mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-03 17:29:01 +00:00
Merge branch 'main' into feat/litellm_sambanova_usage
This commit is contained in:
Jorge Piedrahita Ortiz
GitHub
commit
02a4f9ac59
69 changed files with 1128 additions and 445 deletions
2
.github/TRIAGERS.md
vendored
Normal file
2
.github/TRIAGERS.md
vendored
Normal file
|
|
@ -0,0 +1,2 @@
|
|||
# This file documents Triage members in the Llama Stack community
|
||||
@franciscojavierarceo @leseb
|
||||
29
.github/workflows/changelog.yml
vendored
Normal file
29
.github/workflows/changelog.yml
vendored
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
name: Update Changelog
|
||||
|
||||
on:
|
||||
release:
|
||||
types: [published, unpublished, created, edited, deleted, released]
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
generate_changelog:
|
||||
name: Generate changelog
|
||||
permissions:
|
||||
contents: write # for peter-evans/create-pull-request to create branch
|
||||
pull-requests: write # for peter-evans/create-pull-request to create a PR
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
with:
|
||||
ref: main
|
||||
fetch-depth: 0
|
||||
- run: |
|
||||
python ./scripts/gen-changelog.py
|
||||
- uses: peter-evans/create-pull-request@v7
|
||||
with:
|
||||
title: 'docs: update CHANGELOG.md for ${{ github.ref_name }}'
|
||||
commit-message: 'docs: update CHANGELOG.md for ${{ github.ref_name }}'
|
||||
branch: create-pull-request/changelog
|
||||
signoff: true
|
||||
2
.github/workflows/integration-tests.yml
vendored
2
.github/workflows/integration-tests.yml
vendored
|
|
@ -1,4 +1,4 @@
|
|||
name: Integration tests
|
||||
name: Integration Tests
|
||||
|
||||
on:
|
||||
push:
|
||||
|
|
|
|||
|
|
@ -86,7 +86,7 @@ LLAMA_STACK_CONFIG=
|
|||
|
||||
And then use this dotenv file when running client SDK tests via the following:
|
||||
```bash
|
||||
uv run --env-file .env -- pytest -v tests/api/inference/test_text_inference.py
|
||||
uv run --env-file .env -- pytest -v tests/integration/inference/test_text_inference.py
|
||||
```
|
||||
|
||||
## Pre-commit Hooks
|
||||
|
|
|
|||
23
README.md
23
README.md
|
|
@ -4,7 +4,8 @@
|
|||
[](https://pypi.org/project/llama-stack/)
|
||||
[](https://github.com/meta-llama/llama-stack/blob/main/LICENSE)
|
||||
[](https://discord.gg/llama-stack)
|
||||
|
||||
[](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)
|
||||
|
||||
|
|
@ -72,26 +73,6 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider
|
|||
| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/fireworks.html) |
|
||||
| vLLM | [llamastack/distribution-remote-vllm](https://hub.docker.com/repository/docker/llamastack/distribution-remote-vllm/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/remote-vllm.html) |
|
||||
|
||||
### Installation
|
||||
|
||||
You have two ways to install this repository:
|
||||
|
||||
* **Install as a package**:
|
||||
You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command:
|
||||
```bash
|
||||
pip install llama-stack
|
||||
```
|
||||
|
||||
* **Install from source**:
|
||||
If you prefer to install from the source code, we recommend using [uv](https://github.com/astral-sh/uv).
|
||||
Then, run the following commands:
|
||||
```bash
|
||||
git clone git@github.com:meta-llama/llama-stack.git
|
||||
cd llama-stack
|
||||
|
||||
uv sync
|
||||
uv pip install -e .
|
||||
```
|
||||
|
||||
### Documentation
|
||||
|
||||
|
|
|
|||
263
docs/_static/llama-stack-spec.html
vendored
263
docs/_static/llama-stack-spec.html
vendored
|
|
@ -1101,14 +1101,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/Benchmark"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/Benchmark"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1150,14 +1143,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/Dataset"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/Dataset"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1232,14 +1218,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/Model"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/Model"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1314,14 +1293,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/ScoringFn"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/ScoringFn"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1363,14 +1335,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/Shield"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/Shield"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1673,14 +1638,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/PostTrainingJobArtifactsResponse"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1722,14 +1680,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/PostTrainingJobStatusResponse"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/PostTrainingJobStatusResponse"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1804,14 +1755,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/FileUploadResponse"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/FileUploadResponse"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -1913,14 +1857,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/VectorDB"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/VectorDB"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2246,14 +2183,7 @@
|
|||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/JobStatus"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
"$ref": "#/components/schemas/JobStatus"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -4229,70 +4159,80 @@
|
|||
]
|
||||
},
|
||||
"arguments": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
},
|
||||
{
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"oneOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "integer"
|
||||
},
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "boolean"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"arguments_json": {
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
|
|
@ -7857,7 +7797,8 @@
|
|||
"type": "object",
|
||||
"properties": {
|
||||
"document_id": {
|
||||
"type": "string"
|
||||
"type": "string",
|
||||
"description": "The unique identifier for the document."
|
||||
},
|
||||
"content": {
|
||||
"oneOf": [
|
||||
|
|
@ -7876,10 +7817,12 @@
|
|||
{
|
||||
"$ref": "#/components/schemas/URL"
|
||||
}
|
||||
]
|
||||
],
|
||||
"description": "The content of the document."
|
||||
},
|
||||
"mime_type": {
|
||||
"type": "string"
|
||||
"type": "string",
|
||||
"description": "The MIME type of the document."
|
||||
},
|
||||
"metadata": {
|
||||
"type": "object",
|
||||
|
|
@ -7904,7 +7847,8 @@
|
|||
"type": "object"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"description": "Additional metadata for the document."
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
|
|
@ -7913,7 +7857,8 @@
|
|||
"content",
|
||||
"metadata"
|
||||
],
|
||||
"title": "RAGDocument"
|
||||
"title": "RAGDocument",
|
||||
"description": "A document to be used for document ingestion in the RAG Tool."
|
||||
},
|
||||
"InsertRequest": {
|
||||
"type": "object",
|
||||
|
|
@ -9917,23 +9862,6 @@
|
|||
],
|
||||
"title": "ScoreBatchResponse"
|
||||
},
|
||||
"AlgorithmConfig": {
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/LoraFinetuningConfig"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/QATFinetuningConfig"
|
||||
}
|
||||
],
|
||||
"discriminator": {
|
||||
"propertyName": "type",
|
||||
"mapping": {
|
||||
"LoRA": "#/components/schemas/LoraFinetuningConfig",
|
||||
"QAT": "#/components/schemas/QATFinetuningConfig"
|
||||
}
|
||||
}
|
||||
},
|
||||
"LoraFinetuningConfig": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
|
|
@ -10069,7 +9997,14 @@
|
|||
"type": "string"
|
||||
},
|
||||
"algorithm_config": {
|
||||
"$ref": "#/components/schemas/AlgorithmConfig"
|
||||
"oneOf": [
|
||||
{
|
||||
"$ref": "#/components/schemas/LoraFinetuningConfig"
|
||||
},
|
||||
{
|
||||
"$ref": "#/components/schemas/QATFinetuningConfig"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"additionalProperties": false,
|
||||
|
|
|
|||
111
docs/_static/llama-stack-spec.yaml
vendored
111
docs/_static/llama-stack-spec.yaml
vendored
|
|
@ -757,9 +757,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/Benchmark'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/Benchmark'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -787,9 +785,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/Dataset'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/Dataset'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -840,9 +836,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/Model'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/Model'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -893,9 +887,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/ScoringFn'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/ScoringFn'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -923,9 +915,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/Shield'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/Shield'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -1127,9 +1117,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -1157,9 +1145,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/PostTrainingJobStatusResponse'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/PostTrainingJobStatusResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -1210,9 +1196,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/FileUploadResponse'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/FileUploadResponse'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -1281,9 +1265,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/VectorDB'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/VectorDB'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -1509,9 +1491,7 @@ paths:
|
|||
content:
|
||||
application/json:
|
||||
schema:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/JobStatus'
|
||||
- type: 'null'
|
||||
$ref: '#/components/schemas/JobStatus'
|
||||
'400':
|
||||
$ref: '#/components/responses/BadRequest400'
|
||||
'429':
|
||||
|
|
@ -2884,30 +2864,34 @@ components:
|
|||
title: BuiltinTool
|
||||
- type: string
|
||||
arguments:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
- type: array
|
||||
items:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
- type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
- type: array
|
||||
items:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
- type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: string
|
||||
- type: integer
|
||||
- type: number
|
||||
- type: boolean
|
||||
- type: 'null'
|
||||
arguments_json:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- call_id
|
||||
|
|
@ -5395,6 +5379,7 @@ components:
|
|||
properties:
|
||||
document_id:
|
||||
type: string
|
||||
description: The unique identifier for the document.
|
||||
content:
|
||||
oneOf:
|
||||
- type: string
|
||||
|
|
@ -5403,8 +5388,10 @@ components:
|
|||
items:
|
||||
$ref: '#/components/schemas/InterleavedContentItem'
|
||||
- $ref: '#/components/schemas/URL'
|
||||
description: The content of the document.
|
||||
mime_type:
|
||||
type: string
|
||||
description: The MIME type of the document.
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -5415,12 +5402,15 @@ components:
|
|||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: Additional metadata for the document.
|
||||
additionalProperties: false
|
||||
required:
|
||||
- document_id
|
||||
- content
|
||||
- metadata
|
||||
title: RAGDocument
|
||||
description: >-
|
||||
A document to be used for document ingestion in the RAG Tool.
|
||||
InsertRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6698,15 +6688,6 @@ components:
|
|||
required:
|
||||
- results
|
||||
title: ScoreBatchResponse
|
||||
AlgorithmConfig:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/LoraFinetuningConfig'
|
||||
- $ref: '#/components/schemas/QATFinetuningConfig'
|
||||
discriminator:
|
||||
propertyName: type
|
||||
mapping:
|
||||
LoRA: '#/components/schemas/LoraFinetuningConfig'
|
||||
QAT: '#/components/schemas/QATFinetuningConfig'
|
||||
LoraFinetuningConfig:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6790,7 +6771,9 @@ components:
|
|||
checkpoint_dir:
|
||||
type: string
|
||||
algorithm_config:
|
||||
$ref: '#/components/schemas/AlgorithmConfig'
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/LoraFinetuningConfig'
|
||||
- $ref: '#/components/schemas/QATFinetuningConfig'
|
||||
additionalProperties: false
|
||||
required:
|
||||
- job_uuid
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import sys
|
||||
import fire
|
||||
import ruamel.yaml as yaml
|
||||
|
||||
|
|
@ -21,7 +21,7 @@ from llama_stack.distribution.stack import LlamaStack # noqa: E402
|
|||
|
||||
from .pyopenapi.options import Options # noqa: E402
|
||||
from .pyopenapi.specification import Info, Server # noqa: E402
|
||||
from .pyopenapi.utility import Specification # noqa: E402
|
||||
from .pyopenapi.utility import Specification, validate_api_method_return_types # noqa: E402
|
||||
|
||||
|
||||
def str_presenter(dumper, data):
|
||||
|
|
@ -39,6 +39,14 @@ def main(output_dir: str):
|
|||
if not output_dir.exists():
|
||||
raise ValueError(f"Directory {output_dir} does not exist")
|
||||
|
||||
# Validate API protocols before generating spec
|
||||
print("Validating API method return types...")
|
||||
return_type_errors = validate_api_method_return_types()
|
||||
if return_type_errors:
|
||||
print("\nAPI Method Return Type Validation Errors:\n")
|
||||
for error in return_type_errors:
|
||||
print(error)
|
||||
sys.exit(1)
|
||||
now = str(datetime.now())
|
||||
print(
|
||||
"Converting the spec to YAML (openapi.yaml) and HTML (openapi.html) at " + now
|
||||
|
|
|
|||
|
|
@ -6,16 +6,19 @@
|
|||
|
||||
import json
|
||||
import typing
|
||||
import inspect
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import TextIO
|
||||
from typing import Any, Dict, List, Optional, Protocol, Type, Union, get_type_hints, get_origin, get_args
|
||||
|
||||
from llama_stack.strong_typing.schema import object_to_json, StrictJsonType
|
||||
from llama_stack.distribution.resolver import api_protocol_map
|
||||
|
||||
from .generator import Generator
|
||||
from .options import Options
|
||||
from .specification import Document
|
||||
|
||||
|
||||
THIS_DIR = Path(__file__).parent
|
||||
|
||||
|
||||
|
|
@ -114,3 +117,37 @@ class Specification:
|
|||
)
|
||||
|
||||
f.write(html)
|
||||
|
||||
def is_optional_type(type_: Any) -> bool:
|
||||
"""Check if a type is Optional."""
|
||||
origin = get_origin(type_)
|
||||
args = get_args(type_)
|
||||
return origin is Optional or (origin is Union and type(None) in args)
|
||||
|
||||
|
||||
def validate_api_method_return_types() -> List[str]:
|
||||
"""Validate that all API methods have proper return types."""
|
||||
errors = []
|
||||
protocols = api_protocol_map()
|
||||
|
||||
for protocol_name, protocol in protocols.items():
|
||||
methods = inspect.getmembers(protocol, predicate=inspect.isfunction)
|
||||
|
||||
for method_name, method in methods:
|
||||
if not hasattr(method, '__webmethod__'):
|
||||
continue
|
||||
|
||||
# Only check GET methods
|
||||
if method.__webmethod__.method != "GET":
|
||||
continue
|
||||
|
||||
hints = get_type_hints(method)
|
||||
|
||||
if 'return' not in hints:
|
||||
errors.append(f"Method {protocol_name}.{method_name} has no return type annotation")
|
||||
else:
|
||||
return_type = hints['return']
|
||||
if is_optional_type(return_type):
|
||||
errors.append(f"Method {protocol_name}.{method_name} returns Optional type")
|
||||
|
||||
return errors
|
||||
|
|
|
|||
|
|
@ -15,8 +15,6 @@ Llama Stack defines and standardizes the core building blocks needed to bring ge
|
|||
- **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android
|
||||
- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack
|
||||
|
||||
We focus on making it easy to build production applications with the Llama model family - from the latest Llama 3.3 to specialized models like Llama Guard for safety.
|
||||
|
||||
```{image} ../_static/llama-stack.png
|
||||
:alt: Llama Stack
|
||||
:width: 400px
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
|
|||
|
||||
**Robust Ecosystem**
|
||||
- Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
|
||||
- Ecosystem offers tailored infrastructure, software, and services for deploying Llama models.
|
||||
- Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models.
|
||||
|
||||
|
||||
### Our Philosophy
|
||||
|
|
@ -57,7 +57,6 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
|
|||
- **Composability**: Every component is independent but works together seamlessly
|
||||
- **Production Ready**: Built for real-world applications, not just demos
|
||||
- **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
|
||||
- **Llama First**: Explicit focus on Meta's Llama models and partnering ecosystem
|
||||
|
||||
|
||||
With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.
|
||||
|
|
|
|||
|
|
@ -3,21 +3,36 @@ orphan: true
|
|||
---
|
||||
# Qdrant
|
||||
|
||||
[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
|
||||
[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
|
||||
allows you to store and query vectors directly in memory.
|
||||
That means you'll get fast and efficient vector retrieval.
|
||||
|
||||
> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
|
||||
> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
|
||||
>
|
||||
> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
|
||||
|
||||
|
||||
|
||||
## Features
|
||||
|
||||
- Easy to use
|
||||
- Lightweight and easy to use
|
||||
- Fully integrated with Llama Stack
|
||||
- Apache 2.0 license terms
|
||||
- Store embeddings and their metadata
|
||||
- Supports search by
|
||||
[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
|
||||
and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
|
||||
- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
|
||||
- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
|
||||
- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
|
||||
|
||||
## Usage
|
||||
|
||||
To use Qdrant in your Llama Stack project, follow these steps:
|
||||
|
||||
1. Install the necessary dependencies.
|
||||
2. Configure your Llama Stack project to use Faiss.
|
||||
2. Configure your Llama Stack project to use Qdrant.
|
||||
3. Start storing and querying vectors.
|
||||
|
||||
## Installation
|
||||
|
|
|
|||
|
|
@ -52,7 +52,7 @@ class Benchmarks(Protocol):
|
|||
async def get_benchmark(
|
||||
self,
|
||||
benchmark_id: str,
|
||||
) -> Optional[Benchmark]: ...
|
||||
) -> Benchmark: ...
|
||||
|
||||
@webmethod(route="/eval/benchmarks", method="POST")
|
||||
async def register_benchmark(
|
||||
|
|
|
|||
|
|
@ -121,8 +121,6 @@ class Dataset(CommonDatasetFields, Resource):
|
|||
|
||||
class DatasetInput(CommonDatasetFields, BaseModel):
|
||||
dataset_id: str
|
||||
provider_id: Optional[str] = None
|
||||
provider_dataset_id: Optional[str] = None
|
||||
|
||||
|
||||
class ListDatasetsResponse(BaseModel):
|
||||
|
|
@ -201,7 +199,7 @@ class Datasets(Protocol):
|
|||
async def get_dataset(
|
||||
self,
|
||||
dataset_id: str,
|
||||
) -> Optional[Dataset]: ...
|
||||
) -> Dataset: ...
|
||||
|
||||
@webmethod(route="/datasets", method="GET")
|
||||
async def list_datasets(self) -> ListDatasetsResponse: ...
|
||||
|
|
|
|||
|
|
@ -117,7 +117,7 @@ class Eval(Protocol):
|
|||
"""
|
||||
|
||||
@webmethod(route="/eval/benchmarks/{benchmark_id}/jobs/{job_id}", method="GET")
|
||||
async def job_status(self, benchmark_id: str, job_id: str) -> Optional[JobStatus]:
|
||||
async def job_status(self, benchmark_id: str, job_id: str) -> JobStatus:
|
||||
"""Get the status of a job.
|
||||
|
||||
:param benchmark_id: The ID of the benchmark to run the evaluation on.
|
||||
|
|
|
|||
|
|
@ -115,7 +115,7 @@ class Files(Protocol):
|
|||
async def get_upload_session_info(
|
||||
self,
|
||||
upload_id: str,
|
||||
) -> Optional[FileUploadResponse]:
|
||||
) -> FileUploadResponse:
|
||||
"""
|
||||
Returns information about an existsing upload session
|
||||
|
||||
|
|
|
|||
|
|
@ -66,7 +66,7 @@ class Models(Protocol):
|
|||
async def get_model(
|
||||
self,
|
||||
model_id: str,
|
||||
) -> Optional[Model]: ...
|
||||
) -> Model: ...
|
||||
|
||||
@webmethod(route="/models", method="POST")
|
||||
async def register_model(
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
from datetime import datetime
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Literal, Optional, Protocol, Union
|
||||
from typing import Any, Dict, List, Literal, Optional, Protocol
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from typing_extensions import Annotated
|
||||
|
|
@ -89,7 +89,7 @@ class QATFinetuningConfig(BaseModel):
|
|||
|
||||
|
||||
AlgorithmConfig = register_schema(
|
||||
Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")],
|
||||
Annotated[LoraFinetuningConfig | QATFinetuningConfig, Field(discriminator="type")],
|
||||
name="AlgorithmConfig",
|
||||
)
|
||||
|
||||
|
|
@ -184,7 +184,7 @@ class PostTraining(Protocol):
|
|||
description="Model descriptor from `llama model list`",
|
||||
),
|
||||
checkpoint_dir: Optional[str] = None,
|
||||
algorithm_config: Optional[AlgorithmConfig] = None,
|
||||
algorithm_config: Optional[LoraFinetuningConfig | QATFinetuningConfig] = None,
|
||||
) -> PostTrainingJob: ...
|
||||
|
||||
@webmethod(route="/post-training/preference-optimize", method="POST")
|
||||
|
|
@ -202,10 +202,10 @@ class PostTraining(Protocol):
|
|||
async def get_training_jobs(self) -> ListPostTrainingJobsResponse: ...
|
||||
|
||||
@webmethod(route="/post-training/job/status", method="GET")
|
||||
async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]: ...
|
||||
async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse: ...
|
||||
|
||||
@webmethod(route="/post-training/job/cancel", method="POST")
|
||||
async def cancel_training_job(self, job_uuid: str) -> None: ...
|
||||
|
||||
@webmethod(route="/post-training/job/artifacts", method="GET")
|
||||
async def get_training_job_artifacts(self, job_uuid: str) -> Optional[PostTrainingJobArtifactsResponse]: ...
|
||||
async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse: ...
|
||||
|
|
|
|||
|
|
@ -135,7 +135,7 @@ class ScoringFunctions(Protocol):
|
|||
async def list_scoring_functions(self) -> ListScoringFunctionsResponse: ...
|
||||
|
||||
@webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="GET")
|
||||
async def get_scoring_function(self, scoring_fn_id: str, /) -> Optional[ScoringFn]: ...
|
||||
async def get_scoring_function(self, scoring_fn_id: str, /) -> ScoringFn: ...
|
||||
|
||||
@webmethod(route="/scoring-functions", method="POST")
|
||||
async def register_scoring_function(
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@ class Shields(Protocol):
|
|||
async def list_shields(self) -> ListShieldsResponse: ...
|
||||
|
||||
@webmethod(route="/shields/{identifier:path}", method="GET")
|
||||
async def get_shield(self, identifier: str) -> Optional[Shield]: ...
|
||||
async def get_shield(self, identifier: str) -> Shield: ...
|
||||
|
||||
@webmethod(route="/shields", method="POST")
|
||||
async def register_shield(
|
||||
|
|
|
|||
|
|
@ -17,6 +17,15 @@ from llama_stack.schema_utils import json_schema_type, register_schema, webmetho
|
|||
|
||||
@json_schema_type
|
||||
class RAGDocument(BaseModel):
|
||||
"""
|
||||
A document to be used for document ingestion in the RAG Tool.
|
||||
|
||||
:param document_id: The unique identifier for the document.
|
||||
:param content: The content of the document.
|
||||
:param mime_type: The MIME type of the document.
|
||||
:param metadata: Additional metadata for the document.
|
||||
"""
|
||||
|
||||
document_id: str
|
||||
content: InterleavedContent | URL
|
||||
mime_type: str | None = None
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ class VectorDBs(Protocol):
|
|||
async def get_vector_db(
|
||||
self,
|
||||
vector_db_id: str,
|
||||
) -> Optional[VectorDB]: ...
|
||||
) -> VectorDB: ...
|
||||
|
||||
@webmethod(route="/vector-dbs", method="POST")
|
||||
async def register_vector_db(
|
||||
|
|
|
|||
|
|
@ -125,6 +125,13 @@ class LoggingConfig(BaseModel):
|
|||
)
|
||||
|
||||
|
||||
class AuthenticationConfig(BaseModel):
|
||||
endpoint: str = Field(
|
||||
...,
|
||||
description="Endpoint URL to validate authentication tokens",
|
||||
)
|
||||
|
||||
|
||||
class ServerConfig(BaseModel):
|
||||
port: int = Field(
|
||||
default=8321,
|
||||
|
|
@ -140,6 +147,10 @@ class ServerConfig(BaseModel):
|
|||
default=None,
|
||||
description="Path to TLS key file for HTTPS",
|
||||
)
|
||||
auth: Optional[AuthenticationConfig] = Field(
|
||||
default=None,
|
||||
description="Authentication configuration for the server",
|
||||
)
|
||||
|
||||
|
||||
class StackRunConfig(BaseModel):
|
||||
|
|
|
|||
|
|
@ -20,6 +20,8 @@ from llama_stack.apis.datasets import (
|
|||
DatasetType,
|
||||
DataSource,
|
||||
ListDatasetsResponse,
|
||||
RowsDataSource,
|
||||
URIDataSource,
|
||||
)
|
||||
from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType
|
||||
from llama_stack.apis.resource import ResourceType
|
||||
|
|
@ -219,8 +221,11 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
|
|||
async def list_models(self) -> ListModelsResponse:
|
||||
return ListModelsResponse(data=await self.get_all_with_type("model"))
|
||||
|
||||
async def get_model(self, model_id: str) -> Optional[Model]:
|
||||
return await self.get_object_by_identifier("model", model_id)
|
||||
async def get_model(self, model_id: str) -> Model:
|
||||
model = await self.get_object_by_identifier("model", model_id)
|
||||
if model is None:
|
||||
raise ValueError(f"Model '{model_id}' not found")
|
||||
return model
|
||||
|
||||
async def register_model(
|
||||
self,
|
||||
|
|
@ -267,8 +272,11 @@ class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):
|
|||
async def list_shields(self) -> ListShieldsResponse:
|
||||
return ListShieldsResponse(data=await self.get_all_with_type(ResourceType.shield.value))
|
||||
|
||||
async def get_shield(self, identifier: str) -> Optional[Shield]:
|
||||
return await self.get_object_by_identifier("shield", identifier)
|
||||
async def get_shield(self, identifier: str) -> Shield:
|
||||
shield = await self.get_object_by_identifier("shield", identifier)
|
||||
if shield is None:
|
||||
raise ValueError(f"Shield '{identifier}' not found")
|
||||
return shield
|
||||
|
||||
async def register_shield(
|
||||
self,
|
||||
|
|
@ -303,8 +311,11 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
|
|||
async def list_vector_dbs(self) -> ListVectorDBsResponse:
|
||||
return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db"))
|
||||
|
||||
async def get_vector_db(self, vector_db_id: str) -> Optional[VectorDB]:
|
||||
return await self.get_object_by_identifier("vector_db", vector_db_id)
|
||||
async def get_vector_db(self, vector_db_id: str) -> VectorDB:
|
||||
vector_db = await self.get_object_by_identifier("vector_db", vector_db_id)
|
||||
if vector_db is None:
|
||||
raise ValueError(f"Vector DB '{vector_db_id}' not found")
|
||||
return vector_db
|
||||
|
||||
async def register_vector_db(
|
||||
self,
|
||||
|
|
@ -355,8 +366,11 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
|
|||
async def list_datasets(self) -> ListDatasetsResponse:
|
||||
return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value))
|
||||
|
||||
async def get_dataset(self, dataset_id: str) -> Optional[Dataset]:
|
||||
return await self.get_object_by_identifier("dataset", dataset_id)
|
||||
async def get_dataset(self, dataset_id: str) -> Dataset:
|
||||
dataset = await self.get_object_by_identifier("dataset", dataset_id)
|
||||
if dataset is None:
|
||||
raise ValueError(f"Dataset '{dataset_id}' not found")
|
||||
return dataset
|
||||
|
||||
async def register_dataset(
|
||||
self,
|
||||
|
|
@ -365,6 +379,12 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
|
|||
metadata: Optional[Dict[str, Any]] = None,
|
||||
dataset_id: Optional[str] = None,
|
||||
) -> Dataset:
|
||||
if isinstance(source, dict):
|
||||
if source["type"] == "uri":
|
||||
source = URIDataSource.parse_obj(source)
|
||||
elif source["type"] == "rows":
|
||||
source = RowsDataSource.parse_obj(source)
|
||||
|
||||
if not dataset_id:
|
||||
dataset_id = f"dataset-{str(uuid.uuid4())}"
|
||||
|
||||
|
|
@ -408,8 +428,11 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
|
|||
async def list_scoring_functions(self) -> ListScoringFunctionsResponse:
|
||||
return ListScoringFunctionsResponse(data=await self.get_all_with_type(ResourceType.scoring_function.value))
|
||||
|
||||
async def get_scoring_function(self, scoring_fn_id: str) -> Optional[ScoringFn]:
|
||||
return await self.get_object_by_identifier("scoring_function", scoring_fn_id)
|
||||
async def get_scoring_function(self, scoring_fn_id: str) -> ScoringFn:
|
||||
scoring_fn = await self.get_object_by_identifier("scoring_function", scoring_fn_id)
|
||||
if scoring_fn is None:
|
||||
raise ValueError(f"Scoring function '{scoring_fn_id}' not found")
|
||||
return scoring_fn
|
||||
|
||||
async def register_scoring_function(
|
||||
self,
|
||||
|
|
@ -445,8 +468,11 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):
|
|||
async def list_benchmarks(self) -> ListBenchmarksResponse:
|
||||
return ListBenchmarksResponse(data=await self.get_all_with_type("benchmark"))
|
||||
|
||||
async def get_benchmark(self, benchmark_id: str) -> Optional[Benchmark]:
|
||||
return await self.get_object_by_identifier("benchmark", benchmark_id)
|
||||
async def get_benchmark(self, benchmark_id: str) -> Benchmark:
|
||||
benchmark = await self.get_object_by_identifier("benchmark", benchmark_id)
|
||||
if benchmark is None:
|
||||
raise ValueError(f"Benchmark '{benchmark_id}' not found")
|
||||
return benchmark
|
||||
|
||||
async def register_benchmark(
|
||||
self,
|
||||
|
|
@ -490,7 +516,10 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
|
|||
return ListToolGroupsResponse(data=await self.get_all_with_type("tool_group"))
|
||||
|
||||
async def get_tool_group(self, toolgroup_id: str) -> ToolGroup:
|
||||
return await self.get_object_by_identifier("tool_group", toolgroup_id)
|
||||
tool_group = await self.get_object_by_identifier("tool_group", toolgroup_id)
|
||||
if tool_group is None:
|
||||
raise ValueError(f"Tool group '{toolgroup_id}' not found")
|
||||
return tool_group
|
||||
|
||||
async def get_tool(self, tool_name: str) -> Tool:
|
||||
return await self.get_object_by_identifier("tool", tool_name)
|
||||
|
|
|
|||
69
llama_stack/distribution/server/auth.py
Normal file
69
llama_stack/distribution/server/auth.py
Normal file
|
|
@ -0,0 +1,69 @@
|
|||
# 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 json
|
||||
from urllib.parse import parse_qs
|
||||
|
||||
import httpx
|
||||
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
logger = get_logger(name=__name__, category="auth")
|
||||
|
||||
|
||||
class AuthenticationMiddleware:
|
||||
def __init__(self, app, auth_endpoint):
|
||||
self.app = app
|
||||
self.auth_endpoint = auth_endpoint
|
||||
|
||||
async def __call__(self, scope, receive, send):
|
||||
if scope["type"] == "http":
|
||||
headers = dict(scope.get("headers", []))
|
||||
auth_header = headers.get(b"authorization", b"").decode()
|
||||
|
||||
if not auth_header or not auth_header.startswith("Bearer "):
|
||||
return await self._send_auth_error(send, "Missing or invalid Authorization header")
|
||||
|
||||
api_key = auth_header.split("Bearer ", 1)[1]
|
||||
|
||||
path = scope.get("path", "")
|
||||
request_headers = {k.decode(): v.decode() for k, v in headers.items()}
|
||||
|
||||
query_string = scope.get("query_string", b"").decode()
|
||||
params = parse_qs(query_string)
|
||||
|
||||
auth_data = {
|
||||
"api_key": api_key,
|
||||
"request": {
|
||||
"path": path,
|
||||
"headers": request_headers,
|
||||
"params": params,
|
||||
},
|
||||
}
|
||||
|
||||
# Validate with authentication endpoint
|
||||
try:
|
||||
async with httpx.AsyncClient() as client:
|
||||
response = await client.post(self.auth_endpoint, json=auth_data)
|
||||
if response.status_code != 200:
|
||||
logger.warning(f"Authentication failed: {response.status_code}")
|
||||
return await self._send_auth_error(send, "Authentication failed")
|
||||
except Exception:
|
||||
logger.exception("Error during authentication")
|
||||
return await self._send_auth_error(send, "Authentication service error")
|
||||
|
||||
return await self.app(scope, receive, send)
|
||||
|
||||
async def _send_auth_error(self, send, message):
|
||||
await send(
|
||||
{
|
||||
"type": "http.response.start",
|
||||
"status": 401,
|
||||
"headers": [[b"content-type", b"application/json"]],
|
||||
}
|
||||
)
|
||||
error_msg = json.dumps({"error": {"message": message}}).encode()
|
||||
await send({"type": "http.response.body", "body": error_msg})
|
||||
|
|
@ -52,6 +52,7 @@ from llama_stack.providers.utils.telemetry.tracing import (
|
|||
start_trace,
|
||||
)
|
||||
|
||||
from .auth import AuthenticationMiddleware
|
||||
from .endpoints import get_all_api_endpoints
|
||||
|
||||
REPO_ROOT = Path(__file__).parent.parent.parent.parent
|
||||
|
|
@ -351,6 +352,11 @@ def main():
|
|||
if not os.environ.get("LLAMA_STACK_DISABLE_VERSION_CHECK"):
|
||||
app.add_middleware(ClientVersionMiddleware)
|
||||
|
||||
# Add authentication middleware if configured
|
||||
if config.server.auth and config.server.auth.endpoint:
|
||||
logger.info(f"Enabling authentication with endpoint: {config.server.auth.endpoint}")
|
||||
app.add_middleware(AuthenticationMiddleware, auth_endpoint=config.server.auth.endpoint)
|
||||
|
||||
try:
|
||||
impls = asyncio.run(construct_stack(config))
|
||||
except InvalidProviderError as e:
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def datasets():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def benchmarks():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def models():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def providers():
|
||||
|
|
|
|||
|
|
@ -4,14 +4,15 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from page.distribution.benchmarks import benchmarks
|
||||
from page.distribution.datasets import datasets
|
||||
from page.distribution.models import models
|
||||
from page.distribution.scoring_functions import scoring_functions
|
||||
from page.distribution.shields import shields
|
||||
from page.distribution.vector_dbs import vector_dbs
|
||||
from streamlit_option_menu import option_menu
|
||||
|
||||
from llama_stack.distribution.ui.page.distribution.datasets import datasets
|
||||
from llama_stack.distribution.ui.page.distribution.eval_tasks import benchmarks
|
||||
from llama_stack.distribution.ui.page.distribution.models import models
|
||||
from llama_stack.distribution.ui.page.distribution.scoring_functions import scoring_functions
|
||||
from llama_stack.distribution.ui.page.distribution.shields import shields
|
||||
from llama_stack.distribution.ui.page.distribution.vector_dbs import vector_dbs
|
||||
|
||||
|
||||
def resources_page():
|
||||
options = [
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def scoring_functions():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def shields():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def vector_dbs():
|
||||
|
|
|
|||
|
|
@ -8,8 +8,9 @@ import json
|
|||
|
||||
import pandas as pd
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
from modules.utils import process_dataset
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
from llama_stack.distribution.ui.modules.utils import process_dataset
|
||||
|
||||
|
||||
def application_evaluation_page():
|
||||
|
|
|
|||
|
|
@ -8,7 +8,8 @@ import json
|
|||
|
||||
import pandas as pd
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
|
||||
def select_benchmark_1():
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import streamlit as st
|
||||
from modules.api import llama_stack_api
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
|
||||
# Sidebar configurations
|
||||
with st.sidebar:
|
||||
|
|
|
|||
|
|
@ -7,9 +7,10 @@
|
|||
import streamlit as st
|
||||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.event_logger import EventLogger
|
||||
from llama_stack_client.types.memory_insert_params import Document
|
||||
from modules.api import llama_stack_api
|
||||
from modules.utils import data_url_from_file
|
||||
from llama_stack_client.types.shared.document import Document
|
||||
|
||||
from llama_stack.distribution.ui.modules.api import llama_stack_api
|
||||
from llama_stack.distribution.ui.modules.utils import data_url_from_file
|
||||
|
||||
|
||||
def rag_chat_page():
|
||||
|
|
|
|||
|
|
@ -47,7 +47,14 @@ RecursiveType = Union[Primitive, List[Primitive], Dict[str, Primitive]]
|
|||
class ToolCall(BaseModel):
|
||||
call_id: str
|
||||
tool_name: Union[BuiltinTool, str]
|
||||
arguments: Dict[str, RecursiveType]
|
||||
# Plan is to deprecate the Dict in favor of a JSON string
|
||||
# that is parsed on the client side instead of trying to manage
|
||||
# the recursive type here.
|
||||
# Making this a union so that client side can start prepping for this change.
|
||||
# Eventually, we will remove both the Dict and arguments_json field,
|
||||
# and arguments will just be a str
|
||||
arguments: Union[str, Dict[str, RecursiveType]]
|
||||
arguments_json: Optional[str] = None
|
||||
|
||||
@field_validator("tool_name", mode="before")
|
||||
@classmethod
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@
|
|||
# the top-level of this source tree.
|
||||
|
||||
import io
|
||||
import json
|
||||
import uuid
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
|
@ -203,9 +204,10 @@ class ChatFormat:
|
|||
# This code tries to handle that case
|
||||
if tool_name in BuiltinTool.__members__:
|
||||
tool_name = BuiltinTool[tool_name]
|
||||
tool_arguments = {
|
||||
"query": list(tool_arguments.values())[0],
|
||||
}
|
||||
if isinstance(tool_arguments, dict):
|
||||
tool_arguments = {
|
||||
"query": list(tool_arguments.values())[0],
|
||||
}
|
||||
else:
|
||||
builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
|
||||
if builtin_tool_info is not None:
|
||||
|
|
@ -229,6 +231,7 @@ class ChatFormat:
|
|||
call_id=call_id,
|
||||
tool_name=tool_name,
|
||||
arguments=tool_arguments,
|
||||
arguments_json=json.dumps(tool_arguments),
|
||||
)
|
||||
)
|
||||
content = ""
|
||||
|
|
|
|||
|
|
@ -11,11 +11,8 @@
|
|||
# top-level folder for each specific model found within the models/ directory at
|
||||
# the top-level of this source tree.
|
||||
|
||||
from llama_stack.models.llama.datatypes import (
|
||||
BuiltinTool,
|
||||
StopReason,
|
||||
ToolCall,
|
||||
)
|
||||
|
||||
from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall
|
||||
|
||||
from .prompt_templates import (
|
||||
BuiltinToolGenerator,
|
||||
|
|
|
|||
|
|
@ -35,12 +35,12 @@ class PandasDataframeDataset:
|
|||
else:
|
||||
return self.df.iloc[idx].to_dict()
|
||||
|
||||
def load(self) -> None:
|
||||
async def load(self) -> None:
|
||||
if self.df is not None:
|
||||
return
|
||||
|
||||
if self.dataset_def.source.type == "uri":
|
||||
self.df = get_dataframe_from_uri(self.dataset_def.source.uri)
|
||||
self.df = await get_dataframe_from_uri(self.dataset_def.source.uri)
|
||||
elif self.dataset_def.source.type == "rows":
|
||||
self.df = pandas.DataFrame(self.dataset_def.source.rows)
|
||||
else:
|
||||
|
|
@ -95,7 +95,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
) -> IterrowsResponse:
|
||||
dataset_def = self.dataset_infos[dataset_id]
|
||||
dataset_impl = PandasDataframeDataset(dataset_def)
|
||||
dataset_impl.load()
|
||||
await dataset_impl.load()
|
||||
|
||||
start_index = start_index or 0
|
||||
|
||||
|
|
@ -114,7 +114,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
|
|||
async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None:
|
||||
dataset_def = self.dataset_infos[dataset_id]
|
||||
dataset_impl = PandasDataframeDataset(dataset_def)
|
||||
dataset_impl.load()
|
||||
await dataset_impl.load()
|
||||
|
||||
new_rows_df = pandas.DataFrame(rows)
|
||||
dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import copy
|
||||
import json
|
||||
import logging
|
||||
import multiprocessing
|
||||
|
|
@ -213,7 +214,7 @@ def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage
|
|||
|
||||
def parse_message(json_str: str) -> ProcessingMessage:
|
||||
data = json.loads(json_str)
|
||||
return ProcessingMessageWrapper(**data).payload
|
||||
return copy.deepcopy(ProcessingMessageWrapper(**data).payload)
|
||||
|
||||
|
||||
def worker_process_entrypoint(
|
||||
|
|
|
|||
|
|
@ -582,6 +582,7 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
|
|||
tool_name=t.function.name,
|
||||
# vLLM function args come back as a string. Llama Stack expects JSON.
|
||||
arguments=json.loads(t.function.arguments),
|
||||
arguments_json=t.function.arguments,
|
||||
)
|
||||
for t in vllm_message.tool_calls
|
||||
],
|
||||
|
|
|
|||
|
|
@ -9,6 +9,9 @@
|
|||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import Any
|
||||
|
||||
from llama_stack.apis.common.type_system import (
|
||||
ChatCompletionInputType,
|
||||
DialogType,
|
||||
|
|
@ -20,7 +23,7 @@ from llama_stack.providers.utils.common.data_schema_validator import (
|
|||
validate_dataset_schema,
|
||||
)
|
||||
|
||||
EXPECTED_DATASET_SCHEMA = {
|
||||
EXPECTED_DATASET_SCHEMA: dict[str, list[dict[str, Any]]] = {
|
||||
"instruct": [
|
||||
{
|
||||
ColumnName.chat_completion_input.value: ChatCompletionInputType(),
|
||||
|
|
@ -41,6 +44,9 @@ async def validate_input_dataset_schema(
|
|||
dataset_type: str,
|
||||
) -> None:
|
||||
dataset_def = await datasets_api.get_dataset(dataset_id=dataset_id)
|
||||
if not dataset_def:
|
||||
raise ValueError(f"Dataset {dataset_id} does not exist.")
|
||||
|
||||
if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
|
||||
raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
|
||||
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ class TorchtuneCheckpointer:
|
|||
checkpoint_files: List[str],
|
||||
output_dir: str,
|
||||
model_type: str,
|
||||
) -> None:
|
||||
):
|
||||
# Fail fast if ``checkpoint_files`` is invalid
|
||||
# TODO: support loading more than one file
|
||||
if len(checkpoint_files) != 1:
|
||||
|
|
@ -58,7 +58,7 @@ class TorchtuneCheckpointer:
|
|||
"""
|
||||
Load Meta checkpoint from file. Currently only loading from a single file is supported.
|
||||
"""
|
||||
state_dict: Dict[str:Any] = {}
|
||||
state_dict: Dict[str, Any] = {}
|
||||
model_state_dict = safe_torch_load(self._checkpoint_path)
|
||||
if self._model_type == ModelType.LLAMA3_VISION:
|
||||
from torchtune.models.llama3_2_vision._convert_weights import (
|
||||
|
|
@ -85,10 +85,10 @@ class TorchtuneCheckpointer:
|
|||
state_dict: Dict[str, Any],
|
||||
epoch: int,
|
||||
adapter_only: bool = False,
|
||||
checkpoint_format: str = "meta",
|
||||
checkpoint_format: str | None = None,
|
||||
) -> str:
|
||||
model_file_path = Path(self._output_dir) / f"{self._model_id}-{self._training_algorithm}-{epoch}"
|
||||
if checkpoint_format == "meta":
|
||||
if checkpoint_format == "meta" or checkpoint_format is None:
|
||||
self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
|
||||
elif checkpoint_format == "huggingface":
|
||||
# Note: for saving hugging face format checkpoints, we only suppport saving adapter weights now
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from typing import Any, Callable, Dict
|
||||
from typing import Callable, Dict
|
||||
|
||||
import torch
|
||||
from pydantic import BaseModel
|
||||
|
|
@ -25,10 +25,13 @@ from llama_stack.apis.post_training import DatasetFormat
|
|||
from llama_stack.models.llama.datatypes import Model
|
||||
from llama_stack.models.llama.sku_list import resolve_model
|
||||
|
||||
BuildLoraModelCallable = Callable[..., torch.nn.Module]
|
||||
BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
|
||||
|
||||
|
||||
class ModelConfig(BaseModel):
|
||||
model_definition: Any
|
||||
tokenizer_type: Any
|
||||
model_definition: BuildLoraModelCallable
|
||||
tokenizer_type: BuildTokenizerCallable
|
||||
checkpoint_type: str
|
||||
|
||||
|
||||
|
|
@ -51,10 +54,6 @@ DATA_FORMATS: Dict[str, Transform] = {
|
|||
}
|
||||
|
||||
|
||||
BuildLoraModelCallable = Callable[..., torch.nn.Module]
|
||||
BuildTokenizerCallable = Callable[..., Llama3Tokenizer]
|
||||
|
||||
|
||||
def _validate_model_id(model_id: str) -> Model:
|
||||
model = resolve_model(model_id)
|
||||
if model is None or model.core_model_id.value not in MODEL_CONFIGS:
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ class SFTDataset(Dataset):
|
|||
if "messages" in transformed_sample:
|
||||
validate_messages(transformed_sample["messages"])
|
||||
|
||||
tokenized_dict = self._model_transform(transformed_sample)
|
||||
tokenized_dict: dict[str, Any] = self._model_transform(transformed_sample)
|
||||
|
||||
if not ("tokens" in tokenized_dict and "mask" in tokenized_dict):
|
||||
keys_str = ", ".join(tokenized_dict.keys())
|
||||
|
|
|
|||
|
|
@ -37,10 +37,10 @@ from llama_stack.apis.common.training_types import PostTrainingMetric
|
|||
from llama_stack.apis.datasetio import DatasetIO
|
||||
from llama_stack.apis.datasets import Datasets
|
||||
from llama_stack.apis.post_training import (
|
||||
AlgorithmConfig,
|
||||
Checkpoint,
|
||||
LoraFinetuningConfig,
|
||||
OptimizerConfig,
|
||||
QATFinetuningConfig,
|
||||
TrainingConfig,
|
||||
)
|
||||
from llama_stack.distribution.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
|
||||
|
|
@ -73,6 +73,9 @@ class LoraFinetuningSingleDevice:
|
|||
|
||||
# Currently logging only logs limited training metrics to local disk
|
||||
# will figure out more loggings and how it works with telemetry in future PRs
|
||||
|
||||
_checkpointer: TorchtuneCheckpointer
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: TorchtunePostTrainingConfig,
|
||||
|
|
@ -82,7 +85,7 @@ class LoraFinetuningSingleDevice:
|
|||
logger_config: Dict[str, Any],
|
||||
model: str,
|
||||
checkpoint_dir: Optional[str],
|
||||
algorithm_config: Optional[AlgorithmConfig],
|
||||
algorithm_config: LoraFinetuningConfig | QATFinetuningConfig | None,
|
||||
datasetio_api: DatasetIO,
|
||||
datasets_api: Datasets,
|
||||
) -> None:
|
||||
|
|
@ -109,12 +112,12 @@ class LoraFinetuningSingleDevice:
|
|||
return str(checkpoint_dir)
|
||||
|
||||
if checkpoint_dir and checkpoint_dir != "null":
|
||||
self.checkpoint_dir = config.checkpoint_dir
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
else:
|
||||
model = resolve_model(self.model_id)
|
||||
if model is None:
|
||||
model_obj = resolve_model(self.model_id)
|
||||
if model_obj is None:
|
||||
raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
|
||||
self.checkpoint_dir = model_checkpoint_dir(model)
|
||||
self.checkpoint_dir = model_checkpoint_dir(model_obj)
|
||||
|
||||
self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
|
||||
self._checkpoint_format = config.checkpoint_format
|
||||
|
|
@ -135,16 +138,16 @@ class LoraFinetuningSingleDevice:
|
|||
self.max_validation_steps = training_config.max_validation_steps
|
||||
|
||||
self._clip_grad_norm = 1.0
|
||||
self._enable_activation_checkpointing = (
|
||||
(training_config.efficiency_config.enable_activation_checkpointing)
|
||||
if training_config.efficiency_config
|
||||
else False
|
||||
)
|
||||
self._enable_activation_offloading = (
|
||||
(training_config.efficiency_config.enable_activation_offloading)
|
||||
if training_config.efficiency_config
|
||||
else False
|
||||
)
|
||||
|
||||
self._enable_activation_checkpointing = False
|
||||
self._enable_activation_offloading = False
|
||||
if training_config.efficiency_config:
|
||||
if training_config.efficiency_config.enable_activation_checkpointing:
|
||||
self._enable_activation_checkpointing = (
|
||||
training_config.efficiency_config.enable_activation_checkpointing
|
||||
)
|
||||
if training_config.efficiency_config.enable_activation_offloading:
|
||||
self._enable_activation_offloading = training_config.efficiency_config.enable_activation_offloading
|
||||
|
||||
self.datasetio_api = datasetio_api
|
||||
self.datasets_api = datasets_api
|
||||
|
|
@ -451,12 +454,12 @@ class LoraFinetuningSingleDevice:
|
|||
"""
|
||||
# Initialize tokens count and running loss (for grad accumulation)
|
||||
t0 = time.perf_counter()
|
||||
running_loss = 0
|
||||
running_loss: float = 0.0
|
||||
num_tokens = 0
|
||||
|
||||
# training artifacts
|
||||
checkpoints = []
|
||||
memory_stats = {}
|
||||
memory_stats: Dict[str, Any] = {}
|
||||
|
||||
# self.epochs_run should be non-zero when we're resuming from a checkpoint
|
||||
for curr_epoch in range(self.epochs_run, self.total_epochs):
|
||||
|
|
@ -484,7 +487,7 @@ class LoraFinetuningSingleDevice:
|
|||
# Loss is normalized by default so we multiply by the number of tokens
|
||||
# This way we can normalize by the total number of tokens if we're accumulating gradients
|
||||
current_loss = await self._loss_step(batch) * current_num_tokens
|
||||
running_loss += current_loss
|
||||
running_loss += current_loss.detach().item()
|
||||
current_loss.backward()
|
||||
|
||||
# Step with optimizer
|
||||
|
|
@ -500,7 +503,7 @@ class LoraFinetuningSingleDevice:
|
|||
# Update the number of steps when the weights are updated
|
||||
self.global_step += 1
|
||||
|
||||
loss_to_log = running_loss.item() / num_tokens
|
||||
loss_to_log = running_loss / num_tokens
|
||||
|
||||
pbar.update(1)
|
||||
pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
|
||||
|
|
@ -523,7 +526,7 @@ class LoraFinetuningSingleDevice:
|
|||
)
|
||||
|
||||
# Reset running stats for the next step
|
||||
running_loss = 0
|
||||
running_loss = 0.0
|
||||
num_tokens = 0
|
||||
t0 = time.perf_counter()
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
|
|
@ -37,7 +38,7 @@ class CodeInterpreterToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime):
|
|||
async def initialize(self):
|
||||
pass
|
||||
|
||||
async def register_tool(self, tool: Tool):
|
||||
async def register_tool(self, tool: Tool) -> None:
|
||||
pass
|
||||
|
||||
async def unregister_tool(self, tool_id: str) -> None:
|
||||
|
|
@ -65,7 +66,7 @@ class CodeInterpreterToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime):
|
|||
# Use environment variable to control bwrap usage
|
||||
force_disable_bwrap = os.environ.get("DISABLE_CODE_SANDBOX", "").lower() in ("1", "true", "yes")
|
||||
req = CodeExecutionRequest(scripts=[script], use_bwrap=not force_disable_bwrap)
|
||||
res = self.code_executor.execute(req)
|
||||
res = await asyncio.to_thread(self.code_executor.execute, req)
|
||||
pieces = [res["process_status"]]
|
||||
for out_type in ["stdout", "stderr"]:
|
||||
res_out = res[out_type]
|
||||
|
|
|
|||
19
llama_stack/providers/inline/vector_io/qdrant/__init__.py
Normal file
19
llama_stack/providers/inline/vector_io/qdrant/__init__.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 typing import Dict
|
||||
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
|
||||
from .config import QdrantVectorIOConfig
|
||||
|
||||
|
||||
async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
|
||||
from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
|
||||
|
||||
impl = QdrantVectorIOAdapter(config, deps[Api.inference])
|
||||
await impl.initialize()
|
||||
return impl
|
||||
23
llama_stack/providers/inline/vector_io/qdrant/config.py
Normal file
23
llama_stack/providers/inline/vector_io/qdrant/config.py
Normal file
|
|
@ -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.
|
||||
|
||||
|
||||
from typing import Any, Dict
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from llama_stack.schema_utils import json_schema_type
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class QdrantVectorIOConfig(BaseModel):
|
||||
path: str
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
|
||||
return {
|
||||
"path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
|
||||
}
|
||||
|
|
@ -92,6 +92,14 @@ def available_providers() -> List[ProviderSpec]:
|
|||
),
|
||||
api_dependencies=[Api.inference],
|
||||
),
|
||||
InlineProviderSpec(
|
||||
api=Api.vector_io,
|
||||
provider_type="inline::qdrant",
|
||||
pip_packages=["qdrant-client"],
|
||||
module="llama_stack.providers.inline.vector_io.qdrant",
|
||||
config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
|
||||
api_dependencies=[Api.inference],
|
||||
),
|
||||
remote_provider_spec(
|
||||
Api.vector_io,
|
||||
AdapterSpec(
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@
|
|||
|
||||
import logging
|
||||
import warnings
|
||||
from functools import lru_cache
|
||||
from typing import AsyncIterator, List, Optional, Union
|
||||
|
||||
from openai import APIConnectionError, AsyncOpenAI, BadRequestError
|
||||
|
|
@ -82,12 +83,42 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
# )
|
||||
|
||||
self._config = config
|
||||
# make sure the client lives longer than any async calls
|
||||
self._client = AsyncOpenAI(
|
||||
base_url=f"{self._config.url}/v1",
|
||||
api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
|
||||
timeout=self._config.timeout,
|
||||
)
|
||||
|
||||
@lru_cache # noqa: B019
|
||||
def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
|
||||
"""
|
||||
For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However,
|
||||
some models are hosted on different URLs. This function returns the appropriate client
|
||||
for the given provider_model_id.
|
||||
|
||||
This relies on lru_cache and self._default_client to avoid creating a new client for each request
|
||||
or for each model that is hosted on https://integrate.api.nvidia.com/v1.
|
||||
|
||||
:param provider_model_id: The provider model ID
|
||||
:return: An OpenAI client
|
||||
"""
|
||||
|
||||
@lru_cache # noqa: B019
|
||||
def _get_client_for_base_url(base_url: str) -> AsyncOpenAI:
|
||||
"""
|
||||
Maintain a single OpenAI client per base_url.
|
||||
"""
|
||||
return AsyncOpenAI(
|
||||
base_url=base_url,
|
||||
api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
|
||||
timeout=self._config.timeout,
|
||||
)
|
||||
|
||||
special_model_urls = {
|
||||
"meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct",
|
||||
"meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
|
||||
}
|
||||
|
||||
base_url = f"{self._config.url}/v1"
|
||||
if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
|
||||
base_url = special_model_urls[provider_model_id]
|
||||
|
||||
return _get_client_for_base_url(base_url)
|
||||
|
||||
async def completion(
|
||||
self,
|
||||
|
|
@ -105,9 +136,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
|
||||
await check_health(self._config) # this raises errors
|
||||
|
||||
provider_model_id = self.get_provider_model_id(model_id)
|
||||
request = convert_completion_request(
|
||||
request=CompletionRequest(
|
||||
model=self.get_provider_model_id(model_id),
|
||||
model=provider_model_id,
|
||||
content=content,
|
||||
sampling_params=sampling_params,
|
||||
response_format=response_format,
|
||||
|
|
@ -118,7 +150,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
)
|
||||
|
||||
try:
|
||||
response = await self._client.completions.create(**request)
|
||||
response = await self._get_client(provider_model_id).completions.create(**request)
|
||||
except APIConnectionError as e:
|
||||
raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
|
||||
|
||||
|
|
@ -206,6 +238,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
|
||||
await check_health(self._config) # this raises errors
|
||||
|
||||
provider_model_id = self.get_provider_model_id(model_id)
|
||||
request = await convert_chat_completion_request(
|
||||
request=ChatCompletionRequest(
|
||||
model=self.get_provider_model_id(model_id),
|
||||
|
|
@ -221,7 +254,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
)
|
||||
|
||||
try:
|
||||
response = await self._client.chat.completions.create(**request)
|
||||
response = await self._get_client(provider_model_id).chat.completions.create(**request)
|
||||
except APIConnectionError as e:
|
||||
raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
|
||||
|
||||
|
|
|
|||
|
|
@ -90,15 +90,12 @@ def _convert_to_vllm_tool_calls_in_response(
|
|||
if not tool_calls:
|
||||
return []
|
||||
|
||||
call_function_arguments = None
|
||||
for call in tool_calls:
|
||||
call_function_arguments = json.loads(call.function.arguments)
|
||||
|
||||
return [
|
||||
ToolCall(
|
||||
call_id=call.id,
|
||||
tool_name=call.function.name,
|
||||
arguments=call_function_arguments,
|
||||
arguments=json.loads(call.function.arguments),
|
||||
arguments_json=call.function.arguments,
|
||||
)
|
||||
for call in tool_calls
|
||||
]
|
||||
|
|
@ -183,6 +180,7 @@ async def _process_vllm_chat_completion_stream_response(
|
|||
call_id=tool_call_buf.call_id,
|
||||
tool_name=tool_call_buf.tool_name,
|
||||
arguments=args,
|
||||
arguments_json=args_str,
|
||||
),
|
||||
parse_status=ToolCallParseStatus.succeeded,
|
||||
),
|
||||
|
|
|
|||
|
|
@ -23,7 +23,6 @@ class QdrantVectorIOConfig(BaseModel):
|
|||
prefix: Optional[str] = None
|
||||
timeout: Optional[int] = None
|
||||
host: Optional[str] = None
|
||||
path: Optional[str] = None
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(cls, **kwargs: Any) -> Dict[str, Any]:
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
import logging
|
||||
import uuid
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
from numpy.typing import NDArray
|
||||
from qdrant_client import AsyncQdrantClient, models
|
||||
|
|
@ -16,12 +16,13 @@ from llama_stack.apis.inference import InterleavedContent
|
|||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
|
||||
from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
|
||||
from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
EmbeddingIndex,
|
||||
VectorDBWithIndex,
|
||||
)
|
||||
|
||||
from .config import QdrantVectorIOConfig
|
||||
from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
CHUNK_ID_KEY = "_chunk_id"
|
||||
|
|
@ -99,17 +100,19 @@ class QdrantIndex(EmbeddingIndex):
|
|||
|
||||
|
||||
class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
|
||||
def __init__(self, config: QdrantVectorIOConfig, inference_api: Api.inference) -> None:
|
||||
def __init__(
|
||||
self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
|
||||
) -> None:
|
||||
self.config = config
|
||||
self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
|
||||
self.client: AsyncQdrantClient = None
|
||||
self.cache = {}
|
||||
self.inference_api = inference_api
|
||||
|
||||
async def initialize(self) -> None:
|
||||
pass
|
||||
self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
self.client.close()
|
||||
await self.client.close()
|
||||
|
||||
async def register_vector_db(
|
||||
self,
|
||||
|
|
@ -123,6 +126,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
|
|||
|
||||
self.cache[vector_db.identifier] = index
|
||||
|
||||
async def unregister_vector_db(self, vector_db_id: str) -> None:
|
||||
if vector_db_id in self.cache:
|
||||
await self.cache[vector_db_id].index.delete()
|
||||
del self.cache[vector_db_id]
|
||||
|
||||
async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
|
||||
if vector_db_id in self.cache:
|
||||
return self.cache[vector_db_id]
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import io
|
||||
from urllib.parse import unquote
|
||||
|
|
@ -13,12 +14,15 @@ import pandas
|
|||
from llama_stack.providers.utils.memory.vector_store import parse_data_url
|
||||
|
||||
|
||||
def get_dataframe_from_uri(uri: str):
|
||||
async def get_dataframe_from_uri(uri: str):
|
||||
df = None
|
||||
if uri.endswith(".csv"):
|
||||
df = pandas.read_csv(uri)
|
||||
# Moving to its own thread to avoid io from blocking the eventloop
|
||||
# This isn't ideal as it moves more then just the IO to a new thread
|
||||
# but it is as close as we can easly get
|
||||
df = await asyncio.to_thread(pandas.read_csv, uri)
|
||||
elif uri.endswith(".xlsx"):
|
||||
df = pandas.read_excel(uri)
|
||||
df = await asyncio.to_thread(pandas.read_excel, uri)
|
||||
elif uri.startswith("data:"):
|
||||
parts = parse_data_url(uri)
|
||||
data = parts["data"]
|
||||
|
|
|
|||
|
|
@ -529,7 +529,11 @@ async def convert_message_to_openai_dict_new(
|
|||
) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
|
||||
async def impl(
|
||||
content_: InterleavedContent,
|
||||
) -> Union[str, OpenAIChatCompletionContentPartParam, List[OpenAIChatCompletionContentPartParam]]:
|
||||
) -> Union[
|
||||
str,
|
||||
OpenAIChatCompletionContentPartParam,
|
||||
List[OpenAIChatCompletionContentPartParam],
|
||||
]:
|
||||
# Llama Stack and OpenAI spec match for str and text input
|
||||
if isinstance(content_, str):
|
||||
return content_
|
||||
|
|
@ -570,7 +574,7 @@ async def convert_message_to_openai_dict_new(
|
|||
OpenAIChatCompletionMessageToolCall(
|
||||
id=tool.call_id,
|
||||
function=OpenAIFunction(
|
||||
name=tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value,
|
||||
name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value),
|
||||
arguments=json.dumps(tool.arguments),
|
||||
),
|
||||
type="function",
|
||||
|
|
@ -609,6 +613,7 @@ def convert_tool_call(
|
|||
call_id=tool_call.id,
|
||||
tool_name=tool_call.function.name,
|
||||
arguments=json.loads(tool_call.function.arguments),
|
||||
arguments_json=tool_call.function.arguments,
|
||||
)
|
||||
except Exception:
|
||||
return UnparseableToolCall(
|
||||
|
|
@ -759,6 +764,7 @@ def _convert_openai_tool_calls(
|
|||
call_id=call.id,
|
||||
tool_name=call.function.name,
|
||||
arguments=json.loads(call.function.arguments),
|
||||
arguments_json=call.function.arguments,
|
||||
)
|
||||
for call in tool_calls
|
||||
]
|
||||
|
|
@ -890,7 +896,8 @@ async def convert_openai_chat_completion_stream(
|
|||
# ChatCompletionResponseEvent only supports one per stream
|
||||
if len(choice.delta.tool_calls) > 1:
|
||||
warnings.warn(
|
||||
"multiple tool calls found in a single delta, using the first, ignoring the rest", stacklevel=2
|
||||
"multiple tool calls found in a single delta, using the first, ignoring the rest",
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
if not enable_incremental_tool_calls:
|
||||
|
|
@ -971,6 +978,7 @@ async def convert_openai_chat_completion_stream(
|
|||
call_id=buffer["call_id"],
|
||||
tool_name=buffer["name"],
|
||||
arguments=arguments,
|
||||
arguments_json=buffer["arguments"],
|
||||
)
|
||||
yield ChatCompletionResponseStreamChunk(
|
||||
event=ChatCompletionResponseEvent(
|
||||
|
|
|
|||
|
|
@ -170,7 +170,6 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
default_datasets = [
|
||||
DatasetInput(
|
||||
dataset_id="simpleqa",
|
||||
provider_id="huggingface",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/simpleqa?split=train",
|
||||
|
|
@ -178,7 +177,6 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
),
|
||||
DatasetInput(
|
||||
dataset_id="mmlu_cot",
|
||||
provider_id="huggingface",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/mmlu_cot?split=test&name=all",
|
||||
|
|
@ -186,7 +184,6 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
),
|
||||
DatasetInput(
|
||||
dataset_id="gpqa_cot",
|
||||
provider_id="huggingface",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main",
|
||||
|
|
@ -194,7 +191,6 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
),
|
||||
DatasetInput(
|
||||
dataset_id="math_500",
|
||||
provider_id="huggingface",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/math_500?split=test",
|
||||
|
|
@ -202,7 +198,6 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
),
|
||||
DatasetInput(
|
||||
dataset_id="bfcl",
|
||||
provider_id="huggingface",
|
||||
purpose=DatasetPurpose.eval_messages_answer,
|
||||
source=URIDataSource(
|
||||
uri="huggingface://datasets/llamastack/bfcl_v3?split=train",
|
||||
|
|
|
|||
|
|
@ -164,35 +164,30 @@ datasets:
|
|||
uri: huggingface://datasets/llamastack/simpleqa?split=train
|
||||
metadata: {}
|
||||
dataset_id: simpleqa
|
||||
provider_id: huggingface
|
||||
- purpose: eval/messages-answer
|
||||
source:
|
||||
type: uri
|
||||
uri: huggingface://datasets/llamastack/mmlu_cot?split=test&name=all
|
||||
metadata: {}
|
||||
dataset_id: mmlu_cot
|
||||
provider_id: huggingface
|
||||
- purpose: eval/messages-answer
|
||||
source:
|
||||
type: uri
|
||||
uri: huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main
|
||||
metadata: {}
|
||||
dataset_id: gpqa_cot
|
||||
provider_id: huggingface
|
||||
- purpose: eval/messages-answer
|
||||
source:
|
||||
type: uri
|
||||
uri: huggingface://datasets/llamastack/math_500?split=test
|
||||
metadata: {}
|
||||
dataset_id: math_500
|
||||
provider_id: huggingface
|
||||
- purpose: eval/messages-answer
|
||||
source:
|
||||
type: uri
|
||||
uri: huggingface://datasets/llamastack/bfcl_v3?split=train
|
||||
metadata: {}
|
||||
dataset_id: bfcl
|
||||
provider_id: huggingface
|
||||
scoring_fns: []
|
||||
benchmarks:
|
||||
- dataset_id: simpleqa
|
||||
|
|
|
|||
|
|
@ -56,7 +56,7 @@ dev = [
|
|||
"ruamel.yaml", # needed for openapi generator
|
||||
]
|
||||
# These are the dependencies required for running unit tests.
|
||||
unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet"]
|
||||
unit = ["sqlite-vec", "openai", "aiosqlite", "pypdf", "chardet", "qdrant-client"]
|
||||
# These are the core dependencies required for running integration tests. They are shared across all
|
||||
# providers. If a provider requires additional dependencies, please add them to your environment
|
||||
# separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
|
||||
|
|
@ -114,7 +114,6 @@ exclude = [
|
|||
"./.git",
|
||||
"./docs/*",
|
||||
"./build",
|
||||
"./scripts",
|
||||
"./venv",
|
||||
"*.pyi",
|
||||
".pre-commit-config.yaml",
|
||||
|
|
@ -229,10 +228,6 @@ exclude = [
|
|||
"^llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers\\.py$",
|
||||
"^llama_stack/providers/inline/inference/vllm/",
|
||||
"^llama_stack/providers/inline/post_training/common/validator\\.py$",
|
||||
"^llama_stack/providers/inline/post_training/torchtune/common/checkpointer\\.py$",
|
||||
"^llama_stack/providers/inline/post_training/torchtune/common/utils\\.py$",
|
||||
"^llama_stack/providers/inline/post_training/torchtune/datasets/sft\\.py$",
|
||||
"^llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device\\.py$",
|
||||
"^llama_stack/providers/inline/post_training/torchtune/post_training\\.py$",
|
||||
"^llama_stack/providers/inline/safety/code_scanner/",
|
||||
"^llama_stack/providers/inline/safety/llama_guard/",
|
||||
|
|
@ -248,6 +243,7 @@ exclude = [
|
|||
"^llama_stack/providers/inline/vector_io/chroma/",
|
||||
"^llama_stack/providers/inline/vector_io/faiss/",
|
||||
"^llama_stack/providers/inline/vector_io/milvus/",
|
||||
"^llama_stack/providers/inline/vector_io/qdrant/",
|
||||
"^llama_stack/providers/inline/vector_io/sqlite_vec/",
|
||||
"^llama_stack/providers/remote/agents/sample/",
|
||||
"^llama_stack/providers/remote/datasetio/huggingface/",
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ import requests
|
|||
|
||||
|
||||
def get_all_releases(token):
|
||||
url = f"https://api.github.com/repos/meta-llama/llama-stack/releases"
|
||||
url = "https://api.github.com/repos/meta-llama/llama-stack/releases"
|
||||
headers = {"Accept": "application/vnd.github.v3+json"}
|
||||
|
||||
if token:
|
||||
|
|
@ -22,9 +22,7 @@ def get_all_releases(token):
|
|||
if response.status_code == 200:
|
||||
return response.json()
|
||||
else:
|
||||
raise Exception(
|
||||
f"Error fetching releases: {response.status_code}, {response.text}"
|
||||
)
|
||||
raise Exception(f"Error fetching releases: {response.status_code}, {response.text}")
|
||||
|
||||
|
||||
def clean_release_body(body):
|
||||
|
|
@ -55,7 +53,7 @@ def merge_release_notes(output_file, token=None):
|
|||
releases = get_all_releases(token)
|
||||
|
||||
with open(output_file, "w", encoding="utf-8") as md_file:
|
||||
md_file.write(f"# Changelog\n\n")
|
||||
md_file.write("# Changelog\n\n")
|
||||
|
||||
for release in releases:
|
||||
md_file.write(f"# {release['tag_name']}\n")
|
||||
|
|
|
|||
|
|
@ -165,7 +165,10 @@ class PrepareMessagesTests(unittest.IsolatedAsyncioTestCase):
|
|||
request.model = MODEL
|
||||
request.tool_config.tool_prompt_format = ToolPromptFormat.json
|
||||
prompt = await chat_completion_request_to_prompt(request, request.model)
|
||||
self.assertIn('{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}', prompt)
|
||||
self.assertIn(
|
||||
'{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}',
|
||||
prompt,
|
||||
)
|
||||
|
||||
async def test_user_provided_system_message(self):
|
||||
content = "Hello !"
|
||||
|
|
|
|||
42
tests/unit/providers/vector_io/conftest.py
Normal file
42
tests/unit/providers/vector_io/conftest.py
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
# 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 random
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
|
||||
EMBEDDING_DIMENSION = 384
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_db_id() -> str:
|
||||
return f"test-vector-db-{random.randint(1, 100)}"
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def embedding_dimension() -> int:
|
||||
return EMBEDDING_DIMENSION
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
"""Generates chunks that force multiple batches for a single document to expose ID conflicts."""
|
||||
n, k = 10, 3
|
||||
sample = [
|
||||
Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
|
||||
for j in range(k)
|
||||
for i in range(n)
|
||||
]
|
||||
return sample
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_embeddings(sample_chunks):
|
||||
np.random.seed(42)
|
||||
return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
|
||||
135
tests/unit/providers/vector_io/test_qdrant.py
Normal file
135
tests/unit/providers/vector_io/test_qdrant.py
Normal file
|
|
@ -0,0 +1,135 @@
|
|||
# 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 asyncio
|
||||
import os
|
||||
from typing import Any
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
|
||||
from llama_stack.apis.inference import EmbeddingsResponse, Inference
|
||||
from llama_stack.apis.vector_io import (
|
||||
QueryChunksResponse,
|
||||
VectorDB,
|
||||
VectorDBStore,
|
||||
)
|
||||
from llama_stack.providers.inline.vector_io.qdrant.config import (
|
||||
QdrantVectorIOConfig as InlineQdrantVectorIOConfig,
|
||||
)
|
||||
from llama_stack.providers.remote.vector_io.qdrant.qdrant import (
|
||||
QdrantVectorIOAdapter,
|
||||
)
|
||||
|
||||
# This test is a unit test for the QdrantVectorIOAdapter class. This should only contain
|
||||
# tests which are specific to this class. More general (API-level) tests should be placed in
|
||||
# tests/integration/vector_io/
|
||||
#
|
||||
# How to run this test:
|
||||
#
|
||||
# pytest tests/unit/providers/vector_io/test_qdrant.py \
|
||||
# -v -s --tb=short --disable-warnings --asyncio-mode=auto
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def qdrant_config(tmp_path) -> InlineQdrantVectorIOConfig:
|
||||
return InlineQdrantVectorIOConfig(path=os.path.join(tmp_path, "qdrant.db"))
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def loop():
|
||||
return asyncio.new_event_loop()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_vector_db(vector_db_id) -> MagicMock:
|
||||
mock_vector_db = MagicMock(spec=VectorDB)
|
||||
mock_vector_db.embedding_model = "embedding_model"
|
||||
mock_vector_db.identifier = vector_db_id
|
||||
return mock_vector_db
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_vector_db_store(mock_vector_db) -> MagicMock:
|
||||
mock_store = MagicMock(spec=VectorDBStore)
|
||||
mock_store.get_vector_db = AsyncMock(return_value=mock_vector_db)
|
||||
return mock_store
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_api_service(sample_embeddings):
|
||||
mock_api_service = MagicMock(spec=Inference)
|
||||
mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
|
||||
return mock_api_service
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def qdrant_adapter(qdrant_config, mock_vector_db_store, mock_api_service, loop) -> QdrantVectorIOAdapter:
|
||||
adapter = QdrantVectorIOAdapter(config=qdrant_config, inference_api=mock_api_service)
|
||||
adapter.vector_db_store = mock_vector_db_store
|
||||
await adapter.initialize()
|
||||
yield adapter
|
||||
await adapter.shutdown()
|
||||
|
||||
|
||||
__QUERY = "Sample query"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("max_query_chunks, expected_chunks", [(2, 2), (100, 30)])
|
||||
async def test_qdrant_adapter_returns_expected_chunks(
|
||||
qdrant_adapter: QdrantVectorIOAdapter,
|
||||
vector_db_id,
|
||||
sample_chunks,
|
||||
sample_embeddings,
|
||||
max_query_chunks,
|
||||
expected_chunks,
|
||||
) -> None:
|
||||
assert qdrant_adapter is not None
|
||||
await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
|
||||
|
||||
index = await qdrant_adapter._get_and_cache_vector_db_index(vector_db_id=vector_db_id)
|
||||
assert index is not None
|
||||
|
||||
response = await qdrant_adapter.query_chunks(
|
||||
query=__QUERY,
|
||||
vector_db_id=vector_db_id,
|
||||
params={"max_chunks": max_query_chunks},
|
||||
)
|
||||
assert isinstance(response, QueryChunksResponse)
|
||||
assert len(response.chunks) == expected_chunks
|
||||
|
||||
|
||||
# To by-pass attempt to convert a Mock to JSON
|
||||
def _prepare_for_json(value: Any) -> str:
|
||||
return str(value)
|
||||
|
||||
|
||||
@patch("llama_stack.providers.utils.telemetry.trace_protocol._prepare_for_json", new=_prepare_for_json)
|
||||
@pytest.mark.asyncio
|
||||
async def test_qdrant_register_and_unregister_vector_db(
|
||||
qdrant_adapter: QdrantVectorIOAdapter,
|
||||
mock_vector_db,
|
||||
sample_chunks,
|
||||
) -> None:
|
||||
# Initially, no collections
|
||||
vector_db_id = mock_vector_db.identifier
|
||||
assert len((await qdrant_adapter.client.get_collections()).collections) == 0
|
||||
|
||||
# Register does not create a collection
|
||||
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
|
||||
await qdrant_adapter.register_vector_db(mock_vector_db)
|
||||
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
|
||||
|
||||
# First insert creates the collection
|
||||
await qdrant_adapter.insert_chunks(vector_db_id, sample_chunks)
|
||||
assert await qdrant_adapter.client.collection_exists(vector_db_id)
|
||||
|
||||
# Unregister deletes the collection
|
||||
await qdrant_adapter.unregister_vector_db(vector_db_id)
|
||||
assert not (await qdrant_adapter.client.collection_exists(vector_db_id))
|
||||
assert len((await qdrant_adapter.client.get_collections()).collections) == 0
|
||||
|
|
@ -29,8 +29,6 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
|
|||
# -v -s --tb=short --disable-warnings --asyncio-mode=auto
|
||||
|
||||
SQLITE_VEC_PROVIDER = "sqlite_vec"
|
||||
EMBEDDING_DIMENSION = 384
|
||||
EMBEDDING_MODEL = "all-MiniLM-L6-v2"
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
|
|
@ -50,26 +48,8 @@ def sqlite_connection(loop):
|
|||
|
||||
|
||||
@pytest_asyncio.fixture(scope="session", autouse=True)
|
||||
async def sqlite_vec_index(sqlite_connection):
|
||||
return await SQLiteVecIndex.create(dimension=EMBEDDING_DIMENSION, connection=sqlite_connection, bank_id="test_bank")
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
"""Generates chunks that force multiple batches for a single document to expose ID conflicts."""
|
||||
n, k = 10, 3
|
||||
sample = [
|
||||
Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
|
||||
for j in range(k)
|
||||
for i in range(n)
|
||||
]
|
||||
return sample
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_embeddings(sample_chunks):
|
||||
np.random.seed(42)
|
||||
return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])
|
||||
async def sqlite_vec_index(sqlite_connection, embedding_dimension):
|
||||
return await SQLiteVecIndex.create(dimension=embedding_dimension, connection=sqlite_connection, bank_id="test_bank")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
@ -82,21 +62,21 @@ async def test_add_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings):
|
||||
async def test_query_chunks(sqlite_vec_index, sample_chunks, sample_embeddings, embedding_dimension):
|
||||
await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings)
|
||||
query_embedding = np.random.rand(EMBEDDING_DIMENSION).astype(np.float32)
|
||||
query_embedding = np.random.rand(embedding_dimension).astype(np.float32)
|
||||
response = await sqlite_vec_index.query(query_embedding, k=2, score_threshold=0.0)
|
||||
assert isinstance(response, QueryChunksResponse)
|
||||
assert len(response.chunks) == 2
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks):
|
||||
async def test_chunk_id_conflict(sqlite_vec_index, sample_chunks, embedding_dimension):
|
||||
"""Test that chunk IDs do not conflict across batches when inserting chunks."""
|
||||
# Reduce batch size to force multiple batches for same document
|
||||
# since there are 10 chunks per document and batch size is 2
|
||||
batch_size = 2
|
||||
sample_embeddings = np.random.rand(len(sample_chunks), EMBEDDING_DIMENSION).astype(np.float32)
|
||||
sample_embeddings = np.random.rand(len(sample_chunks), embedding_dimension).astype(np.float32)
|
||||
|
||||
await sqlite_vec_index.add_chunks(sample_chunks, sample_embeddings, batch_size=batch_size)
|
||||
|
||||
|
|
|
|||
124
tests/unit/server/test_auth.py
Normal file
124
tests/unit/server/test_auth.py
Normal file
|
|
@ -0,0 +1,124 @@
|
|||
# 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 AsyncMock, patch
|
||||
|
||||
import pytest
|
||||
from fastapi import FastAPI
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from llama_stack.distribution.server.auth import AuthenticationMiddleware
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_auth_endpoint():
|
||||
return "http://mock-auth-service/validate"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def valid_api_key():
|
||||
return "valid_api_key_12345"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def invalid_api_key():
|
||||
return "invalid_api_key_67890"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def app(mock_auth_endpoint):
|
||||
app = FastAPI()
|
||||
app.add_middleware(AuthenticationMiddleware, auth_endpoint=mock_auth_endpoint)
|
||||
|
||||
@app.get("/test")
|
||||
def test_endpoint():
|
||||
return {"message": "Authentication successful"}
|
||||
|
||||
return app
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def client(app):
|
||||
return TestClient(app)
|
||||
|
||||
|
||||
async def mock_post_success(*args, **kwargs):
|
||||
mock_response = AsyncMock()
|
||||
mock_response.status_code = 200
|
||||
return mock_response
|
||||
|
||||
|
||||
async def mock_post_failure(*args, **kwargs):
|
||||
mock_response = AsyncMock()
|
||||
mock_response.status_code = 401
|
||||
return mock_response
|
||||
|
||||
|
||||
async def mock_post_exception(*args, **kwargs):
|
||||
raise Exception("Connection error")
|
||||
|
||||
|
||||
def test_missing_auth_header(client):
|
||||
response = client.get("/test")
|
||||
assert response.status_code == 401
|
||||
assert "Missing or invalid Authorization header" in response.json()["error"]["message"]
|
||||
|
||||
|
||||
def test_invalid_auth_header_format(client):
|
||||
response = client.get("/test", headers={"Authorization": "InvalidFormat token123"})
|
||||
assert response.status_code == 401
|
||||
assert "Missing or invalid Authorization header" in response.json()["error"]["message"]
|
||||
|
||||
|
||||
@patch("httpx.AsyncClient.post", new=mock_post_success)
|
||||
def test_valid_authentication(client, valid_api_key):
|
||||
response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"})
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"message": "Authentication successful"}
|
||||
|
||||
|
||||
@patch("httpx.AsyncClient.post", new=mock_post_failure)
|
||||
def test_invalid_authentication(client, invalid_api_key):
|
||||
response = client.get("/test", headers={"Authorization": f"Bearer {invalid_api_key}"})
|
||||
assert response.status_code == 401
|
||||
assert "Authentication failed" in response.json()["error"]["message"]
|
||||
|
||||
|
||||
@patch("httpx.AsyncClient.post", new=mock_post_exception)
|
||||
def test_auth_service_error(client, valid_api_key):
|
||||
response = client.get("/test", headers={"Authorization": f"Bearer {valid_api_key}"})
|
||||
assert response.status_code == 401
|
||||
assert "Authentication service error" in response.json()["error"]["message"]
|
||||
|
||||
|
||||
def test_auth_request_payload(client, valid_api_key, mock_auth_endpoint):
|
||||
with patch("httpx.AsyncClient.post") as mock_post:
|
||||
mock_response = AsyncMock()
|
||||
mock_response.status_code = 200
|
||||
mock_post.return_value = mock_response
|
||||
|
||||
client.get(
|
||||
"/test?param1=value1¶m2=value2",
|
||||
headers={
|
||||
"Authorization": f"Bearer {valid_api_key}",
|
||||
"User-Agent": "TestClient",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
)
|
||||
|
||||
# Check that the auth endpoint was called with the correct payload
|
||||
call_args = mock_post.call_args
|
||||
assert call_args is not None
|
||||
|
||||
url, kwargs = call_args[0][0], call_args[1]
|
||||
assert url == mock_auth_endpoint
|
||||
|
||||
payload = kwargs["json"]
|
||||
assert payload["api_key"] == valid_api_key
|
||||
assert payload["request"]["path"] == "/test"
|
||||
assert "authorization" in payload["request"]["headers"]
|
||||
assert "param1" in payload["request"]["params"]
|
||||
assert "param2" in payload["request"]["params"]
|
||||
198
uv.lock
generated
198
uv.lock
generated
|
|
@ -1,5 +1,4 @@
|
|||
version = 1
|
||||
revision = 1
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
|
|
@ -8,9 +7,12 @@ resolution-markers = [
|
|||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.13' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -793,6 +795,107 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/89/30/2bd0eb03a7dee7727cd2ec643d1e992979e62d5e7443507381cce0455132/googleapis_common_protos-1.67.0-py2.py3-none-any.whl", hash = "sha256:579de760800d13616f51cf8be00c876f00a9f146d3e6510e19d1f4111758b741", size = 164985 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "grpcio"
|
||||
version = "1.71.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1c/95/aa11fc09a85d91fbc7dd405dcb2a1e0256989d67bf89fa65ae24b3ba105a/grpcio-1.71.0.tar.gz", hash = "sha256:2b85f7820475ad3edec209d3d89a7909ada16caab05d3f2e08a7e8ae3200a55c", size = 12549828 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7c/c5/ef610b3f988cc0cc67b765f72b8e2db06a1db14e65acb5ae7810a6b7042e/grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd", size = 5210643 },
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/de/c84293c961622df302c0d5d07ec6e2d4cd3874ea42f602be2df09c4ad44f/grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d", size = 11308962 },
|
||||
{ url = "https://files.pythonhosted.org/packages/7c/38/04c9e0dc8c904570c80faa1f1349b190b63e45d6b2782ec8567b050efa9d/grpcio-1.71.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:0ab8b2864396663a5b0b0d6d79495657ae85fa37dcb6498a2669d067c65c11ea", size = 5699236 },
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|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "filelock", marker = "sys_platform != 'darwin'" },
|
||||
|
|
@ -3482,8 +3655,10 @@ resolution-markers = [
|
|||
"python_full_version < '3.11' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.12' and sys_platform == 'darwin'",
|
||||
"python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"python_full_version >= '3.13' and sys_platform == 'darwin'",
|
||||
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
|
||||
|
|
@ -3509,7 +3684,8 @@ source = { registry = "https://download.pytorch.org/whl/cpu" }
|
|||
resolution-markers = [
|
||||
"(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
"(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue