||
VectorStoreFileContentsResponse,
|
||||
VectorStoreFileCounts,
|
||||
VectorStoreFileDeleteResponse,
|
||||
VectorStoreFileLastError,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreFileStatus,
|
||||
VectorStoreListFilesResponse,
|
||||
)
|
||||
from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks
|
||||
|
||||
|
|
@ -70,6 +77,33 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
"""Delete vector store metadata from persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def _save_openai_vector_store_file(
|
||||
self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Save vector store file metadata to persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
|
||||
"""Load vector store file metadata from persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
|
||||
"""Load vector store file contents from persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
|
||||
"""Update vector store file metadata in persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
|
||||
"""Delete vector store file metadata from persistent storage."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def register_vector_db(self, vector_db: VectorDB) -> None:
|
||||
"""Register a vector database (provider-specific implementation)."""
|
||||
|
|
@ -136,18 +170,28 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
await self.register_vector_db(vector_db)
|
||||
|
||||
# Create OpenAI vector store metadata
|
||||
status = "completed"
|
||||
|
||||
# Start with no files attached and update later
|
||||
file_counts = VectorStoreFileCounts(
|
||||
cancelled=0,
|
||||
completed=0,
|
||||
failed=0,
|
||||
in_progress=0,
|
||||
total=0,
|
||||
)
|
||||
store_info = {
|
||||
"id": store_id,
|
||||
"object": "vector_store",
|
||||
"created_at": created_at,
|
||||
"name": store_id,
|
||||
"usage_bytes": 0,
|
||||
"file_counts": {},
|
||||
"status": "completed",
|
||||
"file_counts": file_counts.model_dump(),
|
||||
"status": status,
|
||||
"expires_after": expires_after,
|
||||
"expires_at": None,
|
||||
"last_active_at": created_at,
|
||||
"file_ids": file_ids or [],
|
||||
"file_ids": [],
|
||||
"chunking_strategy": chunking_strategy,
|
||||
}
|
||||
|
||||
|
|
@ -165,18 +209,14 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# Store in memory cache
|
||||
self.openai_vector_stores[store_id] = store_info
|
||||
|
||||
return VectorStoreObject(
|
||||
id=store_id,
|
||||
created_at=created_at,
|
||||
name=store_id,
|
||||
usage_bytes=0,
|
||||
file_counts={},
|
||||
status="completed",
|
||||
expires_after=expires_after,
|
||||
expires_at=None,
|
||||
last_active_at=created_at,
|
||||
metadata=metadata,
|
||||
)
|
||||
# Now that our vector store is created, attach any files that were provided
|
||||
file_ids = file_ids or []
|
||||
tasks = [self.openai_attach_file_to_vector_store(store_id, file_id) for file_id in file_ids]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
# Get the updated store info and return it
|
||||
store_info = self.openai_vector_stores[store_id]
|
||||
return VectorStoreObject.model_validate(store_info)
|
||||
|
||||
async def openai_list_vector_stores(
|
||||
self,
|
||||
|
|
@ -346,33 +386,7 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
if not self._matches_filters(chunk.metadata, filters):
|
||||
continue
|
||||
|
||||
# content is InterleavedContent
|
||||
if isinstance(chunk.content, str):
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content,
|
||||
)
|
||||
]
|
||||
elif isinstance(chunk.content, list):
|
||||
# TODO: Add support for other types of content
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=item.text,
|
||||
)
|
||||
for item in chunk.content
|
||||
if item.type == "text"
|
||||
]
|
||||
else:
|
||||
if chunk.content.type != "text":
|
||||
raise ValueError(f"Unsupported content type: {chunk.content.type}")
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content.text,
|
||||
)
|
||||
]
|
||||
content = self._chunk_to_vector_store_content(chunk)
|
||||
|
||||
response_data_item = VectorStoreSearchResponse(
|
||||
file_id=chunk.metadata.get("file_id", ""),
|
||||
|
|
@ -448,6 +462,36 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
# Unknown filter type, default to no match
|
||||
raise ValueError(f"Unsupported filter type: {filter_type}")
|
||||
|
||||
def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
|
||||
# content is InterleavedContent
|
||||
if isinstance(chunk.content, str):
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content,
|
||||
)
|
||||
]
|
||||
elif isinstance(chunk.content, list):
|
||||
# TODO: Add support for other types of content
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=item.text,
|
||||
)
|
||||
for item in chunk.content
|
||||
if item.type == "text"
|
||||
]
|
||||
else:
|
||||
if chunk.content.type != "text":
|
||||
raise ValueError(f"Unsupported content type: {chunk.content.type}")
|
||||
content = [
|
||||
VectorStoreContent(
|
||||
type="text",
|
||||
text=chunk.content.text,
|
||||
)
|
||||
]
|
||||
return content
|
||||
|
||||
async def openai_attach_file_to_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
|
|
@ -455,14 +499,20 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
attributes: dict[str, Any] | None = None,
|
||||
chunking_strategy: VectorStoreChunkingStrategy | None = None,
|
||||
) -> VectorStoreFileObject:
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
attributes = attributes or {}
|
||||
chunking_strategy = chunking_strategy or VectorStoreChunkingStrategyAuto()
|
||||
created_at = int(time.time())
|
||||
chunks: list[Chunk] = []
|
||||
file_response: OpenAIFileObject | None = None
|
||||
|
||||
vector_store_file_object = VectorStoreFileObject(
|
||||
id=file_id,
|
||||
attributes=attributes,
|
||||
chunking_strategy=chunking_strategy,
|
||||
created_at=int(time.time()),
|
||||
created_at=created_at,
|
||||
status="in_progress",
|
||||
vector_store_id=vector_store_id,
|
||||
)
|
||||
|
|
@ -504,12 +554,12 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
code="server_error",
|
||||
message="No chunks were generated from the file",
|
||||
)
|
||||
return vector_store_file_object
|
||||
|
||||
await self.insert_chunks(
|
||||
vector_db_id=vector_store_id,
|
||||
chunks=chunks,
|
||||
)
|
||||
else:
|
||||
await self.insert_chunks(
|
||||
vector_db_id=vector_store_id,
|
||||
chunks=chunks,
|
||||
)
|
||||
vector_store_file_object.status = "completed"
|
||||
except Exception as e:
|
||||
logger.error(f"Error attaching file to vector store: {e}")
|
||||
vector_store_file_object.status = "failed"
|
||||
|
|
@ -517,8 +567,171 @@ class OpenAIVectorStoreMixin(ABC):
|
|||
code="server_error",
|
||||
message=str(e),
|
||||
)
|
||||
return vector_store_file_object
|
||||
|
||||
vector_store_file_object.status = "completed"
|
||||
# Create OpenAI vector store file metadata
|
||||
file_info = vector_store_file_object.model_dump(exclude={"last_error"})
|
||||
file_info["filename"] = file_response.filename if file_response else ""
|
||||
|
||||
# Save vector store file to persistent storage (provider-specific)
|
||||
dict_chunks = [c.model_dump() for c in chunks]
|
||||
await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
|
||||
|
||||
# Update file_ids and file_counts in vector store metadata
|
||||
store_info = self.openai_vector_stores[vector_store_id].copy()
|
||||
store_info["file_ids"].append(file_id)
|
||||
store_info["file_counts"]["total"] += 1
|
||||
store_info["file_counts"][vector_store_file_object.status] += 1
|
||||
|
||||
# Save updated vector store to persistent storage
|
||||
await self._save_openai_vector_store(vector_store_id, store_info)
|
||||
|
||||
# Update vector store in-memory cache
|
||||
self.openai_vector_stores[vector_store_id] = store_info
|
||||
|
||||
return vector_store_file_object
|
||||
|
||||
async def openai_list_files_in_vector_store(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
limit: int | None = 20,
|
||||
order: str | None = "desc",
|
||||
after: str | None = None,
|
||||
before: str | None = None,
|
||||
filter: VectorStoreFileStatus | None = None,
|
||||
) -> VectorStoreListFilesResponse:
|
||||
"""List files in a vector store."""
|
||||
limit = limit or 20
|
||||
order = order or "desc"
|
||||
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
store_info = self.openai_vector_stores[vector_store_id]
|
||||
|
||||
file_objects: list[VectorStoreFileObject] = []
|
||||
for file_id in store_info["file_ids"]:
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
file_object = VectorStoreFileObject(**file_info)
|
||||
if filter and file_object.status != filter:
|
||||
continue
|
||||
file_objects.append(file_object)
|
||||
|
||||
# Sort by created_at
|
||||
reverse_order = order == "desc"
|
||||
file_objects.sort(key=lambda x: x.created_at, reverse=reverse_order)
|
||||
|
||||
# Apply cursor-based pagination
|
||||
if after:
|
||||
after_index = next((i for i, file in enumerate(file_objects) if file.id == after), -1)
|
||||
if after_index >= 0:
|
||||
file_objects = file_objects[after_index + 1 :]
|
||||
|
||||
if before:
|
||||
before_index = next((i for i, file in enumerate(file_objects) if file.id == before), len(file_objects))
|
||||
file_objects = file_objects[:before_index]
|
||||
|
||||
# Apply limit
|
||||
limited_files = file_objects[:limit]
|
||||
|
||||
# Determine pagination info
|
||||
has_more = len(file_objects) > limit
|
||||
first_id = file_objects[0].id if file_objects else None
|
||||
last_id = file_objects[-1].id if file_objects else None
|
||||
|
||||
return VectorStoreListFilesResponse(
|
||||
data=limited_files,
|
||||
has_more=has_more,
|
||||
first_id=first_id,
|
||||
last_id=last_id,
|
||||
)
|
||||
|
||||
async def openai_retrieve_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileObject:
|
||||
"""Retrieves a vector store file."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
store_info = self.openai_vector_stores[vector_store_id]
|
||||
if file_id not in store_info["file_ids"]:
|
||||
raise ValueError(f"File {file_id} not found in vector store {vector_store_id}")
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
return VectorStoreFileObject(**file_info)
|
||||
|
||||
async def openai_retrieve_vector_store_file_contents(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileContentsResponse:
|
||||
"""Retrieves the contents of a vector store file."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
|
||||
chunks = [Chunk.model_validate(c) for c in dict_chunks]
|
||||
content = []
|
||||
for chunk in chunks:
|
||||
content.extend(self._chunk_to_vector_store_content(chunk))
|
||||
return VectorStoreFileContentsResponse(
|
||||
file_id=file_id,
|
||||
filename=file_info.get("filename", ""),
|
||||
attributes=file_info.get("attributes", {}),
|
||||
content=content,
|
||||
)
|
||||
|
||||
async def openai_update_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
attributes: dict[str, Any],
|
||||
) -> VectorStoreFileObject:
|
||||
"""Updates a vector store file."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
store_info = self.openai_vector_stores[vector_store_id]
|
||||
if file_id not in store_info["file_ids"]:
|
||||
raise ValueError(f"File {file_id} not found in vector store {vector_store_id}")
|
||||
|
||||
file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
|
||||
file_info["attributes"] = attributes
|
||||
await self._update_openai_vector_store_file(vector_store_id, file_id, file_info)
|
||||
return VectorStoreFileObject(**file_info)
|
||||
|
||||
async def openai_delete_vector_store_file(
|
||||
self,
|
||||
vector_store_id: str,
|
||||
file_id: str,
|
||||
) -> VectorStoreFileDeleteResponse:
|
||||
"""Deletes a vector store file."""
|
||||
if vector_store_id not in self.openai_vector_stores:
|
||||
raise ValueError(f"Vector store {vector_store_id} not found")
|
||||
|
||||
store_info = self.openai_vector_stores[vector_store_id].copy()
|
||||
|
||||
file = await self.openai_retrieve_vector_store_file(vector_store_id, file_id)
|
||||
await self._delete_openai_vector_store_file_from_storage(vector_store_id, file_id)
|
||||
|
||||
# TODO: We need to actually delete the embeddings from the underlying vector store...
|
||||
# Also uncomment the corresponding integration test marked as xfail
|
||||
#
|
||||
# test_openai_vector_store_delete_file_removes_from_vector_store in
|
||||
# tests/integration/vector_io/test_openai_vector_stores.py
|
||||
|
||||
# Update in-memory cache
|
||||
store_info["file_ids"].remove(file_id)
|
||||
store_info["file_counts"][file.status] -= 1
|
||||
store_info["file_counts"]["total"] -= 1
|
||||
self.openai_vector_stores[vector_store_id] = store_info
|
||||
|
||||
# Save updated vector store to persistent storage
|
||||
await self._save_openai_vector_store(vector_store_id, store_info)
|
||||
|
||||
return VectorStoreFileDeleteResponse(
|
||||
id=file_id,
|
||||
deleted=True,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -6,8 +6,11 @@
|
|||
|
||||
import logging
|
||||
import time
|
||||
from io import BytesIO
|
||||
|
||||
import pytest
|
||||
from llama_stack_client import BadRequestError, LlamaStackClient
|
||||
from openai import BadRequestError as OpenAIBadRequestError
|
||||
from openai import OpenAI
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
|
|
@ -73,11 +76,23 @@ def compat_client_with_empty_stores(compat_client):
|
|||
logger.warning("Failed to clear vector stores")
|
||||
pass
|
||||
|
||||
def clear_files():
|
||||
try:
|
||||
response = compat_client.files.list()
|
||||
for file in response.data:
|
||||
compat_client.files.delete(file_id=file.id)
|
||||
except Exception:
|
||||
# If the API is not available or fails, just continue
|
||||
logger.warning("Failed to clear files")
|
||||
pass
|
||||
|
||||
clear_vector_stores()
|
||||
clear_files()
|
||||
yield compat_client
|
||||
|
||||
# Clean up after the test
|
||||
clear_vector_stores()
|
||||
clear_files()
|
||||
|
||||
|
||||
def test_openai_create_vector_store(compat_client_with_empty_stores, client_with_models):
|
||||
|
|
@ -423,3 +438,369 @@ def test_openai_vector_store_search_with_max_num_results(
|
|||
|
||||
assert search_response is not None
|
||||
assert len(search_response.data) == 2
|
||||
|
||||
|
||||
def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store attach file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create a file
|
||||
test_content = b"The secret string is foobazbar."
|
||||
with BytesIO(test_content) as file_buffer:
|
||||
file_buffer.name = "openai_test.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
# Attach the file to the vector store
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
|
||||
assert file_attach_response
|
||||
assert file_attach_response.object == "vector_store.file"
|
||||
assert file_attach_response.id == file.id
|
||||
assert file_attach_response.vector_store_id == vector_store.id
|
||||
assert file_attach_response.status == "completed"
|
||||
assert file_attach_response.chunking_strategy.type == "auto"
|
||||
assert file_attach_response.created_at > 0
|
||||
assert not file_attach_response.last_error
|
||||
|
||||
updated_vector_store = compat_client.vector_stores.retrieve(vector_store_id=vector_store.id)
|
||||
assert updated_vector_store.file_counts.completed == 1
|
||||
assert updated_vector_store.file_counts.total == 1
|
||||
assert updated_vector_store.file_counts.cancelled == 0
|
||||
assert updated_vector_store.file_counts.failed == 0
|
||||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
# Search using OpenAI API to confirm our file attached
|
||||
search_response = compat_client.vector_stores.search(
|
||||
vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
|
||||
)
|
||||
assert search_response is not None
|
||||
assert len(search_response.data) > 0
|
||||
top_result = search_response.data[0]
|
||||
top_content = top_result.content[0].text
|
||||
assert "foobazbar" in top_content.lower()
|
||||
|
||||
|
||||
def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store attach files on creation."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create some files and attach them to the vector store
|
||||
valid_file_ids = []
|
||||
for i in range(3):
|
||||
with BytesIO(f"This is a test file {i}".encode()) as file_buffer:
|
||||
file_buffer.name = f"openai_test_{i}.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
valid_file_ids.append(file.id)
|
||||
|
||||
# include an invalid file ID so we can test failed status
|
||||
failed_file_id = "invalid_file_id"
|
||||
file_ids = valid_file_ids + [failed_file_id]
|
||||
num_failed = len(file_ids) - len(valid_file_ids)
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
name="test_store",
|
||||
file_ids=file_ids,
|
||||
)
|
||||
|
||||
assert vector_store.file_counts.completed == len(valid_file_ids)
|
||||
assert vector_store.file_counts.total == len(file_ids)
|
||||
assert vector_store.file_counts.cancelled == 0
|
||||
assert vector_store.file_counts.failed == num_failed
|
||||
assert vector_store.file_counts.in_progress == 0
|
||||
|
||||
files_list = compat_client.vector_stores.files.list(vector_store_id=vector_store.id)
|
||||
assert len(files_list.data) == len(file_ids)
|
||||
assert set(file_ids) == {file.id for file in files_list.data}
|
||||
for file in files_list.data:
|
||||
if file.id in valid_file_ids:
|
||||
assert file.status == "completed"
|
||||
else:
|
||||
assert file.status == "failed"
|
||||
|
||||
failed_list = compat_client.vector_stores.files.list(vector_store_id=vector_store.id, filter="failed")
|
||||
assert len(failed_list.data) == num_failed
|
||||
assert failed_file_id == failed_list.data[0].id
|
||||
|
||||
# Delete the invalid file
|
||||
delete_response = compat_client.vector_stores.files.delete(vector_store_id=vector_store.id, file_id=failed_file_id)
|
||||
assert delete_response.id == failed_file_id
|
||||
|
||||
updated_vector_store = compat_client.vector_stores.retrieve(vector_store_id=vector_store.id)
|
||||
assert updated_vector_store.file_counts.completed == len(valid_file_ids)
|
||||
assert updated_vector_store.file_counts.total == len(valid_file_ids)
|
||||
assert updated_vector_store.file_counts.failed == 0
|
||||
|
||||
|
||||
def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store list files."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create some files and attach them to the vector store
|
||||
file_ids = []
|
||||
for i in range(3):
|
||||
with BytesIO(f"This is a test file {i}".encode()) as file_buffer:
|
||||
file_buffer.name = f"openai_test_{i}.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
file_ids.append(file.id)
|
||||
|
||||
files_list = compat_client.vector_stores.files.list(vector_store_id=vector_store.id)
|
||||
assert files_list
|
||||
assert files_list.object == "list"
|
||||
assert files_list.data
|
||||
assert not files_list.has_more
|
||||
assert len(files_list.data) == 3
|
||||
assert set(file_ids) == {file.id for file in files_list.data}
|
||||
assert files_list.data[0].object == "vector_store.file"
|
||||
assert files_list.data[0].vector_store_id == vector_store.id
|
||||
assert files_list.data[0].status == "completed"
|
||||
assert files_list.data[0].chunking_strategy.type == "auto"
|
||||
assert files_list.data[0].created_at > 0
|
||||
assert files_list.first_id == files_list.data[0].id
|
||||
assert not files_list.data[0].last_error
|
||||
|
||||
first_page = compat_client.vector_stores.files.list(vector_store_id=vector_store.id, limit=2)
|
||||
assert first_page.has_more
|
||||
assert len(first_page.data) == 2
|
||||
assert first_page.first_id == first_page.data[0].id
|
||||
assert first_page.last_id != first_page.data[-1].id
|
||||
|
||||
next_page = compat_client.vector_stores.files.list(
|
||||
vector_store_id=vector_store.id, limit=2, after=first_page.data[-1].id
|
||||
)
|
||||
assert not next_page.has_more
|
||||
assert len(next_page.data) == 1
|
||||
|
||||
updated_vector_store = compat_client.vector_stores.retrieve(vector_store_id=vector_store.id)
|
||||
assert updated_vector_store.file_counts.completed == 3
|
||||
assert updated_vector_store.file_counts.total == 3
|
||||
assert updated_vector_store.file_counts.cancelled == 0
|
||||
assert updated_vector_store.file_counts.failed == 0
|
||||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
|
||||
def test_openai_vector_store_list_files_invalid_vector_store(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store list files with invalid vector store ID."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
with pytest.raises((BadRequestError, OpenAIBadRequestError)):
|
||||
compat_client.vector_stores.files.list(vector_store_id="abc123")
|
||||
|
||||
|
||||
def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store retrieve file contents."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files retrieve contents is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create a file
|
||||
test_content = b"This is a test file"
|
||||
file_name = "openai_test.txt"
|
||||
attributes = {"foo": "bar"}
|
||||
with BytesIO(test_content) as file_buffer:
|
||||
file_buffer.name = file_name
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
# Attach the file to the vector store
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
attributes=attributes,
|
||||
)
|
||||
|
||||
assert file_attach_response.status == "completed"
|
||||
|
||||
file_contents = compat_client.vector_stores.files.content(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
|
||||
assert file_contents
|
||||
assert file_contents.content[0]["type"] == "text"
|
||||
assert file_contents.content[0]["text"] == test_content.decode("utf-8")
|
||||
assert file_contents.filename == file_name
|
||||
assert file_contents.attributes == attributes
|
||||
|
||||
|
||||
def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store delete file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files list is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create some files and attach them to the vector store
|
||||
file_ids = []
|
||||
for i in range(3):
|
||||
with BytesIO(f"This is a test file {i}".encode()) as file_buffer:
|
||||
file_buffer.name = f"openai_test_{i}.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
file_ids.append(file.id)
|
||||
|
||||
files_list = compat_client.vector_stores.files.list(vector_store_id=vector_store.id)
|
||||
assert len(files_list.data) == 3
|
||||
|
||||
# Delete the first file
|
||||
delete_response = compat_client.vector_stores.files.delete(vector_store_id=vector_store.id, file_id=file_ids[0])
|
||||
assert delete_response
|
||||
assert delete_response.id == file_ids[0]
|
||||
assert delete_response.deleted is True
|
||||
assert delete_response.object == "vector_store.file.deleted"
|
||||
|
||||
updated_vector_store = compat_client.vector_stores.retrieve(vector_store_id=vector_store.id)
|
||||
assert updated_vector_store.file_counts.completed == 2
|
||||
assert updated_vector_store.file_counts.total == 2
|
||||
assert updated_vector_store.file_counts.cancelled == 0
|
||||
assert updated_vector_store.file_counts.failed == 0
|
||||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
# Delete the second file
|
||||
delete_response = compat_client.vector_stores.files.delete(vector_store_id=vector_store.id, file_id=file_ids[1])
|
||||
assert delete_response
|
||||
assert delete_response.id == file_ids[1]
|
||||
|
||||
updated_vector_store = compat_client.vector_stores.retrieve(vector_store_id=vector_store.id)
|
||||
assert updated_vector_store.file_counts.completed == 1
|
||||
assert updated_vector_store.file_counts.total == 1
|
||||
assert updated_vector_store.file_counts.cancelled == 0
|
||||
assert updated_vector_store.file_counts.failed == 0
|
||||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
|
||||
# TODO: Remove this xfail once we have a way to remove embeddings from vector store
|
||||
@pytest.mark.xfail(reason="Vector Store Files delete doesn't remove embeddings from vecntor store", strict=True)
|
||||
def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store delete file removes from vector store."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create a file
|
||||
test_content = b"The secret string is foobazbar."
|
||||
with BytesIO(test_content) as file_buffer:
|
||||
file_buffer.name = "openai_test.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
# Attach the file to the vector store
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
assert file_attach_response.status == "completed"
|
||||
|
||||
# Search using OpenAI API to confirm our file attached
|
||||
search_response = compat_client.vector_stores.search(
|
||||
vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
|
||||
)
|
||||
assert "foobazbar" in search_response.data[0].content[0].text.lower()
|
||||
|
||||
# Delete the file
|
||||
compat_client.vector_stores.files.delete(vector_store_id=vector_store.id, file_id=file.id)
|
||||
|
||||
# Search using OpenAI API to confirm our file deleted
|
||||
search_response = compat_client.vector_stores.search(
|
||||
vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
|
||||
)
|
||||
assert not search_response.data
|
||||
|
||||
|
||||
def test_openai_vector_store_update_file(compat_client_with_empty_stores, client_with_models):
|
||||
"""Test OpenAI vector store update file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
if isinstance(compat_client_with_empty_stores, LlamaStackClient):
|
||||
pytest.skip("Vector Store Files update is not yet supported with LlamaStackClient")
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(name="test_store")
|
||||
|
||||
# Create a file
|
||||
test_content = b"This is a test file"
|
||||
with BytesIO(test_content) as file_buffer:
|
||||
file_buffer.name = "openai_test.txt"
|
||||
file = compat_client.files.create(file=file_buffer, purpose="assistants")
|
||||
|
||||
# Attach the file to the vector store
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
attributes={"foo": "bar"},
|
||||
)
|
||||
|
||||
assert file_attach_response.status == "completed"
|
||||
assert file_attach_response.attributes["foo"] == "bar"
|
||||
|
||||
# Update the file's attributes
|
||||
updated_response = compat_client.vector_stores.files.update(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
attributes={"foo": "baz"},
|
||||
)
|
||||
|
||||
assert updated_response.status == "completed"
|
||||
assert updated_response.attributes["foo"] == "baz"
|
||||
|
||||
# Ensure we can retrieve the file and see the updated attributes
|
||||
retrieved_file = compat_client.vector_stores.files.retrieve(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file.id,
|
||||
)
|
||||
assert retrieved_file.attributes["foo"] == "baz"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue