mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-01 16:24:44 +00:00
Merge branch 'main' into out-of-token-budget-fix
This commit is contained in:
Ilya Kolchinsky
GitHub
commit
85ef55391d
61 changed files with 1322 additions and 1598 deletions
|
|
@ -110,31 +110,9 @@ uv run pre-commit run --all-files
|
|||
> [!CAUTION]
|
||||
> Before pushing your changes, make sure that the pre-commit hooks have passed successfully.
|
||||
|
||||
## Running unit tests
|
||||
## Running tests
|
||||
|
||||
You can run the unit tests by running:
|
||||
|
||||
```bash
|
||||
source .venv/bin/activate
|
||||
./scripts/unit-tests.sh [PYTEST_ARGS]
|
||||
```
|
||||
|
||||
Any additional arguments are passed to pytest. For example, you can specify a test directory, a specific test file, or any pytest flags (e.g., -vvv for verbosity). If no test directory is specified, it defaults to "tests/unit", e.g:
|
||||
|
||||
```bash
|
||||
./scripts/unit-tests.sh tests/unit/registry/test_registry.py -vvv
|
||||
```
|
||||
|
||||
If you'd like to run for a non-default version of Python (currently 3.10), pass `PYTHON_VERSION` variable as follows:
|
||||
|
||||
```
|
||||
source .venv/bin/activate
|
||||
PYTHON_VERSION=3.13 ./scripts/unit-tests.sh
|
||||
```
|
||||
|
||||
## Running integration tests
|
||||
|
||||
You can run integration tests following the instructions [here](tests/integration/README.md).
|
||||
You can find the Llama Stack testing documentation here [here](tests/README.md).
|
||||
|
||||
## Adding a new dependency to the project
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@
|
|||
[](https://github.com/meta-llama/llama-stack/actions/workflows/unit-tests.yml?query=branch%3Amain)
|
||||
[](https://github.com/meta-llama/llama-stack/actions/workflows/integration-tests.yml?query=branch%3Amain)
|
||||
|
||||
[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb)
|
||||
[**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) | [**Discord**](https://discord.gg/llama-stack)
|
||||
|
||||
### ✨🎉 Llama 4 Support 🎉✨
|
||||
We released [Version 0.2.0](https://github.com/meta-llama/llama-stack/releases/tag/v0.2.0) with support for the Llama 4 herd of models released by Meta.
|
||||
|
|
|
|||
408
docs/_static/llama-stack-spec.html
vendored
408
docs/_static/llama-stack-spec.html
vendored
|
|
@ -6466,54 +6466,51 @@
|
|||
],
|
||||
"title": "AgentTurnResponseTurnStartPayload"
|
||||
},
|
||||
"OpenAIResponseInputMessage": {
|
||||
"OpenAIResponseInput": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputFunctionToolCallOutput"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseMessage"
|
||||
}
|
||||
]
|
||||
},
|
||||
"OpenAIResponseInputFunctionToolCallOutput": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"content": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputMessageContent"
|
||||
}
|
||||
}
|
||||
]
|
||||
"call_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"role": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string",
|
||||
"const": "system"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "developer"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "user"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "assistant"
|
||||
}
|
||||
]
|
||||
"output": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "message",
|
||||
"default": "message"
|
||||
"const": "function_call_output",
|
||||
"default": "function_call_output"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"content",
|
||||
"role"
|
||||
"call_id",
|
||||
"output",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseInputMessage"
|
||||
"title": "OpenAIResponseInputFunctionToolCallOutput",
|
||||
"description": "This represents the output of a function call that gets passed back to the model."
|
||||
},
|
||||
"OpenAIResponseInputMessageContent": {
|
||||
"oneOf": [
|
||||
|
|
@ -6588,6 +6585,113 @@
|
|||
"title": "OpenAIResponseInputMessageContentText"
|
||||
},
|
||||
"OpenAIResponseInputTool": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputToolWebSearch"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputToolFileSearch"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputToolFunction"
|
||||
}
|
||||
],
|
||||
"discriminator": {
|
||||
"propertyName": "type",
|
||||
"mapping": {
|
||||
"web_search": "#/components/schemas/OpenAIResponseInputToolWebSearch",
|
||||
"file_search": "#/components/schemas/OpenAIResponseInputToolFileSearch",
|
||||
"function": "#/components/schemas/OpenAIResponseInputToolFunction"
|
||||
}
|
||||
}
|
||||
},
|
||||
"OpenAIResponseInputToolFileSearch": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "file_search",
|
||||
"default": "file_search"
|
||||
},
|
||||
"vector_store_id": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"ranking_options": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"ranker": {
|
||||
"type": "string"
|
||||
},
|
||||
"score_threshold": {
|
||||
"type": "number",
|
||||
"default": 0.0
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"title": "FileSearchRankingOptions"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"type",
|
||||
"vector_store_id"
|
||||
],
|
||||
"title": "OpenAIResponseInputToolFileSearch"
|
||||
},
|
||||
"OpenAIResponseInputToolFunction": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "function",
|
||||
"default": "function"
|
||||
},
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"description": {
|
||||
"type": "string"
|
||||
},
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "array"
|
||||
},
|
||||
{
|
||||
"type": "object"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"strict": {
|
||||
"type": "boolean"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"type",
|
||||
"name"
|
||||
],
|
||||
"title": "OpenAIResponseInputToolFunction"
|
||||
},
|
||||
"OpenAIResponseInputToolWebSearch": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"type": {
|
||||
|
|
@ -6614,6 +6718,146 @@
|
|||
],
|
||||
"title": "OpenAIResponseInputToolWebSearch"
|
||||
},
|
||||
"OpenAIResponseMessage": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"content": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputMessageContent"
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageContent"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"role": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string",
|
||||
"const": "system"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "developer"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "user"
|
||||
},
|
||||
{
|
||||
"type": "string",
|
||||
"const": "assistant"
|
||||
}
|
||||
]
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "message",
|
||||
"default": "message"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"content",
|
||||
"role",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseMessage",
|
||||
"description": "Corresponds to the various Message types in the Responses API. They are all under one type because the Responses API gives them all the same \"type\" value, and there is no way to tell them apart in certain scenarios."
|
||||
},
|
||||
"OpenAIResponseOutputMessageContent": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "output_text",
|
||||
"default": "output_text"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"text",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessageContentOutputText"
|
||||
},
|
||||
"OpenAIResponseOutputMessageFunctionToolCall": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"arguments": {
|
||||
"type": "string"
|
||||
},
|
||||
"call_id": {
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "function_call",
|
||||
"default": "function_call"
|
||||
},
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"arguments",
|
||||
"call_id",
|
||||
"name",
|
||||
"type",
|
||||
"id",
|
||||
"status"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessageFunctionToolCall"
|
||||
},
|
||||
"OpenAIResponseOutputMessageWebSearchToolCall": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "web_search_call",
|
||||
"default": "web_search_call"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"id",
|
||||
"status",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
},
|
||||
"CreateOpenaiResponseRequest": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -6625,7 +6869,7 @@
|
|||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/OpenAIResponseInputMessage"
|
||||
"$ref": "#/components/schemas/OpenAIResponseInput"
|
||||
}
|
||||
}
|
||||
],
|
||||
|
|
@ -6743,98 +6987,24 @@
|
|||
"OpenAIResponseOutput": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessage"
|
||||
"$ref": "#/components/schemas/OpenAIResponseMessage"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
|
||||
}
|
||||
],
|
||||
"discriminator": {
|
||||
"propertyName": "type",
|
||||
"mapping": {
|
||||
"message": "#/components/schemas/OpenAIResponseOutputMessage",
|
||||
"web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
"message": "#/components/schemas/OpenAIResponseMessage",
|
||||
"web_search_call": "#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall",
|
||||
"function_call": "#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall"
|
||||
}
|
||||
}
|
||||
},
|
||||
"OpenAIResponseOutputMessage": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"content": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/OpenAIResponseOutputMessageContent"
|
||||
}
|
||||
},
|
||||
"role": {
|
||||
"type": "string",
|
||||
"const": "assistant",
|
||||
"default": "assistant"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "message",
|
||||
"default": "message"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"id",
|
||||
"content",
|
||||
"role",
|
||||
"status",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessage"
|
||||
},
|
||||
"OpenAIResponseOutputMessageContent": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "output_text",
|
||||
"default": "output_text"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"text",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessageContentOutputText"
|
||||
},
|
||||
"OpenAIResponseOutputMessageWebSearchToolCall": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"id": {
|
||||
"type": "string"
|
||||
},
|
||||
"status": {
|
||||
"type": "string"
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"const": "web_search_call",
|
||||
"default": "web_search_call"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
"required": [
|
||||
"id",
|
||||
"status",
|
||||
"type"
|
||||
],
|
||||
"title": "OpenAIResponseOutputMessageWebSearchToolCall"
|
||||
},
|
||||
"OpenAIResponseObjectStream": {
|
||||
"oneOf": [
|
||||
{
|
||||
|
|
|
|||
280
docs/_static/llama-stack-spec.yaml
vendored
280
docs/_static/llama-stack-spec.yaml
vendored
|
|
@ -4534,34 +4534,37 @@ components:
|
|||
- event_type
|
||||
- turn_id
|
||||
title: AgentTurnResponseTurnStartPayload
|
||||
OpenAIResponseInputMessage:
|
||||
OpenAIResponseInput:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputFunctionToolCallOutput'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMessage'
|
||||
"OpenAIResponseInputFunctionToolCallOutput":
|
||||
type: object
|
||||
properties:
|
||||
content:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/OpenAIResponseInputMessageContent'
|
||||
role:
|
||||
oneOf:
|
||||
- type: string
|
||||
const: system
|
||||
- type: string
|
||||
const: developer
|
||||
- type: string
|
||||
const: user
|
||||
- type: string
|
||||
const: assistant
|
||||
call_id:
|
||||
type: string
|
||||
output:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: message
|
||||
default: message
|
||||
const: function_call_output
|
||||
default: function_call_output
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- content
|
||||
- role
|
||||
title: OpenAIResponseInputMessage
|
||||
- call_id
|
||||
- output
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseInputFunctionToolCallOutput
|
||||
description: >-
|
||||
This represents the output of a function call that gets passed back to the
|
||||
model.
|
||||
OpenAIResponseInputMessageContent:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputMessageContentText'
|
||||
|
|
@ -4609,6 +4612,71 @@ components:
|
|||
- type
|
||||
title: OpenAIResponseInputMessageContentText
|
||||
OpenAIResponseInputTool:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputToolWebSearch'
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputToolFileSearch'
|
||||
- $ref: '#/components/schemas/OpenAIResponseInputToolFunction'
|
||||
discriminator:
|
||||
propertyName: type
|
||||
mapping:
|
||||
web_search: '#/components/schemas/OpenAIResponseInputToolWebSearch'
|
||||
file_search: '#/components/schemas/OpenAIResponseInputToolFileSearch'
|
||||
function: '#/components/schemas/OpenAIResponseInputToolFunction'
|
||||
OpenAIResponseInputToolFileSearch:
|
||||
type: object
|
||||
properties:
|
||||
type:
|
||||
type: string
|
||||
const: file_search
|
||||
default: file_search
|
||||
vector_store_id:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
ranking_options:
|
||||
type: object
|
||||
properties:
|
||||
ranker:
|
||||
type: string
|
||||
score_threshold:
|
||||
type: number
|
||||
default: 0.0
|
||||
additionalProperties: false
|
||||
title: FileSearchRankingOptions
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
- vector_store_id
|
||||
title: OpenAIResponseInputToolFileSearch
|
||||
OpenAIResponseInputToolFunction:
|
||||
type: object
|
||||
properties:
|
||||
type:
|
||||
type: string
|
||||
const: function
|
||||
default: function
|
||||
name:
|
||||
type: string
|
||||
description:
|
||||
type: string
|
||||
parameters:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: 'null'
|
||||
- type: boolean
|
||||
- type: number
|
||||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
strict:
|
||||
type: boolean
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
- name
|
||||
title: OpenAIResponseInputToolFunction
|
||||
OpenAIResponseInputToolWebSearch:
|
||||
type: object
|
||||
properties:
|
||||
type:
|
||||
|
|
@ -4625,6 +4693,106 @@ components:
|
|||
required:
|
||||
- type
|
||||
title: OpenAIResponseInputToolWebSearch
|
||||
OpenAIResponseMessage:
|
||||
type: object
|
||||
properties:
|
||||
content:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/OpenAIResponseInputMessageContent'
|
||||
- type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/OpenAIResponseOutputMessageContent'
|
||||
role:
|
||||
oneOf:
|
||||
- type: string
|
||||
const: system
|
||||
- type: string
|
||||
const: developer
|
||||
- type: string
|
||||
const: user
|
||||
- type: string
|
||||
const: assistant
|
||||
type:
|
||||
type: string
|
||||
const: message
|
||||
default: message
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- content
|
||||
- role
|
||||
- type
|
||||
title: OpenAIResponseMessage
|
||||
description: >-
|
||||
Corresponds to the various Message types in the Responses API. They are all
|
||||
under one type because the Responses API gives them all the same "type" value,
|
||||
and there is no way to tell them apart in certain scenarios.
|
||||
OpenAIResponseOutputMessageContent:
|
||||
type: object
|
||||
properties:
|
||||
text:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: output_text
|
||||
default: output_text
|
||||
additionalProperties: false
|
||||
required:
|
||||
- text
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseOutputMessageContentOutputText
|
||||
"OpenAIResponseOutputMessageFunctionToolCall":
|
||||
type: object
|
||||
properties:
|
||||
arguments:
|
||||
type: string
|
||||
call_id:
|
||||
type: string
|
||||
name:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: function_call
|
||||
default: function_call
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- arguments
|
||||
- call_id
|
||||
- name
|
||||
- type
|
||||
- id
|
||||
- status
|
||||
title: >-
|
||||
OpenAIResponseOutputMessageFunctionToolCall
|
||||
"OpenAIResponseOutputMessageWebSearchToolCall":
|
||||
type: object
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: web_search_call
|
||||
default: web_search_call
|
||||
additionalProperties: false
|
||||
required:
|
||||
- id
|
||||
- status
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseOutputMessageWebSearchToolCall
|
||||
CreateOpenaiResponseRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -4633,7 +4801,7 @@ components:
|
|||
- type: string
|
||||
- type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/OpenAIResponseInputMessage'
|
||||
$ref: '#/components/schemas/OpenAIResponseInput'
|
||||
description: Input message(s) to create the response.
|
||||
model:
|
||||
type: string
|
||||
|
|
@ -4717,73 +4885,15 @@ components:
|
|||
title: OpenAIResponseObject
|
||||
OpenAIResponseOutput:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessage'
|
||||
- $ref: '#/components/schemas/OpenAIResponseMessage'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
|
||||
- $ref: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
discriminator:
|
||||
propertyName: type
|
||||
mapping:
|
||||
message: '#/components/schemas/OpenAIResponseOutputMessage'
|
||||
message: '#/components/schemas/OpenAIResponseMessage'
|
||||
web_search_call: '#/components/schemas/OpenAIResponseOutputMessageWebSearchToolCall'
|
||||
OpenAIResponseOutputMessage:
|
||||
type: object
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
content:
|
||||
type: array
|
||||
items:
|
||||
$ref: '#/components/schemas/OpenAIResponseOutputMessageContent'
|
||||
role:
|
||||
type: string
|
||||
const: assistant
|
||||
default: assistant
|
||||
status:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: message
|
||||
default: message
|
||||
additionalProperties: false
|
||||
required:
|
||||
- id
|
||||
- content
|
||||
- role
|
||||
- status
|
||||
- type
|
||||
title: OpenAIResponseOutputMessage
|
||||
OpenAIResponseOutputMessageContent:
|
||||
type: object
|
||||
properties:
|
||||
text:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: output_text
|
||||
default: output_text
|
||||
additionalProperties: false
|
||||
required:
|
||||
- text
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseOutputMessageContentOutputText
|
||||
"OpenAIResponseOutputMessageWebSearchToolCall":
|
||||
type: object
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
type:
|
||||
type: string
|
||||
const: web_search_call
|
||||
default: web_search_call
|
||||
additionalProperties: false
|
||||
required:
|
||||
- id
|
||||
- status
|
||||
- type
|
||||
title: >-
|
||||
OpenAIResponseOutputMessageWebSearchToolCall
|
||||
function_call: '#/components/schemas/OpenAIResponseOutputMessageFunctionToolCall'
|
||||
OpenAIResponseObjectStream:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/OpenAIResponseObjectStreamResponseCreated'
|
||||
|
|
|
|||
|
|
@ -1050,8 +1050,6 @@
|
|||
"text/html": [
|
||||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ToolGroup</span><span style=\"font-weight: bold\">(</span>\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">identifier</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'builtin::code_interpreter'</span>,\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'code-interpreter'</span>,\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">provider_resource_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'builtin::code_interpreter'</span>,\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'tool_group'</span>,\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">args</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>,\n",
|
||||
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">mcp_endpoint</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span>\n",
|
||||
|
|
@ -1061,7 +1059,6 @@
|
|||
"text/plain": [
|
||||
"\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n",
|
||||
"\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n",
|
||||
"\u001b[2;32m│ \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'code-interpreter'\u001b[0m,\n",
|
||||
"\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n",
|
||||
"\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n",
|
||||
"\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n",
|
||||
|
|
|
|||
|
|
@ -337,9 +337,6 @@
|
|||
" provider_id: tavily-search\n",
|
||||
" provider_type: remote::tavily-search\n",
|
||||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||||
" provider_id: code-interpreter\n",
|
||||
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::c</span>ode-interpreter\n",
|
||||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||||
" provider_id: rag-runtime\n",
|
||||
" provider_type: inline::rag-runtime\n",
|
||||
" - config: <span style=\"font-weight: bold\">{}</span>\n",
|
||||
|
|
@ -378,10 +375,6 @@
|
|||
" toolgroup_id: builtin::rag\n",
|
||||
"- args: null\n",
|
||||
" mcp_endpoint: null\n",
|
||||
" provider_id: code-interpreter\n",
|
||||
" toolgroup_id: builtin::code_interpreter\n",
|
||||
"- args: null\n",
|
||||
" mcp_endpoint: null\n",
|
||||
" provider_id: wolfram-alpha\n",
|
||||
" toolgroup_id: builtin::wolfram_alpha\n",
|
||||
"vector_dbs: <span style=\"font-weight: bold\">[]</span>\n",
|
||||
|
|
@ -617,9 +610,6 @@
|
|||
" provider_id: tavily-search\n",
|
||||
" provider_type: remote::tavily-search\n",
|
||||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||||
" provider_id: code-interpreter\n",
|
||||
" provider_type: inlin\u001b[1;92me::c\u001b[0mode-interpreter\n",
|
||||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||||
" provider_id: rag-runtime\n",
|
||||
" provider_type: inline::rag-runtime\n",
|
||||
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
|
||||
|
|
@ -658,10 +648,6 @@
|
|||
" toolgroup_id: builtin::rag\n",
|
||||
"- args: null\n",
|
||||
" mcp_endpoint: null\n",
|
||||
" provider_id: code-interpreter\n",
|
||||
" toolgroup_id: builtin::code_interpreter\n",
|
||||
"- args: null\n",
|
||||
" mcp_endpoint: null\n",
|
||||
" provider_id: wolfram-alpha\n",
|
||||
" toolgroup_id: builtin::wolfram_alpha\n",
|
||||
"vector_dbs: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ def main(output_dir: str):
|
|||
if return_type_errors:
|
||||
print("\nAPI Method Return Type Validation Errors:\n")
|
||||
for error in return_type_errors:
|
||||
print(error)
|
||||
print(error, file=sys.stderr)
|
||||
sys.exit(1)
|
||||
now = str(datetime.now())
|
||||
print(
|
||||
|
|
|
|||
|
|
@ -174,14 +174,25 @@ def _validate_list_parameters_contain_data(method) -> str | None:
|
|||
return "does not have a mandatory data attribute containing the list of objects"
|
||||
|
||||
|
||||
def _validate_has_ellipsis(method) -> str | None:
|
||||
source = inspect.getsource(method)
|
||||
if "..." not in source and not "NotImplementedError" in source:
|
||||
return "does not contain ellipsis (...) in its implementation"
|
||||
|
||||
|
||||
_VALIDATORS = {
|
||||
"GET": [
|
||||
_validate_api_method_return_type,
|
||||
_validate_list_parameters_contain_data,
|
||||
_validate_api_method_doesnt_return_list,
|
||||
_validate_has_ellipsis,
|
||||
],
|
||||
"DELETE": [
|
||||
_validate_api_delete_method_returns_none,
|
||||
_validate_has_ellipsis,
|
||||
],
|
||||
"POST": [
|
||||
_validate_has_ellipsis,
|
||||
],
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -165,34 +165,6 @@ all_tools = client.tools.list_tools()
|
|||
group_tools = client.tools.list_tools(toolgroup_id="search_tools")
|
||||
```
|
||||
|
||||
## Simple Example: Using an Agent with the Code-Interpreter Tool
|
||||
|
||||
```python
|
||||
from llama_stack_client import Agent
|
||||
|
||||
# Instantiate the AI agent with the given configuration
|
||||
agent = Agent(
|
||||
client,
|
||||
name="code-interpreter",
|
||||
description="A code interpreter agent for executing Python code snippets",
|
||||
instructions="""
|
||||
You are a highly reliable, concise, and precise assistant.
|
||||
Always show the generated code, never generate your own code, and never anticipate results.
|
||||
""",
|
||||
model="meta-llama/Llama-3.2-3B-Instruct",
|
||||
tools=["builtin::code_interpreter"],
|
||||
max_infer_iters=5,
|
||||
)
|
||||
|
||||
# Start a session
|
||||
session_id = agent.create_session("tool_session")
|
||||
|
||||
# Send a query to the AI agent for code execution
|
||||
response = agent.create_turn(
|
||||
messages=[{"role": "user", "content": "Run this code: print(3 ** 4 - 5 * 2)"}],
|
||||
session_id=session_id,
|
||||
)
|
||||
```
|
||||
## Simple Example 2: Using an Agent with the Web Search Tool
|
||||
1. Start by registering a Tavily API key at [Tavily](https://tavily.com/).
|
||||
2. [Optional] Provide the API key directly to the Llama Stack server
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ This guide will walk you through the process of adding a new API provider to Lla
|
|||
- Begin by reviewing the [core concepts](../concepts/index.md) of Llama Stack and choose the API your provider belongs to (Inference, Safety, VectorIO, etc.)
|
||||
- Determine the provider type ({repopath}`Remote::llama_stack/providers/remote` or {repopath}`Inline::llama_stack/providers/inline`). Remote providers make requests to external services, while inline providers execute implementation locally.
|
||||
- Add your provider to the appropriate {repopath}`Registry::llama_stack/providers/registry/`. Specify pip dependencies necessary.
|
||||
- Update any distribution {repopath}`Templates::llama_stack/templates/` build.yaml and run.yaml files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
|
||||
- Update any distribution {repopath}`Templates::llama_stack/templates/` `build.yaml` and `run.yaml` files if they should include your provider by default. Run {repopath}`./scripts/distro_codegen.py` if necessary. Note that `distro_codegen.py` will fail if the new provider causes any distribution template to attempt to import provider-specific dependencies. This usually means the distribution's `get_distribution_template()` code path should only import any necessary Config or model alias definitions from each provider and not the provider's actual implementation.
|
||||
|
||||
|
||||
Here are some example PRs to help you get started:
|
||||
|
|
@ -33,6 +33,7 @@ Note that each provider's `sample_run_config()` method (in the configuration cla
|
|||
|
||||
Unit tests are located in {repopath}`tests/unit`. Provider-specific unit tests are located in {repopath}`tests/unit/providers`. These tests are all run automatically as part of the CI process.
|
||||
|
||||
Consult {repopath}`tests/unit/README.md` for more details on how to run the tests manually.
|
||||
|
||||
### 3. Additional end-to-end testing
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ The `llamastack/distribution-watsonx` distribution consists of the following pro
|
|||
| agents | `inline::meta-reference` |
|
||||
| datasetio | `remote::huggingface`, `inline::localfs` |
|
||||
| eval | `inline::meta-reference` |
|
||||
| inference | `remote::watsonx` |
|
||||
| inference | `remote::watsonx`, `inline::sentence-transformers` |
|
||||
| safety | `inline::llama-guard` |
|
||||
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
|
||||
| telemetry | `inline::meta-reference` |
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ The `llamastack/distribution-dell` distribution consists of the following provid
|
|||
| safety | `inline::llama-guard` |
|
||||
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
|
||||
| telemetry | `inline::meta-reference` |
|
||||
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
|
||||
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime` |
|
||||
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -53,7 +53,7 @@ Here's a list of known external providers that you can use with Llama Stack:
|
|||
| Name | Description | API | Type | Repository |
|
||||
|------|-------------|-----|------|------------|
|
||||
| KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
|
||||
| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
|
||||
| KubeFlow Pipelines | Train models with KubeFlow Pipelines | Post Training | Inline **and** Remote | [llama-stack-provider-kfp-trainer](https://github.com/opendatahub-io/llama-stack-provider-kfp-trainer) |
|
||||
| RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) |
|
||||
| TrustyAI LM-Eval | Evaluate models with TrustyAI LM-Eval | Eval | Remote | [llama-stack-provider-lmeval](https://github.com/trustyai-explainability/llama-stack-provider-lmeval) |
|
||||
|
||||
|
|
|
|||
|
|
@ -253,8 +253,6 @@ llama-stack-client toolgroups list
|
|||
+---------------------------+------------------+------+---------------+
|
||||
| identifier | provider_id | args | mcp_endpoint |
|
||||
+===========================+==================+======+===============+
|
||||
| builtin::code_interpreter | code-interpreter | None | None |
|
||||
+---------------------------+------------------+------+---------------+
|
||||
| builtin::rag | rag-runtime | None | None |
|
||||
+---------------------------+------------------+------+---------------+
|
||||
| builtin::websearch | tavily-search | None | None |
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ from llama_stack.apis.tools import ToolDef
|
|||
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
|
||||
|
||||
from .openai_responses import (
|
||||
OpenAIResponseInputMessage,
|
||||
OpenAIResponseInput,
|
||||
OpenAIResponseInputTool,
|
||||
OpenAIResponseObject,
|
||||
OpenAIResponseObjectStream,
|
||||
|
|
@ -415,6 +415,7 @@ class Agents(Protocol):
|
|||
:returns: If stream=False, returns a Turn object.
|
||||
If stream=True, returns an SSE event stream of AgentTurnResponseStreamChunk
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(
|
||||
route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume",
|
||||
|
|
@ -592,7 +593,7 @@ class Agents(Protocol):
|
|||
@webmethod(route="/openai/v1/responses", method="POST")
|
||||
async def create_openai_response(
|
||||
self,
|
||||
input: str | list[OpenAIResponseInputMessage],
|
||||
input: str | list[OpenAIResponseInput],
|
||||
model: str,
|
||||
previous_response_id: str | None = None,
|
||||
store: bool | None = True,
|
||||
|
|
@ -606,3 +607,4 @@ class Agents(Protocol):
|
|||
:param model: The underlying LLM used for completions.
|
||||
:param previous_response_id: (Optional) if specified, the new response will be a continuation of the previous response. This can be used to easily fork-off new responses from existing responses.
|
||||
"""
|
||||
...
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import Annotated, Literal
|
||||
from typing import Annotated, Any, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
|
@ -17,6 +17,28 @@ class OpenAIResponseError(BaseModel):
|
|||
message: str
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputMessageContentText(BaseModel):
|
||||
text: str
|
||||
type: Literal["input_text"] = "input_text"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputMessageContentImage(BaseModel):
|
||||
detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
|
||||
type: Literal["input_image"] = "input_image"
|
||||
# TODO: handle file_id
|
||||
image_url: str | None = None
|
||||
|
||||
|
||||
# TODO: handle file content types
|
||||
OpenAIResponseInputMessageContent = Annotated[
|
||||
OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseOutputMessageContentOutputText(BaseModel):
|
||||
text: str
|
||||
|
|
@ -31,13 +53,22 @@ register_schema(OpenAIResponseOutputMessageContent, name="OpenAIResponseOutputMe
|
|||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseOutputMessage(BaseModel):
|
||||
id: str
|
||||
content: list[OpenAIResponseOutputMessageContent]
|
||||
role: Literal["assistant"] = "assistant"
|
||||
status: str
|
||||
class OpenAIResponseMessage(BaseModel):
|
||||
"""
|
||||
Corresponds to the various Message types in the Responses API.
|
||||
They are all under one type because the Responses API gives them all
|
||||
the same "type" value, and there is no way to tell them apart in certain
|
||||
scenarios.
|
||||
"""
|
||||
|
||||
content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent]
|
||||
role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
|
||||
type: Literal["message"] = "message"
|
||||
|
||||
# The fields below are not used in all scenarios, but are required in others.
|
||||
id: str | None = None
|
||||
status: str | None = None
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
|
||||
|
|
@ -46,8 +77,18 @@ class OpenAIResponseOutputMessageWebSearchToolCall(BaseModel):
|
|||
type: Literal["web_search_call"] = "web_search_call"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseOutputMessageFunctionToolCall(BaseModel):
|
||||
arguments: str
|
||||
call_id: str
|
||||
name: str
|
||||
type: Literal["function_call"] = "function_call"
|
||||
id: str
|
||||
status: str
|
||||
|
||||
|
||||
OpenAIResponseOutput = Annotated[
|
||||
OpenAIResponseOutputMessage | OpenAIResponseOutputMessageWebSearchToolCall,
|
||||
OpenAIResponseMessage | OpenAIResponseOutputMessageWebSearchToolCall | OpenAIResponseOutputMessageFunctionToolCall,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
register_schema(OpenAIResponseOutput, name="OpenAIResponseOutput")
|
||||
|
|
@ -90,32 +131,29 @@ register_schema(OpenAIResponseObjectStream, name="OpenAIResponseObjectStream")
|
|||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputMessageContentText(BaseModel):
|
||||
text: str
|
||||
type: Literal["input_text"] = "input_text"
|
||||
class OpenAIResponseInputFunctionToolCallOutput(BaseModel):
|
||||
"""
|
||||
This represents the output of a function call that gets passed back to the model.
|
||||
"""
|
||||
|
||||
call_id: str
|
||||
output: str
|
||||
type: Literal["function_call_output"] = "function_call_output"
|
||||
id: str | None = None
|
||||
status: str | None = None
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputMessageContentImage(BaseModel):
|
||||
detail: Literal["low"] | Literal["high"] | Literal["auto"] = "auto"
|
||||
type: Literal["input_image"] = "input_image"
|
||||
# TODO: handle file_id
|
||||
image_url: str | None = None
|
||||
|
||||
|
||||
# TODO: handle file content types
|
||||
OpenAIResponseInputMessageContent = Annotated[
|
||||
OpenAIResponseInputMessageContentText | OpenAIResponseInputMessageContentImage,
|
||||
Field(discriminator="type"),
|
||||
OpenAIResponseInput = Annotated[
|
||||
# Responses API allows output messages to be passed in as input
|
||||
OpenAIResponseOutputMessageWebSearchToolCall
|
||||
| OpenAIResponseOutputMessageFunctionToolCall
|
||||
| OpenAIResponseInputFunctionToolCallOutput
|
||||
|
|
||||
# Fallback to the generic message type as a last resort
|
||||
OpenAIResponseMessage,
|
||||
Field(union_mode="left_to_right"),
|
||||
]
|
||||
register_schema(OpenAIResponseInputMessageContent, name="OpenAIResponseInputMessageContent")
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputMessage(BaseModel):
|
||||
content: str | list[OpenAIResponseInputMessageContent]
|
||||
role: Literal["system"] | Literal["developer"] | Literal["user"] | Literal["assistant"]
|
||||
type: Literal["message"] | None = "message"
|
||||
register_schema(OpenAIResponseInput, name="OpenAIResponseInput")
|
||||
|
||||
|
||||
@json_schema_type
|
||||
|
|
@ -126,8 +164,35 @@ class OpenAIResponseInputToolWebSearch(BaseModel):
|
|||
# TODO: add user_location
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputToolFunction(BaseModel):
|
||||
type: Literal["function"] = "function"
|
||||
name: str
|
||||
description: str | None = None
|
||||
parameters: dict[str, Any] | None
|
||||
strict: bool | None = None
|
||||
|
||||
|
||||
class FileSearchRankingOptions(BaseModel):
|
||||
ranker: str | None = None
|
||||
score_threshold: float | None = Field(default=0.0, ge=0.0, le=1.0)
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class OpenAIResponseInputToolFileSearch(BaseModel):
|
||||
type: Literal["file_search"] = "file_search"
|
||||
vector_store_id: list[str]
|
||||
ranking_options: FileSearchRankingOptions | None = None
|
||||
# TODO: add filters
|
||||
|
||||
|
||||
OpenAIResponseInputTool = Annotated[
|
||||
OpenAIResponseInputToolWebSearch,
|
||||
OpenAIResponseInputToolWebSearch | OpenAIResponseInputToolFileSearch | OpenAIResponseInputToolFunction,
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
register_schema(OpenAIResponseInputTool, name="OpenAIResponseInputTool")
|
||||
|
||||
|
||||
class OpenAIResponseInputItemList(BaseModel):
|
||||
data: list[OpenAIResponseInput]
|
||||
object: Literal["list"] = "list"
|
||||
|
|
|
|||
|
|
@ -95,6 +95,7 @@ class Eval(Protocol):
|
|||
:param benchmark_config: The configuration for the benchmark.
|
||||
:return: The job that was created to run the evaluation.
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/eval/benchmarks/{benchmark_id}/evaluations", method="POST")
|
||||
async def evaluate_rows(
|
||||
|
|
@ -112,6 +113,7 @@ class Eval(Protocol):
|
|||
:param benchmark_config: The configuration for the benchmark.
|
||||
:return: EvaluateResponse object containing generations and scores
|
||||
"""
|
||||
...
|
||||
|
||||
@webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET")
|
||||
async def job_status(self, benchmark_id: str, job_id: str) -> Job:
|
||||
|
|
@ -140,3 +142,4 @@ class Eval(Protocol):
|
|||
:param job_id: The ID of the job to get the result of.
|
||||
:return: The result of the job.
|
||||
"""
|
||||
...
|
||||
|
|
|
|||
|
|
@ -249,6 +249,10 @@ class ServerConfig(BaseModel):
|
|||
default=None,
|
||||
description="Path to TLS key file for HTTPS",
|
||||
)
|
||||
tls_cafile: str | None = Field(
|
||||
default=None,
|
||||
description="Path to TLS CA file for HTTPS with mutual TLS authentication",
|
||||
)
|
||||
auth: AuthenticationConfig | None = Field(
|
||||
default=None,
|
||||
description="Authentication configuration for the server",
|
||||
|
|
|
|||
|
|
@ -99,7 +99,7 @@ class ProviderImpl(Providers):
|
|||
try:
|
||||
health = await asyncio.wait_for(impl.health(), timeout=timeout)
|
||||
return api_name, health
|
||||
except asyncio.TimeoutError:
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
return (
|
||||
api_name,
|
||||
HealthResponse(
|
||||
|
|
|
|||
|
|
@ -630,7 +630,7 @@ class InferenceRouter(Inference):
|
|||
continue
|
||||
health = await asyncio.wait_for(impl.health(), timeout=timeout)
|
||||
health_statuses[provider_id] = health
|
||||
except asyncio.TimeoutError:
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
health_statuses[provider_id] = HealthResponse(
|
||||
status=HealthStatus.ERROR,
|
||||
message=f"Health check timed out after {timeout} seconds",
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import asyncio
|
|||
import inspect
|
||||
import json
|
||||
import os
|
||||
import ssl
|
||||
import sys
|
||||
import traceback
|
||||
import warnings
|
||||
|
|
@ -17,6 +18,7 @@ from importlib.metadata import version as parse_version
|
|||
from pathlib import Path
|
||||
from typing import Annotated, Any
|
||||
|
||||
import rich.pretty
|
||||
import yaml
|
||||
from fastapi import Body, FastAPI, HTTPException, Request
|
||||
from fastapi import Path as FastapiPath
|
||||
|
|
@ -114,7 +116,7 @@ def translate_exception(exc: Exception) -> HTTPException | RequestValidationErro
|
|||
return HTTPException(status_code=400, detail=str(exc))
|
||||
elif isinstance(exc, PermissionError):
|
||||
return HTTPException(status_code=403, detail=f"Permission denied: {str(exc)}")
|
||||
elif isinstance(exc, TimeoutError):
|
||||
elif isinstance(exc, asyncio.TimeoutError | TimeoutError):
|
||||
return HTTPException(status_code=504, detail=f"Operation timed out: {str(exc)}")
|
||||
elif isinstance(exc, NotImplementedError):
|
||||
return HTTPException(status_code=501, detail=f"Not implemented: {str(exc)}")
|
||||
|
|
@ -139,7 +141,7 @@ async def shutdown(app):
|
|||
await asyncio.wait_for(impl.shutdown(), timeout=5)
|
||||
else:
|
||||
logger.warning("No shutdown method for %s", impl_name)
|
||||
except asyncio.TimeoutError:
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
logger.exception("Shutdown timeout for %s ", impl_name, exc_info=True)
|
||||
except (Exception, asyncio.CancelledError) as e:
|
||||
logger.exception("Failed to shutdown %s: %s", impl_name, {e})
|
||||
|
|
@ -186,11 +188,30 @@ async def sse_generator(event_gen_coroutine):
|
|||
)
|
||||
|
||||
|
||||
async def log_request_pre_validation(request: Request):
|
||||
if request.method in ("POST", "PUT", "PATCH"):
|
||||
try:
|
||||
body_bytes = await request.body()
|
||||
if body_bytes:
|
||||
try:
|
||||
parsed_body = json.loads(body_bytes.decode())
|
||||
log_output = rich.pretty.pretty_repr(parsed_body)
|
||||
except (json.JSONDecodeError, UnicodeDecodeError):
|
||||
log_output = repr(body_bytes)
|
||||
logger.debug(f"Incoming raw request body for {request.method} {request.url.path}:\n{log_output}")
|
||||
else:
|
||||
logger.debug(f"Incoming {request.method} {request.url.path} request with empty body.")
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not read or log request body for {request.method} {request.url.path}: {e}")
|
||||
|
||||
|
||||
def create_dynamic_typed_route(func: Any, method: str, route: str):
|
||||
async def endpoint(request: Request, **kwargs):
|
||||
# Get auth attributes from the request scope
|
||||
user_attributes = request.scope.get("user_attributes", {})
|
||||
|
||||
await log_request_pre_validation(request)
|
||||
|
||||
# Use context manager with both provider data and auth attributes
|
||||
with request_provider_data_context(request.headers, user_attributes):
|
||||
is_streaming = is_streaming_request(func.__name__, request, **kwargs)
|
||||
|
|
@ -484,7 +505,14 @@ def main(args: argparse.Namespace | None = None):
|
|||
"ssl_keyfile": keyfile,
|
||||
"ssl_certfile": certfile,
|
||||
}
|
||||
logger.info(f"HTTPS enabled with certificates:\n Key: {keyfile}\n Cert: {certfile}")
|
||||
if config.server.tls_cafile:
|
||||
ssl_config["ssl_ca_certs"] = config.server.tls_cafile
|
||||
ssl_config["ssl_cert_reqs"] = ssl.CERT_REQUIRED
|
||||
logger.info(
|
||||
f"HTTPS enabled with certificates:\n Key: {keyfile}\n Cert: {certfile}\n CA: {config.server.tls_cafile}"
|
||||
)
|
||||
else:
|
||||
logger.info(f"HTTPS enabled with certificates:\n Key: {keyfile}\n Cert: {certfile}")
|
||||
|
||||
listen_host = ["::", "0.0.0.0"] if not config.server.disable_ipv6 else "0.0.0.0"
|
||||
logger.info(f"Listening on {listen_host}:{port}")
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ from llama_stack.apis.agents import (
|
|||
AgentTurnCreateRequest,
|
||||
AgentTurnResumeRequest,
|
||||
Document,
|
||||
OpenAIResponseInputMessage,
|
||||
OpenAIResponseInput,
|
||||
OpenAIResponseInputTool,
|
||||
OpenAIResponseObject,
|
||||
Session,
|
||||
|
|
@ -37,8 +37,8 @@ from llama_stack.apis.inference import (
|
|||
from llama_stack.apis.safety import Safety
|
||||
from llama_stack.apis.tools import ToolGroups, ToolRuntime
|
||||
from llama_stack.apis.vector_io import VectorIO
|
||||
from llama_stack.providers.utils.datasetio.pagination import paginate_records
|
||||
from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_impl
|
||||
from llama_stack.providers.utils.pagination import paginate_records
|
||||
|
||||
from .agent_instance import ChatAgent
|
||||
from .config import MetaReferenceAgentsImplConfig
|
||||
|
|
@ -311,7 +311,7 @@ class MetaReferenceAgentsImpl(Agents):
|
|||
|
||||
async def create_openai_response(
|
||||
self,
|
||||
input: str | list[OpenAIResponseInputMessage],
|
||||
input: str | list[OpenAIResponseInput],
|
||||
model: str,
|
||||
previous_response_id: str | None = None,
|
||||
store: bool | None = True,
|
||||
|
|
|
|||
|
|
@ -10,19 +10,26 @@ from collections.abc import AsyncIterator
|
|||
from typing import cast
|
||||
|
||||
from openai.types.chat import ChatCompletionToolParam
|
||||
from pydantic import BaseModel
|
||||
|
||||
from llama_stack.apis.agents.openai_responses import (
|
||||
OpenAIResponseInputMessage,
|
||||
OpenAIResponseInput,
|
||||
OpenAIResponseInputFunctionToolCallOutput,
|
||||
OpenAIResponseInputItemList,
|
||||
OpenAIResponseInputMessageContent,
|
||||
OpenAIResponseInputMessageContentImage,
|
||||
OpenAIResponseInputMessageContentText,
|
||||
OpenAIResponseInputTool,
|
||||
OpenAIResponseInputToolFunction,
|
||||
OpenAIResponseMessage,
|
||||
OpenAIResponseObject,
|
||||
OpenAIResponseObjectStream,
|
||||
OpenAIResponseObjectStreamResponseCompleted,
|
||||
OpenAIResponseObjectStreamResponseCreated,
|
||||
OpenAIResponseOutput,
|
||||
OpenAIResponseOutputMessage,
|
||||
OpenAIResponseOutputMessageContent,
|
||||
OpenAIResponseOutputMessageContentOutputText,
|
||||
OpenAIResponseOutputMessageFunctionToolCall,
|
||||
OpenAIResponseOutputMessageWebSearchToolCall,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import (
|
||||
|
|
@ -32,10 +39,13 @@ from llama_stack.apis.inference.inference import (
|
|||
OpenAIChatCompletionContentPartImageParam,
|
||||
OpenAIChatCompletionContentPartParam,
|
||||
OpenAIChatCompletionContentPartTextParam,
|
||||
OpenAIChatCompletionToolCall,
|
||||
OpenAIChatCompletionToolCallFunction,
|
||||
OpenAIChoice,
|
||||
OpenAIDeveloperMessageParam,
|
||||
OpenAIImageURL,
|
||||
OpenAIMessageParam,
|
||||
OpenAISystemMessageParam,
|
||||
OpenAIToolMessageParam,
|
||||
OpenAIUserMessageParam,
|
||||
)
|
||||
|
|
@ -50,31 +60,110 @@ logger = get_logger(name=__name__, category="openai_responses")
|
|||
OPENAI_RESPONSES_PREFIX = "openai_responses:"
|
||||
|
||||
|
||||
async def _previous_response_to_messages(previous_response: OpenAIResponseObject) -> list[OpenAIMessageParam]:
|
||||
async def _convert_response_content_to_chat_content(
|
||||
content: str | list[OpenAIResponseInputMessageContent] | list[OpenAIResponseOutputMessageContent],
|
||||
) -> str | list[OpenAIChatCompletionContentPartParam]:
|
||||
"""
|
||||
Convert the content parts from an OpenAI Response API request into OpenAI Chat Completion content parts.
|
||||
|
||||
The content schemas of each API look similar, but are not exactly the same.
|
||||
"""
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
|
||||
converted_parts = []
|
||||
for content_part in content:
|
||||
if isinstance(content_part, OpenAIResponseInputMessageContentText):
|
||||
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
|
||||
elif isinstance(content_part, OpenAIResponseOutputMessageContentOutputText):
|
||||
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part.text))
|
||||
elif isinstance(content_part, OpenAIResponseInputMessageContentImage):
|
||||
if content_part.image_url:
|
||||
image_url = OpenAIImageURL(url=content_part.image_url, detail=content_part.detail)
|
||||
converted_parts.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
|
||||
elif isinstance(content_part, str):
|
||||
converted_parts.append(OpenAIChatCompletionContentPartTextParam(text=content_part))
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Llama Stack OpenAI Responses does not yet support content type '{type(content_part)}' in this context"
|
||||
)
|
||||
return converted_parts
|
||||
|
||||
|
||||
async def _convert_response_input_to_chat_messages(
|
||||
input: str | list[OpenAIResponseInput],
|
||||
) -> list[OpenAIMessageParam]:
|
||||
"""
|
||||
Convert the input from an OpenAI Response API request into OpenAI Chat Completion messages.
|
||||
"""
|
||||
messages: list[OpenAIMessageParam] = []
|
||||
for output_message in previous_response.output:
|
||||
if isinstance(output_message, OpenAIResponseOutputMessage):
|
||||
messages.append(OpenAIAssistantMessageParam(content=output_message.content[0].text))
|
||||
if isinstance(input, list):
|
||||
for input_item in input:
|
||||
if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
|
||||
messages.append(
|
||||
OpenAIToolMessageParam(
|
||||
content=input_item.output,
|
||||
tool_call_id=input_item.call_id,
|
||||
)
|
||||
)
|
||||
elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
|
||||
tool_call = OpenAIChatCompletionToolCall(
|
||||
index=0,
|
||||
id=input_item.call_id,
|
||||
function=OpenAIChatCompletionToolCallFunction(
|
||||
name=input_item.name,
|
||||
arguments=input_item.arguments,
|
||||
),
|
||||
)
|
||||
messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
|
||||
else:
|
||||
content = await _convert_response_content_to_chat_content(input_item.content)
|
||||
message_type = await _get_message_type_by_role(input_item.role)
|
||||
if message_type is None:
|
||||
raise ValueError(
|
||||
f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
|
||||
)
|
||||
messages.append(message_type(content=content))
|
||||
else:
|
||||
messages.append(OpenAIUserMessageParam(content=input))
|
||||
return messages
|
||||
|
||||
|
||||
async def _openai_choices_to_output_messages(choices: list[OpenAIChoice]) -> list[OpenAIResponseOutputMessage]:
|
||||
output_messages = []
|
||||
for choice in choices:
|
||||
output_content = ""
|
||||
if isinstance(choice.message.content, str):
|
||||
output_content = choice.message.content
|
||||
elif isinstance(choice.message.content, OpenAIChatCompletionContentPartTextParam):
|
||||
output_content = choice.message.content.text
|
||||
# TODO: handle image content
|
||||
output_messages.append(
|
||||
OpenAIResponseOutputMessage(
|
||||
id=f"msg_{uuid.uuid4()}",
|
||||
content=[OpenAIResponseOutputMessageContentOutputText(text=output_content)],
|
||||
status="completed",
|
||||
)
|
||||
async def _convert_chat_choice_to_response_message(choice: OpenAIChoice) -> OpenAIResponseMessage:
|
||||
"""
|
||||
Convert an OpenAI Chat Completion choice into an OpenAI Response output message.
|
||||
"""
|
||||
output_content = ""
|
||||
if isinstance(choice.message.content, str):
|
||||
output_content = choice.message.content
|
||||
elif isinstance(choice.message.content, OpenAIChatCompletionContentPartTextParam):
|
||||
output_content = choice.message.content.text
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Llama Stack OpenAI Responses does not yet support output content type: {type(choice.message.content)}"
|
||||
)
|
||||
return output_messages
|
||||
|
||||
return OpenAIResponseMessage(
|
||||
id=f"msg_{uuid.uuid4()}",
|
||||
content=[OpenAIResponseOutputMessageContentOutputText(text=output_content)],
|
||||
status="completed",
|
||||
role="assistant",
|
||||
)
|
||||
|
||||
|
||||
async def _get_message_type_by_role(role: str):
|
||||
role_to_type = {
|
||||
"user": OpenAIUserMessageParam,
|
||||
"system": OpenAISystemMessageParam,
|
||||
"assistant": OpenAIAssistantMessageParam,
|
||||
"developer": OpenAIDeveloperMessageParam,
|
||||
}
|
||||
return role_to_type.get(role)
|
||||
|
||||
|
||||
class OpenAIResponsePreviousResponseWithInputItems(BaseModel):
|
||||
input_items: OpenAIResponseInputItemList
|
||||
response: OpenAIResponseObject
|
||||
|
||||
|
||||
class OpenAIResponsesImpl:
|
||||
|
|
@ -90,19 +179,45 @@ class OpenAIResponsesImpl:
|
|||
self.tool_groups_api = tool_groups_api
|
||||
self.tool_runtime_api = tool_runtime_api
|
||||
|
||||
async def get_openai_response(
|
||||
self,
|
||||
id: str,
|
||||
) -> OpenAIResponseObject:
|
||||
async def _get_previous_response_with_input(self, id: str) -> OpenAIResponsePreviousResponseWithInputItems:
|
||||
key = f"{OPENAI_RESPONSES_PREFIX}{id}"
|
||||
response_json = await self.persistence_store.get(key=key)
|
||||
if response_json is None:
|
||||
raise ValueError(f"OpenAI response with id '{id}' not found")
|
||||
return OpenAIResponseObject.model_validate_json(response_json)
|
||||
return OpenAIResponsePreviousResponseWithInputItems.model_validate_json(response_json)
|
||||
|
||||
async def _prepend_previous_response(
|
||||
self, input: str | list[OpenAIResponseInput], previous_response_id: str | None = None
|
||||
):
|
||||
if previous_response_id:
|
||||
previous_response_with_input = await self._get_previous_response_with_input(previous_response_id)
|
||||
|
||||
# previous response input items
|
||||
new_input_items = previous_response_with_input.input_items.data
|
||||
|
||||
# previous response output items
|
||||
new_input_items.extend(previous_response_with_input.response.output)
|
||||
|
||||
# new input items from the current request
|
||||
if isinstance(input, str):
|
||||
new_input_items.append(OpenAIResponseMessage(content=input, role="user"))
|
||||
else:
|
||||
new_input_items.extend(input)
|
||||
|
||||
input = new_input_items
|
||||
|
||||
return input
|
||||
|
||||
async def get_openai_response(
|
||||
self,
|
||||
id: str,
|
||||
) -> OpenAIResponseObject:
|
||||
response_with_input = await self._get_previous_response_with_input(id)
|
||||
return response_with_input.response
|
||||
|
||||
async def create_openai_response(
|
||||
self,
|
||||
input: str | list[OpenAIResponseInputMessage],
|
||||
input: str | list[OpenAIResponseInput],
|
||||
model: str,
|
||||
previous_response_id: str | None = None,
|
||||
store: bool | None = True,
|
||||
|
|
@ -112,31 +227,8 @@ class OpenAIResponsesImpl:
|
|||
):
|
||||
stream = False if stream is None else stream
|
||||
|
||||
messages: list[OpenAIMessageParam] = []
|
||||
if previous_response_id:
|
||||
previous_response = await self.get_openai_response(previous_response_id)
|
||||
messages.extend(await _previous_response_to_messages(previous_response))
|
||||
# TODO: refactor this user_content parsing out into a separate method
|
||||
user_content: str | list[OpenAIChatCompletionContentPartParam] = ""
|
||||
if isinstance(input, list):
|
||||
user_content = []
|
||||
for user_input in input:
|
||||
if isinstance(user_input.content, list):
|
||||
for user_input_content in user_input.content:
|
||||
if isinstance(user_input_content, OpenAIResponseInputMessageContentText):
|
||||
user_content.append(OpenAIChatCompletionContentPartTextParam(text=user_input_content.text))
|
||||
elif isinstance(user_input_content, OpenAIResponseInputMessageContentImage):
|
||||
if user_input_content.image_url:
|
||||
image_url = OpenAIImageURL(
|
||||
url=user_input_content.image_url, detail=user_input_content.detail
|
||||
)
|
||||
user_content.append(OpenAIChatCompletionContentPartImageParam(image_url=image_url))
|
||||
else:
|
||||
user_content.append(OpenAIChatCompletionContentPartTextParam(text=user_input.content))
|
||||
else:
|
||||
user_content = input
|
||||
messages.append(OpenAIUserMessageParam(content=user_content))
|
||||
|
||||
input = await self._prepend_previous_response(input, previous_response_id)
|
||||
messages = await _convert_response_input_to_chat_messages(input)
|
||||
chat_tools = await self._convert_response_tools_to_chat_tools(tools) if tools else None
|
||||
chat_response = await self.inference_api.openai_chat_completion(
|
||||
model=model,
|
||||
|
|
@ -150,6 +242,7 @@ class OpenAIResponsesImpl:
|
|||
# TODO: refactor this into a separate method that handles streaming
|
||||
chat_response_id = ""
|
||||
chat_response_content = []
|
||||
chat_response_tool_calls: dict[int, OpenAIChatCompletionToolCall] = {}
|
||||
# TODO: these chunk_ fields are hacky and only take the last chunk into account
|
||||
chunk_created = 0
|
||||
chunk_model = ""
|
||||
|
|
@ -163,7 +256,26 @@ class OpenAIResponsesImpl:
|
|||
chat_response_content.append(chunk_choice.delta.content or "")
|
||||
if chunk_choice.finish_reason:
|
||||
chunk_finish_reason = chunk_choice.finish_reason
|
||||
assistant_message = OpenAIAssistantMessageParam(content="".join(chat_response_content))
|
||||
|
||||
# Aggregate tool call arguments across chunks, using their index as the aggregation key
|
||||
if chunk_choice.delta.tool_calls:
|
||||
for tool_call in chunk_choice.delta.tool_calls:
|
||||
response_tool_call = chat_response_tool_calls.get(tool_call.index, None)
|
||||
if response_tool_call:
|
||||
response_tool_call.function.arguments += tool_call.function.arguments
|
||||
else:
|
||||
response_tool_call = OpenAIChatCompletionToolCall(**tool_call.model_dump())
|
||||
chat_response_tool_calls[tool_call.index] = response_tool_call
|
||||
|
||||
# Convert the dict of tool calls by index to a list of tool calls to pass back in our response
|
||||
if chat_response_tool_calls:
|
||||
tool_calls = [chat_response_tool_calls[i] for i in sorted(chat_response_tool_calls.keys())]
|
||||
else:
|
||||
tool_calls = None
|
||||
assistant_message = OpenAIAssistantMessageParam(
|
||||
content="".join(chat_response_content),
|
||||
tool_calls=tool_calls,
|
||||
)
|
||||
chat_response = OpenAIChatCompletion(
|
||||
id=chat_response_id,
|
||||
choices=[
|
||||
|
|
@ -181,12 +293,26 @@ class OpenAIResponsesImpl:
|
|||
chat_response = OpenAIChatCompletion(**chat_response.model_dump())
|
||||
|
||||
output_messages: list[OpenAIResponseOutput] = []
|
||||
if chat_response.choices[0].message.tool_calls:
|
||||
output_messages.extend(
|
||||
await self._execute_tool_and_return_final_output(model, stream, chat_response, messages, temperature)
|
||||
)
|
||||
else:
|
||||
output_messages.extend(await _openai_choices_to_output_messages(chat_response.choices))
|
||||
for choice in chat_response.choices:
|
||||
if choice.message.tool_calls and tools:
|
||||
# Assume if the first tool is a function, all tools are functions
|
||||
if isinstance(tools[0], OpenAIResponseInputToolFunction):
|
||||
for tool_call in choice.message.tool_calls:
|
||||
output_messages.append(
|
||||
OpenAIResponseOutputMessageFunctionToolCall(
|
||||
arguments=tool_call.function.arguments or "",
|
||||
call_id=tool_call.id,
|
||||
name=tool_call.function.name or "",
|
||||
id=f"fc_{uuid.uuid4()}",
|
||||
status="completed",
|
||||
)
|
||||
)
|
||||
else:
|
||||
output_messages.extend(
|
||||
await self._execute_tool_and_return_final_output(model, stream, choice, messages, temperature)
|
||||
)
|
||||
else:
|
||||
output_messages.append(await _convert_chat_choice_to_response_message(choice))
|
||||
response = OpenAIResponseObject(
|
||||
created_at=chat_response.created,
|
||||
id=f"resp-{uuid.uuid4()}",
|
||||
|
|
@ -195,13 +321,43 @@ class OpenAIResponsesImpl:
|
|||
status="completed",
|
||||
output=output_messages,
|
||||
)
|
||||
logger.debug(f"OpenAI Responses response: {response}")
|
||||
|
||||
if store:
|
||||
# Store in kvstore
|
||||
|
||||
new_input_id = f"msg_{uuid.uuid4()}"
|
||||
if isinstance(input, str):
|
||||
# synthesize a message from the input string
|
||||
input_content = OpenAIResponseInputMessageContentText(text=input)
|
||||
input_content_item = OpenAIResponseMessage(
|
||||
role="user",
|
||||
content=[input_content],
|
||||
id=new_input_id,
|
||||
)
|
||||
input_items_data = [input_content_item]
|
||||
else:
|
||||
# we already have a list of messages
|
||||
input_items_data = []
|
||||
for input_item in input:
|
||||
if isinstance(input_item, OpenAIResponseMessage):
|
||||
# These may or may not already have an id, so dump to dict, check for id, and add if missing
|
||||
input_item_dict = input_item.model_dump()
|
||||
if "id" not in input_item_dict:
|
||||
input_item_dict["id"] = new_input_id
|
||||
input_items_data.append(OpenAIResponseMessage(**input_item_dict))
|
||||
else:
|
||||
input_items_data.append(input_item)
|
||||
|
||||
input_items = OpenAIResponseInputItemList(data=input_items_data)
|
||||
prev_response = OpenAIResponsePreviousResponseWithInputItems(
|
||||
input_items=input_items,
|
||||
response=response,
|
||||
)
|
||||
key = f"{OPENAI_RESPONSES_PREFIX}{response.id}"
|
||||
await self.persistence_store.set(
|
||||
key=key,
|
||||
value=response.model_dump_json(),
|
||||
value=prev_response.model_dump_json(),
|
||||
)
|
||||
|
||||
if stream:
|
||||
|
|
@ -221,7 +377,9 @@ class OpenAIResponsesImpl:
|
|||
chat_tools: list[ChatCompletionToolParam] = []
|
||||
for input_tool in tools:
|
||||
# TODO: Handle other tool types
|
||||
if input_tool.type == "web_search":
|
||||
if input_tool.type == "function":
|
||||
chat_tools.append(ChatCompletionToolParam(type="function", function=input_tool.model_dump()))
|
||||
elif input_tool.type == "web_search":
|
||||
tool_name = "web_search"
|
||||
tool = await self.tool_groups_api.get_tool(tool_name)
|
||||
tool_def = ToolDefinition(
|
||||
|
|
@ -247,12 +405,11 @@ class OpenAIResponsesImpl:
|
|||
self,
|
||||
model_id: str,
|
||||
stream: bool,
|
||||
chat_response: OpenAIChatCompletion,
|
||||
choice: OpenAIChoice,
|
||||
messages: list[OpenAIMessageParam],
|
||||
temperature: float,
|
||||
) -> list[OpenAIResponseOutput]:
|
||||
output_messages: list[OpenAIResponseOutput] = []
|
||||
choice = chat_response.choices[0]
|
||||
|
||||
# If the choice is not an assistant message, we don't need to execute any tools
|
||||
if not isinstance(choice.message, OpenAIAssistantMessageParam):
|
||||
|
|
@ -262,6 +419,9 @@ class OpenAIResponsesImpl:
|
|||
if not choice.message.tool_calls:
|
||||
return output_messages
|
||||
|
||||
# Copy the messages list to avoid mutating the original list
|
||||
messages = messages.copy()
|
||||
|
||||
# Add the assistant message with tool_calls response to the messages list
|
||||
messages.append(choice.message)
|
||||
|
||||
|
|
@ -307,7 +467,9 @@ class OpenAIResponsesImpl:
|
|||
)
|
||||
# type cast to appease mypy
|
||||
tool_results_chat_response = cast(OpenAIChatCompletion, tool_results_chat_response)
|
||||
tool_final_outputs = await _openai_choices_to_output_messages(tool_results_chat_response.choices)
|
||||
tool_final_outputs = [
|
||||
await _convert_chat_choice_to_response_message(choice) for choice in tool_results_chat_response.choices
|
||||
]
|
||||
# TODO: Wire in annotations with URLs, titles, etc to these output messages
|
||||
output_messages.extend(tool_final_outputs)
|
||||
return output_messages
|
||||
|
|
|
|||
|
|
@ -11,9 +11,9 @@ from llama_stack.apis.common.responses import PaginatedResponse
|
|||
from llama_stack.apis.datasetio import DatasetIO
|
||||
from llama_stack.apis.datasets import Dataset
|
||||
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
|
||||
from llama_stack.providers.utils.datasetio.pagination import paginate_records
|
||||
from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_uri
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.pagination import paginate_records
|
||||
|
||||
from .config import LocalFSDatasetIOConfig
|
||||
|
||||
|
|
|
|||
|
|
@ -105,7 +105,9 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
|
|||
query_config: RAGQueryConfig | None = None,
|
||||
) -> RAGQueryResult:
|
||||
if not vector_db_ids:
|
||||
return RAGQueryResult(content=None)
|
||||
raise ValueError(
|
||||
"No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
|
||||
)
|
||||
|
||||
query_config = query_config or RAGQueryConfig()
|
||||
query = await generate_rag_query(
|
||||
|
|
|
|||
|
|
@ -12,8 +12,8 @@ from llama_stack.apis.common.responses import PaginatedResponse
|
|||
from llama_stack.apis.datasetio import DatasetIO
|
||||
from llama_stack.apis.datasets import Dataset
|
||||
from llama_stack.providers.datatypes import DatasetsProtocolPrivate
|
||||
from llama_stack.providers.utils.datasetio.pagination import paginate_records
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.pagination import paginate_records
|
||||
|
||||
from .config import HuggingfaceDatasetIOConfig
|
||||
|
||||
|
|
|
|||
|
|
@ -61,6 +61,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
|
|||
OpenAICompatCompletionChoice,
|
||||
OpenAICompatCompletionResponse,
|
||||
get_sampling_options,
|
||||
prepare_openai_completion_params,
|
||||
process_chat_completion_response,
|
||||
process_chat_completion_stream_response,
|
||||
process_completion_response,
|
||||
|
|
@ -395,29 +396,25 @@ class OllamaInferenceAdapter(
|
|||
raise ValueError("Ollama does not support non-string prompts for completion")
|
||||
|
||||
model_obj = await self._get_model(model)
|
||||
params = {
|
||||
k: v
|
||||
for k, v in {
|
||||
"model": model_obj.provider_resource_id,
|
||||
"prompt": prompt,
|
||||
"best_of": best_of,
|
||||
"echo": echo,
|
||||
"frequency_penalty": frequency_penalty,
|
||||
"logit_bias": logit_bias,
|
||||
"logprobs": logprobs,
|
||||
"max_tokens": max_tokens,
|
||||
"n": n,
|
||||
"presence_penalty": presence_penalty,
|
||||
"seed": seed,
|
||||
"stop": stop,
|
||||
"stream": stream,
|
||||
"stream_options": stream_options,
|
||||
"temperature": temperature,
|
||||
"top_p": top_p,
|
||||
"user": user,
|
||||
}.items()
|
||||
if v is not None
|
||||
}
|
||||
params = await prepare_openai_completion_params(
|
||||
model=model_obj.provider_resource_id,
|
||||
prompt=prompt,
|
||||
best_of=best_of,
|
||||
echo=echo,
|
||||
frequency_penalty=frequency_penalty,
|
||||
logit_bias=logit_bias,
|
||||
logprobs=logprobs,
|
||||
max_tokens=max_tokens,
|
||||
n=n,
|
||||
presence_penalty=presence_penalty,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
stream_options=stream_options,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
return await self.openai_client.completions.create(**params) # type: ignore
|
||||
|
||||
async def openai_chat_completion(
|
||||
|
|
@ -447,35 +444,31 @@ class OllamaInferenceAdapter(
|
|||
user: str | None = None,
|
||||
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
|
||||
model_obj = await self._get_model(model)
|
||||
params = {
|
||||
k: v
|
||||
for k, v in {
|
||||
"model": model_obj.provider_resource_id,
|
||||
"messages": messages,
|
||||
"frequency_penalty": frequency_penalty,
|
||||
"function_call": function_call,
|
||||
"functions": functions,
|
||||
"logit_bias": logit_bias,
|
||||
"logprobs": logprobs,
|
||||
"max_completion_tokens": max_completion_tokens,
|
||||
"max_tokens": max_tokens,
|
||||
"n": n,
|
||||
"parallel_tool_calls": parallel_tool_calls,
|
||||
"presence_penalty": presence_penalty,
|
||||
"response_format": response_format,
|
||||
"seed": seed,
|
||||
"stop": stop,
|
||||
"stream": stream,
|
||||
"stream_options": stream_options,
|
||||
"temperature": temperature,
|
||||
"tool_choice": tool_choice,
|
||||
"tools": tools,
|
||||
"top_logprobs": top_logprobs,
|
||||
"top_p": top_p,
|
||||
"user": user,
|
||||
}.items()
|
||||
if v is not None
|
||||
}
|
||||
params = await prepare_openai_completion_params(
|
||||
model=model_obj.provider_resource_id,
|
||||
messages=messages,
|
||||
frequency_penalty=frequency_penalty,
|
||||
function_call=function_call,
|
||||
functions=functions,
|
||||
logit_bias=logit_bias,
|
||||
logprobs=logprobs,
|
||||
max_completion_tokens=max_completion_tokens,
|
||||
max_tokens=max_tokens,
|
||||
n=n,
|
||||
parallel_tool_calls=parallel_tool_calls,
|
||||
presence_penalty=presence_penalty,
|
||||
response_format=response_format,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
stream_options=stream_options,
|
||||
temperature=temperature,
|
||||
tool_choice=tool_choice,
|
||||
tools=tools,
|
||||
top_logprobs=top_logprobs,
|
||||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
return await self.openai_client.chat.completions.create(**params) # type: ignore
|
||||
|
||||
async def batch_completion(
|
||||
|
|
|
|||
|
|
@ -26,8 +26,7 @@ from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
|
|||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
ChromaClientType = chromadb.AsyncHttpClient | chromadb.PersistentClient
|
||||
ChromaClientType = chromadb.api.AsyncClientAPI | chromadb.api.ClientAPI
|
||||
|
||||
|
||||
# this is a helper to allow us to use async and non-async chroma clients interchangeably
|
||||
|
|
|
|||
|
|
@ -1,5 +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.
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
inference:
|
||||
tests:
|
||||
- inference/test_vision_inference.py::test_vision_chat_completion_streaming
|
||||
- inference/test_vision_inference.py::test_vision_chat_completion_non_streaming
|
||||
- inference/test_text_inference.py::test_structured_output
|
||||
- inference/test_text_inference.py::test_chat_completion_streaming
|
||||
- inference/test_text_inference.py::test_chat_completion_non_streaming
|
||||
- inference/test_text_inference.py::test_chat_completion_with_tool_calling
|
||||
- inference/test_text_inference.py::test_chat_completion_with_tool_calling_streaming
|
||||
|
||||
scenarios:
|
||||
- provider_fixtures:
|
||||
inference: ollama
|
||||
- fixture_combo_id: fireworks
|
||||
- provider_fixtures:
|
||||
inference: together
|
||||
# - inference: tgi
|
||||
# - inference: vllm_remote
|
||||
|
||||
inference_models:
|
||||
- meta-llama/Llama-3.1-8B-Instruct
|
||||
- meta-llama/Llama-3.2-11B-Vision-Instruct
|
||||
|
||||
|
||||
agents:
|
||||
tests:
|
||||
- agents/test_agents.py::test_agent_turns_with_safety
|
||||
- agents/test_agents.py::test_rag_agent
|
||||
|
||||
scenarios:
|
||||
- fixture_combo_id: ollama
|
||||
- fixture_combo_id: together
|
||||
- fixture_combo_id: fireworks
|
||||
|
||||
inference_models:
|
||||
- meta-llama/Llama-3.2-1B-Instruct
|
||||
|
||||
safety_shield: meta-llama/Llama-Guard-3-1B
|
||||
|
||||
|
||||
memory:
|
||||
tests:
|
||||
- memory/test_memory.py::test_query_documents
|
||||
|
||||
scenarios:
|
||||
- fixture_combo_id: ollama
|
||||
- provider_fixtures:
|
||||
inference: sentence_transformers
|
||||
memory: faiss
|
||||
- fixture_combo_id: chroma
|
||||
|
||||
inference_models:
|
||||
- meta-llama/Llama-3.2-1B-Instruct
|
||||
|
||||
embedding_model: all-MiniLM-L6-v2
|
||||
|
|
@ -1,296 +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 os
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
from dotenv import load_dotenv
|
||||
from pydantic import BaseModel, Field
|
||||
from termcolor import colored
|
||||
|
||||
from llama_stack.distribution.datatypes import Provider
|
||||
from llama_stack.providers.datatypes import RemoteProviderConfig
|
||||
|
||||
from .env import get_env_or_fail
|
||||
from .report import Report
|
||||
|
||||
|
||||
class ProviderFixture(BaseModel):
|
||||
providers: list[Provider]
|
||||
provider_data: dict[str, Any] | None = None
|
||||
|
||||
|
||||
class TestScenario(BaseModel):
|
||||
# provider fixtures can be either a mark or a dictionary of api -> providers
|
||||
provider_fixtures: dict[str, str] = Field(default_factory=dict)
|
||||
fixture_combo_id: str | None = None
|
||||
|
||||
|
||||
class APITestConfig(BaseModel):
|
||||
scenarios: list[TestScenario] = Field(default_factory=list)
|
||||
inference_models: list[str] = Field(default_factory=list)
|
||||
|
||||
# test name format should be <relative_path.py>::<test_name>
|
||||
tests: list[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class MemoryApiTestConfig(APITestConfig):
|
||||
embedding_model: str | None = Field(default_factory=None)
|
||||
|
||||
|
||||
class AgentsApiTestConfig(APITestConfig):
|
||||
safety_shield: str | None = Field(default_factory=None)
|
||||
|
||||
|
||||
class TestConfig(BaseModel):
|
||||
inference: APITestConfig | None = None
|
||||
agents: AgentsApiTestConfig | None = None
|
||||
memory: MemoryApiTestConfig | None = None
|
||||
|
||||
|
||||
def get_test_config_from_config_file(metafunc_config):
|
||||
config_file = metafunc_config.getoption("--config")
|
||||
if config_file is None:
|
||||
return None
|
||||
|
||||
config_file_path = Path(__file__).parent / config_file
|
||||
if not config_file_path.exists():
|
||||
raise ValueError(
|
||||
f"Test config {config_file} was specified but not found. Please make sure it exists in the llama_stack/providers/tests directory."
|
||||
)
|
||||
with open(config_file_path) as config_file:
|
||||
config = yaml.safe_load(config_file)
|
||||
return TestConfig(**config)
|
||||
|
||||
|
||||
def get_test_config_for_api(metafunc_config, api):
|
||||
test_config = get_test_config_from_config_file(metafunc_config)
|
||||
if test_config is None:
|
||||
return None
|
||||
return getattr(test_config, api)
|
||||
|
||||
|
||||
def get_provider_fixture_overrides_from_test_config(metafunc_config, api, default_provider_fixture_combinations):
|
||||
api_config = get_test_config_for_api(metafunc_config, api)
|
||||
if api_config is None:
|
||||
return None
|
||||
|
||||
fixture_combo_ids = set()
|
||||
custom_provider_fixture_combos = []
|
||||
for scenario in api_config.scenarios:
|
||||
if scenario.fixture_combo_id:
|
||||
fixture_combo_ids.add(scenario.fixture_combo_id)
|
||||
else:
|
||||
custom_provider_fixture_combos.append(
|
||||
pytest.param(
|
||||
scenario.provider_fixtures,
|
||||
id=scenario.provider_fixtures.get("inference") or "",
|
||||
)
|
||||
)
|
||||
|
||||
if len(fixture_combo_ids) > 0:
|
||||
for default_fixture in default_provider_fixture_combinations:
|
||||
if default_fixture.id in fixture_combo_ids:
|
||||
custom_provider_fixture_combos.append(default_fixture)
|
||||
return custom_provider_fixture_combos
|
||||
|
||||
|
||||
def remote_stack_fixture() -> ProviderFixture:
|
||||
if url := os.getenv("REMOTE_STACK_URL", None):
|
||||
config = RemoteProviderConfig.from_url(url)
|
||||
else:
|
||||
config = RemoteProviderConfig(
|
||||
host=get_env_or_fail("REMOTE_STACK_HOST"),
|
||||
port=int(get_env_or_fail("REMOTE_STACK_PORT")),
|
||||
)
|
||||
return ProviderFixture(
|
||||
providers=[
|
||||
Provider(
|
||||
provider_id="test::remote",
|
||||
provider_type="test::remote",
|
||||
config=config.model_dump(),
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def pytest_configure(config):
|
||||
config.option.tbstyle = "short"
|
||||
config.option.disable_warnings = True
|
||||
|
||||
"""Load environment variables at start of test run"""
|
||||
# Load from .env file if it exists
|
||||
env_file = Path(__file__).parent / ".env"
|
||||
if env_file.exists():
|
||||
load_dotenv(env_file)
|
||||
|
||||
# Load any environment variables passed via --env
|
||||
env_vars = config.getoption("--env") or []
|
||||
for env_var in env_vars:
|
||||
key, value = env_var.split("=", 1)
|
||||
os.environ[key] = value
|
||||
|
||||
if config.getoption("--output") is not None:
|
||||
config.pluginmanager.register(Report(config.getoption("--output")))
|
||||
|
||||
|
||||
def pytest_addoption(parser):
|
||||
parser.addoption(
|
||||
"--providers",
|
||||
default="",
|
||||
help=(
|
||||
"Provider configuration in format: api1=provider1,api2=provider2. "
|
||||
"Example: --providers inference=ollama,safety=meta-reference"
|
||||
),
|
||||
)
|
||||
parser.addoption(
|
||||
"--config",
|
||||
action="store",
|
||||
help="Set test config file (supported format: YAML), e.g. --config=test_config.yml",
|
||||
)
|
||||
parser.addoption(
|
||||
"--output",
|
||||
action="store",
|
||||
help="Set output file for test report, e.g. --output=pytest_report.md",
|
||||
)
|
||||
"""Add custom command line options"""
|
||||
parser.addoption("--env", action="append", help="Set environment variables, e.g. --env KEY=value")
|
||||
parser.addoption(
|
||||
"--inference-model",
|
||||
action="store",
|
||||
default="meta-llama/Llama-3.2-3B-Instruct",
|
||||
help="Specify the inference model to use for testing",
|
||||
)
|
||||
parser.addoption(
|
||||
"--safety-shield",
|
||||
action="store",
|
||||
default="meta-llama/Llama-Guard-3-1B",
|
||||
help="Specify the safety shield to use for testing",
|
||||
)
|
||||
parser.addoption(
|
||||
"--embedding-model",
|
||||
action="store",
|
||||
default=None,
|
||||
help="Specify the embedding model to use for testing",
|
||||
)
|
||||
parser.addoption(
|
||||
"--judge-model",
|
||||
action="store",
|
||||
default="meta-llama/Llama-3.1-8B-Instruct",
|
||||
help="Specify the judge model to use for testing",
|
||||
)
|
||||
|
||||
|
||||
def make_provider_id(providers: dict[str, str]) -> str:
|
||||
return ":".join(f"{api}={provider}" for api, provider in sorted(providers.items()))
|
||||
|
||||
|
||||
def get_provider_marks(providers: dict[str, str]) -> list[Any]:
|
||||
marks = []
|
||||
for provider in providers.values():
|
||||
marks.append(getattr(pytest.mark, provider))
|
||||
return marks
|
||||
|
||||
|
||||
def get_provider_fixture_overrides(config, available_fixtures: dict[str, list[str]]) -> list[pytest.param] | None:
|
||||
provider_str = config.getoption("--providers")
|
||||
if not provider_str:
|
||||
return None
|
||||
|
||||
fixture_dict = parse_fixture_string(provider_str, available_fixtures)
|
||||
return [
|
||||
pytest.param(
|
||||
fixture_dict,
|
||||
id=make_provider_id(fixture_dict),
|
||||
marks=get_provider_marks(fixture_dict),
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
def parse_fixture_string(provider_str: str, available_fixtures: dict[str, list[str]]) -> dict[str, str]:
|
||||
"""Parse provider string of format 'api1=provider1,api2=provider2'"""
|
||||
if not provider_str:
|
||||
return {}
|
||||
|
||||
fixtures = {}
|
||||
pairs = provider_str.split(",")
|
||||
for pair in pairs:
|
||||
if "=" not in pair:
|
||||
raise ValueError(f"Invalid provider specification: {pair}. Expected format: api=provider")
|
||||
api, fixture = pair.split("=")
|
||||
if api not in available_fixtures:
|
||||
raise ValueError(f"Unknown API: {api}. Available APIs: {list(available_fixtures.keys())}")
|
||||
if fixture not in available_fixtures[api]:
|
||||
raise ValueError(
|
||||
f"Unknown provider '{fixture}' for API '{api}'. Available providers: {list(available_fixtures[api])}"
|
||||
)
|
||||
fixtures[api] = fixture
|
||||
|
||||
# Check that all provided APIs are supported
|
||||
for api in available_fixtures.keys():
|
||||
if api not in fixtures:
|
||||
raise ValueError(
|
||||
f"Missing provider fixture for API '{api}'. Available providers: {list(available_fixtures[api])}"
|
||||
)
|
||||
return fixtures
|
||||
|
||||
|
||||
def pytest_itemcollected(item):
|
||||
# Get all markers as a list
|
||||
filtered = ("asyncio", "parametrize")
|
||||
marks = [mark.name for mark in item.iter_markers() if mark.name not in filtered]
|
||||
if marks:
|
||||
marks = colored(",".join(marks), "yellow")
|
||||
item.name = f"{item.name}[{marks}]"
|
||||
|
||||
|
||||
def pytest_collection_modifyitems(session, config, items):
|
||||
test_config = get_test_config_from_config_file(config)
|
||||
if test_config is None:
|
||||
return
|
||||
|
||||
required_tests = defaultdict(set)
|
||||
for api_test_config in [
|
||||
test_config.inference,
|
||||
test_config.memory,
|
||||
test_config.agents,
|
||||
]:
|
||||
if api_test_config is None:
|
||||
continue
|
||||
for test in api_test_config.tests:
|
||||
arr = test.split("::")
|
||||
if len(arr) != 2:
|
||||
raise ValueError(f"Invalid format for test name {test}")
|
||||
test_path, func_name = arr
|
||||
required_tests[Path(__file__).parent / test_path].add(func_name)
|
||||
|
||||
new_items, deselected_items = [], []
|
||||
for item in items:
|
||||
func_name = getattr(item, "originalname", item.name)
|
||||
if func_name in required_tests[item.fspath]:
|
||||
new_items.append(item)
|
||||
continue
|
||||
deselected_items.append(item)
|
||||
|
||||
items[:] = new_items
|
||||
config.hook.pytest_deselected(items=deselected_items)
|
||||
|
||||
|
||||
pytest_plugins = [
|
||||
"llama_stack.providers.tests.inference.fixtures",
|
||||
"llama_stack.providers.tests.safety.fixtures",
|
||||
"llama_stack.providers.tests.vector_io.fixtures",
|
||||
"llama_stack.providers.tests.agents.fixtures",
|
||||
"llama_stack.providers.tests.datasetio.fixtures",
|
||||
"llama_stack.providers.tests.scoring.fixtures",
|
||||
"llama_stack.providers.tests.eval.fixtures",
|
||||
"llama_stack.providers.tests.post_training.fixtures",
|
||||
"llama_stack.providers.tests.tools.fixtures",
|
||||
]
|
||||
|
|
@ -1,176 +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 collections import defaultdict
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
from pytest import ExitCode
|
||||
from pytest_html.basereport import _process_outcome
|
||||
|
||||
from llama_stack.models.llama.sku_list import all_registered_models
|
||||
from llama_stack.models.llama.sku_types import CoreModelId
|
||||
|
||||
INFERENCE_APIS = ["chat_completion"]
|
||||
FUNCTIONALITIES = ["streaming", "structured_output", "tool_calling"]
|
||||
SUPPORTED_MODELS = {
|
||||
"ollama": {
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_2_1b_instruct.value,
|
||||
CoreModelId.llama3_2_1b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_3_70b_instruct.value,
|
||||
CoreModelId.llama_guard_3_8b.value,
|
||||
CoreModelId.llama_guard_3_1b.value,
|
||||
},
|
||||
"fireworks": {
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_2_1b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_3_70b_instruct.value,
|
||||
CoreModelId.llama_guard_3_8b.value,
|
||||
CoreModelId.llama_guard_3_11b_vision.value,
|
||||
},
|
||||
"together": {
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_3_70b_instruct.value,
|
||||
CoreModelId.llama_guard_3_8b.value,
|
||||
CoreModelId.llama_guard_3_11b_vision.value,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class Report:
|
||||
def __init__(self, output_path):
|
||||
valid_file_format = (
|
||||
output_path.split(".")[1] in ["md", "markdown"] if len(output_path.split(".")) == 2 else False
|
||||
)
|
||||
if not valid_file_format:
|
||||
raise ValueError(f"Invalid output file {output_path}. Markdown file is required")
|
||||
self.output_path = output_path
|
||||
self.test_data = defaultdict(dict)
|
||||
self.inference_tests = defaultdict(dict)
|
||||
|
||||
@pytest.hookimpl
|
||||
def pytest_runtest_logreport(self, report):
|
||||
# This hook is called in several phases, including setup, call and teardown
|
||||
# The test is considered failed / error if any of the outcomes is not "Passed"
|
||||
outcome = _process_outcome(report)
|
||||
data = {
|
||||
"outcome": report.outcome,
|
||||
"longrepr": report.longrepr,
|
||||
"name": report.nodeid,
|
||||
}
|
||||
if report.nodeid not in self.test_data:
|
||||
self.test_data[report.nodeid] = data
|
||||
elif self.test_data[report.nodeid] != outcome and outcome != "Passed":
|
||||
self.test_data[report.nodeid] = data
|
||||
|
||||
@pytest.hookimpl
|
||||
def pytest_sessionfinish(self, session, exitstatus):
|
||||
if exitstatus <= ExitCode.INTERRUPTED:
|
||||
return
|
||||
report = []
|
||||
report.append("# Llama Stack Integration Test Results Report")
|
||||
report.append("\n## Summary")
|
||||
report.append("\n## Supported Models: ")
|
||||
|
||||
header = "| Model Descriptor |"
|
||||
dividor = "|:---|"
|
||||
for k in SUPPORTED_MODELS.keys():
|
||||
header += f"{k} |"
|
||||
dividor += ":---:|"
|
||||
|
||||
report.append(header)
|
||||
report.append(dividor)
|
||||
|
||||
rows = []
|
||||
for model in all_registered_models():
|
||||
if "Instruct" not in model.core_model_id.value and "Guard" not in model.core_model_id.value:
|
||||
continue
|
||||
row = f"| {model.core_model_id.value} |"
|
||||
for k in SUPPORTED_MODELS.keys():
|
||||
if model.core_model_id.value in SUPPORTED_MODELS[k]:
|
||||
row += " ✅ |"
|
||||
else:
|
||||
row += " ❌ |"
|
||||
rows.append(row)
|
||||
report.extend(rows)
|
||||
|
||||
report.append("\n### Tests:")
|
||||
|
||||
for provider in SUPPORTED_MODELS.keys():
|
||||
if provider not in self.inference_tests:
|
||||
continue
|
||||
report.append(f"\n #### {provider}")
|
||||
test_table = [
|
||||
"| Area | Model | API | Functionality Test | Status |",
|
||||
"|:-----|:-----|:-----|:-----|:-----|",
|
||||
]
|
||||
for api in INFERENCE_APIS:
|
||||
tests = self.inference_tests[provider][api]
|
||||
for test_nodeid in tests:
|
||||
row = "|{area} | {model} | {api} | {test} | {result} ".format(
|
||||
area="Text" if "text" in test_nodeid else "Vision",
|
||||
model=("Llama-3.1-8B-Instruct" if "text" in test_nodeid else "Llama3.2-11B-Vision-Instruct"),
|
||||
api=f"/{api}",
|
||||
test=self.get_simple_function_name(test_nodeid),
|
||||
result=("✅" if self.test_data[test_nodeid]["outcome"] == "passed" else "❌"),
|
||||
)
|
||||
test_table += [row]
|
||||
report.extend(test_table)
|
||||
report.append("\n")
|
||||
|
||||
output_file = Path(self.output_path)
|
||||
output_file.write_text("\n".join(report))
|
||||
print(f"\n Report generated: {output_file.absolute()}")
|
||||
|
||||
@pytest.hookimpl(trylast=True)
|
||||
def pytest_collection_modifyitems(self, session, config, items):
|
||||
for item in items:
|
||||
inference = item.callspec.params.get("inference_stack")
|
||||
if "inference" in item.nodeid:
|
||||
func_name = getattr(item, "originalname", item.name)
|
||||
for api in INFERENCE_APIS:
|
||||
if api in func_name:
|
||||
api_tests = self.inference_tests[inference].get(api, set())
|
||||
api_tests.add(item.nodeid)
|
||||
self.inference_tests[inference][api] = api_tests
|
||||
|
||||
def get_simple_function_name(self, nodeid):
|
||||
"""Extract function name from nodeid.
|
||||
|
||||
Examples:
|
||||
- 'tests/test_math.py::test_addition' -> 'test_addition'
|
||||
- 'tests/test_math.py::TestClass::test_method' -> test_method'
|
||||
"""
|
||||
parts = nodeid.split("::")
|
||||
func_name = nodeid # Fallback to full nodeid if pattern doesn't match
|
||||
if len(parts) == 2: # Simple function
|
||||
func_name = parts[1]
|
||||
elif len(parts) == 3: # Class method
|
||||
func_name = parts[2]
|
||||
return func_name.split("[")[0]
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
# Report for cerebras distribution
|
||||
|
||||
## Supported Models
|
||||
| Model Descriptor | cerebras |
|
||||
|:---|:---|
|
||||
| meta-llama/Llama-3-8B-Instruct | ❌ |
|
||||
| meta-llama/Llama-3-70B-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.1-8B-Instruct | ✅ |
|
||||
| meta-llama/Llama-3.1-70B-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.1-405B-Instruct-FP8 | ❌ |
|
||||
| meta-llama/Llama-3.2-1B-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.2-3B-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.2-11B-Vision-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.2-90B-Vision-Instruct | ❌ |
|
||||
| meta-llama/Llama-3.3-70B-Instruct | ✅ |
|
||||
| meta-llama/Llama-Guard-3-11B-Vision | ❌ |
|
||||
| meta-llama/Llama-Guard-3-1B | ❌ |
|
||||
| meta-llama/Llama-Guard-3-8B | ❌ |
|
||||
| meta-llama/Llama-Guard-2-8B | ❌ |
|
||||
|
||||
## Inference
|
||||
| Model | API | Capability | Test | Status |
|
||||
|:----- |:-----|:-----|:-----|:-----|
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | streaming | test_text_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | streaming | test_image_chat_completion_streaming | ❌ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ❌ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | streaming | test_text_completion_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | non_streaming | test_text_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | structured_output | test_text_completion_structured_output | ❌ |
|
||||
|
||||
## Vector IO
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /retrieve | | test_vector_db_retrieve | ✅ |
|
||||
|
||||
## Agents
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /create_agent_turn | rag | test_rag_agent | ✅ |
|
||||
| /create_agent_turn | custom_tool | test_custom_tool | ❌ |
|
||||
|
|
@ -833,6 +833,8 @@
|
|||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
"uvicorn"
|
||||
"uvicorn",
|
||||
"sentence-transformers --no-deps",
|
||||
"torch torchvision --index-url https://download.pytorch.org/whl/cpu"
|
||||
]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,45 +0,0 @@
|
|||
# Report for fireworks distribution
|
||||
|
||||
## Supported Models
|
||||
| Model Descriptor | fireworks |
|
||||
|:---|:---|
|
||||
| Llama-3-8B-Instruct | ❌ |
|
||||
| Llama-3-70B-Instruct | ❌ |
|
||||
| Llama3.1-8B-Instruct | ✅ |
|
||||
| Llama3.1-70B-Instruct | ✅ |
|
||||
| Llama3.1-405B-Instruct | ✅ |
|
||||
| Llama3.2-1B-Instruct | ✅ |
|
||||
| Llama3.2-3B-Instruct | ✅ |
|
||||
| Llama3.2-11B-Vision-Instruct | ✅ |
|
||||
| Llama3.2-90B-Vision-Instruct | ✅ |
|
||||
| Llama3.3-70B-Instruct | ✅ |
|
||||
| Llama-Guard-3-11B-Vision | ✅ |
|
||||
| Llama-Guard-3-1B | ❌ |
|
||||
| Llama-Guard-3-8B | ✅ |
|
||||
| Llama-Guard-2-8B | ❌ |
|
||||
|
||||
## Inference
|
||||
| Model | API | Capability | Test | Status |
|
||||
|:----- |:-----|:-----|:-----|:-----|
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | streaming | test_text_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | streaming | test_image_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | log_probs | test_completion_log_probs_non_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | log_probs | test_completion_log_probs_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | streaming | test_text_completion_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | non_streaming | test_text_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | structured_output | test_text_completion_structured_output | ✅ |
|
||||
|
||||
## Vector IO
|
||||
| Provider | API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|:-----|
|
||||
| inline::faiss | /retrieve | | test_vector_db_retrieve | ✅ |
|
||||
|
||||
## Agents
|
||||
| Provider | API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|:-----|
|
||||
| inline::meta-reference | /create_agent_turn | rag | test_rag_agent | ✅ |
|
||||
| inline::meta-reference | /create_agent_turn | custom_tool | test_custom_tool | ✅ |
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
# Report for ollama distribution
|
||||
|
||||
## Supported Models
|
||||
| Model Descriptor | ollama |
|
||||
|:---|:---|
|
||||
| Llama-3-8B-Instruct | ❌ |
|
||||
| Llama-3-70B-Instruct | ❌ |
|
||||
| Llama3.1-8B-Instruct | ✅ |
|
||||
| Llama3.1-70B-Instruct | ✅ |
|
||||
| Llama3.1-405B-Instruct | ✅ |
|
||||
| Llama3.2-1B-Instruct | ✅ |
|
||||
| Llama3.2-3B-Instruct | ✅ |
|
||||
| Llama3.2-11B-Vision-Instruct | ✅ |
|
||||
| Llama3.2-90B-Vision-Instruct | ✅ |
|
||||
| Llama3.3-70B-Instruct | ✅ |
|
||||
| Llama-Guard-3-11B-Vision | ❌ |
|
||||
| Llama-Guard-3-1B | ✅ |
|
||||
| Llama-Guard-3-8B | ✅ |
|
||||
| Llama-Guard-2-8B | ❌ |
|
||||
|
||||
## Inference
|
||||
| Model | API | Capability | Test | Status |
|
||||
|:----- |:-----|:-----|:-----|:-----|
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | streaming | test_text_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | streaming | test_image_chat_completion_streaming | ❌ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ❌ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | streaming | test_text_completion_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | non_streaming | test_text_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | structured_output | test_text_completion_structured_output | ✅ |
|
||||
|
||||
## Vector IO
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /retrieve | | test_vector_db_retrieve | ✅ |
|
||||
|
||||
## Agents
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /create_agent_turn | rag | test_rag_agent | ✅ |
|
||||
| /create_agent_turn | custom_tool | test_custom_tool | ✅ |
|
||||
|
|
@ -1,44 +0,0 @@
|
|||
# Report for tgi distribution
|
||||
|
||||
## Supported Models
|
||||
| Model Descriptor | tgi |
|
||||
|:---|:---|
|
||||
| Llama-3-8B-Instruct | ✅ |
|
||||
| Llama-3-70B-Instruct | ✅ |
|
||||
| Llama3.1-8B-Instruct | ✅ |
|
||||
| Llama3.1-70B-Instruct | ✅ |
|
||||
| Llama3.1-405B-Instruct | ✅ |
|
||||
| Llama3.2-1B-Instruct | ✅ |
|
||||
| Llama3.2-3B-Instruct | ✅ |
|
||||
| Llama3.2-11B-Vision-Instruct | ✅ |
|
||||
| Llama3.2-90B-Vision-Instruct | ✅ |
|
||||
| Llama3.3-70B-Instruct | ✅ |
|
||||
| Llama-Guard-3-11B-Vision | ✅ |
|
||||
| Llama-Guard-3-1B | ✅ |
|
||||
| Llama-Guard-3-8B | ✅ |
|
||||
| Llama-Guard-2-8B | ✅ |
|
||||
|
||||
## Inference
|
||||
| Model | API | Capability | Test | Status |
|
||||
|:----- |:-----|:-----|:-----|:-----|
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | streaming | test_text_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | streaming | test_image_chat_completion_streaming | ❌ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ❌ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | streaming | test_text_completion_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | non_streaming | test_text_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | structured_output | test_text_completion_structured_output | ✅ |
|
||||
|
||||
## Vector IO
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /retrieve | | test_vector_db_retrieve | ✅ |
|
||||
|
||||
## Agents
|
||||
| API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|
|
||||
| /create_agent_turn | rag | test_rag_agent | ✅ |
|
||||
| /create_agent_turn | custom_tool | test_custom_tool | ✅ |
|
||||
| /create_agent_turn | code_execution | test_code_interpreter_for_attachments | ✅ |
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
# Report for together distribution
|
||||
|
||||
## Supported Models
|
||||
| Model Descriptor | together |
|
||||
|:---|:---|
|
||||
| Llama-3-8B-Instruct | ❌ |
|
||||
| Llama-3-70B-Instruct | ❌ |
|
||||
| Llama3.1-8B-Instruct | ✅ |
|
||||
| Llama3.1-70B-Instruct | ✅ |
|
||||
| Llama3.1-405B-Instruct | ✅ |
|
||||
| Llama3.2-1B-Instruct | ❌ |
|
||||
| Llama3.2-3B-Instruct | ✅ |
|
||||
| Llama3.2-11B-Vision-Instruct | ✅ |
|
||||
| Llama3.2-90B-Vision-Instruct | ✅ |
|
||||
| Llama3.3-70B-Instruct | ✅ |
|
||||
| Llama-Guard-3-11B-Vision | ✅ |
|
||||
| Llama-Guard-3-1B | ❌ |
|
||||
| Llama-Guard-3-8B | ✅ |
|
||||
| Llama-Guard-2-8B | ❌ |
|
||||
|
||||
## Inference
|
||||
| Model | API | Capability | Test | Status |
|
||||
|:----- |:-----|:-----|:-----|:-----|
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | streaming | test_text_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | streaming | test_image_chat_completion_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | non_streaming | test_image_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | non_streaming | test_text_chat_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /chat_completion | tool_calling | test_text_chat_completion_with_tool_calling_and_non_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | log_probs | test_completion_log_probs_non_streaming | ✅ |
|
||||
| Llama-3.2-11B-Vision-Instruct | /chat_completion | log_probs | test_completion_log_probs_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | streaming | test_text_completion_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | non_streaming | test_text_completion_non_streaming | ✅ |
|
||||
| Llama-3.1-8B-Instruct | /completion | structured_output | test_text_completion_structured_output | ✅ |
|
||||
|
||||
## Vector IO
|
||||
| Provider | API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|:-----|
|
||||
| inline::faiss | /retrieve | | test_vector_db_retrieve | ✅ |
|
||||
|
||||
## Agents
|
||||
| Provider | API | Capability | Test | Status |
|
||||
|:-----|:-----|:-----|:-----|:-----|
|
||||
| inline::meta-reference | /create_agent_turn | rag | test_rag_agent | ✅ |
|
||||
| inline::meta-reference | /create_agent_turn | custom_tool | test_custom_tool | ✅ |
|
||||
|
|
@ -4,6 +4,7 @@ distribution_spec:
|
|||
providers:
|
||||
inference:
|
||||
- remote::watsonx
|
||||
- inline::sentence-transformers
|
||||
vector_io:
|
||||
- inline::faiss
|
||||
safety:
|
||||
|
|
|
|||
|
|
@ -18,6 +18,9 @@ providers:
|
|||
url: ${env.WATSONX_BASE_URL:https://us-south.ml.cloud.ibm.com}
|
||||
api_key: ${env.WATSONX_API_KEY:}
|
||||
project_id: ${env.WATSONX_PROJECT_ID:}
|
||||
- provider_id: sentence-transformers
|
||||
provider_type: inline::sentence-transformers
|
||||
config: {}
|
||||
vector_io:
|
||||
- provider_id: faiss
|
||||
provider_type: inline::faiss
|
||||
|
|
@ -191,6 +194,11 @@ models:
|
|||
provider_id: watsonx
|
||||
provider_model_id: meta-llama/llama-guard-3-11b-vision
|
||||
model_type: llm
|
||||
- metadata:
|
||||
embedding_dimension: 384
|
||||
model_id: all-MiniLM-L6-v2
|
||||
provider_id: sentence-transformers
|
||||
model_type: embedding
|
||||
shields: []
|
||||
vector_dbs: []
|
||||
datasets: []
|
||||
|
|
|
|||
|
|
@ -6,7 +6,11 @@
|
|||
|
||||
from pathlib import Path
|
||||
|
||||
from llama_stack.distribution.datatypes import Provider, ToolGroupInput
|
||||
from llama_stack.apis.models.models import ModelType
|
||||
from llama_stack.distribution.datatypes import ModelInput, Provider, ToolGroupInput
|
||||
from llama_stack.providers.inline.inference.sentence_transformers import (
|
||||
SentenceTransformersInferenceConfig,
|
||||
)
|
||||
from llama_stack.providers.remote.inference.watsonx import WatsonXConfig
|
||||
from llama_stack.providers.remote.inference.watsonx.models import MODEL_ENTRIES
|
||||
from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
|
||||
|
|
@ -14,7 +18,7 @@ from llama_stack.templates.template import DistributionTemplate, RunConfigSettin
|
|||
|
||||
def get_distribution_template() -> DistributionTemplate:
|
||||
providers = {
|
||||
"inference": ["remote::watsonx"],
|
||||
"inference": ["remote::watsonx", "inline::sentence-transformers"],
|
||||
"vector_io": ["inline::faiss"],
|
||||
"safety": ["inline::llama-guard"],
|
||||
"agents": ["inline::meta-reference"],
|
||||
|
|
@ -36,6 +40,12 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
config=WatsonXConfig.sample_run_config(),
|
||||
)
|
||||
|
||||
embedding_provider = Provider(
|
||||
provider_id="sentence-transformers",
|
||||
provider_type="inline::sentence-transformers",
|
||||
config=SentenceTransformersInferenceConfig.sample_run_config(),
|
||||
)
|
||||
|
||||
available_models = {
|
||||
"watsonx": MODEL_ENTRIES,
|
||||
}
|
||||
|
|
@ -50,6 +60,15 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
),
|
||||
]
|
||||
|
||||
embedding_model = ModelInput(
|
||||
model_id="all-MiniLM-L6-v2",
|
||||
provider_id="sentence-transformers",
|
||||
model_type=ModelType.embedding,
|
||||
metadata={
|
||||
"embedding_dimension": 384,
|
||||
},
|
||||
)
|
||||
|
||||
default_models = get_model_registry(available_models)
|
||||
return DistributionTemplate(
|
||||
name="watsonx",
|
||||
|
|
@ -62,9 +81,9 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
run_configs={
|
||||
"run.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
"inference": [inference_provider],
|
||||
"inference": [inference_provider, embedding_provider],
|
||||
},
|
||||
default_models=default_models,
|
||||
default_models=default_models + [embedding_model],
|
||||
default_tool_groups=default_tool_groups,
|
||||
),
|
||||
},
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
|||
|
||||
[project]
|
||||
name = "llama_stack"
|
||||
version = "0.2.5"
|
||||
version = "0.2.6"
|
||||
authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }]
|
||||
description = "Llama Stack"
|
||||
readme = "README.md"
|
||||
|
|
@ -27,7 +27,7 @@ dependencies = [
|
|||
"huggingface-hub",
|
||||
"jinja2>=3.1.6",
|
||||
"jsonschema",
|
||||
"llama-stack-client>=0.2.5",
|
||||
"llama-stack-client>=0.2.6",
|
||||
"openai>=1.66",
|
||||
"prompt-toolkit",
|
||||
"python-dotenv",
|
||||
|
|
@ -106,7 +106,7 @@ codegen = ["rich", "pydantic", "jinja2>=3.1.6"]
|
|||
ui = [
|
||||
"streamlit",
|
||||
"pandas",
|
||||
"llama-stack-client>=0.2.5",
|
||||
"llama-stack-client>=0.2.6",
|
||||
"streamlit-option-menu",
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ jiter==0.8.2
|
|||
jsonschema==4.23.0
|
||||
jsonschema-specifications==2024.10.1
|
||||
kubernetes==32.0.1
|
||||
llama-stack-client==0.2.5
|
||||
llama-stack-client==0.2.6
|
||||
lxml==5.3.1
|
||||
markdown-it-py==3.0.0
|
||||
markupsafe==3.0.2
|
||||
|
|
|
|||
9
tests/README.md
Normal file
9
tests/README.md
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
# Llama Stack Tests
|
||||
|
||||
Llama Stack has multiple layers of testing done to ensure continuous functionality and prevent regressions to the codebase.
|
||||
|
||||
| Testing Type | Details |
|
||||
|--------------|---------|
|
||||
| Unit | [unit/README.md](unit/README.md) |
|
||||
| Integration | [integration/README.md](integration/README.md) |
|
||||
| Verification | [verifications/README.md](verifications/README.md) |
|
||||
|
|
@ -53,9 +53,6 @@ providers:
|
|||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: code-interpreter
|
||||
provider_type: inline::code-interpreter
|
||||
config: {}
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
|
|
@ -90,8 +87,6 @@ tool_groups:
|
|||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::code_interpreter
|
||||
provider_id: code-interpreter
|
||||
- toolgroup_id: builtin::wolfram_alpha
|
||||
provider_id: wolfram-alpha
|
||||
server:
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ pytest --help
|
|||
Here are the most important options:
|
||||
- `--stack-config`: specify the stack config to use. You have three ways to point to a stack:
|
||||
- a URL which points to a Llama Stack distribution server
|
||||
- a template (e.g., `fireworks`, `together`) or a path to a run.yaml file
|
||||
- a template (e.g., `fireworks`, `together`) or a path to a `run.yaml` file
|
||||
- a comma-separated list of api=provider pairs, e.g. `inference=fireworks,safety=llama-guard,agents=meta-reference`. This is most useful for testing a single API surface.
|
||||
- `--env`: set environment variables, e.g. --env KEY=value. this is a utility option to set environment variables required by various providers.
|
||||
|
||||
|
|
@ -28,7 +28,6 @@ if no model is specified.
|
|||
|
||||
Experimental, under development, options:
|
||||
- `--record-responses`: record new API responses instead of using cached ones
|
||||
- `--report`: path where the test report should be written, e.g. --report=/path/to/report.md
|
||||
|
||||
|
||||
## Examples
|
||||
|
|
|
|||
|
|
@ -15,8 +15,6 @@ from dotenv import load_dotenv
|
|||
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
from .report import Report
|
||||
|
||||
logger = get_logger(__name__, category="tests")
|
||||
|
||||
|
||||
|
|
@ -60,9 +58,6 @@ def pytest_configure(config):
|
|||
os.environ["DISABLE_CODE_SANDBOX"] = "1"
|
||||
logger.info("Setting DISABLE_CODE_SANDBOX=1 for macOS")
|
||||
|
||||
if config.getoption("--report"):
|
||||
config.pluginmanager.register(Report(config))
|
||||
|
||||
|
||||
def pytest_addoption(parser):
|
||||
parser.addoption(
|
||||
|
|
|
|||
|
|
@ -1,54 +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 llama_stack.providers.datatypes import Api
|
||||
|
||||
INFERENCE_API_CAPA_TEST_MAP = {
|
||||
"chat_completion": {
|
||||
"streaming": [
|
||||
"test_text_chat_completion_streaming",
|
||||
"test_image_chat_completion_streaming",
|
||||
],
|
||||
"non_streaming": [
|
||||
"test_image_chat_completion_non_streaming",
|
||||
"test_text_chat_completion_non_streaming",
|
||||
],
|
||||
"tool_calling": [
|
||||
"test_text_chat_completion_with_tool_calling_and_streaming",
|
||||
"test_text_chat_completion_with_tool_calling_and_non_streaming",
|
||||
],
|
||||
"log_probs": [
|
||||
"test_completion_log_probs_non_streaming",
|
||||
"test_completion_log_probs_streaming",
|
||||
],
|
||||
},
|
||||
"completion": {
|
||||
"streaming": ["test_text_completion_streaming"],
|
||||
"non_streaming": ["test_text_completion_non_streaming"],
|
||||
"structured_output": ["test_text_completion_structured_output"],
|
||||
},
|
||||
}
|
||||
|
||||
VECTORIO_API_TEST_MAP = {
|
||||
"retrieve": {
|
||||
"": ["test_vector_db_retrieve"],
|
||||
}
|
||||
}
|
||||
|
||||
AGENTS_API_TEST_MAP = {
|
||||
"create_agent_turn": {
|
||||
"rag": ["test_rag_agent"],
|
||||
"custom_tool": ["test_custom_tool"],
|
||||
"code_execution": ["test_code_interpreter_for_attachments"],
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
API_MAPS = {
|
||||
Api.inference: INFERENCE_API_CAPA_TEST_MAP,
|
||||
Api.vector_io: VECTORIO_API_TEST_MAP,
|
||||
Api.agents: AGENTS_API_TEST_MAP,
|
||||
}
|
||||
|
|
@ -1,216 +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 collections import defaultdict
|
||||
|
||||
import pytest
|
||||
from pytest import CollectReport
|
||||
from termcolor import cprint
|
||||
|
||||
from llama_stack.models.llama.sku_list import (
|
||||
all_registered_models,
|
||||
llama3_1_instruct_models,
|
||||
llama3_2_instruct_models,
|
||||
llama3_3_instruct_models,
|
||||
llama3_instruct_models,
|
||||
safety_models,
|
||||
)
|
||||
from llama_stack.models.llama.sku_types import CoreModelId
|
||||
from llama_stack.providers.datatypes import Api
|
||||
|
||||
from .metadata import API_MAPS
|
||||
|
||||
|
||||
def featured_models():
|
||||
models = [
|
||||
*llama3_instruct_models(),
|
||||
*llama3_1_instruct_models(),
|
||||
*llama3_2_instruct_models(),
|
||||
*llama3_3_instruct_models(),
|
||||
*safety_models(),
|
||||
]
|
||||
return {model.huggingface_repo: model for model in models if not model.variant}
|
||||
|
||||
|
||||
SUPPORTED_MODELS = {
|
||||
"ollama": {
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_8b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_70b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_1_405b_instruct.value,
|
||||
CoreModelId.llama3_2_1b_instruct.value,
|
||||
CoreModelId.llama3_2_1b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_3b_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_11b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_2_90b_vision_instruct.value,
|
||||
CoreModelId.llama3_3_70b_instruct.value,
|
||||
CoreModelId.llama_guard_3_8b.value,
|
||||
CoreModelId.llama_guard_3_1b.value,
|
||||
},
|
||||
"tgi": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
|
||||
"vllm": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
|
||||
}
|
||||
|
||||
|
||||
class Report:
|
||||
def __init__(self, config):
|
||||
self.distro_name = None
|
||||
self.config = config
|
||||
|
||||
stack_config = self.config.getoption("--stack-config")
|
||||
if stack_config:
|
||||
is_url = stack_config.startswith("http") or "//" in stack_config
|
||||
is_yaml = stack_config.endswith(".yaml")
|
||||
if not is_url and not is_yaml:
|
||||
self.distro_name = stack_config
|
||||
|
||||
self.report_data = defaultdict(dict)
|
||||
# test function -> test nodeid
|
||||
self.test_data = dict()
|
||||
self.test_name_to_nodeid = defaultdict(list)
|
||||
self.vision_model_id = None
|
||||
self.text_model_id = None
|
||||
self.client = None
|
||||
|
||||
@pytest.hookimpl(tryfirst=True)
|
||||
def pytest_runtest_logreport(self, report):
|
||||
# This hook is called in several phases, including setup, call and teardown
|
||||
# The test is considered failed / error if any of the outcomes is not "Passed"
|
||||
outcome = self._process_outcome(report)
|
||||
if report.nodeid not in self.test_data:
|
||||
self.test_data[report.nodeid] = outcome
|
||||
elif self.test_data[report.nodeid] != outcome and outcome != "Passed":
|
||||
self.test_data[report.nodeid] = outcome
|
||||
|
||||
def pytest_sessionfinish(self, session):
|
||||
if not self.client:
|
||||
return
|
||||
|
||||
report = []
|
||||
report.append(f"# Report for {self.distro_name} distribution")
|
||||
report.append("\n## Supported Models")
|
||||
|
||||
header = f"| Model Descriptor | {self.distro_name} |"
|
||||
dividor = "|:---|:---|"
|
||||
|
||||
report.append(header)
|
||||
report.append(dividor)
|
||||
|
||||
rows = []
|
||||
if self.distro_name in SUPPORTED_MODELS:
|
||||
for model in all_registered_models():
|
||||
if ("Instruct" not in model.core_model_id.value and "Guard" not in model.core_model_id.value) or (
|
||||
model.variant
|
||||
):
|
||||
continue
|
||||
row = f"| {model.core_model_id.value} |"
|
||||
if model.core_model_id.value in SUPPORTED_MODELS[self.distro_name]:
|
||||
row += " ✅ |"
|
||||
else:
|
||||
row += " ❌ |"
|
||||
rows.append(row)
|
||||
else:
|
||||
supported_models = {m.identifier for m in self.client.models.list()}
|
||||
for hf_name, model in featured_models().items():
|
||||
row = f"| {model.core_model_id.value} |"
|
||||
if hf_name in supported_models:
|
||||
row += " ✅ |"
|
||||
else:
|
||||
row += " ❌ |"
|
||||
rows.append(row)
|
||||
report.extend(rows)
|
||||
|
||||
report.append("\n## Inference")
|
||||
test_table = [
|
||||
"| Model | API | Capability | Test | Status |",
|
||||
"|:----- |:-----|:-----|:-----|:-----|",
|
||||
]
|
||||
for api, capa_map in API_MAPS[Api.inference].items():
|
||||
for capa, tests in capa_map.items():
|
||||
for test_name in tests:
|
||||
model_id = self.text_model_id if "text" in test_name else self.vision_model_id
|
||||
test_nodeids = self.test_name_to_nodeid[test_name]
|
||||
if not test_nodeids:
|
||||
continue
|
||||
|
||||
# There might be more than one parametrizations for the same test function. We take
|
||||
# the result of the first one for now. Ideally we should mark the test as failed if
|
||||
# any of the parametrizations failed.
|
||||
test_table.append(
|
||||
f"| {model_id} | /{api} | {capa} | {test_name} | {self._print_result_icon(self.test_data[test_nodeids[0]])} |"
|
||||
)
|
||||
|
||||
report.extend(test_table)
|
||||
|
||||
name_map = {Api.vector_io: "Vector IO", Api.agents: "Agents"}
|
||||
providers = self.client.providers.list()
|
||||
for api_group in [Api.vector_io, Api.agents]:
|
||||
api_capitalized = name_map[api_group]
|
||||
report.append(f"\n## {api_capitalized}")
|
||||
test_table = [
|
||||
"| Provider | API | Capability | Test | Status |",
|
||||
"|:-----|:-----|:-----|:-----|:-----|",
|
||||
]
|
||||
provider = [p for p in providers if p.api == str(api_group.name)]
|
||||
provider_str = ",".join(provider) if provider else ""
|
||||
for api, capa_map in API_MAPS[api_group].items():
|
||||
for capa, tests in capa_map.items():
|
||||
for test_name in tests:
|
||||
test_nodeids = self.test_name_to_nodeid[test_name]
|
||||
if not test_nodeids:
|
||||
continue
|
||||
test_table.append(
|
||||
f"| {provider_str} | /{api} | {capa} | {test_name} | {self._print_result_icon(self.test_data[test_nodeids[0]])} |"
|
||||
)
|
||||
report.extend(test_table)
|
||||
|
||||
output_file = self.output_path
|
||||
text = "\n".join(report) + "\n"
|
||||
output_file.write_text(text)
|
||||
cprint(f"\nReport generated: {output_file.absolute()}", "green")
|
||||
|
||||
def pytest_runtest_makereport(self, item, call):
|
||||
func_name = getattr(item, "originalname", item.name)
|
||||
self.test_name_to_nodeid[func_name].append(item.nodeid)
|
||||
|
||||
# Get values from fixtures for report output
|
||||
if model_id := item.funcargs.get("text_model_id"):
|
||||
text_model = model_id.split("/")[1]
|
||||
self.text_model_id = self.text_model_id or text_model
|
||||
elif model_id := item.funcargs.get("vision_model_id"):
|
||||
vision_model = model_id.split("/")[1]
|
||||
self.vision_model_id = self.vision_model_id or vision_model
|
||||
|
||||
if not self.client:
|
||||
self.client = item.funcargs.get("llama_stack_client")
|
||||
|
||||
def _print_result_icon(self, result):
|
||||
if result == "Passed":
|
||||
return "✅"
|
||||
elif result == "Failed" or result == "Error":
|
||||
return "❌"
|
||||
else:
|
||||
# result == "Skipped":
|
||||
return "⏭️"
|
||||
|
||||
def _process_outcome(self, report: CollectReport):
|
||||
if self._is_error(report):
|
||||
return "Error"
|
||||
if hasattr(report, "wasxfail"):
|
||||
if report.outcome in ["passed", "failed"]:
|
||||
return "XPassed"
|
||||
if report.outcome == "skipped":
|
||||
return "XFailed"
|
||||
return report.outcome.capitalize()
|
||||
|
||||
def _is_error(self, report: CollectReport):
|
||||
return report.when in ["setup", "teardown", "collect"] and report.outcome == "failed"
|
||||
21
tests/unit/README.md
Normal file
21
tests/unit/README.md
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
# Llama Stack Unit Tests
|
||||
|
||||
You can run the unit tests by running:
|
||||
|
||||
```bash
|
||||
source .venv/bin/activate
|
||||
./scripts/unit-tests.sh [PYTEST_ARGS]
|
||||
```
|
||||
|
||||
Any additional arguments are passed to pytest. For example, you can specify a test directory, a specific test file, or any pytest flags (e.g., -vvv for verbosity). If no test directory is specified, it defaults to "tests/unit", e.g:
|
||||
|
||||
```bash
|
||||
./scripts/unit-tests.sh tests/unit/registry/test_registry.py -vvv
|
||||
```
|
||||
|
||||
If you'd like to run for a non-default version of Python (currently 3.10), pass `PYTHON_VERSION` variable as follows:
|
||||
|
||||
```
|
||||
source .venv/bin/activate
|
||||
PYTHON_VERSION=3.13 ./scripts/unit-tests.sh
|
||||
```
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
# 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 os
|
||||
|
||||
import yaml
|
||||
|
||||
from llama_stack.apis.inference.inference import (
|
||||
OpenAIChatCompletion,
|
||||
)
|
||||
|
||||
FIXTURES_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
|
||||
def load_chat_completion_fixture(filename: str) -> OpenAIChatCompletion:
|
||||
fixture_path = os.path.join(FIXTURES_DIR, filename)
|
||||
|
||||
with open(fixture_path) as f:
|
||||
data = yaml.safe_load(f)
|
||||
return OpenAIChatCompletion(**data)
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
id: chat-completion-123
|
||||
choices:
|
||||
- message:
|
||||
content: "Dublin"
|
||||
role: assistant
|
||||
finish_reason: stop
|
||||
index: 0
|
||||
created: 1234567890
|
||||
model: meta-llama/Llama-3.1-8B-Instruct
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
id: chat-completion-123
|
||||
choices:
|
||||
- message:
|
||||
tool_calls:
|
||||
- id: tool_call_123
|
||||
type: function
|
||||
function:
|
||||
name: web_search
|
||||
arguments: '{"query":"What is the capital of Ireland?"}'
|
||||
role: assistant
|
||||
finish_reason: stop
|
||||
index: 0
|
||||
created: 1234567890
|
||||
model: meta-llama/Llama-3.1-8B-Instruct
|
||||
|
|
@ -4,27 +4,32 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from unittest.mock import AsyncMock
|
||||
from unittest.mock import AsyncMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.agents.openai_responses import (
|
||||
OpenAIResponseInputItemList,
|
||||
OpenAIResponseInputMessageContentText,
|
||||
OpenAIResponseInputToolWebSearch,
|
||||
OpenAIResponseOutputMessage,
|
||||
OpenAIResponseMessage,
|
||||
OpenAIResponseObject,
|
||||
OpenAIResponseOutputMessageContentOutputText,
|
||||
OpenAIResponseOutputMessageWebSearchToolCall,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChatCompletionToolCall,
|
||||
OpenAIChatCompletionToolCallFunction,
|
||||
OpenAIChoice,
|
||||
OpenAIChatCompletionContentPartTextParam,
|
||||
OpenAIDeveloperMessageParam,
|
||||
OpenAIUserMessageParam,
|
||||
)
|
||||
from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime
|
||||
from llama_stack.providers.inline.agents.meta_reference.openai_responses import (
|
||||
OpenAIResponsePreviousResponseWithInputItems,
|
||||
OpenAIResponsesImpl,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore import KVStore
|
||||
from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -65,21 +70,11 @@ def openai_responses_impl(mock_kvstore, mock_inference_api, mock_tool_groups_api
|
|||
async def test_create_openai_response_with_string_input(openai_responses_impl, mock_inference_api):
|
||||
"""Test creating an OpenAI response with a simple string input."""
|
||||
# Setup
|
||||
input_text = "Hello, world!"
|
||||
input_text = "What is the capital of Ireland?"
|
||||
model = "meta-llama/Llama-3.1-8B-Instruct"
|
||||
|
||||
mock_chat_completion = OpenAIChatCompletion(
|
||||
id="chat-completion-123",
|
||||
choices=[
|
||||
OpenAIChoice(
|
||||
message=OpenAIAssistantMessageParam(content="Hello! How can I help you?"),
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
)
|
||||
],
|
||||
created=1234567890,
|
||||
model=model,
|
||||
)
|
||||
# Load the chat completion fixture
|
||||
mock_chat_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
|
||||
mock_inference_api.openai_chat_completion.return_value = mock_chat_completion
|
||||
|
||||
# Execute
|
||||
|
|
@ -92,7 +87,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
|
|||
# Verify
|
||||
mock_inference_api.openai_chat_completion.assert_called_once_with(
|
||||
model=model,
|
||||
messages=[OpenAIUserMessageParam(role="user", content="Hello, world!", name=None)],
|
||||
messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
|
||||
tools=None,
|
||||
stream=False,
|
||||
temperature=0.1,
|
||||
|
|
@ -100,55 +95,25 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
|
|||
openai_responses_impl.persistence_store.set.assert_called_once()
|
||||
assert result.model == model
|
||||
assert len(result.output) == 1
|
||||
assert isinstance(result.output[0], OpenAIResponseOutputMessage)
|
||||
assert result.output[0].content[0].text == "Hello! How can I help you?"
|
||||
assert isinstance(result.output[0], OpenAIResponseMessage)
|
||||
assert result.output[0].content[0].text == "Dublin"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_openai_response_with_string_input_with_tools(openai_responses_impl, mock_inference_api):
|
||||
"""Test creating an OpenAI response with a simple string input and tools."""
|
||||
# Setup
|
||||
input_text = "What was the score of todays game?"
|
||||
input_text = "What is the capital of Ireland?"
|
||||
model = "meta-llama/Llama-3.1-8B-Instruct"
|
||||
|
||||
mock_chat_completions = [
|
||||
OpenAIChatCompletion(
|
||||
id="chat-completion-123",
|
||||
choices=[
|
||||
OpenAIChoice(
|
||||
message=OpenAIAssistantMessageParam(
|
||||
tool_calls=[
|
||||
OpenAIChatCompletionToolCall(
|
||||
id="tool_call_123",
|
||||
type="function",
|
||||
function=OpenAIChatCompletionToolCallFunction(
|
||||
name="web_search", arguments='{"query":"What was the score of todays game?"}'
|
||||
),
|
||||
)
|
||||
],
|
||||
),
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
)
|
||||
],
|
||||
created=1234567890,
|
||||
model=model,
|
||||
),
|
||||
OpenAIChatCompletion(
|
||||
id="chat-completion-123",
|
||||
choices=[
|
||||
OpenAIChoice(
|
||||
message=OpenAIAssistantMessageParam(content="The score of todays game was 10-12"),
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
)
|
||||
],
|
||||
created=1234567890,
|
||||
model=model,
|
||||
),
|
||||
]
|
||||
# Load the chat completion fixtures
|
||||
tool_call_completion = load_chat_completion_fixture("tool_call_completion.yaml")
|
||||
tool_response_completion = load_chat_completion_fixture("simple_chat_completion.yaml")
|
||||
|
||||
mock_inference_api.openai_chat_completion.side_effect = mock_chat_completions
|
||||
mock_inference_api.openai_chat_completion.side_effect = [
|
||||
tool_call_completion,
|
||||
tool_response_completion,
|
||||
]
|
||||
|
||||
openai_responses_impl.tool_groups_api.get_tool.return_value = Tool(
|
||||
identifier="web_search",
|
||||
|
|
@ -163,7 +128,7 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
|
|||
|
||||
openai_responses_impl.tool_runtime_api.invoke_tool.return_value = ToolInvocationResult(
|
||||
status="completed",
|
||||
content="The score of todays game was 10-12",
|
||||
content="Dublin",
|
||||
)
|
||||
|
||||
# Execute
|
||||
|
|
@ -180,23 +145,172 @@ async def test_create_openai_response_with_string_input_with_tools(openai_respon
|
|||
|
||||
# Verify
|
||||
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
|
||||
assert first_call.kwargs["messages"][0].content == "What was the score of todays game?"
|
||||
assert first_call.kwargs["messages"][0].content == "What is the capital of Ireland?"
|
||||
assert first_call.kwargs["tools"] is not None
|
||||
assert first_call.kwargs["temperature"] == 0.1
|
||||
|
||||
second_call = mock_inference_api.openai_chat_completion.call_args_list[1]
|
||||
assert second_call.kwargs["messages"][-1].content == "The score of todays game was 10-12"
|
||||
assert second_call.kwargs["messages"][-1].content == "Dublin"
|
||||
assert second_call.kwargs["temperature"] == 0.1
|
||||
|
||||
openai_responses_impl.tool_groups_api.get_tool.assert_called_once_with("web_search")
|
||||
openai_responses_impl.tool_runtime_api.invoke_tool.assert_called_once_with(
|
||||
tool_name="web_search",
|
||||
kwargs={"query": "What was the score of todays game?"},
|
||||
kwargs={"query": "What is the capital of Ireland?"},
|
||||
)
|
||||
|
||||
openai_responses_impl.persistence_store.set.assert_called_once()
|
||||
|
||||
# Check that we got the content from our mocked tool execution result
|
||||
assert len(result.output) >= 1
|
||||
assert isinstance(result.output[1], OpenAIResponseOutputMessage)
|
||||
assert result.output[1].content[0].text == "The score of todays game was 10-12"
|
||||
assert isinstance(result.output[1], OpenAIResponseMessage)
|
||||
assert result.output[1].content[0].text == "Dublin"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api):
|
||||
"""Test creating an OpenAI response with multiple messages."""
|
||||
# Setup
|
||||
input_messages = [
|
||||
OpenAIResponseMessage(role="developer", content="You are a helpful assistant", name=None),
|
||||
OpenAIResponseMessage(role="user", content="Name some towns in Ireland", name=None),
|
||||
OpenAIResponseMessage(
|
||||
role="assistant",
|
||||
content=[
|
||||
OpenAIResponseInputMessageContentText(text="Galway, Longford, Sligo"),
|
||||
OpenAIResponseInputMessageContentText(text="Dublin"),
|
||||
],
|
||||
name=None,
|
||||
),
|
||||
OpenAIResponseMessage(role="user", content="Which is the largest town in Ireland?", name=None),
|
||||
]
|
||||
model = "meta-llama/Llama-3.1-8B-Instruct"
|
||||
|
||||
mock_inference_api.openai_chat_completion.return_value = load_chat_completion_fixture("simple_chat_completion.yaml")
|
||||
|
||||
# Execute
|
||||
await openai_responses_impl.create_openai_response(
|
||||
input=input_messages,
|
||||
model=model,
|
||||
temperature=0.1,
|
||||
)
|
||||
|
||||
# Verify the the correct messages were sent to the inference API i.e.
|
||||
# All of the responses message were convered to the chat completion message objects
|
||||
inference_messages = mock_inference_api.openai_chat_completion.call_args_list[0].kwargs["messages"]
|
||||
for i, m in enumerate(input_messages):
|
||||
if isinstance(m.content, str):
|
||||
assert inference_messages[i].content == m.content
|
||||
else:
|
||||
assert inference_messages[i].content[0].text == m.content[0].text
|
||||
assert isinstance(inference_messages[i].content[0], OpenAIChatCompletionContentPartTextParam)
|
||||
assert inference_messages[i].role == m.role
|
||||
if m.role == "user":
|
||||
assert isinstance(inference_messages[i], OpenAIUserMessageParam)
|
||||
elif m.role == "assistant":
|
||||
assert isinstance(inference_messages[i], OpenAIAssistantMessageParam)
|
||||
else:
|
||||
assert isinstance(inference_messages[i], OpenAIDeveloperMessageParam)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prepend_previous_response_none(openai_responses_impl):
|
||||
"""Test prepending no previous response to a new response."""
|
||||
|
||||
input = await openai_responses_impl._prepend_previous_response("fake_input", None)
|
||||
assert input == "fake_input"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch.object(OpenAIResponsesImpl, "_get_previous_response_with_input")
|
||||
async def test_prepend_previous_response_basic(get_previous_response_with_input, openai_responses_impl):
|
||||
"""Test prepending a basic previous response to a new response."""
|
||||
|
||||
input_item_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content=[OpenAIResponseInputMessageContentText(text="fake_previous_input")],
|
||||
role="user",
|
||||
)
|
||||
input_items = OpenAIResponseInputItemList(data=[input_item_message])
|
||||
response_output_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content=[OpenAIResponseOutputMessageContentOutputText(text="fake_response")],
|
||||
status="completed",
|
||||
role="assistant",
|
||||
)
|
||||
response = OpenAIResponseObject(
|
||||
created_at=1,
|
||||
id="resp_123",
|
||||
model="fake_model",
|
||||
output=[response_output_message],
|
||||
status="completed",
|
||||
)
|
||||
previous_response = OpenAIResponsePreviousResponseWithInputItems(
|
||||
input_items=input_items,
|
||||
response=response,
|
||||
)
|
||||
get_previous_response_with_input.return_value = previous_response
|
||||
|
||||
input = await openai_responses_impl._prepend_previous_response("fake_input", "resp_123")
|
||||
|
||||
assert len(input) == 3
|
||||
# Check for previous input
|
||||
assert isinstance(input[0], OpenAIResponseMessage)
|
||||
assert input[0].content[0].text == "fake_previous_input"
|
||||
# Check for previous output
|
||||
assert isinstance(input[1], OpenAIResponseMessage)
|
||||
assert input[1].content[0].text == "fake_response"
|
||||
# Check for new input
|
||||
assert isinstance(input[2], OpenAIResponseMessage)
|
||||
assert input[2].content == "fake_input"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch.object(OpenAIResponsesImpl, "_get_previous_response_with_input")
|
||||
async def test_prepend_previous_response_web_search(get_previous_response_with_input, openai_responses_impl):
|
||||
"""Test prepending a web search previous response to a new response."""
|
||||
|
||||
input_item_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content=[OpenAIResponseInputMessageContentText(text="fake_previous_input")],
|
||||
role="user",
|
||||
)
|
||||
input_items = OpenAIResponseInputItemList(data=[input_item_message])
|
||||
output_web_search = OpenAIResponseOutputMessageWebSearchToolCall(
|
||||
id="ws_123",
|
||||
status="completed",
|
||||
)
|
||||
output_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content=[OpenAIResponseOutputMessageContentOutputText(text="fake_web_search_response")],
|
||||
status="completed",
|
||||
role="assistant",
|
||||
)
|
||||
response = OpenAIResponseObject(
|
||||
created_at=1,
|
||||
id="resp_123",
|
||||
model="fake_model",
|
||||
output=[output_web_search, output_message],
|
||||
status="completed",
|
||||
)
|
||||
previous_response = OpenAIResponsePreviousResponseWithInputItems(
|
||||
input_items=input_items,
|
||||
response=response,
|
||||
)
|
||||
get_previous_response_with_input.return_value = previous_response
|
||||
|
||||
input_messages = [OpenAIResponseMessage(content="fake_input", role="user")]
|
||||
input = await openai_responses_impl._prepend_previous_response(input_messages, "resp_123")
|
||||
|
||||
assert len(input) == 4
|
||||
# Check for previous input
|
||||
assert isinstance(input[0], OpenAIResponseMessage)
|
||||
assert input[0].content[0].text == "fake_previous_input"
|
||||
# Check for previous output web search tool call
|
||||
assert isinstance(input[1], OpenAIResponseOutputMessageWebSearchToolCall)
|
||||
# Check for previous output web search response
|
||||
assert isinstance(input[2], OpenAIResponseMessage)
|
||||
assert input[2].content[0].text == "fake_web_search_response"
|
||||
# Check for new input
|
||||
assert isinstance(input[3], OpenAIResponseMessage)
|
||||
assert input[3].content == "fake_input"
|
||||
|
|
|
|||
19
tests/unit/rag/test_rag_query.py
Normal file
19
tests/unit/rag/test_rag_query.py
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
# 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 unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
|
||||
|
||||
|
||||
class TestRagQuery:
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_raises_on_empty_vector_db_ids(self):
|
||||
rag_tool = MemoryToolRuntimeImpl(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
|
||||
with pytest.raises(ValueError):
|
||||
await rag_tool.query(content=MagicMock(), vector_db_ids=[])
|
||||
|
|
@ -4,7 +4,7 @@ Llama Stack Verifications provide standardized test suites to ensure API compati
|
|||
|
||||
## Overview
|
||||
|
||||
This framework allows you to run the same set of verification tests against different LLM providers' OpenAI-compatible endpoints (Fireworks, Together, Groq, Cerebras, etc., and OpenAI itself) to ensure they meet the expected behavior and interface standards.
|
||||
This framework allows you to run the same set of verification tests against different LLM providers' OpenAI-compatible endpoints (Fireworks, Together, Groq, Cerebras, etc., and OpenAI itself) to ensure they meet the expected behavior and interface standards.
|
||||
|
||||
## Features
|
||||
|
||||
|
|
|
|||
|
|
@ -74,9 +74,6 @@ providers:
|
|||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: code-interpreter
|
||||
provider_type: inline::code-interpreter
|
||||
config: {}
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
|
|
@ -156,8 +153,6 @@ tool_groups:
|
|||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::code_interpreter
|
||||
provider_id: code-interpreter
|
||||
- toolgroup_id: builtin::wolfram_alpha
|
||||
provider_id: wolfram-alpha
|
||||
server:
|
||||
|
|
|
|||
|
|
@ -31,6 +31,26 @@ test_response_web_search:
|
|||
search_context_size: "low"
|
||||
output: "128"
|
||||
|
||||
test_response_custom_tool:
|
||||
test_name: test_response_custom_tool
|
||||
test_params:
|
||||
case:
|
||||
- case_id: "sf_weather"
|
||||
input: "What's the weather like in San Francisco?"
|
||||
tools:
|
||||
- type: function
|
||||
name: get_weather
|
||||
description: Get current temperature for a given location.
|
||||
parameters:
|
||||
additionalProperties: false
|
||||
properties:
|
||||
location:
|
||||
description: "City and country e.g. Bogot\xE1, Colombia"
|
||||
type: string
|
||||
required:
|
||||
- location
|
||||
type: object
|
||||
|
||||
test_response_image:
|
||||
test_name: test_response_image
|
||||
test_params:
|
||||
|
|
@ -59,7 +79,7 @@ test_response_multi_turn_image:
|
|||
- type: input_image
|
||||
image_url: "https://upload.wikimedia.org/wikipedia/commons/f/f7/Llamas%2C_Vernagt-Stausee%2C_Italy.jpg"
|
||||
output: "llama"
|
||||
- input: "Search the web using the search tool for the animal from the previous response. Your search query should be a single phrase that includes the animal's name and the words 'maverick' and 'scout'."
|
||||
- input: "Search the web using the search tool for the animal from the previous response. Your search query should be a single phrase that includes the animal's name and the words 'maverick', 'scout' and 'llm'"
|
||||
tools:
|
||||
- type: web_search
|
||||
output: "model"
|
||||
|
|
|
|||
|
|
@ -124,6 +124,28 @@ def test_response_non_streaming_web_search(request, openai_client, model, provid
|
|||
assert case["output"].lower() in response.output_text.lower().strip()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"case",
|
||||
responses_test_cases["test_response_custom_tool"]["test_params"]["case"],
|
||||
ids=case_id_generator,
|
||||
)
|
||||
def test_response_non_streaming_custom_tool(request, openai_client, model, provider, verification_config, case):
|
||||
test_name_base = get_base_test_name(request)
|
||||
if should_skip_test(verification_config, provider, model, test_name_base):
|
||||
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
|
||||
|
||||
response = openai_client.responses.create(
|
||||
model=model,
|
||||
input=case["input"],
|
||||
tools=case["tools"],
|
||||
stream=False,
|
||||
)
|
||||
assert len(response.output) == 1
|
||||
assert response.output[0].type == "function_call"
|
||||
assert response.output[0].status == "completed"
|
||||
assert response.output[0].name == "get_weather"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"case",
|
||||
responses_test_cases["test_response_image"]["test_params"]["case"],
|
||||
|
|
|
|||
76
uv.lock
generated
76
uv.lock
generated
|
|
@ -1419,7 +1419,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "llama-stack"
|
||||
version = "0.2.5"
|
||||
version = "0.2.6"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "blobfile" },
|
||||
|
|
@ -1533,8 +1533,8 @@ requires-dist = [
|
|||
{ name = "jinja2", marker = "extra == 'codegen'", specifier = ">=3.1.6" },
|
||||
{ name = "jsonschema" },
|
||||
{ name = "kubernetes" },
|
||||
{ name = "llama-stack-client", specifier = ">=0.2.5" },
|
||||
{ name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.5" },
|
||||
{ name = "llama-stack-client", specifier = ">=0.2.6" },
|
||||
{ name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.6" },
|
||||
{ name = "mcp", marker = "extra == 'test'" },
|
||||
{ name = "myst-parser", marker = "extra == 'docs'" },
|
||||
{ name = "nbval", marker = "extra == 'dev'" },
|
||||
|
|
@ -1591,7 +1591,7 @@ provides-extras = ["dev", "unit", "test", "docs", "codegen", "ui"]
|
|||
|
||||
[[package]]
|
||||
name = "llama-stack-client"
|
||||
version = "0.2.5"
|
||||
version = "0.2.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
|
|
@ -1608,9 +1608,9 @@ dependencies = [
|
|||
{ name = "tqdm" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/38/4b/d4758a95a5eed8ad821dd782a3f34843d79dc9adc6bd6e01b13cbec904ca/llama_stack_client-0.2.5.tar.gz", hash = "sha256:509c6336027a13b8b89780a85bd1cd45b3659de3929357fd9d8113ea0d6a3f05", size = 259262, upload-time = "2025-05-03T21:30:29.177Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/14/1e/14e549b5fb7ac09347686f6f1c28ee2bcd16cf575aab934687f20b5cec12/llama_stack_client-0.2.6.tar.gz", hash = "sha256:a03a2b0bd43bdb0083378f481614bb65592d7f669a821d0b618b1dfc7d1c8325", size = 259270, upload-time = "2025-05-12T18:01:30.537Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e8/ac/50eb130fa6126a0a3a1f945b61dc5b3bb11f7badbe877ce0112321851d32/llama_stack_client-0.2.5-py3-none-any.whl", hash = "sha256:504abe51bdd1da658e00f720997e7845d10ca1eb74de52af90f82bfbce8e2e67", size = 292727, upload-time = "2025-05-03T21:30:27.388Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e6/af/93895ce23d3c8e676004e7b69deaea726e81acaaf9cf00090baace904c03/llama_stack_client-0.2.6-py3-none-any.whl", hash = "sha256:9f39dea2dba6767b654d5119f99dfc2b89f838470a547bc5d8def5a230decfcd", size = 292726, upload-time = "2025-05-12T18:01:29.14Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3423,22 +3423,22 @@ wheels = [
|
|||
name = "safetensors"
|
||||
version = "0.5.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/71/7e/2d5d6ee7b40c0682315367ec7475693d110f512922d582fef1bd4a63adc3/safetensors-0.5.3.tar.gz", hash = "sha256:b6b0d6ecacec39a4fdd99cc19f4576f5219ce858e6fd8dbe7609df0b8dc56965", size = 67210 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/71/7e/2d5d6ee7b40c0682315367ec7475693d110f512922d582fef1bd4a63adc3/safetensors-0.5.3.tar.gz", hash = "sha256:b6b0d6ecacec39a4fdd99cc19f4576f5219ce858e6fd8dbe7609df0b8dc56965", size = 67210, upload-time = "2025-02-26T09:15:13.155Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/18/ae/88f6c49dbd0cc4da0e08610019a3c78a7d390879a919411a410a1876d03a/safetensors-0.5.3-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:bd20eb133db8ed15b40110b7c00c6df51655a2998132193de2f75f72d99c7073", size = 436917 },
|
||||
{ url = "https://files.pythonhosted.org/packages/b8/3b/11f1b4a2f5d2ab7da34ecc062b0bc301f2be024d110a6466726bec8c055c/safetensors-0.5.3-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:21d01c14ff6c415c485616b8b0bf961c46b3b343ca59110d38d744e577f9cce7", size = 418419 },
|
||||
{ url = "https://files.pythonhosted.org/packages/5d/9a/add3e6fef267658075c5a41573c26d42d80c935cdc992384dfae435feaef/safetensors-0.5.3-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:11bce6164887cd491ca75c2326a113ba934be596e22b28b1742ce27b1d076467", size = 459493 },
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||||
{ url = "https://files.pythonhosted.org/packages/df/5c/bf2cae92222513cc23b3ff85c4a1bb2811a2c3583ac0f8e8d502751de934/safetensors-0.5.3-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4a243be3590bc3301c821da7a18d87224ef35cbd3e5f5727e4e0728b8172411e", size = 472400 },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/ce/20/1fbe16f9b815f6c5a672f5b760951e20e17e43f67f231428f871909a37f6/safetensors-0.5.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1077f3e94182d72618357b04b5ced540ceb71c8a813d3319f1aba448e68a770d", size = 502241 },
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||||
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sdist = { url = "https://files.pythonhosted.org/packages/c0/29/37877123d6633a188997d75dc17d6f526745d63361794348ce748db23d49/transformers-4.50.3.tar.gz", hash = "sha256:1d795d24925e615a8e63687d077e4f7348c2702eb87032286eaa76d83cdc684f", size = 8774363 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c0/29/37877123d6633a188997d75dc17d6f526745d63361794348ce748db23d49/transformers-4.50.3.tar.gz", hash = "sha256:1d795d24925e615a8e63687d077e4f7348c2702eb87032286eaa76d83cdc684f", size = 8774363, upload-time = "2025-03-28T18:21:02.878Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/aa/22/733a6fc4a6445d835242f64c490fdd30f4a08d58f2b788613de3f9170692/transformers-4.50.3-py3-none-any.whl", hash = "sha256:6111610a43dec24ef32c3df0632c6b25b07d9711c01d9e1077bdd2ff6b14a38c", size = 10180411 },
|
||||
{ url = "https://files.pythonhosted.org/packages/aa/22/733a6fc4a6445d835242f64c490fdd30f4a08d58f2b788613de3f9170692/transformers-4.50.3-py3-none-any.whl", hash = "sha256:6111610a43dec24ef32c3df0632c6b25b07d9711c01d9e1077bdd2ff6b14a38c", size = 10180411, upload-time = "2025-03-28T18:20:59.265Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue