diff --git a/docs/static/llama-stack-spec.html b/docs/static/llama-stack-spec.html
index cc656063d..17eeb8590 100644
--- a/docs/static/llama-stack-spec.html
+++ b/docs/static/llama-stack-spec.html
@@ -2987,165 +2987,6 @@
"deprecated": false
}
},
- "/v1/vector-dbs": {
- "get": {
- "responses": {
- "200": {
- "description": "A ListVectorDBsResponse.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/ListVectorDBsResponse"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "List all vector databases.",
- "description": "List all vector databases.",
- "parameters": [],
- "deprecated": false
- },
- "post": {
- "responses": {
- "200": {
- "description": "A VectorDB.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/VectorDB"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Register a vector database.",
- "description": "Register a vector database.",
- "parameters": [],
- "requestBody": {
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/RegisterVectorDbRequest"
- }
- }
- },
- "required": true
- },
- "deprecated": false
- }
- },
- "/v1/vector-dbs/{vector_db_id}": {
- "get": {
- "responses": {
- "200": {
- "description": "A VectorDB.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/VectorDB"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Get a vector database by its identifier.",
- "description": "Get a vector database by its identifier.",
- "parameters": [
- {
- "name": "vector_db_id",
- "in": "path",
- "description": "The identifier of the vector database to get.",
- "required": true,
- "schema": {
- "type": "string"
- }
- }
- ],
- "deprecated": false
- },
- "delete": {
- "responses": {
- "200": {
- "description": "OK"
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Unregister a vector database.",
- "description": "Unregister a vector database.",
- "parameters": [
- {
- "name": "vector_db_id",
- "in": "path",
- "description": "The identifier of the vector database to unregister.",
- "required": true,
- "schema": {
- "type": "string"
- }
- }
- ],
- "deprecated": false
- }
- },
"/v1/vector-io/insert": {
"post": {
"responses": {
@@ -11784,111 +11625,6 @@
],
"title": "RegisterToolGroupRequest"
},
- "VectorDB": {
- "type": "object",
- "properties": {
- "identifier": {
- "type": "string"
- },
- "provider_resource_id": {
- "type": "string"
- },
- "provider_id": {
- "type": "string"
- },
- "type": {
- "type": "string",
- "enum": [
- "model",
- "shield",
- "vector_db",
- "dataset",
- "scoring_function",
- "benchmark",
- "tool",
- "tool_group",
- "prompt"
- ],
- "const": "vector_db",
- "default": "vector_db",
- "description": "Type of resource, always 'vector_db' for vector databases"
- },
- "embedding_model": {
- "type": "string",
- "description": "Name of the embedding model to use for vector generation"
- },
- "embedding_dimension": {
- "type": "integer",
- "description": "Dimension of the embedding vectors"
- },
- "vector_db_name": {
- "type": "string"
- }
- },
- "additionalProperties": false,
- "required": [
- "identifier",
- "provider_id",
- "type",
- "embedding_model",
- "embedding_dimension"
- ],
- "title": "VectorDB",
- "description": "Vector database resource for storing and querying vector embeddings."
- },
- "ListVectorDBsResponse": {
- "type": "object",
- "properties": {
- "data": {
- "type": "array",
- "items": {
- "$ref": "#/components/schemas/VectorDB"
- },
- "description": "List of vector databases"
- }
- },
- "additionalProperties": false,
- "required": [
- "data"
- ],
- "title": "ListVectorDBsResponse",
- "description": "Response from listing vector databases."
- },
- "RegisterVectorDbRequest": {
- "type": "object",
- "properties": {
- "vector_db_id": {
- "type": "string",
- "description": "The identifier of the vector database to register."
- },
- "embedding_model": {
- "type": "string",
- "description": "The embedding model to use."
- },
- "embedding_dimension": {
- "type": "integer",
- "description": "The dimension of the embedding model."
- },
- "provider_id": {
- "type": "string",
- "description": "The identifier of the provider."
- },
- "vector_db_name": {
- "type": "string",
- "description": "The name of the vector database."
- },
- "provider_vector_db_id": {
- "type": "string",
- "description": "The identifier of the vector database in the provider."
- }
- },
- "additionalProperties": false,
- "required": [
- "vector_db_id",
- "embedding_model"
- ],
- "title": "RegisterVectorDbRequest"
- },
"Chunk": {
"type": "object",
"properties": {
@@ -13364,10 +13100,6 @@
"name": "ToolRuntime",
"description": ""
},
- {
- "name": "VectorDBs",
- "description": ""
- },
{
"name": "VectorIO",
"description": ""
@@ -13393,7 +13125,6 @@
"Telemetry",
"ToolGroups",
"ToolRuntime",
- "VectorDBs",
"VectorIO"
]
}
diff --git a/docs/static/llama-stack-spec.yaml b/docs/static/llama-stack-spec.yaml
index 66e84b4f2..5ebb297d4 100644
--- a/docs/static/llama-stack-spec.yaml
+++ b/docs/static/llama-stack-spec.yaml
@@ -2275,120 +2275,6 @@ paths:
schema:
type: string
deprecated: false
- /v1/vector-dbs:
- get:
- responses:
- '200':
- description: A ListVectorDBsResponse.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/ListVectorDBsResponse'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: List all vector databases.
- description: List all vector databases.
- parameters: []
- deprecated: false
- post:
- responses:
- '200':
- description: A VectorDB.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/VectorDB'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Register a vector database.
- description: Register a vector database.
- parameters: []
- requestBody:
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/RegisterVectorDbRequest'
- required: true
- deprecated: false
- /v1/vector-dbs/{vector_db_id}:
- get:
- responses:
- '200':
- description: A VectorDB.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/VectorDB'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Get a vector database by its identifier.
- description: Get a vector database by its identifier.
- parameters:
- - name: vector_db_id
- in: path
- description: >-
- The identifier of the vector database to get.
- required: true
- schema:
- type: string
- deprecated: false
- delete:
- responses:
- '200':
- description: OK
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Unregister a vector database.
- description: Unregister a vector database.
- parameters:
- - name: vector_db_id
- in: path
- description: >-
- The identifier of the vector database to unregister.
- required: true
- schema:
- type: string
- deprecated: false
/v1/vector-io/insert:
post:
responses:
@@ -8908,91 +8794,6 @@ components:
- toolgroup_id
- provider_id
title: RegisterToolGroupRequest
- VectorDB:
- type: object
- properties:
- identifier:
- type: string
- provider_resource_id:
- type: string
- provider_id:
- type: string
- type:
- type: string
- enum:
- - model
- - shield
- - vector_db
- - dataset
- - scoring_function
- - benchmark
- - tool
- - tool_group
- - prompt
- const: vector_db
- default: vector_db
- description: >-
- Type of resource, always 'vector_db' for vector databases
- embedding_model:
- type: string
- description: >-
- Name of the embedding model to use for vector generation
- embedding_dimension:
- type: integer
- description: Dimension of the embedding vectors
- vector_db_name:
- type: string
- additionalProperties: false
- required:
- - identifier
- - provider_id
- - type
- - embedding_model
- - embedding_dimension
- title: VectorDB
- description: >-
- Vector database resource for storing and querying vector embeddings.
- ListVectorDBsResponse:
- type: object
- properties:
- data:
- type: array
- items:
- $ref: '#/components/schemas/VectorDB'
- description: List of vector databases
- additionalProperties: false
- required:
- - data
- title: ListVectorDBsResponse
- description: Response from listing vector databases.
- RegisterVectorDbRequest:
- type: object
- properties:
- vector_db_id:
- type: string
- description: >-
- The identifier of the vector database to register.
- embedding_model:
- type: string
- description: The embedding model to use.
- embedding_dimension:
- type: integer
- description: The dimension of the embedding model.
- provider_id:
- type: string
- description: The identifier of the provider.
- vector_db_name:
- type: string
- description: The name of the vector database.
- provider_vector_db_id:
- type: string
- description: >-
- The identifier of the vector database in the provider.
- additionalProperties: false
- required:
- - vector_db_id
- - embedding_model
- title: RegisterVectorDbRequest
Chunk:
type: object
properties:
@@ -10162,8 +9963,6 @@ tags:
description: ''
- name: ToolRuntime
description: ''
- - name: VectorDBs
- description: ''
- name: VectorIO
description: ''
x-tagGroups:
@@ -10185,5 +9984,4 @@ x-tagGroups:
- Telemetry
- ToolGroups
- ToolRuntime
- - VectorDBs
- VectorIO
diff --git a/docs/static/stainless-llama-stack-spec.html b/docs/static/stainless-llama-stack-spec.html
index 10305b239..eb0ffc840 100644
--- a/docs/static/stainless-llama-stack-spec.html
+++ b/docs/static/stainless-llama-stack-spec.html
@@ -2987,165 +2987,6 @@
"deprecated": false
}
},
- "/v1/vector-dbs": {
- "get": {
- "responses": {
- "200": {
- "description": "A ListVectorDBsResponse.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/ListVectorDBsResponse"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "List all vector databases.",
- "description": "List all vector databases.",
- "parameters": [],
- "deprecated": false
- },
- "post": {
- "responses": {
- "200": {
- "description": "A VectorDB.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/VectorDB"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Register a vector database.",
- "description": "Register a vector database.",
- "parameters": [],
- "requestBody": {
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/RegisterVectorDbRequest"
- }
- }
- },
- "required": true
- },
- "deprecated": false
- }
- },
- "/v1/vector-dbs/{vector_db_id}": {
- "get": {
- "responses": {
- "200": {
- "description": "A VectorDB.",
- "content": {
- "application/json": {
- "schema": {
- "$ref": "#/components/schemas/VectorDB"
- }
- }
- }
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Get a vector database by its identifier.",
- "description": "Get a vector database by its identifier.",
- "parameters": [
- {
- "name": "vector_db_id",
- "in": "path",
- "description": "The identifier of the vector database to get.",
- "required": true,
- "schema": {
- "type": "string"
- }
- }
- ],
- "deprecated": false
- },
- "delete": {
- "responses": {
- "200": {
- "description": "OK"
- },
- "400": {
- "$ref": "#/components/responses/BadRequest400"
- },
- "429": {
- "$ref": "#/components/responses/TooManyRequests429"
- },
- "500": {
- "$ref": "#/components/responses/InternalServerError500"
- },
- "default": {
- "$ref": "#/components/responses/DefaultError"
- }
- },
- "tags": [
- "VectorDBs"
- ],
- "summary": "Unregister a vector database.",
- "description": "Unregister a vector database.",
- "parameters": [
- {
- "name": "vector_db_id",
- "in": "path",
- "description": "The identifier of the vector database to unregister.",
- "required": true,
- "schema": {
- "type": "string"
- }
- }
- ],
- "deprecated": false
- }
- },
"/v1/vector-io/insert": {
"post": {
"responses": {
@@ -13793,111 +13634,6 @@
],
"title": "RegisterToolGroupRequest"
},
- "VectorDB": {
- "type": "object",
- "properties": {
- "identifier": {
- "type": "string"
- },
- "provider_resource_id": {
- "type": "string"
- },
- "provider_id": {
- "type": "string"
- },
- "type": {
- "type": "string",
- "enum": [
- "model",
- "shield",
- "vector_db",
- "dataset",
- "scoring_function",
- "benchmark",
- "tool",
- "tool_group",
- "prompt"
- ],
- "const": "vector_db",
- "default": "vector_db",
- "description": "Type of resource, always 'vector_db' for vector databases"
- },
- "embedding_model": {
- "type": "string",
- "description": "Name of the embedding model to use for vector generation"
- },
- "embedding_dimension": {
- "type": "integer",
- "description": "Dimension of the embedding vectors"
- },
- "vector_db_name": {
- "type": "string"
- }
- },
- "additionalProperties": false,
- "required": [
- "identifier",
- "provider_id",
- "type",
- "embedding_model",
- "embedding_dimension"
- ],
- "title": "VectorDB",
- "description": "Vector database resource for storing and querying vector embeddings."
- },
- "ListVectorDBsResponse": {
- "type": "object",
- "properties": {
- "data": {
- "type": "array",
- "items": {
- "$ref": "#/components/schemas/VectorDB"
- },
- "description": "List of vector databases"
- }
- },
- "additionalProperties": false,
- "required": [
- "data"
- ],
- "title": "ListVectorDBsResponse",
- "description": "Response from listing vector databases."
- },
- "RegisterVectorDbRequest": {
- "type": "object",
- "properties": {
- "vector_db_id": {
- "type": "string",
- "description": "The identifier of the vector database to register."
- },
- "embedding_model": {
- "type": "string",
- "description": "The embedding model to use."
- },
- "embedding_dimension": {
- "type": "integer",
- "description": "The dimension of the embedding model."
- },
- "provider_id": {
- "type": "string",
- "description": "The identifier of the provider."
- },
- "vector_db_name": {
- "type": "string",
- "description": "The name of the vector database."
- },
- "provider_vector_db_id": {
- "type": "string",
- "description": "The identifier of the vector database in the provider."
- }
- },
- "additionalProperties": false,
- "required": [
- "vector_db_id",
- "embedding_model"
- ],
- "title": "RegisterVectorDbRequest"
- },
"Chunk": {
"type": "object",
"properties": {
@@ -18941,10 +18677,6 @@
"name": "ToolRuntime",
"description": ""
},
- {
- "name": "VectorDBs",
- "description": ""
- },
{
"name": "VectorIO",
"description": ""
@@ -18975,7 +18707,6 @@
"Telemetry",
"ToolGroups",
"ToolRuntime",
- "VectorDBs",
"VectorIO"
]
}
diff --git a/docs/static/stainless-llama-stack-spec.yaml b/docs/static/stainless-llama-stack-spec.yaml
index afeeabc62..87fc52750 100644
--- a/docs/static/stainless-llama-stack-spec.yaml
+++ b/docs/static/stainless-llama-stack-spec.yaml
@@ -2278,120 +2278,6 @@ paths:
schema:
type: string
deprecated: false
- /v1/vector-dbs:
- get:
- responses:
- '200':
- description: A ListVectorDBsResponse.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/ListVectorDBsResponse'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: List all vector databases.
- description: List all vector databases.
- parameters: []
- deprecated: false
- post:
- responses:
- '200':
- description: A VectorDB.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/VectorDB'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Register a vector database.
- description: Register a vector database.
- parameters: []
- requestBody:
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/RegisterVectorDbRequest'
- required: true
- deprecated: false
- /v1/vector-dbs/{vector_db_id}:
- get:
- responses:
- '200':
- description: A VectorDB.
- content:
- application/json:
- schema:
- $ref: '#/components/schemas/VectorDB'
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Get a vector database by its identifier.
- description: Get a vector database by its identifier.
- parameters:
- - name: vector_db_id
- in: path
- description: >-
- The identifier of the vector database to get.
- required: true
- schema:
- type: string
- deprecated: false
- delete:
- responses:
- '200':
- description: OK
- '400':
- $ref: '#/components/responses/BadRequest400'
- '429':
- $ref: >-
- #/components/responses/TooManyRequests429
- '500':
- $ref: >-
- #/components/responses/InternalServerError500
- default:
- $ref: '#/components/responses/DefaultError'
- tags:
- - VectorDBs
- summary: Unregister a vector database.
- description: Unregister a vector database.
- parameters:
- - name: vector_db_id
- in: path
- description: >-
- The identifier of the vector database to unregister.
- required: true
- schema:
- type: string
- deprecated: false
/v1/vector-io/insert:
post:
responses:
@@ -10353,91 +10239,6 @@ components:
- toolgroup_id
- provider_id
title: RegisterToolGroupRequest
- VectorDB:
- type: object
- properties:
- identifier:
- type: string
- provider_resource_id:
- type: string
- provider_id:
- type: string
- type:
- type: string
- enum:
- - model
- - shield
- - vector_db
- - dataset
- - scoring_function
- - benchmark
- - tool
- - tool_group
- - prompt
- const: vector_db
- default: vector_db
- description: >-
- Type of resource, always 'vector_db' for vector databases
- embedding_model:
- type: string
- description: >-
- Name of the embedding model to use for vector generation
- embedding_dimension:
- type: integer
- description: Dimension of the embedding vectors
- vector_db_name:
- type: string
- additionalProperties: false
- required:
- - identifier
- - provider_id
- - type
- - embedding_model
- - embedding_dimension
- title: VectorDB
- description: >-
- Vector database resource for storing and querying vector embeddings.
- ListVectorDBsResponse:
- type: object
- properties:
- data:
- type: array
- items:
- $ref: '#/components/schemas/VectorDB'
- description: List of vector databases
- additionalProperties: false
- required:
- - data
- title: ListVectorDBsResponse
- description: Response from listing vector databases.
- RegisterVectorDbRequest:
- type: object
- properties:
- vector_db_id:
- type: string
- description: >-
- The identifier of the vector database to register.
- embedding_model:
- type: string
- description: The embedding model to use.
- embedding_dimension:
- type: integer
- description: The dimension of the embedding model.
- provider_id:
- type: string
- description: The identifier of the provider.
- vector_db_name:
- type: string
- description: The name of the vector database.
- provider_vector_db_id:
- type: string
- description: >-
- The identifier of the vector database in the provider.
- additionalProperties: false
- required:
- - vector_db_id
- - embedding_model
- title: RegisterVectorDbRequest
Chunk:
type: object
properties:
@@ -14210,8 +14011,6 @@ tags:
description: ''
- name: ToolRuntime
description: ''
- - name: VectorDBs
- description: ''
- name: VectorIO
description: ''
x-tagGroups:
@@ -14238,5 +14037,4 @@ x-tagGroups:
- Telemetry
- ToolGroups
- ToolRuntime
- - VectorDBs
- VectorIO
diff --git a/llama_stack/apis/datatypes.py b/llama_stack/apis/datatypes.py
index e522682c6..8fbf21f3e 100644
--- a/llama_stack/apis/datatypes.py
+++ b/llama_stack/apis/datatypes.py
@@ -96,7 +96,6 @@ class Api(Enum, metaclass=DynamicApiMeta):
:cvar telemetry: Observability and system monitoring
:cvar models: Model metadata and management
:cvar shields: Safety shield implementations
- :cvar vector_dbs: Vector database management
:cvar datasets: Dataset creation and management
:cvar scoring_functions: Scoring function definitions
:cvar benchmarks: Benchmark suite management
@@ -122,7 +121,6 @@ class Api(Enum, metaclass=DynamicApiMeta):
models = "models"
shields = "shields"
- vector_dbs = "vector_dbs"
datasets = "datasets"
scoring_functions = "scoring_functions"
benchmarks = "benchmarks"
diff --git a/llama_stack/apis/vector_dbs/vector_dbs.py b/llama_stack/apis/vector_dbs/vector_dbs.py
index 521d129c6..53bf181e9 100644
--- a/llama_stack/apis/vector_dbs/vector_dbs.py
+++ b/llama_stack/apis/vector_dbs/vector_dbs.py
@@ -4,14 +4,12 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
-from typing import Literal, Protocol, runtime_checkable
+from typing import Literal
from pydantic import BaseModel
from llama_stack.apis.resource import Resource, ResourceType
-from llama_stack.apis.version import LLAMA_STACK_API_V1
-from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
-from llama_stack.schema_utils import json_schema_type, webmethod
+from llama_stack.schema_utils import json_schema_type
@json_schema_type
@@ -61,57 +59,3 @@ class ListVectorDBsResponse(BaseModel):
"""
data: list[VectorDB]
-
-
-@runtime_checkable
-@trace_protocol
-class VectorDBs(Protocol):
- @webmethod(route="/vector-dbs", method="GET", level=LLAMA_STACK_API_V1)
- async def list_vector_dbs(self) -> ListVectorDBsResponse:
- """List all vector databases.
-
- :returns: A ListVectorDBsResponse.
- """
- ...
-
- @webmethod(route="/vector-dbs/{vector_db_id:path}", method="GET", level=LLAMA_STACK_API_V1)
- async def get_vector_db(
- self,
- vector_db_id: str,
- ) -> VectorDB:
- """Get a vector database by its identifier.
-
- :param vector_db_id: The identifier of the vector database to get.
- :returns: A VectorDB.
- """
- ...
-
- @webmethod(route="/vector-dbs", method="POST", level=LLAMA_STACK_API_V1)
- async def register_vector_db(
- self,
- vector_db_id: str,
- embedding_model: str,
- embedding_dimension: int | None = 384,
- provider_id: str | None = None,
- vector_db_name: str | None = None,
- provider_vector_db_id: str | None = None,
- ) -> VectorDB:
- """Register a vector database.
-
- :param vector_db_id: The identifier of the vector database to register.
- :param embedding_model: The embedding model to use.
- :param embedding_dimension: The dimension of the embedding model.
- :param provider_id: The identifier of the provider.
- :param vector_db_name: The name of the vector database.
- :param provider_vector_db_id: The identifier of the vector database in the provider.
- :returns: A VectorDB.
- """
- ...
-
- @webmethod(route="/vector-dbs/{vector_db_id:path}", method="DELETE", level=LLAMA_STACK_API_V1)
- async def unregister_vector_db(self, vector_db_id: str) -> None:
- """Unregister a vector database.
-
- :param vector_db_id: The identifier of the vector database to unregister.
- """
- ...
diff --git a/llama_stack/core/distribution.py b/llama_stack/core/distribution.py
index 0ebb847af..124eaa02c 100644
--- a/llama_stack/core/distribution.py
+++ b/llama_stack/core/distribution.py
@@ -47,10 +47,6 @@ def builtin_automatically_routed_apis() -> list[AutoRoutedApiInfo]:
routing_table_api=Api.shields,
router_api=Api.safety,
),
- AutoRoutedApiInfo(
- routing_table_api=Api.vector_dbs,
- router_api=Api.vector_io,
- ),
AutoRoutedApiInfo(
routing_table_api=Api.datasets,
router_api=Api.datasetio,
diff --git a/llama_stack/core/resolver.py b/llama_stack/core/resolver.py
index 749253865..6bc7a36f6 100644
--- a/llama_stack/core/resolver.py
+++ b/llama_stack/core/resolver.py
@@ -28,7 +28,6 @@ from llama_stack.apis.scoring_functions import ScoringFunctions
from llama_stack.apis.shields import Shields
from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import ToolGroups, ToolRuntime
-from llama_stack.apis.vector_dbs import VectorDBs
from llama_stack.apis.vector_io import VectorIO
from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
from llama_stack.core.client import get_client_impl
@@ -55,7 +54,6 @@ from llama_stack.providers.datatypes import (
ScoringFunctionsProtocolPrivate,
ShieldsProtocolPrivate,
ToolGroupsProtocolPrivate,
- VectorDBsProtocolPrivate,
)
logger = get_logger(name=__name__, category="core")
@@ -81,7 +79,6 @@ def api_protocol_map(external_apis: dict[Api, ExternalApiSpec] | None = None) ->
Api.inspect: Inspect,
Api.batches: Batches,
Api.vector_io: VectorIO,
- Api.vector_dbs: VectorDBs,
Api.models: Models,
Api.safety: Safety,
Api.shields: Shields,
@@ -125,7 +122,6 @@ def additional_protocols_map() -> dict[Api, Any]:
return {
Api.inference: (ModelsProtocolPrivate, Models, Api.models),
Api.tool_groups: (ToolGroupsProtocolPrivate, ToolGroups, Api.tool_groups),
- Api.vector_io: (VectorDBsProtocolPrivate, VectorDBs, Api.vector_dbs),
Api.safety: (ShieldsProtocolPrivate, Shields, Api.shields),
Api.datasetio: (DatasetsProtocolPrivate, Datasets, Api.datasets),
Api.scoring: (
diff --git a/llama_stack/core/routers/__init__.py b/llama_stack/core/routers/__init__.py
index f129f8ede..a1a8b0144 100644
--- a/llama_stack/core/routers/__init__.py
+++ b/llama_stack/core/routers/__init__.py
@@ -26,10 +26,8 @@ async def get_routing_table_impl(
from ..routing_tables.scoring_functions import ScoringFunctionsRoutingTable
from ..routing_tables.shields import ShieldsRoutingTable
from ..routing_tables.toolgroups import ToolGroupsRoutingTable
- from ..routing_tables.vector_dbs import VectorDBsRoutingTable
api_to_tables = {
- "vector_dbs": VectorDBsRoutingTable,
"models": ModelsRoutingTable,
"shields": ShieldsRoutingTable,
"datasets": DatasetsRoutingTable,
diff --git a/llama_stack/core/routing_tables/common.py b/llama_stack/core/routing_tables/common.py
index 0800b909b..0b5aa7843 100644
--- a/llama_stack/core/routing_tables/common.py
+++ b/llama_stack/core/routing_tables/common.py
@@ -134,15 +134,12 @@ class CommonRoutingTableImpl(RoutingTable):
from .scoring_functions import ScoringFunctionsRoutingTable
from .shields import ShieldsRoutingTable
from .toolgroups import ToolGroupsRoutingTable
- from .vector_dbs import VectorDBsRoutingTable
def apiname_object():
if isinstance(self, ModelsRoutingTable):
return ("Inference", "model")
elif isinstance(self, ShieldsRoutingTable):
return ("Safety", "shield")
- elif isinstance(self, VectorDBsRoutingTable):
- return ("VectorIO", "vector_db")
elif isinstance(self, DatasetsRoutingTable):
return ("DatasetIO", "dataset")
elif isinstance(self, ScoringFunctionsRoutingTable):
diff --git a/llama_stack/core/routing_tables/vector_dbs.py b/llama_stack/core/routing_tables/vector_dbs.py
deleted file mode 100644
index 932bbdba8..000000000
--- a/llama_stack/core/routing_tables/vector_dbs.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-from typing import Any
-
-from pydantic import TypeAdapter
-
-from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError, VectorStoreNotFoundError
-from llama_stack.apis.models import ModelType
-from llama_stack.apis.resource import ResourceType
-from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
-from llama_stack.apis.vector_io.vector_io import (
- SearchRankingOptions,
- VectorStoreChunkingStrategy,
- VectorStoreDeleteResponse,
- VectorStoreFileContentsResponse,
- VectorStoreFileDeleteResponse,
- VectorStoreFileObject,
- VectorStoreFileStatus,
- VectorStoreObject,
- VectorStoreSearchResponsePage,
-)
-from llama_stack.core.datatypes import (
- VectorDBWithOwner,
-)
-from llama_stack.log import get_logger
-
-from .common import CommonRoutingTableImpl, lookup_model
-
-logger = get_logger(name=__name__, category="core::routing_tables")
-
-
-class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
- async def list_vector_dbs(self) -> ListVectorDBsResponse:
- return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db"))
-
- async def get_vector_db(self, vector_db_id: str) -> VectorDB:
- vector_db = await self.get_object_by_identifier("vector_db", vector_db_id)
- if vector_db is None:
- raise VectorStoreNotFoundError(vector_db_id)
- return vector_db
-
- async def register_vector_db(
- self,
- vector_db_id: str,
- embedding_model: str,
- embedding_dimension: int | None = 384,
- provider_id: str | None = None,
- provider_vector_db_id: str | None = None,
- vector_db_name: str | None = None,
- ) -> VectorDB:
- if provider_id is None:
- if len(self.impls_by_provider_id) > 0:
- provider_id = list(self.impls_by_provider_id.keys())[0]
- if len(self.impls_by_provider_id) > 1:
- logger.warning(
- f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}."
- )
- else:
- raise ValueError("No provider available. Please configure a vector_io provider.")
- model = await lookup_model(self, embedding_model)
- if model is None:
- raise ModelNotFoundError(embedding_model)
- if model.model_type != ModelType.embedding:
- raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
- if "embedding_dimension" not in model.metadata:
- raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
-
- provider = self.impls_by_provider_id[provider_id]
- logger.warning(
- "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
- )
- vector_store = await provider.openai_create_vector_store(
- name=vector_db_name or vector_db_id,
- embedding_model=embedding_model,
- embedding_dimension=model.metadata["embedding_dimension"],
- provider_id=provider_id,
- provider_vector_db_id=provider_vector_db_id,
- )
-
- vector_store_id = vector_store.id
- actual_provider_vector_db_id = provider_vector_db_id or vector_store_id
- logger.warning(
- f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name"
- )
-
- vector_db_data = {
- "identifier": vector_store_id,
- "type": ResourceType.vector_db.value,
- "provider_id": provider_id,
- "provider_resource_id": actual_provider_vector_db_id,
- "embedding_model": embedding_model,
- "embedding_dimension": model.metadata["embedding_dimension"],
- "vector_db_name": vector_store.name,
- }
- vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
- await self.register_object(vector_db)
- return vector_db
-
- async def unregister_vector_db(self, vector_db_id: str) -> None:
- existing_vector_db = await self.get_vector_db(vector_db_id)
- await self.unregister_object(existing_vector_db)
-
- async def openai_retrieve_vector_store(
- self,
- vector_store_id: str,
- ) -> VectorStoreObject:
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.get_provider_impl(vector_store_id)
- return await provider.openai_retrieve_vector_store(vector_store_id)
-
- async def openai_update_vector_store(
- self,
- vector_store_id: str,
- name: str | None = None,
- expires_after: dict[str, Any] | None = None,
- metadata: dict[str, Any] | None = None,
- ) -> VectorStoreObject:
- await self.assert_action_allowed("update", "vector_db", vector_store_id)
- provider = await self.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,
- metadata=metadata,
- )
-
- async def openai_delete_vector_store(
- self,
- vector_store_id: str,
- ) -> VectorStoreDeleteResponse:
- await self.assert_action_allowed("delete", "vector_db", vector_store_id)
- provider = await self.get_provider_impl(vector_store_id)
- result = await provider.openai_delete_vector_store(vector_store_id)
- await self.unregister_vector_db(vector_store_id)
- return result
-
- async def openai_search_vector_store(
- self,
- vector_store_id: str,
- query: str | list[str],
- filters: dict[str, Any] | None = None,
- max_num_results: int | None = 10,
- ranking_options: SearchRankingOptions | None = None,
- rewrite_query: bool | None = False,
- search_mode: str | None = "vector",
- ) -> VectorStoreSearchResponsePage:
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.get_provider_impl(vector_store_id)
- return await provider.openai_search_vector_store(
- vector_store_id=vector_store_id,
- query=query,
- filters=filters,
- max_num_results=max_num_results,
- ranking_options=ranking_options,
- rewrite_query=rewrite_query,
- search_mode=search_mode,
- )
-
- async def openai_attach_file_to_vector_store(
- self,
- vector_store_id: str,
- file_id: str,
- attributes: dict[str, Any] | None = None,
- chunking_strategy: VectorStoreChunkingStrategy | None = None,
- ) -> VectorStoreFileObject:
- await self.assert_action_allowed("update", "vector_db", vector_store_id)
- provider = await self.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,
- 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]:
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.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:
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.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:
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.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:
- await self.assert_action_allowed("update", "vector_db", vector_store_id)
- provider = await self.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:
- await self.assert_action_allowed("delete", "vector_db", vector_store_id)
- provider = await self.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 openai_create_vector_store_file_batch(
- self,
- vector_store_id: str,
- file_ids: list[str],
- attributes: dict[str, Any] | None = None,
- chunking_strategy: Any | None = None,
- ):
- await self.assert_action_allowed("update", "vector_db", vector_store_id)
- provider = await self.get_provider_impl(vector_store_id)
- return await provider.openai_create_vector_store_file_batch(
- vector_store_id=vector_store_id,
- file_ids=file_ids,
- attributes=attributes,
- chunking_strategy=chunking_strategy,
- )
-
- async def openai_retrieve_vector_store_file_batch(
- self,
- batch_id: str,
- vector_store_id: str,
- ):
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.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,
- )
-
- async def openai_list_files_in_vector_store_file_batch(
- self,
- batch_id: str,
- vector_store_id: str,
- after: str | None = None,
- before: str | None = None,
- filter: str | None = None,
- limit: int | None = 20,
- order: str | None = "desc",
- ):
- await self.assert_action_allowed("read", "vector_db", vector_store_id)
- provider = await self.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,
- before=before,
- filter=filter,
- limit=limit,
- order=order,
- )
-
- async def openai_cancel_vector_store_file_batch(
- self,
- batch_id: str,
- vector_store_id: str,
- ):
- await self.assert_action_allowed("update", "vector_db", vector_store_id)
- provider = await self.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,
- )
diff --git a/llama_stack/core/stack.py b/llama_stack/core/stack.py
index 2eab9344f..f161ac358 100644
--- a/llama_stack/core/stack.py
+++ b/llama_stack/core/stack.py
@@ -33,7 +33,6 @@ from llama_stack.apis.shields import Shields
from llama_stack.apis.synthetic_data_generation import SyntheticDataGeneration
from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
-from llama_stack.apis.vector_dbs import VectorDBs
from llama_stack.apis.vector_io import VectorIO
from llama_stack.core.conversations.conversations import ConversationServiceConfig, ConversationServiceImpl
from llama_stack.core.datatypes import Provider, StackRunConfig
@@ -53,7 +52,6 @@ logger = get_logger(name=__name__, category="core")
class LlamaStack(
Providers,
- VectorDBs,
Inference,
Agents,
Safety,
@@ -83,7 +81,6 @@ class LlamaStack(
RESOURCES = [
("models", Api.models, "register_model", "list_models"),
("shields", Api.shields, "register_shield", "list_shields"),
- ("vector_dbs", Api.vector_dbs, "register_vector_db", "list_vector_dbs"),
("datasets", Api.datasets, "register_dataset", "list_datasets"),
(
"scoring_fns",
diff --git a/llama_stack/core/ui/page/distribution/resources.py b/llama_stack/core/ui/page/distribution/resources.py
index c56fcfff3..6e7122ceb 100644
--- a/llama_stack/core/ui/page/distribution/resources.py
+++ b/llama_stack/core/ui/page/distribution/resources.py
@@ -11,19 +11,17 @@ from llama_stack.core.ui.page.distribution.eval_tasks import benchmarks
from llama_stack.core.ui.page.distribution.models import models
from llama_stack.core.ui.page.distribution.scoring_functions import scoring_functions
from llama_stack.core.ui.page.distribution.shields import shields
-from llama_stack.core.ui.page.distribution.vector_dbs import vector_dbs
def resources_page():
options = [
"Models",
- "Vector Databases",
"Shields",
"Scoring Functions",
"Datasets",
"Benchmarks",
]
- icons = ["magic", "memory", "shield", "file-bar-graph", "database", "list-task"]
+ icons = ["magic", "shield", "file-bar-graph", "database", "list-task"]
selected_resource = option_menu(
None,
options,
@@ -37,8 +35,6 @@ def resources_page():
)
if selected_resource == "Benchmarks":
benchmarks()
- elif selected_resource == "Vector Databases":
- vector_dbs()
elif selected_resource == "Datasets":
datasets()
elif selected_resource == "Models":
diff --git a/llama_stack/core/ui/page/distribution/vector_dbs.py b/llama_stack/core/ui/page/distribution/vector_dbs.py
deleted file mode 100644
index e81077d2a..000000000
--- a/llama_stack/core/ui/page/distribution/vector_dbs.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import streamlit as st
-
-from llama_stack.core.ui.modules.api import llama_stack_api
-
-
-def vector_dbs():
- st.header("Vector Databases")
- vector_dbs_info = {v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()}
-
- if len(vector_dbs_info) > 0:
- selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys()))
- st.json(vector_dbs_info[selected_vector_db])
- else:
- st.info("No vector databases found")
diff --git a/llama_stack/core/ui/page/playground/rag.py b/llama_stack/core/ui/page/playground/rag.py
deleted file mode 100644
index 2ffae1c33..000000000
--- a/llama_stack/core/ui/page/playground/rag.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import uuid
-
-import streamlit as st
-from llama_stack_client import Agent, AgentEventLogger, RAGDocument
-
-from llama_stack.apis.common.content_types import ToolCallDelta
-from llama_stack.core.ui.modules.api import llama_stack_api
-from llama_stack.core.ui.modules.utils import data_url_from_file
-
-
-def rag_chat_page():
- st.title("🦙 RAG")
-
- def reset_agent_and_chat():
- st.session_state.clear()
- st.cache_resource.clear()
-
- def should_disable_input():
- return "displayed_messages" in st.session_state and len(st.session_state.displayed_messages) > 0
-
- def log_message(message):
- with st.chat_message(message["role"]):
- if "tool_output" in message and message["tool_output"]:
- with st.expander(label="Tool Output", expanded=False, icon="🛠"):
- st.write(message["tool_output"])
- st.markdown(message["content"])
-
- with st.sidebar:
- # File/Directory Upload Section
- st.subheader("Upload Documents", divider=True)
- uploaded_files = st.file_uploader(
- "Upload file(s) or directory",
- accept_multiple_files=True,
- type=["txt", "pdf", "doc", "docx"], # Add more file types as needed
- )
- # Process uploaded files
- if uploaded_files:
- st.success(f"Successfully uploaded {len(uploaded_files)} files")
- # Add memory bank name input field
- vector_db_name = st.text_input(
- "Document Collection Name",
- value="rag_vector_db",
- help="Enter a unique identifier for this document collection",
- )
- if st.button("Create Document Collection"):
- documents = [
- RAGDocument(
- document_id=uploaded_file.name,
- content=data_url_from_file(uploaded_file),
- )
- for i, uploaded_file in enumerate(uploaded_files)
- ]
-
- providers = llama_stack_api.client.providers.list()
- vector_io_provider = None
-
- for x in providers:
- if x.api == "vector_io":
- vector_io_provider = x.provider_id
-
- llama_stack_api.client.vector_dbs.register(
- vector_db_id=vector_db_name, # Use the user-provided name
- embedding_dimension=384,
- embedding_model="all-MiniLM-L6-v2",
- provider_id=vector_io_provider,
- )
-
- # insert documents using the custom vector db name
- llama_stack_api.client.tool_runtime.rag_tool.insert(
- vector_db_id=vector_db_name, # Use the user-provided name
- documents=documents,
- chunk_size_in_tokens=512,
- )
- st.success("Vector database created successfully!")
-
- st.subheader("RAG Parameters", divider=True)
-
- rag_mode = st.radio(
- "RAG mode",
- ["Direct", "Agent-based"],
- captions=[
- "RAG is performed by directly retrieving the information and augmenting the user query",
- "RAG is performed by an agent activating a dedicated knowledge search tool.",
- ],
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
-
- # select memory banks
- vector_dbs = llama_stack_api.client.vector_dbs.list()
- vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
- selected_vector_dbs = st.multiselect(
- label="Select Document Collections to use in RAG queries",
- options=vector_dbs,
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
-
- st.subheader("Inference Parameters", divider=True)
- available_models = llama_stack_api.client.models.list()
- available_models = [model.identifier for model in available_models if model.model_type == "llm"]
- selected_model = st.selectbox(
- label="Choose a model",
- options=available_models,
- index=0,
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
- system_prompt = st.text_area(
- "System Prompt",
- value="You are a helpful assistant. ",
- help="Initial instructions given to the AI to set its behavior and context",
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
- temperature = st.slider(
- "Temperature",
- min_value=0.0,
- max_value=1.0,
- value=0.0,
- step=0.1,
- help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable",
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
-
- top_p = st.slider(
- "Top P",
- min_value=0.0,
- max_value=1.0,
- value=0.95,
- step=0.1,
- on_change=reset_agent_and_chat,
- disabled=should_disable_input(),
- )
-
- # Add clear chat button to sidebar
- if st.button("Clear Chat", use_container_width=True):
- reset_agent_and_chat()
- st.rerun()
-
- # Chat Interface
- if "messages" not in st.session_state:
- st.session_state.messages = []
- if "displayed_messages" not in st.session_state:
- st.session_state.displayed_messages = []
-
- # Display chat history
- for message in st.session_state.displayed_messages:
- log_message(message)
-
- if temperature > 0.0:
- strategy = {
- "type": "top_p",
- "temperature": temperature,
- "top_p": top_p,
- }
- else:
- strategy = {"type": "greedy"}
-
- @st.cache_resource
- def create_agent():
- return Agent(
- llama_stack_api.client,
- model=selected_model,
- instructions=system_prompt,
- sampling_params={
- "strategy": strategy,
- },
- tools=[
- dict(
- name="builtin::rag/knowledge_search",
- args={
- "vector_db_ids": list(selected_vector_dbs),
- },
- )
- ],
- )
-
- if rag_mode == "Agent-based":
- agent = create_agent()
- if "agent_session_id" not in st.session_state:
- st.session_state["agent_session_id"] = agent.create_session(session_name=f"rag_demo_{uuid.uuid4()}")
-
- session_id = st.session_state["agent_session_id"]
-
- def agent_process_prompt(prompt):
- # Add user message to chat history
- st.session_state.messages.append({"role": "user", "content": prompt})
-
- # Send the prompt to the agent
- response = agent.create_turn(
- messages=[
- {
- "role": "user",
- "content": prompt,
- }
- ],
- session_id=session_id,
- )
-
- # Display assistant response
- with st.chat_message("assistant"):
- retrieval_message_placeholder = st.expander(label="Tool Output", expanded=False, icon="🛠")
- message_placeholder = st.empty()
- full_response = ""
- retrieval_response = ""
- for log in AgentEventLogger().log(response):
- log.print()
- if log.role == "tool_execution":
- retrieval_response += log.content.replace("====", "").strip()
- retrieval_message_placeholder.write(retrieval_response)
- else:
- full_response += log.content
- message_placeholder.markdown(full_response + "▌")
- message_placeholder.markdown(full_response)
-
- st.session_state.messages.append({"role": "assistant", "content": full_response})
- st.session_state.displayed_messages.append(
- {"role": "assistant", "content": full_response, "tool_output": retrieval_response}
- )
-
- def direct_process_prompt(prompt):
- # Add the system prompt in the beginning of the conversation
- if len(st.session_state.messages) == 0:
- st.session_state.messages.append({"role": "system", "content": system_prompt})
-
- # Query the vector DB
- rag_response = llama_stack_api.client.tool_runtime.rag_tool.query(
- content=prompt, vector_db_ids=list(selected_vector_dbs)
- )
- prompt_context = rag_response.content
-
- with st.chat_message("assistant"):
- with st.expander(label="Retrieval Output", expanded=False):
- st.write(prompt_context)
-
- retrieval_message_placeholder = st.empty()
- message_placeholder = st.empty()
- full_response = ""
- retrieval_response = ""
-
- # Construct the extended prompt
- extended_prompt = f"Please answer the following query using the context below.\n\nCONTEXT:\n{prompt_context}\n\nQUERY:\n{prompt}"
-
- # Run inference directly
- st.session_state.messages.append({"role": "user", "content": extended_prompt})
- response = llama_stack_api.client.inference.chat_completion(
- messages=st.session_state.messages,
- model_id=selected_model,
- sampling_params={
- "strategy": strategy,
- },
- stream=True,
- )
-
- # Display assistant response
- for chunk in response:
- response_delta = chunk.event.delta
- if isinstance(response_delta, ToolCallDelta):
- retrieval_response += response_delta.tool_call.replace("====", "").strip()
- retrieval_message_placeholder.info(retrieval_response)
- else:
- full_response += chunk.event.delta.text
- message_placeholder.markdown(full_response + "▌")
- message_placeholder.markdown(full_response)
-
- response_dict = {"role": "assistant", "content": full_response, "stop_reason": "end_of_message"}
- st.session_state.messages.append(response_dict)
- st.session_state.displayed_messages.append(response_dict)
-
- # Chat input
- if prompt := st.chat_input("Ask a question about your documents"):
- # Add user message to chat history
- st.session_state.displayed_messages.append({"role": "user", "content": prompt})
-
- # Display user message
- with st.chat_message("user"):
- st.markdown(prompt)
-
- # store the prompt to process it after page refresh
- st.session_state.prompt = prompt
-
- # force page refresh to disable the settings widgets
- st.rerun()
-
- if "prompt" in st.session_state and st.session_state.prompt is not None:
- if rag_mode == "Agent-based":
- agent_process_prompt(st.session_state.prompt)
- else: # rag_mode == "Direct"
- direct_process_prompt(st.session_state.prompt)
- st.session_state.prompt = None
-
-
-rag_chat_page()
diff --git a/llama_stack/providers/registry/agents.py b/llama_stack/providers/registry/agents.py
index d7e9bed88..b246ae062 100644
--- a/llama_stack/providers/registry/agents.py
+++ b/llama_stack/providers/registry/agents.py
@@ -32,7 +32,6 @@ def available_providers() -> list[ProviderSpec]:
Api.inference,
Api.safety,
Api.vector_io,
- Api.vector_dbs,
Api.tool_runtime,
Api.tool_groups,
Api.conversations,
diff --git a/tests/integration/agents/test_agents.py b/tests/integration/agents/test_agents.py
index 07ba7bb01..3542facef 100644
--- a/tests/integration/agents/test_agents.py
+++ b/tests/integration/agents/test_agents.py
@@ -8,7 +8,6 @@ from typing import Any
from uuid import uuid4
import pytest
-import requests
from llama_stack_client import Agent, AgentEventLogger, Document
from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
@@ -443,118 +442,6 @@ def run_agent_with_tool_choice(client, agent_config, tool_choice):
return [step for step in response.steps if step.step_type == "tool_execution"]
-@pytest.mark.parametrize("rag_tool_name", ["builtin::rag/knowledge_search", "builtin::rag"])
-def test_rag_agent(llama_stack_client, agent_config, rag_tool_name):
- urls = ["chat.rst", "llama3.rst", "memory_optimizations.rst", "lora_finetune.rst"]
- documents = [
- Document(
- document_id=f"num-{i}",
- content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
- mime_type="text/plain",
- metadata={},
- )
- for i, url in enumerate(urls)
- ]
- vector_db_id = f"test-vector-db-{uuid4()}"
- llama_stack_client.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model="all-MiniLM-L6-v2",
- embedding_dimension=384,
- )
- llama_stack_client.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=vector_db_id,
- # small chunks help to get specific info out of the docs
- chunk_size_in_tokens=256,
- )
- agent_config = {
- **agent_config,
- "tools": [
- dict(
- name=rag_tool_name,
- args={
- "vector_db_ids": [vector_db_id],
- },
- )
- ],
- }
- rag_agent = Agent(llama_stack_client, **agent_config)
- session_id = rag_agent.create_session(f"test-session-{uuid4()}")
- user_prompts = [
- (
- "Instead of the standard multi-head attention, what attention type does Llama3-8B use?",
- "grouped",
- ),
- ]
- for prompt, expected_kw in user_prompts:
- response = rag_agent.create_turn(
- messages=[{"role": "user", "content": prompt}],
- session_id=session_id,
- stream=False,
- )
- # rag is called
- tool_execution_step = next(step for step in response.steps if step.step_type == "tool_execution")
- assert tool_execution_step.tool_calls[0].tool_name == "knowledge_search"
- # document ids are present in metadata
- assert all(
- doc_id.startswith("num-") for doc_id in tool_execution_step.tool_responses[0].metadata["document_ids"]
- )
- if expected_kw:
- assert expected_kw in response.output_message.content.lower()
-
-
-def test_rag_agent_with_attachments(llama_stack_client, agent_config_without_safety):
- urls = ["llama3.rst", "lora_finetune.rst"]
- documents = [
- # passign as url
- Document(
- document_id="num-0",
- content={
- "type": "url",
- "uri": f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{urls[0]}",
- },
- mime_type="text/plain",
- metadata={},
- ),
- # passing as str
- Document(
- document_id="num-1",
- content=requests.get(
- f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{urls[1]}"
- ).text[:500],
- mime_type="text/plain",
- metadata={},
- ),
- ]
- rag_agent = Agent(llama_stack_client, **agent_config_without_safety)
- session_id = rag_agent.create_session(f"test-session-{uuid4()}")
- user_prompts = [
- (
- "I am attaching some documentation for Torchtune. Help me answer questions I will ask next.",
- documents,
- ),
- (
- "Tell me how to use LoRA in 100 words or less",
- None,
- ),
- ]
-
- for prompt in user_prompts:
- response = rag_agent.create_turn(
- messages=[
- {
- "role": "user",
- "content": prompt[0],
- }
- ],
- documents=prompt[1],
- session_id=session_id,
- stream=False,
- )
-
- assert "lora" in response.output_message.content.lower()
-
-
@pytest.mark.parametrize(
"client_tools",
[(get_boiling_point, False), (get_boiling_point_with_metadata, True)],
diff --git a/tests/integration/tool_runtime/test_rag_tool.py b/tests/integration/tool_runtime/test_rag_tool.py
deleted file mode 100644
index b78c39af8..000000000
--- a/tests/integration/tool_runtime/test_rag_tool.py
+++ /dev/null
@@ -1,459 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import pytest
-from llama_stack_client import BadRequestError
-from llama_stack_client.types import Document
-
-
-@pytest.fixture(scope="function")
-def client_with_empty_registry(client_with_models):
- def clear_registry():
- vector_dbs = [vector_db.identifier for vector_db in client_with_models.vector_dbs.list()]
- for vector_db_id in vector_dbs:
- client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id)
-
- clear_registry()
-
- try:
- client_with_models.toolgroups.register(toolgroup_id="builtin::rag", provider_id="rag-runtime")
- except Exception:
- pass
-
- yield client_with_models
-
- clear_registry()
-
-
-@pytest.fixture(scope="session")
-def sample_documents():
- return [
- Document(
- document_id="test-doc-1",
- content="Python is a high-level programming language.",
- metadata={"category": "programming", "difficulty": "beginner"},
- ),
- Document(
- document_id="test-doc-2",
- content="Machine learning is a subset of artificial intelligence.",
- metadata={"category": "AI", "difficulty": "advanced"},
- ),
- Document(
- document_id="test-doc-3",
- content="Data structures are fundamental to computer science.",
- metadata={"category": "computer science", "difficulty": "intermediate"},
- ),
- Document(
- document_id="test-doc-4",
- content="Neural networks are inspired by biological neural networks.",
- metadata={"category": "AI", "difficulty": "advanced"},
- ),
- ]
-
-
-def assert_valid_chunk_response(response):
- assert len(response.chunks) > 0
- assert len(response.scores) > 0
- assert len(response.chunks) == len(response.scores)
- for chunk in response.chunks:
- assert isinstance(chunk.content, str)
-
-
-def assert_valid_text_response(response):
- assert len(response.content) > 0
- assert all(isinstance(chunk.text, str) for chunk in response.content)
-
-
-def test_vector_db_insert_inline_and_query(
- client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension
-):
- vector_db_name = "test_vector_db"
- vector_db = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
- vector_db_id = vector_db.identifier
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=sample_documents,
- chunk_size_in_tokens=512,
- vector_db_id=vector_db_id,
- )
-
- # Query with a direct match
- query1 = "programming language"
- response1 = client_with_empty_registry.vector_io.query(
- vector_db_id=vector_db_id,
- query=query1,
- )
- assert_valid_chunk_response(response1)
- assert any("Python" in chunk.content for chunk in response1.chunks)
-
- # Query with semantic similarity
- query2 = "AI and brain-inspired computing"
- response2 = client_with_empty_registry.vector_io.query(
- vector_db_id=vector_db_id,
- query=query2,
- )
- assert_valid_chunk_response(response2)
- assert any("neural networks" in chunk.content.lower() for chunk in response2.chunks)
-
- # Query with limit on number of results (max_chunks=2)
- query3 = "computer"
- response3 = client_with_empty_registry.vector_io.query(
- vector_db_id=vector_db_id,
- query=query3,
- params={"max_chunks": 2},
- )
- assert_valid_chunk_response(response3)
- assert len(response3.chunks) <= 2
-
- # Query with threshold on similarity score
- query4 = "computer"
- response4 = client_with_empty_registry.vector_io.query(
- vector_db_id=vector_db_id,
- query=query4,
- params={"score_threshold": 0.01},
- )
- assert_valid_chunk_response(response4)
- assert all(score >= 0.01 for score in response4.scores)
-
-
-def test_vector_db_insert_from_url_and_query(
- client_with_empty_registry, sample_documents, embedding_model_id, embedding_dimension
-):
- providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
- assert len(providers) > 0
-
- vector_db_id = "test_vector_db"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- # list to check memory bank is successfully registered
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- # VectorDB is being migrated to VectorStore, so the ID will be different
- # Just check that at least one vector DB was registered
- assert len(available_vector_dbs) > 0
- # Use the actual registered vector_db_id for subsequent operations
- actual_vector_db_id = available_vector_dbs[0]
-
- urls = [
- "memory_optimizations.rst",
- "chat.rst",
- "llama3.rst",
- ]
- documents = [
- Document(
- document_id=f"num-{i}",
- content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
- mime_type="text/plain",
- metadata={},
- )
- for i, url in enumerate(urls)
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=512,
- )
-
- # Query for the name of method
- response1 = client_with_empty_registry.vector_io.query(
- vector_db_id=actual_vector_db_id,
- query="What's the name of the fine-tunning method used?",
- )
- assert_valid_chunk_response(response1)
- assert any("lora" in chunk.content.lower() for chunk in response1.chunks)
-
- # Query for the name of model
- response2 = client_with_empty_registry.vector_io.query(
- vector_db_id=actual_vector_db_id,
- query="Which Llama model is mentioned?",
- )
- assert_valid_chunk_response(response2)
- assert any("llama2" in chunk.content.lower() for chunk in response2.chunks)
-
-
-def test_rag_tool_openai_apis(client_with_empty_registry, embedding_model_id, embedding_dimension):
- vector_db_id = "test_openai_vector_db"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- actual_vector_db_id = available_vector_dbs[0]
-
- # different document formats that should work with OpenAI APIs
- documents = [
- Document(
- document_id="text-doc",
- content="This is a plain text document about machine learning algorithms.",
- metadata={"type": "text", "category": "AI"},
- ),
- Document(
- document_id="url-doc",
- content="https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/chat.rst",
- mime_type="text/plain",
- metadata={"type": "url", "source": "pytorch"},
- ),
- Document(
- document_id="data-url-doc",
- content="data:text/plain;base64,VGhpcyBpcyBhIGRhdGEgVVJMIGRvY3VtZW50IGFib3V0IGRlZXAgbGVhcm5pbmcu", # "This is a data URL document about deep learning."
- metadata={"type": "data_url", "encoding": "base64"},
- ),
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=256,
- )
-
- files_list = client_with_empty_registry.files.list()
- assert len(files_list.data) >= len(documents), (
- f"Expected at least {len(documents)} files, got {len(files_list.data)}"
- )
-
- vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store(
- vector_store_id=actual_vector_db_id
- )
- assert len(vector_store_files.data) >= len(documents), f"Expected at least {len(documents)} files in vector store"
-
- response = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="Tell me about machine learning and deep learning",
- )
-
- assert_valid_text_response(response)
- content_text = " ".join([chunk.text for chunk in response.content]).lower()
- assert "machine learning" in content_text or "deep learning" in content_text
-
-
-def test_rag_tool_exception_handling(client_with_empty_registry, embedding_model_id, embedding_dimension):
- vector_db_id = "test_exception_handling"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- actual_vector_db_id = available_vector_dbs[0]
-
- documents = [
- Document(
- document_id="valid-doc",
- content="This is a valid document that should be processed successfully.",
- metadata={"status": "valid"},
- ),
- Document(
- document_id="invalid-url-doc",
- content="https://nonexistent-domain-12345.com/invalid.txt",
- metadata={"status": "invalid_url"},
- ),
- Document(
- document_id="another-valid-doc",
- content="This is another valid document for testing resilience.",
- metadata={"status": "valid"},
- ),
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=256,
- )
-
- response = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="valid document",
- )
-
- assert_valid_text_response(response)
- content_text = " ".join([chunk.text for chunk in response.content]).lower()
- assert "valid document" in content_text
-
-
-def test_rag_tool_insert_and_query(client_with_empty_registry, embedding_model_id, embedding_dimension):
- providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
- assert len(providers) > 0
-
- vector_db_id = "test_vector_db"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- # VectorDB is being migrated to VectorStore, so the ID will be different
- # Just check that at least one vector DB was registered
- assert len(available_vector_dbs) > 0
- # Use the actual registered vector_db_id for subsequent operations
- actual_vector_db_id = available_vector_dbs[0]
-
- urls = [
- "memory_optimizations.rst",
- "chat.rst",
- "llama3.rst",
- ]
- documents = [
- Document(
- document_id=f"num-{i}",
- content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
- mime_type="text/plain",
- metadata={"author": "llama", "source": url},
- )
- for i, url in enumerate(urls)
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=512,
- )
-
- response_with_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="What is the name of the method used for fine-tuning?",
- )
- assert_valid_text_response(response_with_metadata)
- assert any("metadata:" in chunk.text.lower() for chunk in response_with_metadata.content)
-
- response_without_metadata = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="What is the name of the method used for fine-tuning?",
- query_config={
- "include_metadata_in_content": True,
- "chunk_template": "Result {index}\nContent: {chunk.content}\n",
- },
- )
- assert_valid_text_response(response_without_metadata)
- assert not any("metadata:" in chunk.text.lower() for chunk in response_without_metadata.content)
-
- with pytest.raises((ValueError, BadRequestError)):
- client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="What is the name of the method used for fine-tuning?",
- query_config={
- "chunk_template": "This should raise a ValueError because it is missing the proper template variables",
- },
- )
-
-
-def test_rag_tool_query_generation(client_with_empty_registry, embedding_model_id, embedding_dimension):
- vector_db_id = "test_query_generation_db"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- actual_vector_db_id = available_vector_dbs[0]
-
- documents = [
- Document(
- document_id="ai-doc",
- content="Artificial intelligence and machine learning are transforming technology.",
- metadata={"category": "AI"},
- ),
- Document(
- document_id="banana-doc",
- content="Don't bring a banana to a knife fight.",
- metadata={"category": "wisdom"},
- ),
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=256,
- )
-
- response = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="Tell me about AI",
- )
-
- assert_valid_text_response(response)
- content_text = " ".join([chunk.text for chunk in response.content]).lower()
- assert "artificial intelligence" in content_text or "machine learning" in content_text
-
-
-def test_rag_tool_pdf_data_url_handling(client_with_empty_registry, embedding_model_id, embedding_dimension):
- vector_db_id = "test_pdf_data_url_db"
-
- client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_id,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
- )
-
- available_vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- actual_vector_db_id = available_vector_dbs[0]
-
- sample_pdf = b"%PDF-1.3\n3 0 obj\n<>\nendobj\n4 0 obj\n<>\nstream\nx\x9c\x15\xcc1\x0e\x820\x18@\xe1\x9dS\xbcM]jk$\xd5\xd5(\x83!\x86\xa1\x17\xf8\xa3\xa5`LIh+\xd7W\xc6\xf7\r\xef\xc0\xbd\xd2\xaa\xb6,\xd5\xc5\xb1o\x0c\xa6VZ\xe3znn%\xf3o\xab\xb1\xe7\xa3:Y\xdc\x8bm\xeb\xf3&1\xc8\xd7\xd3\x97\xc82\xe6\x81\x87\xe42\xcb\x87Vb(\x12<\xdd<=}Jc\x0cL\x91\xee\xda$\xb5\xc3\xbd\xd7\xe9\x0f\x8d\x97 $\nendstream\nendobj\n1 0 obj\n<>\nendobj\n5 0 obj\n<>\nendobj\n2 0 obj\n<<\n/ProcSet [/PDF /Text /ImageB /ImageC /ImageI]\n/Font <<\n/F1 5 0 R\n>>\n/XObject <<\n>>\n>>\nendobj\n6 0 obj\n<<\n/Producer (PyFPDF 1.7.2 http://pyfpdf.googlecode.com/)\n/Title (This is a sample title.)\n/Author (Llama Stack Developers)\n/CreationDate (D:20250312165548)\n>>\nendobj\n7 0 obj\n<<\n/Type /Catalog\n/Pages 1 0 R\n/OpenAction [3 0 R /FitH null]\n/PageLayout /OneColumn\n>>\nendobj\nxref\n0 8\n0000000000 65535 f \n0000000272 00000 n \n0000000455 00000 n \n0000000009 00000 n \n0000000087 00000 n \n0000000359 00000 n \n0000000559 00000 n \n0000000734 00000 n \ntrailer\n<<\n/Size 8\n/Root 7 0 R\n/Info 6 0 R\n>>\nstartxref\n837\n%%EOF\n"
-
- import base64
-
- pdf_base64 = base64.b64encode(sample_pdf).decode("utf-8")
- pdf_data_url = f"data:application/pdf;base64,{pdf_base64}"
-
- documents = [
- Document(
- document_id="test-pdf-data-url",
- content=pdf_data_url,
- metadata={"type": "pdf", "source": "data_url"},
- ),
- ]
-
- client_with_empty_registry.tool_runtime.rag_tool.insert(
- documents=documents,
- vector_db_id=actual_vector_db_id,
- chunk_size_in_tokens=256,
- )
-
- files_list = client_with_empty_registry.files.list()
- assert len(files_list.data) >= 1, "PDF should have been uploaded to Files API"
-
- pdf_file = None
- for file in files_list.data:
- if file.filename and "test-pdf-data-url" in file.filename:
- pdf_file = file
- break
-
- assert pdf_file is not None, "PDF file should be found in Files API"
- assert pdf_file.bytes == len(sample_pdf), f"File size should match original PDF ({len(sample_pdf)} bytes)"
-
- file_content = client_with_empty_registry.files.retrieve_content(pdf_file.id)
- assert file_content.startswith(b"%PDF-"), "Retrieved file should be a valid PDF"
-
- vector_store_files = client_with_empty_registry.vector_io.openai_list_files_in_vector_store(
- vector_store_id=actual_vector_db_id
- )
- assert len(vector_store_files.data) >= 1, "PDF should be attached to vector store"
-
- response = client_with_empty_registry.tool_runtime.rag_tool.query(
- vector_db_ids=[actual_vector_db_id],
- content="sample title",
- )
-
- assert_valid_text_response(response)
- content_text = " ".join([chunk.text for chunk in response.content]).lower()
- assert "sample title" in content_text or "title" in content_text
diff --git a/tests/integration/vector_io/recordings/0017fb171832738ab63935b610003c8fee26dcc73e8f5c5a741b8ba8870ee5cd.json b/tests/integration/vector_io/recordings/0017fb171832738ab63935b610003c8fee26dcc73e8f5c5a741b8ba8870ee5cd.json
new file mode 100644
index 000000000..8888e3cee
--- /dev/null
+++ b/tests/integration/vector_io/recordings/0017fb171832738ab63935b610003c8fee26dcc73e8f5c5a741b8ba8870ee5cd.json
@@ -0,0 +1,1597 @@
+{
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+ "request": {
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+ ],
+ "encoding_format": "float"
+ },
+ "endpoint": "/v1/embeddings",
+ "model": "all-minilm:l6-v2"
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diff --git a/tests/integration/vector_io/recordings/00af71a2d4b59c7120e3972032ff25aeba47766d18eb6714743f3a95753165f5.json b/tests/integration/vector_io/recordings/00af71a2d4b59c7120e3972032ff25aeba47766d18eb6714743f3a95753165f5.json
new file mode 100644
index 000000000..aa779f6fe
--- /dev/null
+++ b/tests/integration/vector_io/recordings/00af71a2d4b59c7120e3972032ff25aeba47766d18eb6714743f3a95753165f5.json
@@ -0,0 +1,424 @@
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diff --git a/tests/integration/vector_io/recordings/1521a1b6cbd87d9cec77af07f594020d09b39efc765efe4a9ab077c06bf179c3.json b/tests/integration/vector_io/recordings/1521a1b6cbd87d9cec77af07f594020d09b39efc765efe4a9ab077c06bf179c3.json
new file mode 100644
index 000000000..d1973ad77
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diff --git a/tests/integration/vector_io/recordings/18a3cfa248028cec285ca25857a14f208ab554d1549db0c478310cd0bb69a115.json b/tests/integration/vector_io/recordings/18a3cfa248028cec285ca25857a14f208ab554d1549db0c478310cd0bb69a115.json
new file mode 100644
index 000000000..b0361db91
--- /dev/null
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diff --git a/tests/integration/vector_io/recordings/1b5e124ccc2b9a27ecf8ddd316895a25ca47835176f3fa88fc464c11ee4cd1b6.json b/tests/integration/vector_io/recordings/1b5e124ccc2b9a27ecf8ddd316895a25ca47835176f3fa88fc464c11ee4cd1b6.json
new file mode 100644
index 000000000..4ba758f28
--- /dev/null
+++ b/tests/integration/vector_io/recordings/1b5e124ccc2b9a27ecf8ddd316895a25ca47835176f3fa88fc464c11ee4cd1b6.json
@@ -0,0 +1,1597 @@
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diff --git a/tests/integration/vector_io/recordings/1bbfdd713cbcc75ea98571495883fcde333b1db79ddc8e397bdaabdf0b2e8667.json b/tests/integration/vector_io/recordings/1bbfdd713cbcc75ea98571495883fcde333b1db79ddc8e397bdaabdf0b2e8667.json
new file mode 100644
index 000000000..fb2d7dec6
--- /dev/null
+++ b/tests/integration/vector_io/recordings/1bbfdd713cbcc75ea98571495883fcde333b1db79ddc8e397bdaabdf0b2e8667.json
@@ -0,0 +1,424 @@
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diff --git a/tests/integration/vector_io/recordings/307bb7dfec58ba06444e457140e82c0d2bf5d1c5b6a4f2f003689bbe6ca4f82b.json b/tests/integration/vector_io/recordings/307bb7dfec58ba06444e457140e82c0d2bf5d1c5b6a4f2f003689bbe6ca4f82b.json
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new file mode 100644
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diff --git a/tests/integration/vector_io/recordings/366e166c930f24a4695f2f7ab5744a0a47cb8ef262adfa4c1f5bbc81207effa9.json b/tests/integration/vector_io/recordings/366e166c930f24a4695f2f7ab5744a0a47cb8ef262adfa4c1f5bbc81207effa9.json
new file mode 100644
index 000000000..571549135
--- /dev/null
+++ b/tests/integration/vector_io/recordings/366e166c930f24a4695f2f7ab5744a0a47cb8ef262adfa4c1f5bbc81207effa9.json
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diff --git a/tests/integration/vector_io/recordings/42e67e71def8f49274d9aa7745b38840d864e664bec3e3bbac6fdbda12fd02cb.json b/tests/integration/vector_io/recordings/42e67e71def8f49274d9aa7745b38840d864e664bec3e3bbac6fdbda12fd02cb.json
new file mode 100644
index 000000000..897d431f3
--- /dev/null
+++ b/tests/integration/vector_io/recordings/42e67e71def8f49274d9aa7745b38840d864e664bec3e3bbac6fdbda12fd02cb.json
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diff --git a/tests/integration/vector_io/recordings/4962d607edc12a614f39f4b7043e12162b14335d2a1c94b84a346c56868ede2e.json b/tests/integration/vector_io/recordings/4962d607edc12a614f39f4b7043e12162b14335d2a1c94b84a346c56868ede2e.json
new file mode 100644
index 000000000..b986315d3
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diff --git a/tests/integration/vector_io/recordings/4c044ae708862abba94b8e757d05cf3c7120e1dd8484d3947a86fb29af24a8a1.json b/tests/integration/vector_io/recordings/4c044ae708862abba94b8e757d05cf3c7120e1dd8484d3947a86fb29af24a8a1.json
new file mode 100644
index 000000000..47f9844a0
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+ "Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
+ "Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
+ "Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning."
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diff --git a/tests/integration/vector_io/recordings/56571b92d8276796c890f4d6c37610fb6a2e48e3710a39d161668a56abb2735a.json b/tests/integration/vector_io/recordings/56571b92d8276796c890f4d6c37610fb6a2e48e3710a39d161668a56abb2735a.json
new file mode 100644
index 000000000..bf18ef5a8
--- /dev/null
+++ b/tests/integration/vector_io/recordings/56571b92d8276796c890f4d6c37610fb6a2e48e3710a39d161668a56abb2735a.json
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+ "Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.",
+ "Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
+ "Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
+ "Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning."
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diff --git a/tests/integration/vector_io/recordings/5908f48af1663d749291fac1fb0d3e534a7a446c2ffc07dab51a151694dea1be.json b/tests/integration/vector_io/recordings/5908f48af1663d749291fac1fb0d3e534a7a446c2ffc07dab51a151694dea1be.json
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diff --git a/tests/integration/vector_io/recordings/5da809595a176c88b2fb6e2b1cd9af7eba4e2dbbf73151ffd920846f6f115a2f.json b/tests/integration/vector_io/recordings/5da809595a176c88b2fb6e2b1cd9af7eba4e2dbbf73151ffd920846f6f115a2f.json
new file mode 100644
index 000000000..fc1085a74
--- /dev/null
+++ b/tests/integration/vector_io/recordings/5da809595a176c88b2fb6e2b1cd9af7eba4e2dbbf73151ffd920846f6f115a2f.json
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diff --git a/tests/integration/vector_io/recordings/640b6fe648864089776d64b6b3eb9a28b427e3f3ab8f518f301ed74983a78b60.json b/tests/integration/vector_io/recordings/640b6fe648864089776d64b6b3eb9a28b427e3f3ab8f518f301ed74983a78b60.json
new file mode 100644
index 000000000..538d00aa2
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diff --git a/tests/integration/vector_io/recordings/6e1eed3c7720ad57fb0c8baa6aa48aba9df6bc0e32d5bdde7100e4828aef7102.json b/tests/integration/vector_io/recordings/6e1eed3c7720ad57fb0c8baa6aa48aba9df6bc0e32d5bdde7100e4828aef7102.json
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new file mode 100644
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diff --git a/tests/integration/vector_io/recordings/951a2cde454e88a99039a4b5dd9390f6039f5c312942529cee7839c7596320c9.json b/tests/integration/vector_io/recordings/951a2cde454e88a99039a4b5dd9390f6039f5c312942529cee7839c7596320c9.json
new file mode 100644
index 000000000..9a3d53dc8
--- /dev/null
+++ b/tests/integration/vector_io/recordings/951a2cde454e88a99039a4b5dd9390f6039f5c312942529cee7839c7596320c9.json
@@ -0,0 +1,1597 @@
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diff --git a/tests/integration/vector_io/recordings/9c30695933702b8bb0b90a9b79c8def97ef6742dbdfd8ca22f06523a5ca97c55.json b/tests/integration/vector_io/recordings/9c30695933702b8bb0b90a9b79c8def97ef6742dbdfd8ca22f06523a5ca97c55.json
new file mode 100644
index 000000000..cdb0099ba
--- /dev/null
+++ b/tests/integration/vector_io/recordings/9c30695933702b8bb0b90a9b79c8def97ef6742dbdfd8ca22f06523a5ca97c55.json
@@ -0,0 +1,1597 @@
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diff --git a/tests/integration/vector_io/recordings/a8668c968c057b15c396b11e3954ddd82f666eba0901113902d42fc247219a53.json b/tests/integration/vector_io/recordings/a8668c968c057b15c396b11e3954ddd82f666eba0901113902d42fc247219a53.json
new file mode 100644
index 000000000..b3830ecc9
--- /dev/null
+++ b/tests/integration/vector_io/recordings/a8668c968c057b15c396b11e3954ddd82f666eba0901113902d42fc247219a53.json
@@ -0,0 +1,1597 @@
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diff --git a/tests/integration/vector_io/recordings/b03801ff92bb7b6cc494872e562ac62e07757316fd788b1b57e712dc1a56ba79.json b/tests/integration/vector_io/recordings/b03801ff92bb7b6cc494872e562ac62e07757316fd788b1b57e712dc1a56ba79.json
new file mode 100644
index 000000000..c5accc1fd
--- /dev/null
+++ b/tests/integration/vector_io/recordings/b03801ff92bb7b6cc494872e562ac62e07757316fd788b1b57e712dc1a56ba79.json
@@ -0,0 +1,1597 @@
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diff --git a/tests/integration/vector_io/recordings/b2b1c7e034a8bdbf121e9c500adae09fc5f77c370fd9c3c9fe84950f06a6a265.json b/tests/integration/vector_io/recordings/b2b1c7e034a8bdbf121e9c500adae09fc5f77c370fd9c3c9fe84950f06a6a265.json
new file mode 100644
index 000000000..b835e0669
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diff --git a/tests/integration/vector_io/recordings/b3f9fc35d4be85f421610c60333f0c2dde2b01f41cd085428225eb809eb0c811.json b/tests/integration/vector_io/recordings/b3f9fc35d4be85f421610c60333f0c2dde2b01f41cd085428225eb809eb0c811.json
new file mode 100644
index 000000000..c0b5fd01f
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diff --git a/tests/integration/vector_io/recordings/bb1f8baef275f8ca4f93bf233718b3492c8deabaa3706cf9e84968ba096bb134.json b/tests/integration/vector_io/recordings/bb1f8baef275f8ca4f93bf233718b3492c8deabaa3706cf9e84968ba096bb134.json
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diff --git a/tests/integration/vector_io/recordings/c1e423bedc7bcf7a702e343caef852b2eb8b34ed12a270d051ea68827fe2fdba.json b/tests/integration/vector_io/recordings/c1e423bedc7bcf7a702e343caef852b2eb8b34ed12a270d051ea68827fe2fdba.json
new file mode 100644
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diff --git a/tests/integration/vector_io/recordings/c1e565d209eb2d78bbe8d2a5e792c7292fd7172b327274f284f68be814898f2e.json b/tests/integration/vector_io/recordings/c1e565d209eb2d78bbe8d2a5e792c7292fd7172b327274f284f68be814898f2e.json
new file mode 100644
index 000000000..0ca3a4a47
--- /dev/null
+++ b/tests/integration/vector_io/recordings/c1e565d209eb2d78bbe8d2a5e792c7292fd7172b327274f284f68be814898f2e.json
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diff --git a/tests/integration/vector_io/recordings/d74e7e352b27eff8032641831fb79602fd2b48f5bfc786d89a5512041b5c3f62.json b/tests/integration/vector_io/recordings/d74e7e352b27eff8032641831fb79602fd2b48f5bfc786d89a5512041b5c3f62.json
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new file mode 100644
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diff --git a/tests/integration/vector_io/recordings/de4148bb72ba07cb93bd73dee30cc85c83935df23940549e2fb0f2262910ab31.json b/tests/integration/vector_io/recordings/de4148bb72ba07cb93bd73dee30cc85c83935df23940549e2fb0f2262910ab31.json
new file mode 100644
index 000000000..436da8c57
--- /dev/null
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diff --git a/tests/integration/vector_io/recordings/e19bb342a5817d18312a1d772ff8cb3ea9e67d4dff338329bf3eed04f830e59f.json b/tests/integration/vector_io/recordings/e19bb342a5817d18312a1d772ff8cb3ea9e67d4dff338329bf3eed04f830e59f.json
new file mode 100644
index 000000000..bd8782c6b
--- /dev/null
+++ b/tests/integration/vector_io/recordings/e19bb342a5817d18312a1d772ff8cb3ea9e67d4dff338329bf3eed04f830e59f.json
@@ -0,0 +1,424 @@
+{
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diff --git a/tests/integration/vector_io/recordings/e2420f11a4b9fdecb676fd8799ab413bbba50d086ed2b1d1b177f2413bbf06cb.json b/tests/integration/vector_io/recordings/e2420f11a4b9fdecb676fd8799ab413bbba50d086ed2b1d1b177f2413bbf06cb.json
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diff --git a/tests/integration/vector_io/recordings/ed11e85201f572a15902bb32f19a7cd21581442421f5818fbee4ac0cb8610fdc.json b/tests/integration/vector_io/recordings/ed11e85201f572a15902bb32f19a7cd21581442421f5818fbee4ac0cb8610fdc.json
new file mode 100644
index 000000000..a25f83a85
--- /dev/null
+++ b/tests/integration/vector_io/recordings/ed11e85201f572a15902bb32f19a7cd21581442421f5818fbee4ac0cb8610fdc.json
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diff --git a/tests/integration/vector_io/recordings/ee4aad5aa1b20c8a0118046a0c23a5070fba0e83d0f0c0677a0af67e38b94db7.json b/tests/integration/vector_io/recordings/ee4aad5aa1b20c8a0118046a0c23a5070fba0e83d0f0c0677a0af67e38b94db7.json
new file mode 100644
index 000000000..fa388d30f
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diff --git a/tests/integration/vector_io/recordings/f6a8befc1dfce931b48319e478c0286c5fd73de20a28d6991749ef662ab8a99b.json b/tests/integration/vector_io/recordings/f6a8befc1dfce931b48319e478c0286c5fd73de20a28d6991749ef662ab8a99b.json
new file mode 100644
index 000000000..6adcb4c11
--- /dev/null
+++ b/tests/integration/vector_io/recordings/f6a8befc1dfce931b48319e478c0286c5fd73de20a28d6991749ef662ab8a99b.json
@@ -0,0 +1,424 @@
+{
+ "test_id": "tests/integration/vector_io/test_openai_vector_stores.py::test_openai_vector_store_with_chunks[client_with_models-emb=ollama/all-minilm:l6-v2:dim=384]",
+ "request": {
+ "method": "POST",
+ "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+ "headers": {},
+ "body": {
+ "model": "all-minilm:l6-v2",
+ "input": [
+ "What is Python programming language?"
+ ],
+ "encoding_format": "float"
+ },
+ "endpoint": "/v1/embeddings",
+ "model": "all-minilm:l6-v2"
+ },
+ "response": {
+ "body": {
+ "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+ "__data__": {
+ "data": [
+ {
+ "embedding": [
+ -0.062304743,
+ 0.04315718,
+ -0.056847535,
+ 0.03486019,
+ -0.045148205,
+ -0.1325256,
+ 0.021795923,
+ 0.039035086,
+ -0.048403695,
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+ 0.006355416,
+ 0.07870429,
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+ 0.03968397,
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+ 0.012665322,
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+ 0.030793479,
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+ 0.0005291749,
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+ 0.040542576,
+ 0.0062958263,
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+ 0.0016065374,
+ 0.012649728,
+ -0.036491103,
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+ 0.012404348,
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+ 0.020217113,
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+ 2.9781768e-33,
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+ 0.010422842,
+ -0.022787437,
+ 0.11515081,
+ 0.18532182,
+ -0.026544156
+ ],
+ "index": 0,
+ "object": "embedding"
+ }
+ ],
+ "model": "all-minilm:l6-v2",
+ "object": "list",
+ "usage": {
+ "prompt_tokens": 6,
+ "total_tokens": 6
+ }
+ }
+ },
+ "is_streaming": false
+ },
+ "id_normalization_mapping": {}
+}
diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py
index e850f2aee..347b43145 100644
--- a/tests/integration/vector_io/test_openai_vector_stores.py
+++ b/tests/integration/vector_io/test_openai_vector_stores.py
@@ -8,8 +8,9 @@ import time
from io import BytesIO
import pytest
-from llama_stack_client import BadRequestError
+from llama_stack_client import BadRequestError, NotFoundError
from openai import BadRequestError as OpenAIBadRequestError
+from openai import NotFoundError as OpenAINotFoundError
from llama_stack.apis.vector_io import Chunk
from llama_stack.core.library_client import LlamaStackAsLibraryClient
@@ -132,14 +133,22 @@ def compat_client_with_empty_stores(compat_client):
clear_files()
-def test_openai_create_vector_store(compat_client_with_empty_stores, client_with_models):
+def test_openai_create_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test creating a vector store using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a vector store
vector_store = client.vector_stores.create(
- name="Vs_test_vector_store", metadata={"purpose": "testing", "environment": "integration"}
+ name="Vs_test_vector_store",
+ metadata={"purpose": "testing", "environment": "integration"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert vector_store is not None
@@ -152,15 +161,33 @@ def test_openai_create_vector_store(compat_client_with_empty_stores, client_with
assert hasattr(vector_store, "created_at")
-def test_openai_list_vector_stores(compat_client_with_empty_stores, client_with_models):
+def test_openai_list_vector_stores(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test listing vector stores using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a few vector stores
- store1 = client.vector_stores.create(name="store1", metadata={"type": "test"})
- store2 = client.vector_stores.create(name="store2", metadata={"type": "test"})
+ store1 = client.vector_stores.create(
+ name="store1",
+ metadata={"type": "test"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
+ store2 = client.vector_stores.create(
+ name="store2",
+ metadata={"type": "test"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# List vector stores
response = client.vector_stores.list()
@@ -179,14 +206,24 @@ def test_openai_list_vector_stores(compat_client_with_empty_stores, client_with_
assert len(limited_response.data) == 1
-def test_openai_retrieve_vector_store(compat_client_with_empty_stores, client_with_models):
+def test_openai_retrieve_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test retrieving a specific vector store using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a vector store
- created_store = client.vector_stores.create(name="retrieve_test_store", metadata={"purpose": "retrieval_test"})
+ created_store = client.vector_stores.create(
+ name="retrieve_test_store",
+ metadata={"purpose": "retrieval_test"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Retrieve the store
retrieved_store = client.vector_stores.retrieve(vector_store_id=created_store.id)
@@ -198,14 +235,24 @@ def test_openai_retrieve_vector_store(compat_client_with_empty_stores, client_wi
assert retrieved_store.object == "vector_store"
-def test_openai_update_vector_store(compat_client_with_empty_stores, client_with_models):
+def test_openai_update_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test modifying a vector store using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a vector store
- created_store = client.vector_stores.create(name="original_name", metadata={"version": "1.0"})
+ created_store = client.vector_stores.create(
+ name="original_name",
+ metadata={"version": "1.0"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
time.sleep(1)
# Modify the store
modified_store = client.vector_stores.update(
@@ -221,14 +268,24 @@ def test_openai_update_vector_store(compat_client_with_empty_stores, client_with
assert modified_store.last_active_at > created_store.last_active_at
-def test_openai_delete_vector_store(compat_client_with_empty_stores, client_with_models):
+def test_openai_delete_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test deleting a vector store using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a vector store
- created_store = client.vector_stores.create(name="delete_test_store", metadata={"purpose": "deletion_test"})
+ created_store = client.vector_stores.create(
+ name="delete_test_store",
+ metadata={"purpose": "deletion_test"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Delete the store
delete_response = client.vector_stores.delete(vector_store_id=created_store.id)
@@ -243,14 +300,24 @@ def test_openai_delete_vector_store(compat_client_with_empty_stores, client_with
client.vector_stores.retrieve(vector_store_id=created_store.id)
-def test_openai_vector_store_search_empty(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_search_empty(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test searching an empty vector store using OpenAI API."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
client = compat_client_with_empty_stores
# Create a vector store
- vector_store = client.vector_stores.create(name="search_test_store", metadata={"purpose": "search_testing"})
+ vector_store = client.vector_stores.create(
+ name="search_test_store",
+ metadata={"purpose": "search_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Search the empty store
search_response = client.vector_stores.search(
@@ -264,7 +331,9 @@ def test_openai_vector_store_search_empty(compat_client_with_empty_stores, clien
assert search_response.has_more is False
-def test_openai_vector_store_with_chunks(compat_client_with_empty_stores, client_with_models, sample_chunks):
+def test_openai_vector_store_with_chunks(
+ compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
+):
"""Test vector store functionality with actual chunks using both OpenAI and native APIs."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -272,7 +341,15 @@ def test_openai_vector_store_with_chunks(compat_client_with_empty_stores, client
llama_client = client_with_models
# Create a vector store using OpenAI API
- vector_store = compat_client.vector_stores.create(name="chunks_test_store", metadata={"purpose": "chunks_testing"})
+ vector_store = compat_client.vector_stores.create(
+ name="chunks_test_store",
+ metadata={"purpose": "chunks_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Insert chunks using the native LlamaStack API (since OpenAI API doesn't have direct chunk insertion)
llama_client.vector_io.insert(
@@ -314,7 +391,12 @@ def test_openai_vector_store_with_chunks(compat_client_with_empty_stores, client
],
)
def test_openai_vector_store_search_relevance(
- compat_client_with_empty_stores, client_with_models, sample_chunks, test_case
+ compat_client_with_empty_stores,
+ client_with_models,
+ sample_chunks,
+ test_case,
+ embedding_model_id,
+ embedding_dimension,
):
"""Test that OpenAI vector store search returns relevant results for different queries."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -326,7 +408,13 @@ def test_openai_vector_store_search_relevance(
# Create a vector store
vector_store = compat_client.vector_stores.create(
- name=f"relevance_test_{expected_doc_id}", metadata={"purpose": "relevance_testing"}
+ name=f"relevance_test_{expected_doc_id}",
+ metadata={"purpose": "relevance_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Insert chunks using native API
@@ -355,7 +443,7 @@ def test_openai_vector_store_search_relevance(
def test_openai_vector_store_search_with_ranking_options(
- compat_client_with_empty_stores, client_with_models, sample_chunks
+ compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
):
"""Test OpenAI vector store search with ranking options."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -365,7 +453,13 @@ def test_openai_vector_store_search_with_ranking_options(
# Create a vector store
vector_store = compat_client.vector_stores.create(
- name="ranking_test_store", metadata={"purpose": "ranking_testing"}
+ name="ranking_test_store",
+ metadata={"purpose": "ranking_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Insert chunks
@@ -392,7 +486,7 @@ def test_openai_vector_store_search_with_ranking_options(
def test_openai_vector_store_search_with_high_score_filter(
- compat_client_with_empty_stores, client_with_models, sample_chunks
+ compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
):
"""Test that searching with text very similar to a document and high score threshold returns only that document."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -402,7 +496,13 @@ def test_openai_vector_store_search_with_high_score_filter(
# Create a vector store
vector_store = compat_client.vector_stores.create(
- name="high_score_filter_test", metadata={"purpose": "high_score_filtering"}
+ name="high_score_filter_test",
+ metadata={"purpose": "high_score_filtering"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Insert chunks
@@ -447,7 +547,7 @@ def test_openai_vector_store_search_with_high_score_filter(
def test_openai_vector_store_search_with_max_num_results(
- compat_client_with_empty_stores, client_with_models, sample_chunks
+ compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
):
"""Test OpenAI vector store search with max_num_results."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -457,7 +557,13 @@ def test_openai_vector_store_search_with_max_num_results(
# Create a vector store
vector_store = compat_client.vector_stores.create(
- name="max_num_results_test_store", metadata={"purpose": "max_num_results_testing"}
+ name="max_num_results_test_store",
+ metadata={"purpose": "max_num_results_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Insert chunks
@@ -477,14 +583,23 @@ def test_openai_vector_store_search_with_max_num_results(
assert len(search_response.data) == 2
-def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_attach_file(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store attach file."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create a file
test_content = b"The secret string is foobazbar."
@@ -525,7 +640,9 @@ def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client
assert "foobazbar" in top_content.lower()
-def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_attach_files_on_creation(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store attach files on creation."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -547,6 +664,11 @@ def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_s
vector_store = compat_client.vector_stores.create(
name="test_store",
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert vector_store.file_counts.completed == len(valid_file_ids)
@@ -578,14 +700,23 @@ def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_s
assert updated_vector_store.file_counts.failed == 0
-def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_list_files(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store list files."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create some files and attach them to the vector store
file_ids = []
@@ -639,7 +770,9 @@ def test_openai_vector_store_list_files(compat_client_with_empty_stores, client_
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):
+def test_openai_vector_store_list_files_invalid_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store list files with invalid vector store ID."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
@@ -647,20 +780,29 @@ def test_openai_vector_store_list_files_invalid_vector_store(compat_client_with_
if isinstance(compat_client, LlamaStackAsLibraryClient):
errors = ValueError
else:
- errors = (BadRequestError, OpenAIBadRequestError)
+ errors = (NotFoundError, OpenAINotFoundError)
with pytest.raises(errors):
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):
+def test_openai_vector_store_retrieve_file_contents(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store retrieve file contents."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create a file
test_content = b"This is a test file"
@@ -675,6 +817,11 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto
vector_store_id=vector_store.id,
file_id=file.id,
attributes=attributes,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert file_attach_response.status == "completed"
@@ -697,14 +844,23 @@ def test_openai_vector_store_retrieve_file_contents(compat_client_with_empty_sto
assert file_contents.attributes == attributes
-def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_delete_file(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store delete file."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create some files and attach them to the vector store
file_ids = []
@@ -749,14 +905,23 @@ def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client
assert updated_vector_store.file_counts.in_progress == 0
-def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_delete_file_removes_from_vector_store(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store delete file removes from vector store."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create a file
test_content = b"The secret string is foobazbar."
@@ -787,14 +952,23 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client
assert not search_response.data
-def test_openai_vector_store_update_file(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_update_file(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test OpenAI vector store update file."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create a file
test_content = b"This is a test file"
@@ -830,7 +1004,9 @@ def test_openai_vector_store_update_file(compat_client_with_empty_stores, client
assert retrieved_file.attributes["foo"] == "baz"
-def test_create_vector_store_files_duplicate_vector_store_name(compat_client_with_empty_stores, client_with_models):
+def test_create_vector_store_files_duplicate_vector_store_name(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""
This test confirms that client.vector_stores.create() creates a unique ID
"""
@@ -848,6 +1024,11 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
vector_store = compat_client.vector_stores.create(
name="test_store_with_files",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert vector_store.file_counts.completed == 0
assert vector_store.file_counts.total == 0
@@ -857,6 +1038,11 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
vector_store2 = compat_client.vector_stores.create(
name="test_store_with_files",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
vector_stores_list = compat_client.vector_stores.list()
@@ -865,6 +1051,11 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
created_file = compat_client.vector_stores.files.create(
vector_store_id=vector_store.id,
file_id=file_ids[0],
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert created_file.status == "completed"
@@ -872,6 +1063,11 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
created_file_from_non_deleted_vector_store = compat_client.vector_stores.files.create(
vector_store_id=vector_store.id,
file_id=file_ids[1],
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert created_file_from_non_deleted_vector_store.status == "completed"
@@ -880,13 +1076,20 @@ def test_create_vector_store_files_duplicate_vector_store_name(compat_client_wit
@pytest.mark.parametrize("search_mode", ["vector", "keyword", "hybrid"])
-def test_openai_vector_store_search_modes(llama_stack_client, client_with_models, sample_chunks, search_mode):
+def test_openai_vector_store_search_modes(
+ llama_stack_client, client_with_models, sample_chunks, search_mode, embedding_model_id, embedding_dimension
+):
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_models, search_mode)
vector_store = llama_stack_client.vector_stores.create(
name=f"search_mode_test_{search_mode}",
metadata={"purpose": "search_mode_testing"},
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
client_with_models.vector_io.insert(
@@ -904,14 +1107,23 @@ def test_openai_vector_store_search_modes(llama_stack_client, client_with_models
assert search_response is not None
-def test_openai_vector_store_file_batch_create_and_retrieve(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_file_batch_create_and_retrieve(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test creating and retrieving a vector store file batch."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="batch_test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="batch_test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create multiple files
file_ids = []
@@ -925,6 +1137,11 @@ def test_openai_vector_store_file_batch_create_and_retrieve(compat_client_with_e
batch = compat_client.vector_stores.file_batches.create(
vector_store_id=vector_store.id,
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert batch is not None
@@ -957,14 +1174,23 @@ def test_openai_vector_store_file_batch_create_and_retrieve(compat_client_with_e
assert retrieved_batch.status == "completed" # Should be completed after processing
-def test_openai_vector_store_file_batch_list_files(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_file_batch_list_files(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test listing files in a vector store file batch."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="batch_list_test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="batch_list_test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create multiple files
file_ids = []
@@ -978,6 +1204,11 @@ def test_openai_vector_store_file_batch_list_files(compat_client_with_empty_stor
batch = compat_client.vector_stores.file_batches.create(
vector_store_id=vector_store.id,
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Wait for batch processing to complete
@@ -1040,14 +1271,23 @@ def test_openai_vector_store_file_batch_list_files(compat_client_with_empty_stor
assert first_page_ids.isdisjoint(second_page_ids)
-def test_openai_vector_store_file_batch_cancel(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_file_batch_cancel(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test cancelling a vector store file batch."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="batch_cancel_test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="batch_cancel_test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create a batch to test cancellation
file_ids = []
@@ -1061,6 +1301,11 @@ def test_openai_vector_store_file_batch_cancel(compat_client_with_empty_stores,
batch = compat_client.vector_stores.file_batches.create(
vector_store_id=vector_store.id,
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
try:
@@ -1085,14 +1330,23 @@ def test_openai_vector_store_file_batch_cancel(compat_client_with_empty_stores,
assert final_batch.status in ["completed", "cancelled"]
-def test_openai_vector_store_file_batch_retrieve_contents(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_file_batch_retrieve_contents(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test retrieving file contents after file batch processing."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="batch_contents_test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="batch_contents_test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Create multiple files with known content
file_data = [
@@ -1111,6 +1365,11 @@ def test_openai_vector_store_file_batch_retrieve_contents(compat_client_with_emp
batch = compat_client.vector_stores.file_batches.create(
vector_store_id=vector_store.id,
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
# Wait for batch processing to complete
@@ -1148,14 +1407,23 @@ def test_openai_vector_store_file_batch_retrieve_contents(compat_client_with_emp
assert file_data[i][1].decode("utf-8") in content_text
-def test_openai_vector_store_file_batch_error_handling(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_file_batch_error_handling(
+ compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
+):
"""Test error handling for file batch operations."""
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
compat_client = compat_client_with_empty_stores
# Create a vector store
- vector_store = compat_client.vector_stores.create(name="batch_error_test_store")
+ vector_store = compat_client.vector_stores.create(
+ name="batch_error_test_store",
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
+ )
# Test with invalid file IDs (should handle gracefully)
file_ids = ["invalid_file_id_1", "invalid_file_id_2"]
@@ -1163,6 +1431,11 @@ def test_openai_vector_store_file_batch_error_handling(compat_client_with_empty_
batch = compat_client.vector_stores.file_batches.create(
vector_store_id=vector_store.id,
file_ids=file_ids,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
assert batch is not None
@@ -1170,21 +1443,25 @@ def test_openai_vector_store_file_batch_error_handling(compat_client_with_empty_
# Invalid files should be marked as failed
assert batch.file_counts.failed >= 0 # Implementation may vary
- # Determine expected errors based on client type
+ # Test retrieving non-existent batch (returns BadRequestError)
if isinstance(compat_client, LlamaStackAsLibraryClient):
- errors = ValueError
+ batch_errors = ValueError
else:
- errors = (BadRequestError, OpenAIBadRequestError)
+ batch_errors = (BadRequestError, OpenAIBadRequestError)
- # Test retrieving non-existent batch
- with pytest.raises(errors): # Should raise an error for non-existent batch
+ with pytest.raises(batch_errors): # Should raise an error for non-existent batch
compat_client.vector_stores.file_batches.retrieve(
vector_store_id=vector_store.id,
batch_id="non_existent_batch_id",
)
- # Test operations on non-existent vector store
- with pytest.raises(errors): # Should raise an error for non-existent vector store
+ # Test operations on non-existent vector store (returns NotFoundError)
+ if isinstance(compat_client, LlamaStackAsLibraryClient):
+ vector_store_errors = ValueError
+ else:
+ vector_store_errors = (NotFoundError, OpenAINotFoundError)
+
+ with pytest.raises(vector_store_errors): # Should raise an error for non-existent vector store
compat_client.vector_stores.file_batches.create(
vector_store_id="non_existent_vector_store",
file_ids=["any_file_id"],
diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py
index 7bfe31dd6..f2205ed0a 100644
--- a/tests/integration/vector_io/test_vector_io.py
+++ b/tests/integration/vector_io/test_vector_io.py
@@ -34,9 +34,9 @@ def sample_chunks():
@pytest.fixture(scope="function")
def client_with_empty_registry(client_with_models):
def clear_registry():
- vector_dbs = [vector_db.identifier for vector_db in client_with_models.vector_dbs.list()]
- for vector_db_id in vector_dbs:
- client_with_models.vector_dbs.unregister(vector_db_id=vector_db_id)
+ vector_stores = client_with_models.vector_stores.list()
+ for vector_store in vector_stores.data:
+ client_with_models.vector_stores.delete(vector_store_id=vector_store.id)
clear_registry()
yield client_with_models
@@ -48,47 +48,50 @@ def client_with_empty_registry(client_with_models):
def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension):
vector_db_name = "test_vector_db"
- register_response = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
+ create_response = client_with_empty_registry.vector_stores.create(
+ name=vector_db_name,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
- actual_vector_db_id = register_response.identifier
+ actual_vector_db_id = create_response.id
- # Retrieve the memory bank and validate its properties
- response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=actual_vector_db_id)
+ # Retrieve the vector store and validate its properties
+ response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_db_id)
assert response is not None
- assert response.identifier == actual_vector_db_id
- assert response.embedding_model == embedding_model_id
- assert response.identifier.startswith("vs_")
+ assert response.id == actual_vector_db_id
+ assert response.name == vector_db_name
+ assert response.id.startswith("vs_")
def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension):
vector_db_name = "test_vector_db"
- response = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
+ response = client_with_empty_registry.vector_stores.create(
+ name=vector_db_name,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
- actual_vector_db_id = response.identifier
+ actual_vector_db_id = response.id
assert actual_vector_db_id.startswith("vs_")
assert actual_vector_db_id != vector_db_name
- vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- assert vector_dbs_after_register == [actual_vector_db_id]
-
vector_stores = client_with_empty_registry.vector_stores.list()
assert len(vector_stores.data) == 1
vector_store = vector_stores.data[0]
assert vector_store.id == actual_vector_db_id
assert vector_store.name == vector_db_name
- client_with_empty_registry.vector_dbs.unregister(vector_db_id=actual_vector_db_id)
+ client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_db_id)
- vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
- assert len(vector_dbs) == 0
+ vector_stores = client_with_empty_registry.vector_stores.list()
+ assert len(vector_stores.data) == 0
@pytest.mark.parametrize(
@@ -103,13 +106,16 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
)
def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case):
vector_db_name = "test_vector_db"
- register_response = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
+ create_response = client_with_empty_registry.vector_stores.create(
+ name=vector_db_name,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
- actual_vector_db_id = register_response.identifier
+ actual_vector_db_id = create_response.id
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_db_id,
@@ -142,13 +148,16 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
"remote::qdrant": {"score_threshold": -1.0},
}
vector_db_name = "test_precomputed_embeddings_db"
- register_response = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
+ register_response = client_with_empty_registry.vector_stores.create(
+ name=vector_db_name,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
- actual_vector_db_id = register_response.identifier
+ actual_vector_db_id = register_response.id
chunks_with_embeddings = [
Chunk(
@@ -189,13 +198,16 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
"inline::qdrant": {"score_threshold": 0.0},
}
vector_db_name = "test_precomputed_embeddings_db"
- register_response = client_with_empty_registry.vector_dbs.register(
- vector_db_id=vector_db_name,
- embedding_model=embedding_model_id,
- embedding_dimension=embedding_dimension,
+ register_response = client_with_empty_registry.vector_stores.create(
+ name=vector_db_name,
+ extra_body={
+ "embedding_model": embedding_model_id,
+ "embedding_dimension": embedding_dimension,
+ "provider_id": "my_provider",
+ },
)
- actual_vector_db_id = register_response.identifier
+ actual_vector_db_id = register_response.id
chunks_with_embeddings = [
Chunk(
diff --git a/tests/unit/distribution/routers/test_routing_tables.py b/tests/unit/distribution/routers/test_routing_tables.py
index 8b03ec260..beb0b4a95 100644
--- a/tests/unit/distribution/routers/test_routing_tables.py
+++ b/tests/unit/distribution/routers/test_routing_tables.py
@@ -17,7 +17,6 @@ from llama_stack.apis.datatypes import Api
from llama_stack.apis.models import Model, ModelType
from llama_stack.apis.shields.shields import Shield
from llama_stack.apis.tools import ListToolDefsResponse, ToolDef, ToolGroup
-from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.core.datatypes import RegistryEntrySource
from llama_stack.core.routing_tables.benchmarks import BenchmarksRoutingTable
from llama_stack.core.routing_tables.datasets import DatasetsRoutingTable
@@ -25,7 +24,6 @@ from llama_stack.core.routing_tables.models import ModelsRoutingTable
from llama_stack.core.routing_tables.scoring_functions import ScoringFunctionsRoutingTable
from llama_stack.core.routing_tables.shields import ShieldsRoutingTable
from llama_stack.core.routing_tables.toolgroups import ToolGroupsRoutingTable
-from llama_stack.core.routing_tables.vector_dbs import VectorDBsRoutingTable
class Impl:
@@ -146,31 +144,6 @@ class ToolGroupsImpl(Impl):
)
-class VectorDBImpl(Impl):
- def __init__(self):
- super().__init__(Api.vector_io)
-
- async def register_vector_db(self, vector_db: VectorDB):
- return vector_db
-
- async def unregister_vector_db(self, vector_db_id: str):
- return vector_db_id
-
- async def openai_create_vector_store(self, **kwargs):
- import time
- import uuid
-
- from llama_stack.apis.vector_io.vector_io import VectorStoreFileCounts, VectorStoreObject
-
- vector_store_id = kwargs.get("provider_vector_db_id") or f"vs_{uuid.uuid4()}"
- return VectorStoreObject(
- id=vector_store_id,
- name=kwargs.get("name", vector_store_id),
- created_at=int(time.time()),
- file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0),
- )
-
-
async def test_models_routing_table(cached_disk_dist_registry):
table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {})
await table.initialize()
@@ -263,40 +236,6 @@ async def test_shields_routing_table(cached_disk_dist_registry):
await table.unregister_shield(identifier="non-existent")
-async def test_vectordbs_routing_table(cached_disk_dist_registry):
- table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {})
- await table.initialize()
-
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {})
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- # Register multiple vector databases and verify listing
- vdb1 = await table.register_vector_db(vector_db_id="test-vectordb", embedding_model="test_provider/test-model")
- vdb2 = await table.register_vector_db(vector_db_id="test-vectordb-2", embedding_model="test_provider/test-model")
- vector_dbs = await table.list_vector_dbs()
-
- assert len(vector_dbs.data) == 2
- vector_db_ids = {v.identifier for v in vector_dbs.data}
- assert vdb1.identifier in vector_db_ids
- assert vdb2.identifier in vector_db_ids
-
- # Verify they have UUID-based identifiers
- assert vdb1.identifier.startswith("vs_")
- assert vdb2.identifier.startswith("vs_")
-
- await table.unregister_vector_db(vector_db_id=vdb1.identifier)
- await table.unregister_vector_db(vector_db_id=vdb2.identifier)
-
- vector_dbs = await table.list_vector_dbs()
- assert len(vector_dbs.data) == 0
-
-
async def test_datasets_routing_table(cached_disk_dist_registry):
table = DatasetsRoutingTable({"localfs": DatasetsImpl()}, cached_disk_dist_registry, {})
await table.initialize()
diff --git a/tests/unit/distribution/routing_tables/test_vector_dbs.py b/tests/unit/distribution/routing_tables/test_vector_dbs.py
deleted file mode 100644
index 3444f64c2..000000000
--- a/tests/unit/distribution/routing_tables/test_vector_dbs.py
+++ /dev/null
@@ -1,381 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-# Unit tests for the routing tables vector_dbs
-
-import time
-import uuid
-from unittest.mock import AsyncMock
-
-import pytest
-
-from llama_stack.apis.datatypes import Api
-from llama_stack.apis.models import ModelType
-from llama_stack.apis.vector_dbs.vector_dbs import VectorDB
-from llama_stack.apis.vector_io.vector_io import (
- VectorStoreContent,
- VectorStoreDeleteResponse,
- VectorStoreFileContentsResponse,
- VectorStoreFileCounts,
- VectorStoreFileDeleteResponse,
- VectorStoreFileObject,
- VectorStoreObject,
- VectorStoreSearchResponsePage,
-)
-from llama_stack.core.access_control.datatypes import AccessRule, Scope
-from llama_stack.core.datatypes import User
-from llama_stack.core.request_headers import request_provider_data_context
-from llama_stack.core.routing_tables.vector_dbs import VectorDBsRoutingTable
-from tests.unit.distribution.routers.test_routing_tables import Impl, InferenceImpl, ModelsRoutingTable
-
-
-class VectorDBImpl(Impl):
- def __init__(self):
- super().__init__(Api.vector_io)
- self.vector_stores = {}
-
- async def register_vector_db(self, vector_db: VectorDB):
- return vector_db
-
- async def unregister_vector_db(self, vector_db_id: str):
- return vector_db_id
-
- async def openai_retrieve_vector_store(self, vector_store_id):
- return VectorStoreObject(
- id=vector_store_id,
- name="Test Store",
- created_at=int(time.time()),
- file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0),
- )
-
- async def openai_update_vector_store(self, vector_store_id, **kwargs):
- return VectorStoreObject(
- id=vector_store_id,
- name="Updated Store",
- created_at=int(time.time()),
- file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0),
- )
-
- async def openai_delete_vector_store(self, vector_store_id):
- return VectorStoreDeleteResponse(id=vector_store_id, object="vector_store.deleted", deleted=True)
-
- async def openai_search_vector_store(self, vector_store_id, query, **kwargs):
- return VectorStoreSearchResponsePage(
- object="vector_store.search_results.page", search_query="query", data=[], has_more=False, next_page=None
- )
-
- async def openai_attach_file_to_vector_store(self, vector_store_id, file_id, **kwargs):
- return VectorStoreFileObject(
- id=file_id,
- status="completed",
- chunking_strategy={"type": "auto"},
- created_at=int(time.time()),
- vector_store_id=vector_store_id,
- )
-
- async def openai_list_files_in_vector_store(self, vector_store_id, **kwargs):
- return [
- VectorStoreFileObject(
- id="1",
- status="completed",
- chunking_strategy={"type": "auto"},
- created_at=int(time.time()),
- vector_store_id=vector_store_id,
- )
- ]
-
- async def openai_retrieve_vector_store_file(self, vector_store_id, file_id):
- return VectorStoreFileObject(
- id=file_id,
- status="completed",
- chunking_strategy={"type": "auto"},
- created_at=int(time.time()),
- vector_store_id=vector_store_id,
- )
-
- async def openai_retrieve_vector_store_file_contents(self, vector_store_id, file_id):
- return VectorStoreFileContentsResponse(
- file_id=file_id,
- filename="Sample File name",
- attributes={"key": "value"},
- content=[VectorStoreContent(type="text", text="Sample content")],
- )
-
- async def openai_update_vector_store_file(self, vector_store_id, file_id, **kwargs):
- return VectorStoreFileObject(
- id=file_id,
- status="completed",
- chunking_strategy={"type": "auto"},
- created_at=int(time.time()),
- vector_store_id=vector_store_id,
- )
-
- async def openai_delete_vector_store_file(self, vector_store_id, file_id):
- return VectorStoreFileDeleteResponse(id=file_id, deleted=True)
-
- async def openai_create_vector_store(
- self,
- name=None,
- embedding_model=None,
- embedding_dimension=None,
- provider_id=None,
- provider_vector_db_id=None,
- **kwargs,
- ):
- vector_store_id = provider_vector_db_id or f"vs_{uuid.uuid4()}"
- vector_store = VectorStoreObject(
- id=vector_store_id,
- name=name or vector_store_id,
- created_at=int(time.time()),
- file_counts=VectorStoreFileCounts(completed=0, cancelled=0, failed=0, in_progress=0, total=0),
- )
- self.vector_stores[vector_store_id] = vector_store
- return vector_store
-
- async def openai_list_vector_stores(self, **kwargs):
- from llama_stack.apis.vector_io.vector_io import VectorStoreListResponse
-
- return VectorStoreListResponse(
- data=list(self.vector_stores.values()), has_more=False, first_id=None, last_id=None
- )
-
-
-async def test_vectordbs_routing_table(cached_disk_dist_registry):
- n = 10
- table = VectorDBsRoutingTable({"test_provider": VectorDBImpl()}, cached_disk_dist_registry, {})
- await table.initialize()
-
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {})
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- # Register multiple vector databases and verify listing
- vdb_dict = {}
- for i in range(n):
- vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model")
-
- vector_dbs = await table.list_vector_dbs()
-
- assert len(vector_dbs.data) == len(vdb_dict)
- vector_db_ids = {v.identifier for v in vector_dbs.data}
- for k in vdb_dict:
- assert vdb_dict[k].identifier in vector_db_ids
- for k in vdb_dict:
- await table.unregister_vector_db(vector_db_id=vdb_dict[k].identifier)
-
- vector_dbs = await table.list_vector_dbs()
- assert len(vector_dbs.data) == 0
-
-
-async def test_vector_db_and_vector_store_id_mapping(cached_disk_dist_registry):
- n = 10
- impl = VectorDBImpl()
- table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {})
- await table.initialize()
-
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {})
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- vdb_dict = {}
- for i in range(n):
- vdb_dict[i] = await table.register_vector_db(vector_db_id=f"test-vectordb-{i}", embedding_model="test-model")
-
- vector_dbs = await table.list_vector_dbs()
- vector_db_ids = {v.identifier for v in vector_dbs.data}
-
- vector_stores = await impl.openai_list_vector_stores()
- vector_store_ids = {v.id for v in vector_stores.data}
-
- assert vector_db_ids == vector_store_ids, (
- f"Vector DB IDs {vector_db_ids} don't match vector store IDs {vector_store_ids}"
- )
-
- for vector_store in vector_stores.data:
- vector_db = await table.get_vector_db(vector_store.id)
- assert vector_store.name == vector_db.vector_db_name, (
- f"Vector store name {vector_store.name} doesn't match vector store ID {vector_store.id}"
- )
-
- for vector_db_id in vector_db_ids:
- await table.unregister_vector_db(vector_db_id)
-
- assert len((await table.list_vector_dbs()).data) == 0
-
-
-async def test_vector_db_id_becomes_vector_store_name(cached_disk_dist_registry):
- impl = VectorDBImpl()
- table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, {})
- await table.initialize()
-
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, {})
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- user_provided_id = "my-custom-vector-db"
- await table.register_vector_db(vector_db_id=user_provided_id, embedding_model="test-model")
-
- vector_stores = await impl.openai_list_vector_stores()
- assert len(vector_stores.data) == 1
-
- vector_store = vector_stores.data[0]
-
- assert vector_store.name == user_provided_id
-
- assert vector_store.id.startswith("vs_")
- assert vector_store.id != user_provided_id
-
- vector_dbs = await table.list_vector_dbs()
- assert len(vector_dbs.data) == 1
- assert vector_dbs.data[0].identifier == vector_store.id
-
- await table.unregister_vector_db(vector_store.id)
-
-
-async def test_openai_vector_stores_routing_table_roles(cached_disk_dist_registry):
- impl = VectorDBImpl()
- impl.openai_retrieve_vector_store = AsyncMock(return_value="OK")
- table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, policy=[])
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, policy=[])
- authorized_table = "vs1"
- authorized_team = "team1"
- unauthorized_team = "team2"
-
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- authorized_user = User(principal="alice", attributes={"roles": [authorized_team]})
- with request_provider_data_context({}, authorized_user):
- registered_vdb = await table.register_vector_db(vector_db_id="vs1", embedding_model="test-model")
- authorized_table = registered_vdb.identifier # Use the actual generated ID
-
- # Authorized reader
- with request_provider_data_context({}, authorized_user):
- res = await table.openai_retrieve_vector_store(authorized_table)
- assert res == "OK"
-
- # Authorized updater
- impl.openai_update_vector_store_file = AsyncMock(return_value="UPDATED")
- with request_provider_data_context({}, authorized_user):
- res = await table.openai_update_vector_store_file(authorized_table, file_id="file1", attributes={"foo": "bar"})
- assert res == "UPDATED"
-
- # Unauthorized reader
- unauthorized_user = User(principal="eve", attributes={"roles": [unauthorized_team]})
- with request_provider_data_context({}, unauthorized_user):
- with pytest.raises(ValueError):
- await table.openai_retrieve_vector_store(authorized_table)
-
- # Unauthorized updater
- with request_provider_data_context({}, unauthorized_user):
- with pytest.raises(ValueError):
- await table.openai_update_vector_store_file(authorized_table, file_id="file1", attributes={"foo": "bar"})
-
- # Authorized deleter
- impl.openai_delete_vector_store_file = AsyncMock(return_value="DELETED")
- with request_provider_data_context({}, authorized_user):
- res = await table.openai_delete_vector_store_file(authorized_table, file_id="file1")
- assert res == "DELETED"
-
- # Unauthorized deleter
- with request_provider_data_context({}, unauthorized_user):
- with pytest.raises(ValueError):
- await table.openai_delete_vector_store_file(authorized_table, file_id="file1")
-
-
-async def test_openai_vector_stores_routing_table_actions(cached_disk_dist_registry):
- impl = VectorDBImpl()
-
- policy = [
- AccessRule(permit=Scope(actions=["create", "read", "update", "delete"]), when="user with admin in roles"),
- AccessRule(permit=Scope(actions=["read"]), when="user with reader in roles"),
- ]
-
- table = VectorDBsRoutingTable({"test_provider": impl}, cached_disk_dist_registry, policy=policy)
- m_table = ModelsRoutingTable({"test_provider": InferenceImpl()}, cached_disk_dist_registry, policy=[])
-
- vector_db_id = "vs1"
- file_id = "file-1"
-
- admin_user = User(principal="admin", attributes={"roles": ["admin"]})
- read_only_user = User(principal="reader", attributes={"roles": ["reader"]})
- no_access_user = User(principal="outsider", attributes={"roles": ["no_access"]})
-
- await m_table.initialize()
- await m_table.register_model(
- model_id="test-model",
- provider_id="test_provider",
- metadata={"embedding_dimension": 128},
- model_type=ModelType.embedding,
- )
-
- with request_provider_data_context({}, admin_user):
- registered_vdb = await table.register_vector_db(vector_db_id=vector_db_id, embedding_model="test-model")
- vector_db_id = registered_vdb.identifier # Use the actual generated ID
-
- read_methods = [
- (table.openai_retrieve_vector_store, (vector_db_id,), {}),
- (table.openai_search_vector_store, (vector_db_id, "query"), {}),
- (table.openai_list_files_in_vector_store, (vector_db_id,), {}),
- (table.openai_retrieve_vector_store_file, (vector_db_id, file_id), {}),
- (table.openai_retrieve_vector_store_file_contents, (vector_db_id, file_id), {}),
- ]
- update_methods = [
- (table.openai_update_vector_store, (vector_db_id,), {"name": "Updated DB"}),
- (table.openai_attach_file_to_vector_store, (vector_db_id, file_id), {}),
- (table.openai_update_vector_store_file, (vector_db_id, file_id), {"attributes": {"key": "value"}}),
- ]
- delete_methods = [
- (table.openai_delete_vector_store_file, (vector_db_id, file_id), {}),
- (table.openai_delete_vector_store, (vector_db_id,), {}),
- ]
-
- for user in [admin_user, read_only_user]:
- with request_provider_data_context({}, user):
- for method, args, kwargs in read_methods:
- result = await method(*args, **kwargs)
- assert result is not None, f"Read operation failed with user {user.principal}"
-
- with request_provider_data_context({}, no_access_user):
- for method, args, kwargs in read_methods:
- with pytest.raises(ValueError):
- await method(*args, **kwargs)
-
- with request_provider_data_context({}, admin_user):
- for method, args, kwargs in update_methods:
- result = await method(*args, **kwargs)
- assert result is not None, "Update operation failed with admin user"
-
- with request_provider_data_context({}, admin_user):
- for method, args, kwargs in delete_methods:
- result = await method(*args, **kwargs)
- assert result is not None, "Delete operation failed with admin user"
-
- for user in [read_only_user, no_access_user]:
- with request_provider_data_context({}, user):
- for method, args, kwargs in delete_methods:
- with pytest.raises(ValueError):
- await method(*args, **kwargs)