mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 09:53:45 +00:00
Merge 3170870410 into sapling-pr-archive-ehhuang
This commit is contained in:
ehhuang
GitHub
commit
1b357c3441
1003 changed files with 119481 additions and 4453 deletions
1
.github/workflows/README.md
vendored
1
.github/workflows/README.md
vendored
|
|
@ -18,6 +18,7 @@ Llama Stack uses GitHub Actions for Continuous Integration (CI). Below is a tabl
|
||||||
| Python Package Build Test | [python-build-test.yml](python-build-test.yml) | Test building the llama-stack PyPI project |
|
| Python Package Build Test | [python-build-test.yml](python-build-test.yml) | Test building the llama-stack PyPI project |
|
||||||
| Integration Tests (Record) | [record-integration-tests.yml](record-integration-tests.yml) | Run the integration test suite from tests/integration |
|
| Integration Tests (Record) | [record-integration-tests.yml](record-integration-tests.yml) | Run the integration test suite from tests/integration |
|
||||||
| Check semantic PR titles | [semantic-pr.yml](semantic-pr.yml) | Ensure that PR titles follow the conventional commit spec |
|
| Check semantic PR titles | [semantic-pr.yml](semantic-pr.yml) | Ensure that PR titles follow the conventional commit spec |
|
||||||
|
| Stainless SDK Builds | [stainless-builds.yml](stainless-builds.yml) | Build Stainless SDK from OpenAPI spec changes |
|
||||||
| Close stale issues and PRs | [stale_bot.yml](stale_bot.yml) | Run the Stale Bot action |
|
| Close stale issues and PRs | [stale_bot.yml](stale_bot.yml) | Run the Stale Bot action |
|
||||||
| Test External Providers Installed via Module | [test-external-provider-module.yml](test-external-provider-module.yml) | Test External Provider installation via Python module |
|
| Test External Providers Installed via Module | [test-external-provider-module.yml](test-external-provider-module.yml) | Test External Provider installation via Python module |
|
||||||
| Test External API and Providers | [test-external.yml](test-external.yml) | Test the External API and Provider mechanisms |
|
| Test External API and Providers | [test-external.yml](test-external.yml) | Test the External API and Provider mechanisms |
|
||||||
|
|
|
||||||
110
.github/workflows/stainless-builds.yml
vendored
Normal file
110
.github/workflows/stainless-builds.yml
vendored
Normal file
|
|
@ -0,0 +1,110 @@
|
||||||
|
name: Stainless SDK Builds
|
||||||
|
run-name: Build Stainless SDK from OpenAPI spec changes
|
||||||
|
|
||||||
|
# This workflow uses pull_request_target, which allows it to run on pull requests
|
||||||
|
# from forks with access to secrets. This is safe because the workflow definition
|
||||||
|
# comes from the base branch (trusted), and the action only reads OpenAPI spec
|
||||||
|
# files without executing any code from the PR.
|
||||||
|
|
||||||
|
on:
|
||||||
|
pull_request_target:
|
||||||
|
types:
|
||||||
|
- opened
|
||||||
|
- synchronize
|
||||||
|
- reopened
|
||||||
|
- closed
|
||||||
|
paths:
|
||||||
|
- "client-sdks/stainless/**"
|
||||||
|
|
||||||
|
concurrency:
|
||||||
|
group: ${{ github.workflow }}-${{ github.event.pull_request.number }}
|
||||||
|
cancel-in-progress: true
|
||||||
|
|
||||||
|
env:
|
||||||
|
# Stainless organization name.
|
||||||
|
STAINLESS_ORG: llamastack
|
||||||
|
|
||||||
|
# Stainless project name.
|
||||||
|
STAINLESS_PROJECT: llama-stack-client
|
||||||
|
|
||||||
|
# Path to your OpenAPI spec.
|
||||||
|
OAS_PATH: ./client-sdks/stainless/openapi.yml
|
||||||
|
|
||||||
|
# Path to your Stainless config. Optional; only provide this if you prefer
|
||||||
|
# to maintain the ground truth Stainless config in your own repo.
|
||||||
|
CONFIG_PATH: ./client-sdks/stainless/config.yml
|
||||||
|
|
||||||
|
# When to fail the job based on build conclusion.
|
||||||
|
# Options: "never" | "note" | "warning" | "error" | "fatal".
|
||||||
|
FAIL_ON: error
|
||||||
|
|
||||||
|
# In your repo secrets, configure:
|
||||||
|
# - STAINLESS_API_KEY: a Stainless API key, which you can generate on the
|
||||||
|
# Stainless organization dashboard
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
preview:
|
||||||
|
if: github.event.action != 'closed'
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
permissions:
|
||||||
|
contents: read
|
||||||
|
pull-requests: write
|
||||||
|
steps:
|
||||||
|
# Checkout the PR's code to access the OpenAPI spec and config files.
|
||||||
|
# This is necessary to read the spec/config from the PR (including from forks).
|
||||||
|
- name: Checkout repository
|
||||||
|
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
|
||||||
|
with:
|
||||||
|
repository: ${{ github.event.pull_request.head.repo.full_name }}
|
||||||
|
ref: ${{ github.event.pull_request.head.sha }}
|
||||||
|
fetch-depth: 2
|
||||||
|
|
||||||
|
# This action builds preview SDKs from the OpenAPI spec changes and
|
||||||
|
# posts/updates a comment on the PR with build results and links to the preview.
|
||||||
|
- name: Run preview builds
|
||||||
|
uses: stainless-api/upload-openapi-spec-action/preview@32823b096b4319c53ee948d702d9052873af485f # 1.6.0
|
||||||
|
with:
|
||||||
|
stainless_api_key: ${{ secrets.STAINLESS_API_KEY }}
|
||||||
|
org: ${{ env.STAINLESS_ORG }}
|
||||||
|
project: ${{ env.STAINLESS_PROJECT }}
|
||||||
|
oas_path: ${{ env.OAS_PATH }}
|
||||||
|
config_path: ${{ env.CONFIG_PATH }}
|
||||||
|
fail_on: ${{ env.FAIL_ON }}
|
||||||
|
base_sha: ${{ github.event.pull_request.base.sha }}
|
||||||
|
base_ref: ${{ github.event.pull_request.base.ref }}
|
||||||
|
head_sha: ${{ github.event.pull_request.head.sha }}
|
||||||
|
|
||||||
|
merge:
|
||||||
|
if: github.event.action == 'closed' && github.event.pull_request.merged == true
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
permissions:
|
||||||
|
contents: read
|
||||||
|
pull-requests: write
|
||||||
|
steps:
|
||||||
|
# Checkout the PR's code to access the OpenAPI spec and config files.
|
||||||
|
# This is necessary to read the spec/config from the PR (including from forks).
|
||||||
|
- name: Checkout repository
|
||||||
|
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
|
||||||
|
with:
|
||||||
|
repository: ${{ github.event.pull_request.head.repo.full_name }}
|
||||||
|
ref: ${{ github.event.pull_request.head.sha }}
|
||||||
|
fetch-depth: 2
|
||||||
|
|
||||||
|
# Note that this only merges in changes that happened on the last build on
|
||||||
|
# preview/${{ github.head_ref }}. It's possible that there are OAS/config
|
||||||
|
# changes that haven't been built, if the preview-sdk job didn't finish
|
||||||
|
# before this step starts. In theory we want to wait for all builds
|
||||||
|
# against preview/${{ github.head_ref }} to complete, but assuming that
|
||||||
|
# the preview-sdk job happens before the PR merge, it should be fine.
|
||||||
|
- name: Run merge build
|
||||||
|
uses: stainless-api/upload-openapi-spec-action/merge@32823b096b4319c53ee948d702d9052873af485f # 1.6.0
|
||||||
|
with:
|
||||||
|
stainless_api_key: ${{ secrets.STAINLESS_API_KEY }}
|
||||||
|
org: ${{ env.STAINLESS_ORG }}
|
||||||
|
project: ${{ env.STAINLESS_PROJECT }}
|
||||||
|
oas_path: ${{ env.OAS_PATH }}
|
||||||
|
config_path: ${{ env.CONFIG_PATH }}
|
||||||
|
fail_on: ${{ env.FAIL_ON }}
|
||||||
|
base_sha: ${{ github.event.pull_request.base.sha }}
|
||||||
|
base_ref: ${{ github.event.pull_request.base.ref }}
|
||||||
|
head_sha: ${{ github.event.pull_request.head.sha }}
|
||||||
|
|
@ -1,7 +1,7 @@
|
||||||
These are the source-of-truth configuration files used to generate the Stainless client SDKs via Stainless.
|
These are the source-of-truth configuration files used to generate the Stainless client SDKs via Stainless.
|
||||||
|
|
||||||
- `openapi.yml`: this is the OpenAPI specification for the Llama Stack API.
|
- `openapi.yml`: this is the OpenAPI specification for the Llama Stack API.
|
||||||
- `openapi.stainless.yml`: this is the Stainless _configuration_ which instructs Stainless how to generate the client SDKs.
|
- `config.yml`: this is the Stainless _configuration_ which instructs Stainless how to generate the client SDKs.
|
||||||
|
|
||||||
A small side note: notice the `.yml` suffixes since Stainless uses that suffix typically for its configuration files.
|
A small side note: notice the `.yml` suffixes since Stainless uses that suffix typically for its configuration files.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -115,6 +115,9 @@ resources:
|
||||||
sampling_params: SamplingParams
|
sampling_params: SamplingParams
|
||||||
scoring_result: ScoringResult
|
scoring_result: ScoringResult
|
||||||
system_message: SystemMessage
|
system_message: SystemMessage
|
||||||
|
query_result: RAGQueryResult
|
||||||
|
document: RAGDocument
|
||||||
|
query_config: RAGQueryConfig
|
||||||
toolgroups:
|
toolgroups:
|
||||||
models:
|
models:
|
||||||
tool_group: ToolGroup
|
tool_group: ToolGroup
|
||||||
|
|
@ -140,6 +143,11 @@ resources:
|
||||||
endpoint: get /v1/tool-runtime/list-tools
|
endpoint: get /v1/tool-runtime/list-tools
|
||||||
paginated: false
|
paginated: false
|
||||||
invoke_tool: post /v1/tool-runtime/invoke
|
invoke_tool: post /v1/tool-runtime/invoke
|
||||||
|
subresources:
|
||||||
|
rag_tool:
|
||||||
|
methods:
|
||||||
|
insert: post /v1/tool-runtime/rag-tool/insert
|
||||||
|
query: post /v1/tool-runtime/rag-tool/query
|
||||||
|
|
||||||
responses:
|
responses:
|
||||||
models:
|
models:
|
||||||
|
|
@ -332,21 +340,18 @@ resources:
|
||||||
endpoint: get /v1/inspect/routes
|
endpoint: get /v1/inspect/routes
|
||||||
paginated: false
|
paginated: false
|
||||||
|
|
||||||
|
|
||||||
moderations:
|
moderations:
|
||||||
models:
|
models:
|
||||||
create_response: ModerationObject
|
create_response: ModerationObject
|
||||||
methods:
|
methods:
|
||||||
create: post /v1/moderations
|
create: post /v1/moderations
|
||||||
|
|
||||||
|
|
||||||
safety:
|
safety:
|
||||||
models:
|
models:
|
||||||
run_shield_response: RunShieldResponse
|
run_shield_response: RunShieldResponse
|
||||||
methods:
|
methods:
|
||||||
run_shield: post /v1/safety/run-shield
|
run_shield: post /v1/safety/run-shield
|
||||||
|
|
||||||
|
|
||||||
shields:
|
shields:
|
||||||
models:
|
models:
|
||||||
shield: Shield
|
shield: Shield
|
||||||
|
|
@ -455,7 +460,6 @@ resources:
|
||||||
iterrows: get /v1beta/datasetio/iterrows/{dataset_id}
|
iterrows: get /v1beta/datasetio/iterrows/{dataset_id}
|
||||||
appendrows: post /v1beta/datasetio/append-rows/{dataset_id}
|
appendrows: post /v1beta/datasetio/append-rows/{dataset_id}
|
||||||
|
|
||||||
|
|
||||||
settings:
|
settings:
|
||||||
license: MIT
|
license: MIT
|
||||||
unwrap_response_fields: [data]
|
unwrap_response_fields: [data]
|
||||||
|
|
@ -466,7 +470,7 @@ openapi:
|
||||||
reason: Better return_type using enum
|
reason: Better return_type using enum
|
||||||
args:
|
args:
|
||||||
target:
|
target:
|
||||||
- '$.components.schemas'
|
- "$.components.schemas"
|
||||||
object:
|
object:
|
||||||
ReturnType:
|
ReturnType:
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
|
|
@ -491,10 +495,10 @@ openapi:
|
||||||
args:
|
args:
|
||||||
filter:
|
filter:
|
||||||
only:
|
only:
|
||||||
- '$.components.schemas.ScoringFn.properties.return_type'
|
- "$.components.schemas.ScoringFn.properties.return_type"
|
||||||
- '$.components.schemas.RegisterScoringFunctionRequest.properties.return_type'
|
- "$.components.schemas.RegisterScoringFunctionRequest.properties.return_type"
|
||||||
value:
|
value:
|
||||||
$ref: '#/components/schemas/ReturnType'
|
$ref: "#/components/schemas/ReturnType"
|
||||||
- command: oneOfToAnyOf
|
- command: oneOfToAnyOf
|
||||||
reason: Prism (mock server) doesn't like one of our requests as it technically matches multiple variants
|
reason: Prism (mock server) doesn't like one of our requests as it technically matches multiple variants
|
||||||
|
|
||||||
|
|
@ -963,7 +963,7 @@ paths:
|
||||||
Optional filter to control which routes are returned. Can be an API level
|
Optional filter to control which routes are returned. Can be an API level
|
||||||
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
||||||
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
||||||
returns only non-deprecated v1 routes.
|
returns all non-deprecated routes.
|
||||||
required: false
|
required: false
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
|
|
@ -998,39 +998,6 @@ paths:
|
||||||
description: List models using the OpenAI API.
|
description: List models using the OpenAI API.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Model.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Model'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Register model.
|
|
||||||
description: >-
|
|
||||||
Register model.
|
|
||||||
|
|
||||||
Register a model.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterModelRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/models/{model_id}:
|
/v1/models/{model_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1065,36 +1032,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Unregister model.
|
|
||||||
description: >-
|
|
||||||
Unregister model.
|
|
||||||
|
|
||||||
Unregister a model.
|
|
||||||
parameters:
|
|
||||||
- name: model_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the model to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/moderations:
|
/v1/moderations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1725,32 +1662,6 @@ paths:
|
||||||
description: List all scoring functions.
|
description: List all scoring functions.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Register a scoring function.
|
|
||||||
description: Register a scoring function.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterScoringFunctionRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring-functions/{scoring_fn_id}:
|
/v1/scoring-functions/{scoring_fn_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1782,33 +1693,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Unregister a scoring function.
|
|
||||||
description: Unregister a scoring function.
|
|
||||||
parameters:
|
|
||||||
- name: scoring_fn_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring/score:
|
/v1/scoring/score:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1897,36 +1781,6 @@ paths:
|
||||||
description: List all shields.
|
description: List all shields.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Shield.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Shield'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Register a shield.
|
|
||||||
description: Register a shield.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterShieldRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/shields/{identifier}:
|
/v1/shields/{identifier}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1958,33 +1812,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Unregister a shield.
|
|
||||||
description: Unregister a shield.
|
|
||||||
parameters:
|
|
||||||
- name: identifier
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tool-runtime/invoke:
|
/v1/tool-runtime/invoke:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2080,32 +1907,6 @@ paths:
|
||||||
description: List tool groups with optional provider.
|
description: List tool groups with optional provider.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Register a tool group.
|
|
||||||
description: Register a tool group.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterToolGroupRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/toolgroups/{toolgroup_id}:
|
/v1/toolgroups/{toolgroup_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2137,32 +1938,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Unregister a tool group.
|
|
||||||
description: Unregister a tool group.
|
|
||||||
parameters:
|
|
||||||
- name: toolgroup_id
|
|
||||||
in: path
|
|
||||||
description: The ID of the tool group to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tools:
|
/v1/tools:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3171,7 +2946,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterDatasetRequest'
|
$ref: '#/components/schemas/RegisterDatasetRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1beta/datasets/{dataset_id}:
|
/v1beta/datasets/{dataset_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3228,7 +3003,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks:
|
/v1alpha/eval/benchmarks:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3279,7 +3054,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}:
|
/v1alpha/eval/benchmarks/{benchmark_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3336,7 +3111,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -6280,46 +6055,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: OpenAIListModelsResponse
|
title: OpenAIListModelsResponse
|
||||||
ModelType:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- llm
|
|
||||||
- embedding
|
|
||||||
- rerank
|
|
||||||
title: ModelType
|
|
||||||
description: >-
|
|
||||||
Enumeration of supported model types in Llama Stack.
|
|
||||||
RegisterModelRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
model_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the model to register.
|
|
||||||
provider_model_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the model in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: Any additional metadata for this model.
|
|
||||||
model_type:
|
|
||||||
$ref: '#/components/schemas/ModelType'
|
|
||||||
description: The type of model to register.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- model_id
|
|
||||||
title: RegisterModelRequest
|
|
||||||
Model:
|
Model:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -6377,6 +6112,15 @@ components:
|
||||||
title: Model
|
title: Model
|
||||||
description: >-
|
description: >-
|
||||||
A model resource representing an AI model registered in Llama Stack.
|
A model resource representing an AI model registered in Llama Stack.
|
||||||
|
ModelType:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- llm
|
||||||
|
- embedding
|
||||||
|
- rerank
|
||||||
|
title: ModelType
|
||||||
|
description: >-
|
||||||
|
Enumeration of supported model types in Llama Stack.
|
||||||
RunModerationRequest:
|
RunModerationRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -6791,6 +6535,8 @@ components:
|
||||||
const: web_search_preview
|
const: web_search_preview
|
||||||
- type: string
|
- type: string
|
||||||
const: web_search_preview_2025_03_11
|
const: web_search_preview_2025_03_11
|
||||||
|
- type: string
|
||||||
|
const: web_search_2025_08_26
|
||||||
default: web_search
|
default: web_search
|
||||||
description: Web search tool type variant to use
|
description: Web search tool type variant to use
|
||||||
search_context_size:
|
search_context_size:
|
||||||
|
|
@ -6880,6 +6626,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
input:
|
input:
|
||||||
type: array
|
type: array
|
||||||
items:
|
items:
|
||||||
|
|
@ -7238,6 +6989,11 @@ components:
|
||||||
(Optional) Additional fields to include in the response.
|
(Optional) Additional fields to include in the response.
|
||||||
max_infer_iters:
|
max_infer_iters:
|
||||||
type: integer
|
type: integer
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response.
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- input
|
- input
|
||||||
|
|
@ -7319,6 +7075,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- created_at
|
- created_at
|
||||||
|
|
@ -9113,61 +8874,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListScoringFunctionsResponse
|
title: ListScoringFunctionsResponse
|
||||||
ParamType:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/StringType'
|
|
||||||
- $ref: '#/components/schemas/NumberType'
|
|
||||||
- $ref: '#/components/schemas/BooleanType'
|
|
||||||
- $ref: '#/components/schemas/ArrayType'
|
|
||||||
- $ref: '#/components/schemas/ObjectType'
|
|
||||||
- $ref: '#/components/schemas/JsonType'
|
|
||||||
- $ref: '#/components/schemas/UnionType'
|
|
||||||
- $ref: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
- $ref: '#/components/schemas/CompletionInputType'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
string: '#/components/schemas/StringType'
|
|
||||||
number: '#/components/schemas/NumberType'
|
|
||||||
boolean: '#/components/schemas/BooleanType'
|
|
||||||
array: '#/components/schemas/ArrayType'
|
|
||||||
object: '#/components/schemas/ObjectType'
|
|
||||||
json: '#/components/schemas/JsonType'
|
|
||||||
union: '#/components/schemas/UnionType'
|
|
||||||
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
completion_input: '#/components/schemas/CompletionInputType'
|
|
||||||
RegisterScoringFunctionRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to register.
|
|
||||||
description:
|
|
||||||
type: string
|
|
||||||
description: The description of the scoring function.
|
|
||||||
return_type:
|
|
||||||
$ref: '#/components/schemas/ParamType'
|
|
||||||
description: The return type of the scoring function.
|
|
||||||
provider_scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider scoring function to use for the scoring function.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the scoring function.
|
|
||||||
params:
|
|
||||||
$ref: '#/components/schemas/ScoringFnParams'
|
|
||||||
description: >-
|
|
||||||
The parameters for the scoring function for benchmark eval, these can
|
|
||||||
be overridden for app eval.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- scoring_fn_id
|
|
||||||
- description
|
|
||||||
- return_type
|
|
||||||
title: RegisterScoringFunctionRequest
|
|
||||||
ScoreRequest:
|
ScoreRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -9343,35 +9049,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListShieldsResponse
|
title: ListShieldsResponse
|
||||||
RegisterShieldRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to register.
|
|
||||||
provider_shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
params:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The parameters of the shield.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- shield_id
|
|
||||||
title: RegisterShieldRequest
|
|
||||||
InvokeToolRequest:
|
InvokeToolRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -9632,37 +9309,6 @@ components:
|
||||||
title: ListToolGroupsResponse
|
title: ListToolGroupsResponse
|
||||||
description: >-
|
description: >-
|
||||||
Response containing a list of tool groups.
|
Response containing a list of tool groups.
|
||||||
RegisterToolGroupRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
toolgroup_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the tool group to register.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the tool group.
|
|
||||||
mcp_endpoint:
|
|
||||||
$ref: '#/components/schemas/URL'
|
|
||||||
description: >-
|
|
||||||
The MCP endpoint to use for the tool group.
|
|
||||||
args:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
A dictionary of arguments to pass to the tool group.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- toolgroup_id
|
|
||||||
- provider_id
|
|
||||||
title: RegisterToolGroupRequest
|
|
||||||
Chunk:
|
Chunk:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -10808,68 +10454,6 @@ components:
|
||||||
- data
|
- data
|
||||||
title: ListDatasetsResponse
|
title: ListDatasetsResponse
|
||||||
description: Response from listing datasets.
|
description: Response from listing datasets.
|
||||||
DataSource:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/URIDataSource'
|
|
||||||
- $ref: '#/components/schemas/RowsDataSource'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
uri: '#/components/schemas/URIDataSource'
|
|
||||||
rows: '#/components/schemas/RowsDataSource'
|
|
||||||
RegisterDatasetRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
purpose:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- post-training/messages
|
|
||||||
- eval/question-answer
|
|
||||||
- eval/messages-answer
|
|
||||||
description: >-
|
|
||||||
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
|
||||||
contains a messages column with list of messages for post-training. {
|
|
||||||
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
|
||||||
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
|
||||||
contains a question column and an answer column for evaluation. { "question":
|
|
||||||
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
|
||||||
The dataset contains a messages column with list of messages and an answer
|
|
||||||
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
|
||||||
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
|
||||||
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
|
||||||
my name?"}, ], "answer": "John Doe" }
|
|
||||||
source:
|
|
||||||
$ref: '#/components/schemas/DataSource'
|
|
||||||
description: >-
|
|
||||||
The data source of the dataset. Ensure that the data source schema is
|
|
||||||
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
|
||||||
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
|
||||||
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
|
||||||
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
|
||||||
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
|
||||||
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
|
||||||
} ] }
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset. If not provided, an ID will be generated.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- purpose
|
|
||||||
- source
|
|
||||||
title: RegisterDatasetRequest
|
|
||||||
Benchmark:
|
Benchmark:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -10937,47 +10521,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListBenchmarksResponse
|
title: ListBenchmarksResponse
|
||||||
RegisterBenchmarkRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the benchmark to register.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset to use for the benchmark.
|
|
||||||
scoring_functions:
|
|
||||||
type: array
|
|
||||||
items:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The scoring functions to use for the benchmark.
|
|
||||||
provider_benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider benchmark to use for the benchmark.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the benchmark.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The metadata to use for the benchmark.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- benchmark_id
|
|
||||||
- dataset_id
|
|
||||||
- scoring_functions
|
|
||||||
title: RegisterBenchmarkRequest
|
|
||||||
BenchmarkConfig:
|
BenchmarkConfig:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -11839,6 +11382,109 @@ components:
|
||||||
- hyperparam_search_config
|
- hyperparam_search_config
|
||||||
- logger_config
|
- logger_config
|
||||||
title: SupervisedFineTuneRequest
|
title: SupervisedFineTuneRequest
|
||||||
|
DataSource:
|
||||||
|
oneOf:
|
||||||
|
- $ref: '#/components/schemas/URIDataSource'
|
||||||
|
- $ref: '#/components/schemas/RowsDataSource'
|
||||||
|
discriminator:
|
||||||
|
propertyName: type
|
||||||
|
mapping:
|
||||||
|
uri: '#/components/schemas/URIDataSource'
|
||||||
|
rows: '#/components/schemas/RowsDataSource'
|
||||||
|
RegisterDatasetRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
purpose:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- post-training/messages
|
||||||
|
- eval/question-answer
|
||||||
|
- eval/messages-answer
|
||||||
|
description: >-
|
||||||
|
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
||||||
|
contains a messages column with list of messages for post-training. {
|
||||||
|
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
||||||
|
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
||||||
|
contains a question column and an answer column for evaluation. { "question":
|
||||||
|
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
||||||
|
The dataset contains a messages column with list of messages and an answer
|
||||||
|
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
||||||
|
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
||||||
|
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
||||||
|
my name?"}, ], "answer": "John Doe" }
|
||||||
|
source:
|
||||||
|
$ref: '#/components/schemas/DataSource'
|
||||||
|
description: >-
|
||||||
|
The data source of the dataset. Ensure that the data source schema is
|
||||||
|
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
||||||
|
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
||||||
|
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
||||||
|
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
||||||
|
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
||||||
|
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
||||||
|
} ] }
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: >-
|
||||||
|
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset. If not provided, an ID will be generated.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- purpose
|
||||||
|
- source
|
||||||
|
title: RegisterDatasetRequest
|
||||||
|
RegisterBenchmarkRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: The ID of the benchmark to register.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset to use for the benchmark.
|
||||||
|
scoring_functions:
|
||||||
|
type: array
|
||||||
|
items:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The scoring functions to use for the benchmark.
|
||||||
|
provider_benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider benchmark to use for the benchmark.
|
||||||
|
provider_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider to use for the benchmark.
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: The metadata to use for the benchmark.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- benchmark_id
|
||||||
|
- dataset_id
|
||||||
|
- scoring_functions
|
||||||
|
title: RegisterBenchmarkRequest
|
||||||
responses:
|
responses:
|
||||||
BadRequest400:
|
BadRequest400:
|
||||||
description: The request was invalid or malformed
|
description: The request was invalid or malformed
|
||||||
|
|
|
||||||
|
|
@ -10,7 +10,7 @@ import TabItem from '@theme/TabItem';
|
||||||
|
|
||||||
# Kubernetes Deployment Guide
|
# Kubernetes Deployment Guide
|
||||||
|
|
||||||
Deploy Llama Stack and vLLM servers in a Kubernetes cluster instead of running them locally. This guide covers both local development with Kind and production deployment on AWS EKS.
|
Deploy Llama Stack and vLLM servers in a Kubernetes cluster instead of running them locally. This guide covers deployment using the Kubernetes operator to manage the Llama Stack server with Kind. The vLLM inference server is deployed manually.
|
||||||
|
|
||||||
## Prerequisites
|
## Prerequisites
|
||||||
|
|
||||||
|
|
@ -110,115 +110,176 @@ spec:
|
||||||
EOF
|
EOF
|
||||||
```
|
```
|
||||||
|
|
||||||
### Step 3: Configure Llama Stack
|
### Step 3: Install Kubernetes Operator
|
||||||
|
|
||||||
Update your run configuration:
|
Install the Llama Stack Kubernetes operator to manage Llama Stack deployments:
|
||||||
|
|
||||||
```yaml
|
|
||||||
providers:
|
|
||||||
inference:
|
|
||||||
- provider_id: vllm
|
|
||||||
provider_type: remote::vllm
|
|
||||||
config:
|
|
||||||
url: http://vllm-server.default.svc.cluster.local:8000/v1
|
|
||||||
max_tokens: 4096
|
|
||||||
api_token: fake
|
|
||||||
```
|
|
||||||
|
|
||||||
Build container image:
|
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
tmp_dir=$(mktemp -d) && cat >$tmp_dir/Containerfile.llama-stack-run-k8s <<EOF
|
# Install from the latest main branch
|
||||||
FROM distribution-myenv:dev
|
kubectl apply -f https://raw.githubusercontent.com/llamastack/llama-stack-k8s-operator/main/release/operator.yaml
|
||||||
RUN apt-get update && apt-get install -y git
|
|
||||||
RUN git clone https://github.com/meta-llama/llama-stack.git /app/llama-stack-source
|
# Or install a specific version (e.g., v0.4.0)
|
||||||
ADD ./vllm-llama-stack-run-k8s.yaml /app/config.yaml
|
# kubectl apply -f https://raw.githubusercontent.com/llamastack/llama-stack-k8s-operator/v0.4.0/release/operator.yaml
|
||||||
EOF
|
|
||||||
podman build -f $tmp_dir/Containerfile.llama-stack-run-k8s -t llama-stack-run-k8s $tmp_dir
|
|
||||||
```
|
```
|
||||||
|
|
||||||
### Step 4: Deploy Llama Stack Server
|
Verify the operator is running:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
kubectl get pods -n llama-stack-operator-system
|
||||||
|
```
|
||||||
|
|
||||||
|
For more information about the operator, see the [llama-stack-k8s-operator repository](https://github.com/llamastack/llama-stack-k8s-operator).
|
||||||
|
|
||||||
|
### Step 4: Deploy Llama Stack Server using Operator
|
||||||
|
|
||||||
|
Create a `LlamaStackDistribution` custom resource to deploy the Llama Stack server. The operator will automatically create the necessary Deployment, Service, and other resources.
|
||||||
|
You can optionally override the default `run.yaml` using `spec.server.userConfig` with a ConfigMap (see [userConfig spec](https://github.com/llamastack/llama-stack-k8s-operator/blob/main/docs/api-overview.md#userconfigspec)).
|
||||||
|
|
||||||
```yaml
|
```yaml
|
||||||
cat <<EOF | kubectl apply -f -
|
cat <<EOF | kubectl apply -f -
|
||||||
apiVersion: v1
|
apiVersion: llamastack.io/v1alpha1
|
||||||
kind: PersistentVolumeClaim
|
kind: LlamaStackDistribution
|
||||||
metadata:
|
metadata:
|
||||||
name: llama-pvc
|
name: llamastack-vllm
|
||||||
spec:
|
|
||||||
accessModes:
|
|
||||||
- ReadWriteOnce
|
|
||||||
resources:
|
|
||||||
requests:
|
|
||||||
storage: 1Gi
|
|
||||||
---
|
|
||||||
apiVersion: apps/v1
|
|
||||||
kind: Deployment
|
|
||||||
metadata:
|
|
||||||
name: llama-stack-server
|
|
||||||
spec:
|
spec:
|
||||||
replicas: 1
|
replicas: 1
|
||||||
selector:
|
server:
|
||||||
matchLabels:
|
distribution:
|
||||||
app.kubernetes.io/name: llama-stack
|
name: starter
|
||||||
template:
|
containerSpec:
|
||||||
metadata:
|
port: 8321
|
||||||
labels:
|
env:
|
||||||
app.kubernetes.io/name: llama-stack
|
- name: VLLM_URL
|
||||||
spec:
|
value: "http://vllm-server.default.svc.cluster.local:8000/v1"
|
||||||
containers:
|
- name: VLLM_MAX_TOKENS
|
||||||
- name: llama-stack
|
value: "4096"
|
||||||
image: localhost/llama-stack-run-k8s:latest
|
- name: VLLM_API_TOKEN
|
||||||
imagePullPolicy: IfNotPresent
|
value: "fake"
|
||||||
command: ["llama", "stack", "run", "/app/config.yaml"]
|
# Optional: override run.yaml from a ConfigMap using userConfig
|
||||||
ports:
|
userConfig:
|
||||||
- containerPort: 5000
|
configMap:
|
||||||
volumeMounts:
|
name: llama-stack-config
|
||||||
- name: llama-storage
|
storage:
|
||||||
mountPath: /root/.llama
|
size: "20Gi"
|
||||||
volumes:
|
mountPath: "/home/lls/.lls"
|
||||||
- name: llama-storage
|
|
||||||
persistentVolumeClaim:
|
|
||||||
claimName: llama-pvc
|
|
||||||
---
|
|
||||||
apiVersion: v1
|
|
||||||
kind: Service
|
|
||||||
metadata:
|
|
||||||
name: llama-stack-service
|
|
||||||
spec:
|
|
||||||
selector:
|
|
||||||
app.kubernetes.io/name: llama-stack
|
|
||||||
ports:
|
|
||||||
- protocol: TCP
|
|
||||||
port: 5000
|
|
||||||
targetPort: 5000
|
|
||||||
type: ClusterIP
|
|
||||||
EOF
|
EOF
|
||||||
```
|
```
|
||||||
|
|
||||||
|
**Configuration Options:**
|
||||||
|
|
||||||
|
- `replicas`: Number of Llama Stack server instances to run
|
||||||
|
- `server.distribution.name`: The distribution to use (e.g., `starter` for the starter distribution). See the [list of supported distributions](https://github.com/llamastack/llama-stack-k8s-operator/blob/main/distributions.json) in the operator repository.
|
||||||
|
- `server.distribution.image`: (Optional) Custom container image for non-supported distributions. Use this field when deploying a distribution that is not in the supported list. If specified, this takes precedence over `name`.
|
||||||
|
- `server.containerSpec.port`: Port on which the Llama Stack server listens (default: 8321)
|
||||||
|
- `server.containerSpec.env`: Environment variables to configure providers:
|
||||||
|
- `server.userConfig`: (Optional) Override the default `run.yaml` using a ConfigMap. See [userConfig spec](https://github.com/llamastack/llama-stack-k8s-operator/blob/main/docs/api-overview.md#userconfigspec).
|
||||||
|
- `server.storage.size`: Size of the persistent volume for model and data storage
|
||||||
|
- `server.storage.mountPath`: Where to mount the storage in the container
|
||||||
|
|
||||||
|
**Note:** For a complete list of supported distributions, see [distributions.json](https://github.com/llamastack/llama-stack-k8s-operator/blob/main/distributions.json) in the operator repository. To use a custom or non-supported distribution, set the `server.distribution.image` field with your container image instead of `server.distribution.name`.
|
||||||
|
|
||||||
|
The operator automatically creates:
|
||||||
|
- A Deployment for the Llama Stack server
|
||||||
|
- A Service to access the server
|
||||||
|
- A PersistentVolumeClaim for storage
|
||||||
|
- All necessary RBAC resources
|
||||||
|
|
||||||
|
|
||||||
|
Check the status of your deployment:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
kubectl get llamastackdistribution
|
||||||
|
kubectl describe llamastackdistribution llamastack-vllm
|
||||||
|
```
|
||||||
|
|
||||||
### Step 5: Test Deployment
|
### Step 5: Test Deployment
|
||||||
|
|
||||||
|
Wait for the Llama Stack server pod to be ready:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
# Port forward and test
|
# Check the status of the LlamaStackDistribution
|
||||||
kubectl port-forward service/llama-stack-service 5000:5000
|
kubectl get llamastackdistribution llamastack-vllm
|
||||||
llama-stack-client --endpoint http://localhost:5000 inference chat-completion --message "hello, what model are you?"
|
|
||||||
|
# Check the pods created by the operator
|
||||||
|
kubectl get pods -l app.kubernetes.io/name=llama-stack
|
||||||
|
|
||||||
|
# Wait for the pod to be ready
|
||||||
|
kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=llama-stack --timeout=300s
|
||||||
|
```
|
||||||
|
|
||||||
|
Get the service name created by the operator (it typically follows the pattern `<llamastackdistribution-name>-service`):
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# List services to find the service name
|
||||||
|
kubectl get services | grep llamastack
|
||||||
|
|
||||||
|
# Port forward and test (replace SERVICE_NAME with the actual service name)
|
||||||
|
kubectl port-forward service/llamastack-vllm-service 8321:8321
|
||||||
|
```
|
||||||
|
|
||||||
|
In another terminal, test the deployment:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
llama-stack-client --endpoint http://localhost:8321 inference chat-completion --message "hello, what model are you?"
|
||||||
```
|
```
|
||||||
|
|
||||||
## Troubleshooting
|
## Troubleshooting
|
||||||
|
|
||||||
**Check pod status:**
|
### vLLM Server Issues
|
||||||
|
|
||||||
|
**Check vLLM pod status:**
|
||||||
```bash
|
```bash
|
||||||
kubectl get pods -l app.kubernetes.io/name=vllm
|
kubectl get pods -l app.kubernetes.io/name=vllm
|
||||||
kubectl logs -l app.kubernetes.io/name=vllm
|
kubectl logs -l app.kubernetes.io/name=vllm
|
||||||
```
|
```
|
||||||
|
|
||||||
**Test service connectivity:**
|
**Test vLLM service connectivity:**
|
||||||
```bash
|
```bash
|
||||||
kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- curl http://vllm-server:8000/v1/models
|
kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- curl http://vllm-server:8000/v1/models
|
||||||
```
|
```
|
||||||
|
|
||||||
|
### Llama Stack Server Issues
|
||||||
|
|
||||||
|
**Check LlamaStackDistribution status:**
|
||||||
|
```bash
|
||||||
|
# Get detailed status
|
||||||
|
kubectl describe llamastackdistribution llamastack-vllm
|
||||||
|
|
||||||
|
# Check for events
|
||||||
|
kubectl get events --sort-by='.lastTimestamp' | grep llamastack-vllm
|
||||||
|
```
|
||||||
|
|
||||||
|
**Check operator-managed pods:**
|
||||||
|
```bash
|
||||||
|
# List all pods managed by the operator
|
||||||
|
kubectl get pods -l app.kubernetes.io/name=llama-stack
|
||||||
|
|
||||||
|
# Check pod logs (replace POD_NAME with actual pod name)
|
||||||
|
kubectl logs -l app.kubernetes.io/name=llama-stack
|
||||||
|
```
|
||||||
|
|
||||||
|
**Check operator status:**
|
||||||
|
```bash
|
||||||
|
# Verify the operator is running
|
||||||
|
kubectl get pods -n llama-stack-operator-system
|
||||||
|
|
||||||
|
# Check operator logs if issues persist
|
||||||
|
kubectl logs -n llama-stack-operator-system -l control-plane=controller-manager
|
||||||
|
```
|
||||||
|
|
||||||
|
**Verify service connectivity:**
|
||||||
|
```bash
|
||||||
|
# Get the service endpoint
|
||||||
|
kubectl get svc llamastack-vllm-service
|
||||||
|
|
||||||
|
# Test connectivity from within the cluster
|
||||||
|
kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- curl http://llamastack-vllm-service:8321/health
|
||||||
|
```
|
||||||
|
|
||||||
## Related Resources
|
## Related Resources
|
||||||
|
|
||||||
- **[Deployment Overview](/docs/deploying/)** - Overview of deployment options
|
- **[Deployment Overview](/docs/deploying/)** - Overview of deployment options
|
||||||
- **[Distributions](/docs/distributions)** - Understanding Llama Stack distributions
|
- **[Distributions](/docs/distributions)** - Understanding Llama Stack distributions
|
||||||
- **[Configuration](/docs/distributions/configuration)** - Detailed configuration options
|
- **[Configuration](/docs/distributions/configuration)** - Detailed configuration options
|
||||||
|
- **[LlamaStack Operator](https://github.com/llamastack/llama-stack-k8s-operator)** - Overview of llama-stack kubernetes operator
|
||||||
|
- **[LlamaStackDistribution](https://github.com/llamastack/llama-stack-k8s-operator/blob/main/docs/api-overview.md)** - API Spec of the llama-stack operator Custom Resource.
|
||||||
|
|
|
||||||
|
|
@ -11,7 +11,7 @@ If you are planning to use an external service for Inference (even Ollama or TGI
|
||||||
This avoids the overhead of setting up a server.
|
This avoids the overhead of setting up a server.
|
||||||
```bash
|
```bash
|
||||||
# setup
|
# setup
|
||||||
uv pip install llama-stack
|
uv pip install llama-stack llama-stack-client
|
||||||
llama stack list-deps starter | xargs -L1 uv pip install
|
llama stack list-deps starter | xargs -L1 uv pip install
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -19,3 +19,4 @@ This section provides an overview of the distributions available in Llama Stack.
|
||||||
- **[Starting Llama Stack Server](./starting_llama_stack_server.mdx)** - How to run distributions
|
- **[Starting Llama Stack Server](./starting_llama_stack_server.mdx)** - How to run distributions
|
||||||
- **[Importing as Library](./importing_as_library.mdx)** - Use distributions in your code
|
- **[Importing as Library](./importing_as_library.mdx)** - Use distributions in your code
|
||||||
- **[Configuration Reference](./configuration.mdx)** - Configuration file format details
|
- **[Configuration Reference](./configuration.mdx)** - Configuration file format details
|
||||||
|
- **[Llama Stack UI](./llama_stack_ui.mdx)** - Web-based user interface for interacting with Llama Stack servers
|
||||||
|
|
|
||||||
109
docs/docs/distributions/llama_stack_ui.mdx
Normal file
109
docs/docs/distributions/llama_stack_ui.mdx
Normal file
|
|
@ -0,0 +1,109 @@
|
||||||
|
---
|
||||||
|
title: Llama Stack UI
|
||||||
|
description: Web-based user interface for interacting with Llama Stack servers
|
||||||
|
sidebar_label: Llama Stack UI
|
||||||
|
sidebar_position: 8
|
||||||
|
---
|
||||||
|
|
||||||
|
# Llama Stack UI
|
||||||
|
|
||||||
|
The Llama Stack UI is a web-based interface for interacting with Llama Stack servers. Built with Next.js and React, it provides a visual way to work with agents, manage resources, and view logs.
|
||||||
|
|
||||||
|
## Features
|
||||||
|
|
||||||
|
- **Logs & Monitoring**: View chat completions, agent responses, and vector store activity
|
||||||
|
- **Vector Stores**: Create and manage vector databases for RAG (Retrieval-Augmented Generation) workflows
|
||||||
|
- **Prompt Management**: Create and manage reusable prompts
|
||||||
|
|
||||||
|
## Prerequisites
|
||||||
|
|
||||||
|
You need a running Llama Stack server. The UI is a client that connects to the Llama Stack backend.
|
||||||
|
|
||||||
|
If you don't have a Llama Stack server running yet, see the [Starting Llama Stack Server](../getting_started/starting_llama_stack_server.mdx) guide.
|
||||||
|
|
||||||
|
## Running the UI
|
||||||
|
|
||||||
|
### Option 1: Using npx (Recommended for Quick Start)
|
||||||
|
|
||||||
|
The fastest way to get started is using `npx`:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
npx llama-stack-ui
|
||||||
|
```
|
||||||
|
|
||||||
|
This will start the UI server on `http://localhost:8322` (default port).
|
||||||
|
|
||||||
|
### Option 2: Using Docker
|
||||||
|
|
||||||
|
Run the UI in a container:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
docker run -p 8322:8322 llamastack/ui
|
||||||
|
```
|
||||||
|
|
||||||
|
Access the UI at `http://localhost:8322`.
|
||||||
|
|
||||||
|
## Environment Variables
|
||||||
|
|
||||||
|
The UI can be configured using the following environment variables:
|
||||||
|
|
||||||
|
| Variable | Description | Default |
|
||||||
|
|----------|-------------|---------|
|
||||||
|
| `LLAMA_STACK_BACKEND_URL` | URL of your Llama Stack server | `http://localhost:8321` |
|
||||||
|
| `LLAMA_STACK_UI_PORT` | Port for the UI server | `8322` |
|
||||||
|
|
||||||
|
If the Llama Stack server is running with authentication enabled, you can configure the UI to use it by setting the following environment variables:
|
||||||
|
|
||||||
|
| Variable | Description | Default |
|
||||||
|
|----------|-------------|---------|
|
||||||
|
| `NEXTAUTH_URL` | NextAuth URL for authentication | `http://localhost:8322` |
|
||||||
|
| `GITHUB_CLIENT_ID` | GitHub OAuth client ID (optional, for authentication) | - |
|
||||||
|
| `GITHUB_CLIENT_SECRET` | GitHub OAuth client secret (optional, for authentication) | - |
|
||||||
|
|
||||||
|
### Setting Environment Variables
|
||||||
|
|
||||||
|
#### For npx:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
LLAMA_STACK_BACKEND_URL=http://localhost:8321 \
|
||||||
|
LLAMA_STACK_UI_PORT=8080 \
|
||||||
|
npx llama-stack-ui
|
||||||
|
```
|
||||||
|
|
||||||
|
#### For Docker:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
docker run -p 8080:8080 \
|
||||||
|
-e LLAMA_STACK_BACKEND_URL=http://localhost:8321 \
|
||||||
|
-e LLAMA_STACK_UI_PORT=8080 \
|
||||||
|
llamastack/ui
|
||||||
|
```
|
||||||
|
|
||||||
|
## Using the UI
|
||||||
|
|
||||||
|
### Managing Resources
|
||||||
|
|
||||||
|
- **Vector Stores**: Create vector databases for RAG workflows, view stored documents and embeddings
|
||||||
|
- **Prompts**: Create and manage reusable prompt templates
|
||||||
|
- **Chat Completions**: View history of chat interactions
|
||||||
|
- **Responses**: Browse detailed agent responses and tool calls
|
||||||
|
|
||||||
|
## Development
|
||||||
|
|
||||||
|
If you want to run the UI from source for development:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# From the project root
|
||||||
|
cd src/llama_stack_ui
|
||||||
|
|
||||||
|
# Install dependencies
|
||||||
|
npm install
|
||||||
|
|
||||||
|
# Set environment variables
|
||||||
|
export LLAMA_STACK_BACKEND_URL=http://localhost:8321
|
||||||
|
|
||||||
|
# Start the development server
|
||||||
|
npm run dev
|
||||||
|
```
|
||||||
|
|
||||||
|
The development server will start on `http://localhost:8322` with hot reloading enabled.
|
||||||
143
docs/docs/distributions/remote_hosted_distro/oci.md
Normal file
143
docs/docs/distributions/remote_hosted_distro/oci.md
Normal file
|
|
@ -0,0 +1,143 @@
|
||||||
|
---
|
||||||
|
orphan: true
|
||||||
|
---
|
||||||
|
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
|
||||||
|
# OCI Distribution
|
||||||
|
|
||||||
|
The `llamastack/distribution-oci` distribution consists of the following provider configurations.
|
||||||
|
|
||||||
|
| API | Provider(s) |
|
||||||
|
|-----|-------------|
|
||||||
|
| agents | `inline::meta-reference` |
|
||||||
|
| datasetio | `remote::huggingface`, `inline::localfs` |
|
||||||
|
| eval | `inline::meta-reference` |
|
||||||
|
| files | `inline::localfs` |
|
||||||
|
| inference | `remote::oci` |
|
||||||
|
| safety | `inline::llama-guard` |
|
||||||
|
| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
|
||||||
|
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol` |
|
||||||
|
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
|
||||||
|
|
||||||
|
|
||||||
|
### Environment Variables
|
||||||
|
|
||||||
|
The following environment variables can be configured:
|
||||||
|
|
||||||
|
- `OCI_AUTH_TYPE`: OCI authentication type (instance_principal or config_file) (default: `instance_principal`)
|
||||||
|
- `OCI_REGION`: OCI region (e.g., us-ashburn-1, us-chicago-1, us-phoenix-1, eu-frankfurt-1) (default: ``)
|
||||||
|
- `OCI_COMPARTMENT_OCID`: OCI compartment ID for the Generative AI service (default: ``)
|
||||||
|
- `OCI_CONFIG_FILE_PATH`: OCI config file path (required if OCI_AUTH_TYPE is config_file) (default: `~/.oci/config`)
|
||||||
|
- `OCI_CLI_PROFILE`: OCI CLI profile name to use from config file (default: `DEFAULT`)
|
||||||
|
|
||||||
|
|
||||||
|
## Prerequisites
|
||||||
|
### Oracle Cloud Infrastructure Setup
|
||||||
|
|
||||||
|
Before using the OCI Generative AI distribution, ensure you have:
|
||||||
|
|
||||||
|
1. **Oracle Cloud Infrastructure Account**: Sign up at [Oracle Cloud Infrastructure](https://cloud.oracle.com/)
|
||||||
|
2. **Generative AI Service Access**: Enable the Generative AI service in your OCI tenancy
|
||||||
|
3. **Compartment**: Create or identify a compartment where you'll deploy Generative AI models
|
||||||
|
4. **Authentication**: Configure authentication using either:
|
||||||
|
- **Instance Principal** (recommended for cloud-hosted deployments)
|
||||||
|
- **API Key** (for on-premises or development environments)
|
||||||
|
|
||||||
|
### Authentication Methods
|
||||||
|
|
||||||
|
#### Instance Principal Authentication (Recommended)
|
||||||
|
Instance Principal authentication allows OCI resources to authenticate using the identity of the compute instance they're running on. This is the most secure method for production deployments.
|
||||||
|
|
||||||
|
Requirements:
|
||||||
|
- Instance must be running in an Oracle Cloud Infrastructure compartment
|
||||||
|
- Instance must have appropriate IAM policies to access Generative AI services
|
||||||
|
|
||||||
|
#### API Key Authentication
|
||||||
|
For development or on-premises deployments, follow [this doc](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm) to learn how to create your API signing key for your config file.
|
||||||
|
|
||||||
|
### Required IAM Policies
|
||||||
|
|
||||||
|
Ensure your OCI user or instance has the following policy statements:
|
||||||
|
|
||||||
|
```
|
||||||
|
Allow group <group_name> to use generative-ai-inference-endpoints in compartment <compartment_name>
|
||||||
|
Allow group <group_name> to manage generative-ai-inference-endpoints in compartment <compartment_name>
|
||||||
|
```
|
||||||
|
|
||||||
|
## Supported Services
|
||||||
|
|
||||||
|
### Inference: OCI Generative AI
|
||||||
|
Oracle Cloud Infrastructure Generative AI provides access to high-performance AI models through OCI's Platform-as-a-Service offering. The service supports:
|
||||||
|
|
||||||
|
- **Chat Completions**: Conversational AI with context awareness
|
||||||
|
- **Text Generation**: Complete prompts and generate text content
|
||||||
|
|
||||||
|
#### Available Models
|
||||||
|
Common OCI Generative AI models include access to Meta, Cohere, OpenAI, Grok, and more models.
|
||||||
|
|
||||||
|
### Safety: Llama Guard
|
||||||
|
For content safety and moderation, this distribution uses Meta's LlamaGuard model through the OCI Generative AI service to provide:
|
||||||
|
- Content filtering and moderation
|
||||||
|
- Policy compliance checking
|
||||||
|
- Harmful content detection
|
||||||
|
|
||||||
|
### Vector Storage: Multiple Options
|
||||||
|
The distribution supports several vector storage providers:
|
||||||
|
- **FAISS**: Local in-memory vector search
|
||||||
|
- **ChromaDB**: Distributed vector database
|
||||||
|
- **PGVector**: PostgreSQL with vector extensions
|
||||||
|
|
||||||
|
### Additional Services
|
||||||
|
- **Dataset I/O**: Local filesystem and Hugging Face integration
|
||||||
|
- **Tool Runtime**: Web search (Brave, Tavily) and RAG capabilities
|
||||||
|
- **Evaluation**: Meta reference evaluation framework
|
||||||
|
|
||||||
|
## Running Llama Stack with OCI
|
||||||
|
|
||||||
|
You can run the OCI distribution via Docker or local virtual environment.
|
||||||
|
|
||||||
|
### Via venv
|
||||||
|
|
||||||
|
If you've set up your local development environment, you can also build the image using your local virtual environment.
|
||||||
|
|
||||||
|
```bash
|
||||||
|
OCI_AUTH=$OCI_AUTH_TYPE OCI_REGION=$OCI_REGION OCI_COMPARTMENT_OCID=$OCI_COMPARTMENT_OCID llama stack run --port 8321 oci
|
||||||
|
```
|
||||||
|
|
||||||
|
### Configuration Examples
|
||||||
|
|
||||||
|
#### Using Instance Principal (Recommended for Production)
|
||||||
|
```bash
|
||||||
|
export OCI_AUTH_TYPE=instance_principal
|
||||||
|
export OCI_REGION=us-chicago-1
|
||||||
|
export OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..<your-compartment-id>
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Using API Key Authentication (Development)
|
||||||
|
```bash
|
||||||
|
export OCI_AUTH_TYPE=config_file
|
||||||
|
export OCI_CONFIG_FILE_PATH=~/.oci/config
|
||||||
|
export OCI_CLI_PROFILE=DEFAULT
|
||||||
|
export OCI_REGION=us-chicago-1
|
||||||
|
export OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..your-compartment-id
|
||||||
|
```
|
||||||
|
|
||||||
|
## Regional Endpoints
|
||||||
|
|
||||||
|
OCI Generative AI is available in multiple regions. The service automatically routes to the appropriate regional endpoint based on your configuration. For a full list of regional model availability, visit:
|
||||||
|
|
||||||
|
https://docs.oracle.com/en-us/iaas/Content/generative-ai/overview.htm#regions
|
||||||
|
|
||||||
|
## Troubleshooting
|
||||||
|
|
||||||
|
### Common Issues
|
||||||
|
|
||||||
|
1. **Authentication Errors**: Verify your OCI credentials and IAM policies
|
||||||
|
2. **Model Not Found**: Ensure the model OCID is correct and the model is available in your region
|
||||||
|
3. **Permission Denied**: Check compartment permissions and Generative AI service access
|
||||||
|
4. **Region Unavailable**: Verify the specified region supports Generative AI services
|
||||||
|
|
||||||
|
### Getting Help
|
||||||
|
|
||||||
|
For additional support:
|
||||||
|
- [OCI Generative AI Documentation](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)
|
||||||
|
- [Llama Stack Issues](https://github.com/meta-llama/llama-stack/issues)
|
||||||
|
|
@ -144,7 +144,7 @@ source .venv/bin/activate
|
||||||
```bash
|
```bash
|
||||||
uv venv client --python 3.12
|
uv venv client --python 3.12
|
||||||
source client/bin/activate
|
source client/bin/activate
|
||||||
pip install llama-stack-client
|
uv pip install llama-stack-client
|
||||||
```
|
```
|
||||||
</TabItem>
|
</TabItem>
|
||||||
</Tabs>
|
</Tabs>
|
||||||
|
|
|
||||||
41
docs/docs/providers/inference/remote_oci.mdx
Normal file
41
docs/docs/providers/inference/remote_oci.mdx
Normal file
|
|
@ -0,0 +1,41 @@
|
||||||
|
---
|
||||||
|
description: |
|
||||||
|
Oracle Cloud Infrastructure (OCI) Generative AI inference provider for accessing OCI's Generative AI Platform-as-a-Service models.
|
||||||
|
Provider documentation
|
||||||
|
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
|
||||||
|
sidebar_label: Remote - Oci
|
||||||
|
title: remote::oci
|
||||||
|
---
|
||||||
|
|
||||||
|
# remote::oci
|
||||||
|
|
||||||
|
## Description
|
||||||
|
|
||||||
|
|
||||||
|
Oracle Cloud Infrastructure (OCI) Generative AI inference provider for accessing OCI's Generative AI Platform-as-a-Service models.
|
||||||
|
Provider documentation
|
||||||
|
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
|
||||||
|
|
||||||
|
|
||||||
|
## Configuration
|
||||||
|
|
||||||
|
| Field | Type | Required | Default | Description |
|
||||||
|
|-------|------|----------|---------|-------------|
|
||||||
|
| `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. |
|
||||||
|
| `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically from the provider |
|
||||||
|
| `api_key` | `pydantic.types.SecretStr \| None` | No | | Authentication credential for the provider |
|
||||||
|
| `oci_auth_type` | `<class 'str'>` | No | instance_principal | OCI authentication type (must be one of: instance_principal, config_file) |
|
||||||
|
| `oci_region` | `<class 'str'>` | No | us-ashburn-1 | OCI region (e.g., us-ashburn-1) |
|
||||||
|
| `oci_compartment_id` | `<class 'str'>` | No | | OCI compartment ID for the Generative AI service |
|
||||||
|
| `oci_config_file_path` | `<class 'str'>` | No | ~/.oci/config | OCI config file path (required if oci_auth_type is config_file) |
|
||||||
|
| `oci_config_profile` | `<class 'str'>` | No | DEFAULT | OCI config profile (required if oci_auth_type is config_file) |
|
||||||
|
|
||||||
|
## Sample Configuration
|
||||||
|
|
||||||
|
```yaml
|
||||||
|
oci_auth_type: ${env.OCI_AUTH_TYPE:=instance_principal}
|
||||||
|
oci_config_file_path: ${env.OCI_CONFIG_FILE_PATH:=~/.oci/config}
|
||||||
|
oci_config_profile: ${env.OCI_CLI_PROFILE:=DEFAULT}
|
||||||
|
oci_region: ${env.OCI_REGION:=us-ashburn-1}
|
||||||
|
oci_compartment_id: ${env.OCI_COMPARTMENT_OCID:=}
|
||||||
|
```
|
||||||
|
|
@ -48,11 +48,9 @@ Both OpenAI and Llama Stack support a web-search built-in tool. The [OpenAI doc
|
||||||
|
|
||||||
> The type of the web search tool. One of `web_search` or `web_search_2025_08_26`.
|
> The type of the web search tool. One of `web_search` or `web_search_2025_08_26`.
|
||||||
|
|
||||||
In contrast, the [Llama Stack documentation](https://llamastack.github.io/docs/api/create-a-new-open-ai-response) says that the allowed values for `type` for web search are `MOD1`, `MOD2` and `MOD3`.
|
Llama Stack now supports both `web_search` and `web_search_2025_08_26` types, matching OpenAI's API. For backward compatibility, Llama Stack also supports `web_search_preview` and `web_search_preview_2025_03_11` types.
|
||||||
Is that correct? If so, what are the meanings of each of them? It might make sense for the allowed values for OpenAI map to some values for Llama Stack so that code written to the OpenAI specification
|
|
||||||
also work with Llama Stack.
|
|
||||||
|
|
||||||
The OpenAI web search tool also has fields for `filters` and `user_location` which are not documented as options for Llama Stack. If feasible, it would be good to support these too.
|
The OpenAI web search tool also has fields for `filters` and `user_location` which are not yet implemented in Llama Stack. If feasible, it would be good to support these too.
|
||||||
|
|
||||||
---
|
---
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -37,7 +37,7 @@
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"# NBVAL_SKIP\n",
|
"# NBVAL_SKIP\n",
|
||||||
"!pip install -U llama-stack\n",
|
"!pip install -U llama-stack llama-stack-client\n",
|
||||||
"llama stack list-deps fireworks | xargs -L1 uv pip install\n"
|
"llama stack list-deps fireworks | xargs -L1 uv pip install\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
|
|
||||||
|
|
@ -44,7 +44,7 @@
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"# NBVAL_SKIP\n",
|
"# NBVAL_SKIP\n",
|
||||||
"!pip install -U llama-stack"
|
"!pip install -U llama-stack llama-stack-client\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|
|
||||||
|
|
@ -74,6 +74,7 @@
|
||||||
"source": [
|
"source": [
|
||||||
"```bash\n",
|
"```bash\n",
|
||||||
"uv sync --extra dev\n",
|
"uv sync --extra dev\n",
|
||||||
|
"uv pip install -U llama-stack-client\n",
|
||||||
"uv pip install -e .\n",
|
"uv pip install -e .\n",
|
||||||
"source .venv/bin/activate\n",
|
"source .venv/bin/activate\n",
|
||||||
"```"
|
"```"
|
||||||
|
|
|
||||||
|
|
@ -57,6 +57,7 @@ const sidebars: SidebarsConfig = {
|
||||||
'distributions/importing_as_library',
|
'distributions/importing_as_library',
|
||||||
'distributions/configuration',
|
'distributions/configuration',
|
||||||
'distributions/starting_llama_stack_server',
|
'distributions/starting_llama_stack_server',
|
||||||
|
'distributions/llama_stack_ui',
|
||||||
{
|
{
|
||||||
type: 'category',
|
type: 'category',
|
||||||
label: 'Self-Hosted Distributions',
|
label: 'Self-Hosted Distributions',
|
||||||
|
|
|
||||||
1094
docs/static/deprecated-llama-stack-spec.yaml
vendored
1094
docs/static/deprecated-llama-stack-spec.yaml
vendored
File diff suppressed because it is too large
Load diff
214
docs/static/experimental-llama-stack-spec.yaml
vendored
214
docs/static/experimental-llama-stack-spec.yaml
vendored
|
|
@ -162,7 +162,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterDatasetRequest'
|
$ref: '#/components/schemas/RegisterDatasetRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1beta/datasets/{dataset_id}:
|
/v1beta/datasets/{dataset_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -219,7 +219,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks:
|
/v1alpha/eval/benchmarks:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -270,7 +270,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}:
|
/v1alpha/eval/benchmarks/{benchmark_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -327,7 +327,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -936,68 +936,6 @@ components:
|
||||||
- data
|
- data
|
||||||
title: ListDatasetsResponse
|
title: ListDatasetsResponse
|
||||||
description: Response from listing datasets.
|
description: Response from listing datasets.
|
||||||
DataSource:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/URIDataSource'
|
|
||||||
- $ref: '#/components/schemas/RowsDataSource'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
uri: '#/components/schemas/URIDataSource'
|
|
||||||
rows: '#/components/schemas/RowsDataSource'
|
|
||||||
RegisterDatasetRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
purpose:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- post-training/messages
|
|
||||||
- eval/question-answer
|
|
||||||
- eval/messages-answer
|
|
||||||
description: >-
|
|
||||||
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
|
||||||
contains a messages column with list of messages for post-training. {
|
|
||||||
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
|
||||||
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
|
||||||
contains a question column and an answer column for evaluation. { "question":
|
|
||||||
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
|
||||||
The dataset contains a messages column with list of messages and an answer
|
|
||||||
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
|
||||||
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
|
||||||
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
|
||||||
my name?"}, ], "answer": "John Doe" }
|
|
||||||
source:
|
|
||||||
$ref: '#/components/schemas/DataSource'
|
|
||||||
description: >-
|
|
||||||
The data source of the dataset. Ensure that the data source schema is
|
|
||||||
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
|
||||||
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
|
||||||
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
|
||||||
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
|
||||||
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
|
||||||
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
|
||||||
} ] }
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset. If not provided, an ID will be generated.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- purpose
|
|
||||||
- source
|
|
||||||
title: RegisterDatasetRequest
|
|
||||||
Benchmark:
|
Benchmark:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -1065,47 +1003,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListBenchmarksResponse
|
title: ListBenchmarksResponse
|
||||||
RegisterBenchmarkRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the benchmark to register.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset to use for the benchmark.
|
|
||||||
scoring_functions:
|
|
||||||
type: array
|
|
||||||
items:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The scoring functions to use for the benchmark.
|
|
||||||
provider_benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider benchmark to use for the benchmark.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the benchmark.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The metadata to use for the benchmark.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- benchmark_id
|
|
||||||
- dataset_id
|
|
||||||
- scoring_functions
|
|
||||||
title: RegisterBenchmarkRequest
|
|
||||||
AggregationFunctionType:
|
AggregationFunctionType:
|
||||||
type: string
|
type: string
|
||||||
enum:
|
enum:
|
||||||
|
|
@ -2254,6 +2151,109 @@ components:
|
||||||
- hyperparam_search_config
|
- hyperparam_search_config
|
||||||
- logger_config
|
- logger_config
|
||||||
title: SupervisedFineTuneRequest
|
title: SupervisedFineTuneRequest
|
||||||
|
DataSource:
|
||||||
|
oneOf:
|
||||||
|
- $ref: '#/components/schemas/URIDataSource'
|
||||||
|
- $ref: '#/components/schemas/RowsDataSource'
|
||||||
|
discriminator:
|
||||||
|
propertyName: type
|
||||||
|
mapping:
|
||||||
|
uri: '#/components/schemas/URIDataSource'
|
||||||
|
rows: '#/components/schemas/RowsDataSource'
|
||||||
|
RegisterDatasetRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
purpose:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- post-training/messages
|
||||||
|
- eval/question-answer
|
||||||
|
- eval/messages-answer
|
||||||
|
description: >-
|
||||||
|
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
||||||
|
contains a messages column with list of messages for post-training. {
|
||||||
|
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
||||||
|
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
||||||
|
contains a question column and an answer column for evaluation. { "question":
|
||||||
|
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
||||||
|
The dataset contains a messages column with list of messages and an answer
|
||||||
|
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
||||||
|
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
||||||
|
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
||||||
|
my name?"}, ], "answer": "John Doe" }
|
||||||
|
source:
|
||||||
|
$ref: '#/components/schemas/DataSource'
|
||||||
|
description: >-
|
||||||
|
The data source of the dataset. Ensure that the data source schema is
|
||||||
|
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
||||||
|
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
||||||
|
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
||||||
|
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
||||||
|
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
||||||
|
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
||||||
|
} ] }
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: >-
|
||||||
|
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset. If not provided, an ID will be generated.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- purpose
|
||||||
|
- source
|
||||||
|
title: RegisterDatasetRequest
|
||||||
|
RegisterBenchmarkRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: The ID of the benchmark to register.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset to use for the benchmark.
|
||||||
|
scoring_functions:
|
||||||
|
type: array
|
||||||
|
items:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The scoring functions to use for the benchmark.
|
||||||
|
provider_benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider benchmark to use for the benchmark.
|
||||||
|
provider_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider to use for the benchmark.
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: The metadata to use for the benchmark.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- benchmark_id
|
||||||
|
- dataset_id
|
||||||
|
- scoring_functions
|
||||||
|
title: RegisterBenchmarkRequest
|
||||||
responses:
|
responses:
|
||||||
BadRequest400:
|
BadRequest400:
|
||||||
description: The request was invalid or malformed
|
description: The request was invalid or malformed
|
||||||
|
|
|
||||||
408
docs/static/llama-stack-spec.yaml
vendored
408
docs/static/llama-stack-spec.yaml
vendored
|
|
@ -960,7 +960,7 @@ paths:
|
||||||
Optional filter to control which routes are returned. Can be an API level
|
Optional filter to control which routes are returned. Can be an API level
|
||||||
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
||||||
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
||||||
returns only non-deprecated v1 routes.
|
returns all non-deprecated routes.
|
||||||
required: false
|
required: false
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
|
|
@ -995,39 +995,6 @@ paths:
|
||||||
description: List models using the OpenAI API.
|
description: List models using the OpenAI API.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Model.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Model'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Register model.
|
|
||||||
description: >-
|
|
||||||
Register model.
|
|
||||||
|
|
||||||
Register a model.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterModelRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/models/{model_id}:
|
/v1/models/{model_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1062,36 +1029,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Unregister model.
|
|
||||||
description: >-
|
|
||||||
Unregister model.
|
|
||||||
|
|
||||||
Unregister a model.
|
|
||||||
parameters:
|
|
||||||
- name: model_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the model to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/moderations:
|
/v1/moderations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1722,32 +1659,6 @@ paths:
|
||||||
description: List all scoring functions.
|
description: List all scoring functions.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Register a scoring function.
|
|
||||||
description: Register a scoring function.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterScoringFunctionRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring-functions/{scoring_fn_id}:
|
/v1/scoring-functions/{scoring_fn_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1779,33 +1690,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Unregister a scoring function.
|
|
||||||
description: Unregister a scoring function.
|
|
||||||
parameters:
|
|
||||||
- name: scoring_fn_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring/score:
|
/v1/scoring/score:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1894,36 +1778,6 @@ paths:
|
||||||
description: List all shields.
|
description: List all shields.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Shield.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Shield'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Register a shield.
|
|
||||||
description: Register a shield.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterShieldRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/shields/{identifier}:
|
/v1/shields/{identifier}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1955,33 +1809,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Unregister a shield.
|
|
||||||
description: Unregister a shield.
|
|
||||||
parameters:
|
|
||||||
- name: identifier
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tool-runtime/invoke:
|
/v1/tool-runtime/invoke:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2077,32 +1904,6 @@ paths:
|
||||||
description: List tool groups with optional provider.
|
description: List tool groups with optional provider.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Register a tool group.
|
|
||||||
description: Register a tool group.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterToolGroupRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/toolgroups/{toolgroup_id}:
|
/v1/toolgroups/{toolgroup_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2134,32 +1935,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Unregister a tool group.
|
|
||||||
description: Unregister a tool group.
|
|
||||||
parameters:
|
|
||||||
- name: toolgroup_id
|
|
||||||
in: path
|
|
||||||
description: The ID of the tool group to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tools:
|
/v1/tools:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -5564,46 +5339,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: OpenAIListModelsResponse
|
title: OpenAIListModelsResponse
|
||||||
ModelType:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- llm
|
|
||||||
- embedding
|
|
||||||
- rerank
|
|
||||||
title: ModelType
|
|
||||||
description: >-
|
|
||||||
Enumeration of supported model types in Llama Stack.
|
|
||||||
RegisterModelRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
model_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the model to register.
|
|
||||||
provider_model_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the model in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: Any additional metadata for this model.
|
|
||||||
model_type:
|
|
||||||
$ref: '#/components/schemas/ModelType'
|
|
||||||
description: The type of model to register.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- model_id
|
|
||||||
title: RegisterModelRequest
|
|
||||||
Model:
|
Model:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -5661,6 +5396,15 @@ components:
|
||||||
title: Model
|
title: Model
|
||||||
description: >-
|
description: >-
|
||||||
A model resource representing an AI model registered in Llama Stack.
|
A model resource representing an AI model registered in Llama Stack.
|
||||||
|
ModelType:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- llm
|
||||||
|
- embedding
|
||||||
|
- rerank
|
||||||
|
title: ModelType
|
||||||
|
description: >-
|
||||||
|
Enumeration of supported model types in Llama Stack.
|
||||||
RunModerationRequest:
|
RunModerationRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -6075,6 +5819,8 @@ components:
|
||||||
const: web_search_preview
|
const: web_search_preview
|
||||||
- type: string
|
- type: string
|
||||||
const: web_search_preview_2025_03_11
|
const: web_search_preview_2025_03_11
|
||||||
|
- type: string
|
||||||
|
const: web_search_2025_08_26
|
||||||
default: web_search
|
default: web_search
|
||||||
description: Web search tool type variant to use
|
description: Web search tool type variant to use
|
||||||
search_context_size:
|
search_context_size:
|
||||||
|
|
@ -6164,6 +5910,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
input:
|
input:
|
||||||
type: array
|
type: array
|
||||||
items:
|
items:
|
||||||
|
|
@ -6522,6 +6273,11 @@ components:
|
||||||
(Optional) Additional fields to include in the response.
|
(Optional) Additional fields to include in the response.
|
||||||
max_infer_iters:
|
max_infer_iters:
|
||||||
type: integer
|
type: integer
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response.
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- input
|
- input
|
||||||
|
|
@ -6603,6 +6359,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- created_at
|
- created_at
|
||||||
|
|
@ -8397,61 +8158,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListScoringFunctionsResponse
|
title: ListScoringFunctionsResponse
|
||||||
ParamType:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/StringType'
|
|
||||||
- $ref: '#/components/schemas/NumberType'
|
|
||||||
- $ref: '#/components/schemas/BooleanType'
|
|
||||||
- $ref: '#/components/schemas/ArrayType'
|
|
||||||
- $ref: '#/components/schemas/ObjectType'
|
|
||||||
- $ref: '#/components/schemas/JsonType'
|
|
||||||
- $ref: '#/components/schemas/UnionType'
|
|
||||||
- $ref: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
- $ref: '#/components/schemas/CompletionInputType'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
string: '#/components/schemas/StringType'
|
|
||||||
number: '#/components/schemas/NumberType'
|
|
||||||
boolean: '#/components/schemas/BooleanType'
|
|
||||||
array: '#/components/schemas/ArrayType'
|
|
||||||
object: '#/components/schemas/ObjectType'
|
|
||||||
json: '#/components/schemas/JsonType'
|
|
||||||
union: '#/components/schemas/UnionType'
|
|
||||||
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
completion_input: '#/components/schemas/CompletionInputType'
|
|
||||||
RegisterScoringFunctionRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to register.
|
|
||||||
description:
|
|
||||||
type: string
|
|
||||||
description: The description of the scoring function.
|
|
||||||
return_type:
|
|
||||||
$ref: '#/components/schemas/ParamType'
|
|
||||||
description: The return type of the scoring function.
|
|
||||||
provider_scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider scoring function to use for the scoring function.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the scoring function.
|
|
||||||
params:
|
|
||||||
$ref: '#/components/schemas/ScoringFnParams'
|
|
||||||
description: >-
|
|
||||||
The parameters for the scoring function for benchmark eval, these can
|
|
||||||
be overridden for app eval.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- scoring_fn_id
|
|
||||||
- description
|
|
||||||
- return_type
|
|
||||||
title: RegisterScoringFunctionRequest
|
|
||||||
ScoreRequest:
|
ScoreRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -8627,35 +8333,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListShieldsResponse
|
title: ListShieldsResponse
|
||||||
RegisterShieldRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to register.
|
|
||||||
provider_shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
params:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The parameters of the shield.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- shield_id
|
|
||||||
title: RegisterShieldRequest
|
|
||||||
InvokeToolRequest:
|
InvokeToolRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -8916,37 +8593,6 @@ components:
|
||||||
title: ListToolGroupsResponse
|
title: ListToolGroupsResponse
|
||||||
description: >-
|
description: >-
|
||||||
Response containing a list of tool groups.
|
Response containing a list of tool groups.
|
||||||
RegisterToolGroupRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
toolgroup_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the tool group to register.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the tool group.
|
|
||||||
mcp_endpoint:
|
|
||||||
$ref: '#/components/schemas/URL'
|
|
||||||
description: >-
|
|
||||||
The MCP endpoint to use for the tool group.
|
|
||||||
args:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
A dictionary of arguments to pass to the tool group.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- toolgroup_id
|
|
||||||
- provider_id
|
|
||||||
title: RegisterToolGroupRequest
|
|
||||||
Chunk:
|
Chunk:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
|
||||||
622
docs/static/stainless-llama-stack-spec.yaml
vendored
622
docs/static/stainless-llama-stack-spec.yaml
vendored
|
|
@ -963,7 +963,7 @@ paths:
|
||||||
Optional filter to control which routes are returned. Can be an API level
|
Optional filter to control which routes are returned. Can be an API level
|
||||||
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level,
|
||||||
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
or 'deprecated' to show deprecated routes across all levels. If not specified,
|
||||||
returns only non-deprecated v1 routes.
|
returns all non-deprecated routes.
|
||||||
required: false
|
required: false
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
|
|
@ -998,39 +998,6 @@ paths:
|
||||||
description: List models using the OpenAI API.
|
description: List models using the OpenAI API.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Model.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Model'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Register model.
|
|
||||||
description: >-
|
|
||||||
Register model.
|
|
||||||
|
|
||||||
Register a model.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterModelRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/models/{model_id}:
|
/v1/models/{model_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1065,36 +1032,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Models
|
|
||||||
summary: Unregister model.
|
|
||||||
description: >-
|
|
||||||
Unregister model.
|
|
||||||
|
|
||||||
Unregister a model.
|
|
||||||
parameters:
|
|
||||||
- name: model_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the model to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/moderations:
|
/v1/moderations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1725,32 +1662,6 @@ paths:
|
||||||
description: List all scoring functions.
|
description: List all scoring functions.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Register a scoring function.
|
|
||||||
description: Register a scoring function.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterScoringFunctionRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring-functions/{scoring_fn_id}:
|
/v1/scoring-functions/{scoring_fn_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1782,33 +1693,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ScoringFunctions
|
|
||||||
summary: Unregister a scoring function.
|
|
||||||
description: Unregister a scoring function.
|
|
||||||
parameters:
|
|
||||||
- name: scoring_fn_id
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/scoring/score:
|
/v1/scoring/score:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1897,36 +1781,6 @@ paths:
|
||||||
description: List all shields.
|
description: List all shields.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: A Shield.
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/Shield'
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Register a shield.
|
|
||||||
description: Register a shield.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterShieldRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/shields/{identifier}:
|
/v1/shields/{identifier}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -1958,33 +1812,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- Shields
|
|
||||||
summary: Unregister a shield.
|
|
||||||
description: Unregister a shield.
|
|
||||||
parameters:
|
|
||||||
- name: identifier
|
|
||||||
in: path
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tool-runtime/invoke:
|
/v1/tool-runtime/invoke:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2080,32 +1907,6 @@ paths:
|
||||||
description: List tool groups with optional provider.
|
description: List tool groups with optional provider.
|
||||||
parameters: []
|
parameters: []
|
||||||
deprecated: false
|
deprecated: false
|
||||||
post:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Register a tool group.
|
|
||||||
description: Register a tool group.
|
|
||||||
parameters: []
|
|
||||||
requestBody:
|
|
||||||
content:
|
|
||||||
application/json:
|
|
||||||
schema:
|
|
||||||
$ref: '#/components/schemas/RegisterToolGroupRequest'
|
|
||||||
required: true
|
|
||||||
deprecated: false
|
|
||||||
/v1/toolgroups/{toolgroup_id}:
|
/v1/toolgroups/{toolgroup_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -2137,32 +1938,6 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: false
|
||||||
delete:
|
|
||||||
responses:
|
|
||||||
'200':
|
|
||||||
description: OK
|
|
||||||
'400':
|
|
||||||
$ref: '#/components/responses/BadRequest400'
|
|
||||||
'429':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/TooManyRequests429
|
|
||||||
'500':
|
|
||||||
$ref: >-
|
|
||||||
#/components/responses/InternalServerError500
|
|
||||||
default:
|
|
||||||
$ref: '#/components/responses/DefaultError'
|
|
||||||
tags:
|
|
||||||
- ToolGroups
|
|
||||||
summary: Unregister a tool group.
|
|
||||||
description: Unregister a tool group.
|
|
||||||
parameters:
|
|
||||||
- name: toolgroup_id
|
|
||||||
in: path
|
|
||||||
description: The ID of the tool group to unregister.
|
|
||||||
required: true
|
|
||||||
schema:
|
|
||||||
type: string
|
|
||||||
deprecated: false
|
|
||||||
/v1/tools:
|
/v1/tools:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3171,7 +2946,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterDatasetRequest'
|
$ref: '#/components/schemas/RegisterDatasetRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1beta/datasets/{dataset_id}:
|
/v1beta/datasets/{dataset_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3228,7 +3003,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks:
|
/v1alpha/eval/benchmarks:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3279,7 +3054,7 @@ paths:
|
||||||
schema:
|
schema:
|
||||||
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
$ref: '#/components/schemas/RegisterBenchmarkRequest'
|
||||||
required: true
|
required: true
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}:
|
/v1alpha/eval/benchmarks/{benchmark_id}:
|
||||||
get:
|
get:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -3336,7 +3111,7 @@ paths:
|
||||||
required: true
|
required: true
|
||||||
schema:
|
schema:
|
||||||
type: string
|
type: string
|
||||||
deprecated: false
|
deprecated: true
|
||||||
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
|
||||||
post:
|
post:
|
||||||
responses:
|
responses:
|
||||||
|
|
@ -6280,46 +6055,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: OpenAIListModelsResponse
|
title: OpenAIListModelsResponse
|
||||||
ModelType:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- llm
|
|
||||||
- embedding
|
|
||||||
- rerank
|
|
||||||
title: ModelType
|
|
||||||
description: >-
|
|
||||||
Enumeration of supported model types in Llama Stack.
|
|
||||||
RegisterModelRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
model_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the model to register.
|
|
||||||
provider_model_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the model in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: Any additional metadata for this model.
|
|
||||||
model_type:
|
|
||||||
$ref: '#/components/schemas/ModelType'
|
|
||||||
description: The type of model to register.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- model_id
|
|
||||||
title: RegisterModelRequest
|
|
||||||
Model:
|
Model:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -6377,6 +6112,15 @@ components:
|
||||||
title: Model
|
title: Model
|
||||||
description: >-
|
description: >-
|
||||||
A model resource representing an AI model registered in Llama Stack.
|
A model resource representing an AI model registered in Llama Stack.
|
||||||
|
ModelType:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- llm
|
||||||
|
- embedding
|
||||||
|
- rerank
|
||||||
|
title: ModelType
|
||||||
|
description: >-
|
||||||
|
Enumeration of supported model types in Llama Stack.
|
||||||
RunModerationRequest:
|
RunModerationRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -6791,6 +6535,8 @@ components:
|
||||||
const: web_search_preview
|
const: web_search_preview
|
||||||
- type: string
|
- type: string
|
||||||
const: web_search_preview_2025_03_11
|
const: web_search_preview_2025_03_11
|
||||||
|
- type: string
|
||||||
|
const: web_search_2025_08_26
|
||||||
default: web_search
|
default: web_search
|
||||||
description: Web search tool type variant to use
|
description: Web search tool type variant to use
|
||||||
search_context_size:
|
search_context_size:
|
||||||
|
|
@ -6880,6 +6626,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
input:
|
input:
|
||||||
type: array
|
type: array
|
||||||
items:
|
items:
|
||||||
|
|
@ -7238,6 +6989,11 @@ components:
|
||||||
(Optional) Additional fields to include in the response.
|
(Optional) Additional fields to include in the response.
|
||||||
max_infer_iters:
|
max_infer_iters:
|
||||||
type: integer
|
type: integer
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response.
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- input
|
- input
|
||||||
|
|
@ -7319,6 +7075,11 @@ components:
|
||||||
type: string
|
type: string
|
||||||
description: >-
|
description: >-
|
||||||
(Optional) System message inserted into the model's context
|
(Optional) System message inserted into the model's context
|
||||||
|
max_tool_calls:
|
||||||
|
type: integer
|
||||||
|
description: >-
|
||||||
|
(Optional) Max number of total calls to built-in tools that can be processed
|
||||||
|
in a response
|
||||||
additionalProperties: false
|
additionalProperties: false
|
||||||
required:
|
required:
|
||||||
- created_at
|
- created_at
|
||||||
|
|
@ -9113,61 +8874,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListScoringFunctionsResponse
|
title: ListScoringFunctionsResponse
|
||||||
ParamType:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/StringType'
|
|
||||||
- $ref: '#/components/schemas/NumberType'
|
|
||||||
- $ref: '#/components/schemas/BooleanType'
|
|
||||||
- $ref: '#/components/schemas/ArrayType'
|
|
||||||
- $ref: '#/components/schemas/ObjectType'
|
|
||||||
- $ref: '#/components/schemas/JsonType'
|
|
||||||
- $ref: '#/components/schemas/UnionType'
|
|
||||||
- $ref: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
- $ref: '#/components/schemas/CompletionInputType'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
string: '#/components/schemas/StringType'
|
|
||||||
number: '#/components/schemas/NumberType'
|
|
||||||
boolean: '#/components/schemas/BooleanType'
|
|
||||||
array: '#/components/schemas/ArrayType'
|
|
||||||
object: '#/components/schemas/ObjectType'
|
|
||||||
json: '#/components/schemas/JsonType'
|
|
||||||
union: '#/components/schemas/UnionType'
|
|
||||||
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
|
|
||||||
completion_input: '#/components/schemas/CompletionInputType'
|
|
||||||
RegisterScoringFunctionRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the scoring function to register.
|
|
||||||
description:
|
|
||||||
type: string
|
|
||||||
description: The description of the scoring function.
|
|
||||||
return_type:
|
|
||||||
$ref: '#/components/schemas/ParamType'
|
|
||||||
description: The return type of the scoring function.
|
|
||||||
provider_scoring_fn_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider scoring function to use for the scoring function.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the scoring function.
|
|
||||||
params:
|
|
||||||
$ref: '#/components/schemas/ScoringFnParams'
|
|
||||||
description: >-
|
|
||||||
The parameters for the scoring function for benchmark eval, these can
|
|
||||||
be overridden for app eval.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- scoring_fn_id
|
|
||||||
- description
|
|
||||||
- return_type
|
|
||||||
title: RegisterScoringFunctionRequest
|
|
||||||
ScoreRequest:
|
ScoreRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -9343,35 +9049,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListShieldsResponse
|
title: ListShieldsResponse
|
||||||
RegisterShieldRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield to register.
|
|
||||||
provider_shield_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The identifier of the shield in the provider.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: The identifier of the provider.
|
|
||||||
params:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The parameters of the shield.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- shield_id
|
|
||||||
title: RegisterShieldRequest
|
|
||||||
InvokeToolRequest:
|
InvokeToolRequest:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -9632,37 +9309,6 @@ components:
|
||||||
title: ListToolGroupsResponse
|
title: ListToolGroupsResponse
|
||||||
description: >-
|
description: >-
|
||||||
Response containing a list of tool groups.
|
Response containing a list of tool groups.
|
||||||
RegisterToolGroupRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
toolgroup_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the tool group to register.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the tool group.
|
|
||||||
mcp_endpoint:
|
|
||||||
$ref: '#/components/schemas/URL'
|
|
||||||
description: >-
|
|
||||||
The MCP endpoint to use for the tool group.
|
|
||||||
args:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
A dictionary of arguments to pass to the tool group.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- toolgroup_id
|
|
||||||
- provider_id
|
|
||||||
title: RegisterToolGroupRequest
|
|
||||||
Chunk:
|
Chunk:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -10808,68 +10454,6 @@ components:
|
||||||
- data
|
- data
|
||||||
title: ListDatasetsResponse
|
title: ListDatasetsResponse
|
||||||
description: Response from listing datasets.
|
description: Response from listing datasets.
|
||||||
DataSource:
|
|
||||||
oneOf:
|
|
||||||
- $ref: '#/components/schemas/URIDataSource'
|
|
||||||
- $ref: '#/components/schemas/RowsDataSource'
|
|
||||||
discriminator:
|
|
||||||
propertyName: type
|
|
||||||
mapping:
|
|
||||||
uri: '#/components/schemas/URIDataSource'
|
|
||||||
rows: '#/components/schemas/RowsDataSource'
|
|
||||||
RegisterDatasetRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
purpose:
|
|
||||||
type: string
|
|
||||||
enum:
|
|
||||||
- post-training/messages
|
|
||||||
- eval/question-answer
|
|
||||||
- eval/messages-answer
|
|
||||||
description: >-
|
|
||||||
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
|
||||||
contains a messages column with list of messages for post-training. {
|
|
||||||
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
|
||||||
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
|
||||||
contains a question column and an answer column for evaluation. { "question":
|
|
||||||
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
|
||||||
The dataset contains a messages column with list of messages and an answer
|
|
||||||
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
|
||||||
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
|
||||||
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
|
||||||
my name?"}, ], "answer": "John Doe" }
|
|
||||||
source:
|
|
||||||
$ref: '#/components/schemas/DataSource'
|
|
||||||
description: >-
|
|
||||||
The data source of the dataset. Ensure that the data source schema is
|
|
||||||
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
|
||||||
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
|
||||||
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
|
||||||
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
|
||||||
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
|
||||||
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
|
||||||
} ] }
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: >-
|
|
||||||
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset. If not provided, an ID will be generated.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- purpose
|
|
||||||
- source
|
|
||||||
title: RegisterDatasetRequest
|
|
||||||
Benchmark:
|
Benchmark:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -10937,47 +10521,6 @@ components:
|
||||||
required:
|
required:
|
||||||
- data
|
- data
|
||||||
title: ListBenchmarksResponse
|
title: ListBenchmarksResponse
|
||||||
RegisterBenchmarkRequest:
|
|
||||||
type: object
|
|
||||||
properties:
|
|
||||||
benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: The ID of the benchmark to register.
|
|
||||||
dataset_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the dataset to use for the benchmark.
|
|
||||||
scoring_functions:
|
|
||||||
type: array
|
|
||||||
items:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The scoring functions to use for the benchmark.
|
|
||||||
provider_benchmark_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider benchmark to use for the benchmark.
|
|
||||||
provider_id:
|
|
||||||
type: string
|
|
||||||
description: >-
|
|
||||||
The ID of the provider to use for the benchmark.
|
|
||||||
metadata:
|
|
||||||
type: object
|
|
||||||
additionalProperties:
|
|
||||||
oneOf:
|
|
||||||
- type: 'null'
|
|
||||||
- type: boolean
|
|
||||||
- type: number
|
|
||||||
- type: string
|
|
||||||
- type: array
|
|
||||||
- type: object
|
|
||||||
description: The metadata to use for the benchmark.
|
|
||||||
additionalProperties: false
|
|
||||||
required:
|
|
||||||
- benchmark_id
|
|
||||||
- dataset_id
|
|
||||||
- scoring_functions
|
|
||||||
title: RegisterBenchmarkRequest
|
|
||||||
BenchmarkConfig:
|
BenchmarkConfig:
|
||||||
type: object
|
type: object
|
||||||
properties:
|
properties:
|
||||||
|
|
@ -11839,6 +11382,109 @@ components:
|
||||||
- hyperparam_search_config
|
- hyperparam_search_config
|
||||||
- logger_config
|
- logger_config
|
||||||
title: SupervisedFineTuneRequest
|
title: SupervisedFineTuneRequest
|
||||||
|
DataSource:
|
||||||
|
oneOf:
|
||||||
|
- $ref: '#/components/schemas/URIDataSource'
|
||||||
|
- $ref: '#/components/schemas/RowsDataSource'
|
||||||
|
discriminator:
|
||||||
|
propertyName: type
|
||||||
|
mapping:
|
||||||
|
uri: '#/components/schemas/URIDataSource'
|
||||||
|
rows: '#/components/schemas/RowsDataSource'
|
||||||
|
RegisterDatasetRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
purpose:
|
||||||
|
type: string
|
||||||
|
enum:
|
||||||
|
- post-training/messages
|
||||||
|
- eval/question-answer
|
||||||
|
- eval/messages-answer
|
||||||
|
description: >-
|
||||||
|
The purpose of the dataset. One of: - "post-training/messages": The dataset
|
||||||
|
contains a messages column with list of messages for post-training. {
|
||||||
|
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
|
||||||
|
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
|
||||||
|
contains a question column and an answer column for evaluation. { "question":
|
||||||
|
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
|
||||||
|
The dataset contains a messages column with list of messages and an answer
|
||||||
|
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
|
||||||
|
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
|
||||||
|
Doe. How can I help you today?"}, {"role": "user", "content": "What's
|
||||||
|
my name?"}, ], "answer": "John Doe" }
|
||||||
|
source:
|
||||||
|
$ref: '#/components/schemas/DataSource'
|
||||||
|
description: >-
|
||||||
|
The data source of the dataset. Ensure that the data source schema is
|
||||||
|
compatible with the purpose of the dataset. Examples: - { "type": "uri",
|
||||||
|
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
|
||||||
|
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
|
||||||
|
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
|
||||||
|
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
|
||||||
|
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
|
||||||
|
} ] }
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: >-
|
||||||
|
The metadata for the dataset. - E.g. {"description": "My dataset"}.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset. If not provided, an ID will be generated.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- purpose
|
||||||
|
- source
|
||||||
|
title: RegisterDatasetRequest
|
||||||
|
RegisterBenchmarkRequest:
|
||||||
|
type: object
|
||||||
|
properties:
|
||||||
|
benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: The ID of the benchmark to register.
|
||||||
|
dataset_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the dataset to use for the benchmark.
|
||||||
|
scoring_functions:
|
||||||
|
type: array
|
||||||
|
items:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The scoring functions to use for the benchmark.
|
||||||
|
provider_benchmark_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider benchmark to use for the benchmark.
|
||||||
|
provider_id:
|
||||||
|
type: string
|
||||||
|
description: >-
|
||||||
|
The ID of the provider to use for the benchmark.
|
||||||
|
metadata:
|
||||||
|
type: object
|
||||||
|
additionalProperties:
|
||||||
|
oneOf:
|
||||||
|
- type: 'null'
|
||||||
|
- type: boolean
|
||||||
|
- type: number
|
||||||
|
- type: string
|
||||||
|
- type: array
|
||||||
|
- type: object
|
||||||
|
description: The metadata to use for the benchmark.
|
||||||
|
additionalProperties: false
|
||||||
|
required:
|
||||||
|
- benchmark_id
|
||||||
|
- dataset_id
|
||||||
|
- scoring_functions
|
||||||
|
title: RegisterBenchmarkRequest
|
||||||
responses:
|
responses:
|
||||||
BadRequest400:
|
BadRequest400:
|
||||||
description: The request was invalid or malformed
|
description: The request was invalid or malformed
|
||||||
|
|
|
||||||
|
|
@ -24,13 +24,13 @@ classifiers = [
|
||||||
"Topic :: Scientific/Engineering :: Information Analysis",
|
"Topic :: Scientific/Engineering :: Information Analysis",
|
||||||
]
|
]
|
||||||
dependencies = [
|
dependencies = [
|
||||||
|
"PyYAML>=6.0",
|
||||||
"aiohttp",
|
"aiohttp",
|
||||||
"fastapi>=0.115.0,<1.0", # server
|
"fastapi>=0.115.0,<1.0", # server
|
||||||
"fire", # for MCP in LLS client
|
"fire", # for MCP in LLS client
|
||||||
"httpx",
|
"httpx",
|
||||||
"jinja2>=3.1.6",
|
"jinja2>=3.1.6",
|
||||||
"jsonschema",
|
"jsonschema",
|
||||||
"llama-stack-client>=0.3.0",
|
|
||||||
"openai>=2.5.0",
|
"openai>=2.5.0",
|
||||||
"prompt-toolkit",
|
"prompt-toolkit",
|
||||||
"python-dotenv",
|
"python-dotenv",
|
||||||
|
|
@ -51,6 +51,11 @@ dependencies = [
|
||||||
"sqlalchemy[asyncio]>=2.0.41", # server - for conversations
|
"sqlalchemy[asyncio]>=2.0.41", # server - for conversations
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[project.optional-dependencies]
|
||||||
|
client = [
|
||||||
|
"llama-stack-client>=0.3.0", # Optional for library-only usage
|
||||||
|
]
|
||||||
|
|
||||||
[dependency-groups]
|
[dependency-groups]
|
||||||
dev = [
|
dev = [
|
||||||
"pytest>=8.4",
|
"pytest>=8.4",
|
||||||
|
|
@ -96,6 +101,7 @@ type_checking = [
|
||||||
"lm-format-enforcer",
|
"lm-format-enforcer",
|
||||||
"mcp",
|
"mcp",
|
||||||
"ollama",
|
"ollama",
|
||||||
|
"llama-stack-client>=0.3.0",
|
||||||
]
|
]
|
||||||
# These are the dependencies required for running unit tests.
|
# These are the dependencies required for running unit tests.
|
||||||
unit = [
|
unit = [
|
||||||
|
|
@ -292,6 +298,7 @@ exclude = [
|
||||||
"^src/llama_stack/providers/remote/agents/sample/",
|
"^src/llama_stack/providers/remote/agents/sample/",
|
||||||
"^src/llama_stack/providers/remote/datasetio/huggingface/",
|
"^src/llama_stack/providers/remote/datasetio/huggingface/",
|
||||||
"^src/llama_stack/providers/remote/datasetio/nvidia/",
|
"^src/llama_stack/providers/remote/datasetio/nvidia/",
|
||||||
|
"^src/llama_stack/providers/remote/inference/oci/",
|
||||||
"^src/llama_stack/providers/remote/inference/bedrock/",
|
"^src/llama_stack/providers/remote/inference/bedrock/",
|
||||||
"^src/llama_stack/providers/remote/inference/nvidia/",
|
"^src/llama_stack/providers/remote/inference/nvidia/",
|
||||||
"^src/llama_stack/providers/remote/inference/passthrough/",
|
"^src/llama_stack/providers/remote/inference/passthrough/",
|
||||||
|
|
|
||||||
|
|
@ -3,8 +3,3 @@
|
||||||
#
|
#
|
||||||
# This source code is licensed under the terms described in the LICENSE file in
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
# the root directory of this source tree.
|
# the root directory of this source tree.
|
||||||
|
|
||||||
from llama_stack.core.library_client import ( # noqa: F401
|
|
||||||
AsyncLlamaStackAsLibraryClient,
|
|
||||||
LlamaStackAsLibraryClient,
|
|
||||||
)
|
|
||||||
|
|
|
||||||
|
|
@ -87,6 +87,7 @@ class Agents(Protocol):
|
||||||
"List of guardrails to apply during response generation. Guardrails provide safety and content moderation."
|
"List of guardrails to apply during response generation. Guardrails provide safety and content moderation."
|
||||||
),
|
),
|
||||||
] = None,
|
] = None,
|
||||||
|
max_tool_calls: int | None = None,
|
||||||
) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
|
) -> OpenAIResponseObject | AsyncIterator[OpenAIResponseObjectStream]:
|
||||||
"""Create a model response.
|
"""Create a model response.
|
||||||
|
|
||||||
|
|
@ -97,6 +98,7 @@ class Agents(Protocol):
|
||||||
:param conversation: (Optional) The ID of a conversation to add the response to. Must begin with 'conv_'. Input and output messages will be automatically added to the conversation.
|
:param conversation: (Optional) The ID of a conversation to add the response to. Must begin with 'conv_'. Input and output messages will be automatically added to the conversation.
|
||||||
:param include: (Optional) Additional fields to include in the response.
|
:param include: (Optional) Additional fields to include in the response.
|
||||||
:param guardrails: (Optional) List of guardrails to apply during response generation. Can be guardrail IDs (strings) or guardrail specifications.
|
:param guardrails: (Optional) List of guardrails to apply during response generation. Can be guardrail IDs (strings) or guardrail specifications.
|
||||||
|
:param max_tool_calls: (Optional) Max number of total calls to built-in tools that can be processed in a response.
|
||||||
:returns: An OpenAIResponseObject.
|
:returns: An OpenAIResponseObject.
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
|
||||||
|
|
@ -403,7 +403,7 @@ class OpenAIResponseText(BaseModel):
|
||||||
|
|
||||||
|
|
||||||
# Must match type Literals of OpenAIResponseInputToolWebSearch below
|
# Must match type Literals of OpenAIResponseInputToolWebSearch below
|
||||||
WebSearchToolTypes = ["web_search", "web_search_preview", "web_search_preview_2025_03_11"]
|
WebSearchToolTypes = ["web_search", "web_search_preview", "web_search_preview_2025_03_11", "web_search_2025_08_26"]
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
@json_schema_type
|
||||||
|
|
@ -415,9 +415,12 @@ class OpenAIResponseInputToolWebSearch(BaseModel):
|
||||||
"""
|
"""
|
||||||
|
|
||||||
# Must match values of WebSearchToolTypes above
|
# Must match values of WebSearchToolTypes above
|
||||||
type: Literal["web_search"] | Literal["web_search_preview"] | Literal["web_search_preview_2025_03_11"] = (
|
type: (
|
||||||
"web_search"
|
Literal["web_search"]
|
||||||
)
|
| Literal["web_search_preview"]
|
||||||
|
| Literal["web_search_preview_2025_03_11"]
|
||||||
|
| Literal["web_search_2025_08_26"]
|
||||||
|
) = "web_search"
|
||||||
# TODO: actually use search_context_size somewhere...
|
# TODO: actually use search_context_size somewhere...
|
||||||
search_context_size: str | None = Field(default="medium", pattern="^low|medium|high$")
|
search_context_size: str | None = Field(default="medium", pattern="^low|medium|high$")
|
||||||
# TODO: add user_location
|
# TODO: add user_location
|
||||||
|
|
@ -591,6 +594,7 @@ class OpenAIResponseObject(BaseModel):
|
||||||
:param truncation: (Optional) Truncation strategy applied to the response
|
:param truncation: (Optional) Truncation strategy applied to the response
|
||||||
:param usage: (Optional) Token usage information for the response
|
:param usage: (Optional) Token usage information for the response
|
||||||
:param instructions: (Optional) System message inserted into the model's context
|
:param instructions: (Optional) System message inserted into the model's context
|
||||||
|
:param max_tool_calls: (Optional) Max number of total calls to built-in tools that can be processed in a response
|
||||||
"""
|
"""
|
||||||
|
|
||||||
created_at: int
|
created_at: int
|
||||||
|
|
@ -612,6 +616,7 @@ class OpenAIResponseObject(BaseModel):
|
||||||
truncation: str | None = None
|
truncation: str | None = None
|
||||||
usage: OpenAIResponseUsage | None = None
|
usage: OpenAIResponseUsage | None = None
|
||||||
instructions: str | None = None
|
instructions: str | None = None
|
||||||
|
max_tool_calls: int | None = None
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
@json_schema_type
|
||||||
|
|
|
||||||
|
|
@ -74,7 +74,7 @@ class Benchmarks(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/eval/benchmarks", method="POST", level=LLAMA_STACK_API_V1ALPHA)
|
@webmethod(route="/eval/benchmarks", method="POST", level=LLAMA_STACK_API_V1ALPHA, deprecated=True)
|
||||||
async def register_benchmark(
|
async def register_benchmark(
|
||||||
self,
|
self,
|
||||||
benchmark_id: str,
|
benchmark_id: str,
|
||||||
|
|
@ -95,7 +95,7 @@ class Benchmarks(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE", level=LLAMA_STACK_API_V1ALPHA)
|
@webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE", level=LLAMA_STACK_API_V1ALPHA, deprecated=True)
|
||||||
async def unregister_benchmark(self, benchmark_id: str) -> None:
|
async def unregister_benchmark(self, benchmark_id: str) -> None:
|
||||||
"""Unregister a benchmark.
|
"""Unregister a benchmark.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -146,7 +146,7 @@ class ListDatasetsResponse(BaseModel):
|
||||||
|
|
||||||
|
|
||||||
class Datasets(Protocol):
|
class Datasets(Protocol):
|
||||||
@webmethod(route="/datasets", method="POST", level=LLAMA_STACK_API_V1BETA)
|
@webmethod(route="/datasets", method="POST", level=LLAMA_STACK_API_V1BETA, deprecated=True)
|
||||||
async def register_dataset(
|
async def register_dataset(
|
||||||
self,
|
self,
|
||||||
purpose: DatasetPurpose,
|
purpose: DatasetPurpose,
|
||||||
|
|
@ -235,7 +235,7 @@ class Datasets(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/datasets/{dataset_id:path}", method="DELETE", level=LLAMA_STACK_API_V1BETA)
|
@webmethod(route="/datasets/{dataset_id:path}", method="DELETE", level=LLAMA_STACK_API_V1BETA, deprecated=True)
|
||||||
async def unregister_dataset(
|
async def unregister_dataset(
|
||||||
self,
|
self,
|
||||||
dataset_id: str,
|
dataset_id: str,
|
||||||
|
|
|
||||||
|
|
@ -1,43 +0,0 @@
|
||||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
||||||
# All rights reserved.
|
|
||||||
#
|
|
||||||
# This source code is licensed under the terms described in the LICENSE file in
|
|
||||||
# the root directory of this source tree.
|
|
||||||
|
|
||||||
from termcolor import cprint
|
|
||||||
|
|
||||||
from llama_stack.apis.inference import (
|
|
||||||
ChatCompletionResponseEventType,
|
|
||||||
ChatCompletionResponseStreamChunk,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
class LogEvent:
|
|
||||||
def __init__(
|
|
||||||
self,
|
|
||||||
content: str = "",
|
|
||||||
end: str = "\n",
|
|
||||||
color="white",
|
|
||||||
):
|
|
||||||
self.content = content
|
|
||||||
self.color = color
|
|
||||||
self.end = "\n" if end is None else end
|
|
||||||
|
|
||||||
def print(self, flush=True):
|
|
||||||
cprint(f"{self.content}", color=self.color, end=self.end, flush=flush)
|
|
||||||
|
|
||||||
|
|
||||||
class EventLogger:
|
|
||||||
async def log(self, event_generator):
|
|
||||||
async for chunk in event_generator:
|
|
||||||
if isinstance(chunk, ChatCompletionResponseStreamChunk):
|
|
||||||
event = chunk.event
|
|
||||||
if event.event_type == ChatCompletionResponseEventType.start:
|
|
||||||
yield LogEvent("Assistant> ", color="cyan", end="")
|
|
||||||
elif event.event_type == ChatCompletionResponseEventType.progress:
|
|
||||||
yield LogEvent(event.delta, color="yellow", end="")
|
|
||||||
elif event.event_type == ChatCompletionResponseEventType.complete:
|
|
||||||
yield LogEvent("")
|
|
||||||
else:
|
|
||||||
yield LogEvent("Assistant> ", color="cyan", end="")
|
|
||||||
yield LogEvent(chunk.completion_message.content, color="yellow")
|
|
||||||
|
|
@ -5,7 +5,7 @@
|
||||||
# the root directory of this source tree.
|
# the root directory of this source tree.
|
||||||
|
|
||||||
from collections.abc import AsyncIterator
|
from collections.abc import AsyncIterator
|
||||||
from enum import Enum
|
from enum import Enum, StrEnum
|
||||||
from typing import (
|
from typing import (
|
||||||
Annotated,
|
Annotated,
|
||||||
Any,
|
Any,
|
||||||
|
|
@ -15,28 +15,18 @@ from typing import (
|
||||||
)
|
)
|
||||||
|
|
||||||
from fastapi import Body
|
from fastapi import Body
|
||||||
from pydantic import BaseModel, Field, field_validator
|
from pydantic import BaseModel, Field
|
||||||
from typing_extensions import TypedDict
|
from typing_extensions import TypedDict
|
||||||
|
|
||||||
from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent
|
from llama_stack.apis.common.content_types import InterleavedContent
|
||||||
from llama_stack.apis.common.responses import MetricResponseMixin, Order
|
from llama_stack.apis.common.responses import (
|
||||||
|
Order,
|
||||||
|
)
|
||||||
from llama_stack.apis.common.tracing import telemetry_traceable
|
from llama_stack.apis.common.tracing import telemetry_traceable
|
||||||
from llama_stack.apis.models import Model
|
from llama_stack.apis.models import Model
|
||||||
from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA
|
from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA
|
||||||
from llama_stack.models.llama.datatypes import (
|
|
||||||
BuiltinTool,
|
|
||||||
StopReason,
|
|
||||||
ToolCall,
|
|
||||||
ToolDefinition,
|
|
||||||
ToolPromptFormat,
|
|
||||||
)
|
|
||||||
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
|
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
|
||||||
|
|
||||||
register_schema(ToolCall)
|
|
||||||
register_schema(ToolDefinition)
|
|
||||||
|
|
||||||
from enum import StrEnum
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
@json_schema_type
|
||||||
class GreedySamplingStrategy(BaseModel):
|
class GreedySamplingStrategy(BaseModel):
|
||||||
|
|
@ -201,58 +191,6 @@ class ToolResponseMessage(BaseModel):
|
||||||
content: InterleavedContent
|
content: InterleavedContent
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class CompletionMessage(BaseModel):
|
|
||||||
"""A message containing the model's (assistant) response in a chat conversation.
|
|
||||||
|
|
||||||
:param role: Must be "assistant" to identify this as the model's response
|
|
||||||
:param content: The content of the model's response
|
|
||||||
:param stop_reason: Reason why the model stopped generating. Options are:
|
|
||||||
- `StopReason.end_of_turn`: The model finished generating the entire response.
|
|
||||||
- `StopReason.end_of_message`: The model finished generating but generated a partial response -- usually, a tool call. The user may call the tool and continue the conversation with the tool's response.
|
|
||||||
- `StopReason.out_of_tokens`: The model ran out of token budget.
|
|
||||||
:param tool_calls: List of tool calls. Each tool call is a ToolCall object.
|
|
||||||
"""
|
|
||||||
|
|
||||||
role: Literal["assistant"] = "assistant"
|
|
||||||
content: InterleavedContent
|
|
||||||
stop_reason: StopReason
|
|
||||||
tool_calls: list[ToolCall] | None = Field(default_factory=lambda: [])
|
|
||||||
|
|
||||||
|
|
||||||
Message = Annotated[
|
|
||||||
UserMessage | SystemMessage | ToolResponseMessage | CompletionMessage,
|
|
||||||
Field(discriminator="role"),
|
|
||||||
]
|
|
||||||
register_schema(Message, name="Message")
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class ToolResponse(BaseModel):
|
|
||||||
"""Response from a tool invocation.
|
|
||||||
|
|
||||||
:param call_id: Unique identifier for the tool call this response is for
|
|
||||||
:param tool_name: Name of the tool that was invoked
|
|
||||||
:param content: The response content from the tool
|
|
||||||
:param metadata: (Optional) Additional metadata about the tool response
|
|
||||||
"""
|
|
||||||
|
|
||||||
call_id: str
|
|
||||||
tool_name: BuiltinTool | str
|
|
||||||
content: InterleavedContent
|
|
||||||
metadata: dict[str, Any] | None = None
|
|
||||||
|
|
||||||
@field_validator("tool_name", mode="before")
|
|
||||||
@classmethod
|
|
||||||
def validate_field(cls, v):
|
|
||||||
if isinstance(v, str):
|
|
||||||
try:
|
|
||||||
return BuiltinTool(v)
|
|
||||||
except ValueError:
|
|
||||||
return v
|
|
||||||
return v
|
|
||||||
|
|
||||||
|
|
||||||
class ToolChoice(Enum):
|
class ToolChoice(Enum):
|
||||||
"""Whether tool use is required or automatic. This is a hint to the model which may not be followed. It depends on the Instruction Following capabilities of the model.
|
"""Whether tool use is required or automatic. This is a hint to the model which may not be followed. It depends on the Instruction Following capabilities of the model.
|
||||||
|
|
||||||
|
|
@ -289,22 +227,6 @@ class ChatCompletionResponseEventType(Enum):
|
||||||
progress = "progress"
|
progress = "progress"
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class ChatCompletionResponseEvent(BaseModel):
|
|
||||||
"""An event during chat completion generation.
|
|
||||||
|
|
||||||
:param event_type: Type of the event
|
|
||||||
:param delta: Content generated since last event. This can be one or more tokens, or a tool call.
|
|
||||||
:param logprobs: Optional log probabilities for generated tokens
|
|
||||||
:param stop_reason: Optional reason why generation stopped, if complete
|
|
||||||
"""
|
|
||||||
|
|
||||||
event_type: ChatCompletionResponseEventType
|
|
||||||
delta: ContentDelta
|
|
||||||
logprobs: list[TokenLogProbs] | None = None
|
|
||||||
stop_reason: StopReason | None = None
|
|
||||||
|
|
||||||
|
|
||||||
class ResponseFormatType(StrEnum):
|
class ResponseFormatType(StrEnum):
|
||||||
"""Types of formats for structured (guided) decoding.
|
"""Types of formats for structured (guided) decoding.
|
||||||
|
|
||||||
|
|
@ -357,34 +279,6 @@ class CompletionRequest(BaseModel):
|
||||||
logprobs: LogProbConfig | None = None
|
logprobs: LogProbConfig | None = None
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class CompletionResponse(MetricResponseMixin):
|
|
||||||
"""Response from a completion request.
|
|
||||||
|
|
||||||
:param content: The generated completion text
|
|
||||||
:param stop_reason: Reason why generation stopped
|
|
||||||
:param logprobs: Optional log probabilities for generated tokens
|
|
||||||
"""
|
|
||||||
|
|
||||||
content: str
|
|
||||||
stop_reason: StopReason
|
|
||||||
logprobs: list[TokenLogProbs] | None = None
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class CompletionResponseStreamChunk(MetricResponseMixin):
|
|
||||||
"""A chunk of a streamed completion response.
|
|
||||||
|
|
||||||
:param delta: New content generated since last chunk. This can be one or more tokens.
|
|
||||||
:param stop_reason: Optional reason why generation stopped, if complete
|
|
||||||
:param logprobs: Optional log probabilities for generated tokens
|
|
||||||
"""
|
|
||||||
|
|
||||||
delta: str
|
|
||||||
stop_reason: StopReason | None = None
|
|
||||||
logprobs: list[TokenLogProbs] | None = None
|
|
||||||
|
|
||||||
|
|
||||||
class SystemMessageBehavior(Enum):
|
class SystemMessageBehavior(Enum):
|
||||||
"""Config for how to override the default system prompt.
|
"""Config for how to override the default system prompt.
|
||||||
|
|
||||||
|
|
@ -398,70 +292,6 @@ class SystemMessageBehavior(Enum):
|
||||||
replace = "replace"
|
replace = "replace"
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class ToolConfig(BaseModel):
|
|
||||||
"""Configuration for tool use.
|
|
||||||
|
|
||||||
:param tool_choice: (Optional) Whether tool use is automatic, required, or none. Can also specify a tool name to use a specific tool. Defaults to ToolChoice.auto.
|
|
||||||
:param tool_prompt_format: (Optional) Instructs the model how to format tool calls. By default, Llama Stack will attempt to use a format that is best adapted to the model.
|
|
||||||
- `ToolPromptFormat.json`: The tool calls are formatted as a JSON object.
|
|
||||||
- `ToolPromptFormat.function_tag`: The tool calls are enclosed in a <function=function_name> tag.
|
|
||||||
- `ToolPromptFormat.python_list`: The tool calls are output as Python syntax -- a list of function calls.
|
|
||||||
:param system_message_behavior: (Optional) Config for how to override the default system prompt.
|
|
||||||
- `SystemMessageBehavior.append`: Appends the provided system message to the default system prompt.
|
|
||||||
- `SystemMessageBehavior.replace`: Replaces the default system prompt with the provided system message. The system message can include the string
|
|
||||||
'{{function_definitions}}' to indicate where the function definitions should be inserted.
|
|
||||||
"""
|
|
||||||
|
|
||||||
tool_choice: ToolChoice | str | None = Field(default=ToolChoice.auto)
|
|
||||||
tool_prompt_format: ToolPromptFormat | None = Field(default=None)
|
|
||||||
system_message_behavior: SystemMessageBehavior | None = Field(default=SystemMessageBehavior.append)
|
|
||||||
|
|
||||||
def model_post_init(self, __context: Any) -> None:
|
|
||||||
if isinstance(self.tool_choice, str):
|
|
||||||
try:
|
|
||||||
self.tool_choice = ToolChoice[self.tool_choice]
|
|
||||||
except KeyError:
|
|
||||||
pass
|
|
||||||
|
|
||||||
|
|
||||||
# This is an internally used class
|
|
||||||
@json_schema_type
|
|
||||||
class ChatCompletionRequest(BaseModel):
|
|
||||||
model: str
|
|
||||||
messages: list[Message]
|
|
||||||
sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
|
|
||||||
|
|
||||||
tools: list[ToolDefinition] | None = Field(default_factory=lambda: [])
|
|
||||||
tool_config: ToolConfig | None = Field(default_factory=ToolConfig)
|
|
||||||
|
|
||||||
response_format: ResponseFormat | None = None
|
|
||||||
stream: bool | None = False
|
|
||||||
logprobs: LogProbConfig | None = None
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class ChatCompletionResponseStreamChunk(MetricResponseMixin):
|
|
||||||
"""A chunk of a streamed chat completion response.
|
|
||||||
|
|
||||||
:param event: The event containing the new content
|
|
||||||
"""
|
|
||||||
|
|
||||||
event: ChatCompletionResponseEvent
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
|
||||||
class ChatCompletionResponse(MetricResponseMixin):
|
|
||||||
"""Response from a chat completion request.
|
|
||||||
|
|
||||||
:param completion_message: The complete response message
|
|
||||||
:param logprobs: Optional log probabilities for generated tokens
|
|
||||||
"""
|
|
||||||
|
|
||||||
completion_message: CompletionMessage
|
|
||||||
logprobs: list[TokenLogProbs] | None = None
|
|
||||||
|
|
||||||
|
|
||||||
@json_schema_type
|
@json_schema_type
|
||||||
class EmbeddingsResponse(BaseModel):
|
class EmbeddingsResponse(BaseModel):
|
||||||
"""Response containing generated embeddings.
|
"""Response containing generated embeddings.
|
||||||
|
|
|
||||||
|
|
@ -76,7 +76,7 @@ class Inspect(Protocol):
|
||||||
|
|
||||||
List all available API routes with their methods and implementing providers.
|
List all available API routes with their methods and implementing providers.
|
||||||
|
|
||||||
:param api_filter: Optional filter to control which routes are returned. Can be an API level ('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level, or 'deprecated' to show deprecated routes across all levels. If not specified, returns only non-deprecated v1 routes.
|
:param api_filter: Optional filter to control which routes are returned. Can be an API level ('v1', 'v1alpha', 'v1beta') to show non-deprecated routes at that level, or 'deprecated' to show deprecated routes across all levels. If not specified, returns all non-deprecated routes.
|
||||||
:returns: Response containing information about all available routes.
|
:returns: Response containing information about all available routes.
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
|
||||||
|
|
@ -136,7 +136,7 @@ class Models(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/models", method="POST", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/models", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def register_model(
|
async def register_model(
|
||||||
self,
|
self,
|
||||||
model_id: str,
|
model_id: str,
|
||||||
|
|
@ -158,7 +158,7 @@ class Models(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/models/{model_id:path}", method="DELETE", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/models/{model_id:path}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def unregister_model(
|
async def unregister_model(
|
||||||
self,
|
self,
|
||||||
model_id: str,
|
model_id: str,
|
||||||
|
|
|
||||||
|
|
@ -178,7 +178,7 @@ class ScoringFunctions(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/scoring-functions", method="POST", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/scoring-functions", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def register_scoring_function(
|
async def register_scoring_function(
|
||||||
self,
|
self,
|
||||||
scoring_fn_id: str,
|
scoring_fn_id: str,
|
||||||
|
|
@ -199,7 +199,9 @@ class ScoringFunctions(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="DELETE", level=LLAMA_STACK_API_V1)
|
@webmethod(
|
||||||
|
route="/scoring-functions/{scoring_fn_id:path}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True
|
||||||
|
)
|
||||||
async def unregister_scoring_function(self, scoring_fn_id: str) -> None:
|
async def unregister_scoring_function(self, scoring_fn_id: str) -> None:
|
||||||
"""Unregister a scoring function.
|
"""Unregister a scoring function.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -67,7 +67,7 @@ class Shields(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/shields", method="POST", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/shields", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def register_shield(
|
async def register_shield(
|
||||||
self,
|
self,
|
||||||
shield_id: str,
|
shield_id: str,
|
||||||
|
|
@ -85,7 +85,7 @@ class Shields(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/shields/{identifier:path}", method="DELETE", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/shields/{identifier:path}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def unregister_shield(self, identifier: str) -> None:
|
async def unregister_shield(self, identifier: str) -> None:
|
||||||
"""Unregister a shield.
|
"""Unregister a shield.
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -109,7 +109,7 @@ class ListToolDefsResponse(BaseModel):
|
||||||
@runtime_checkable
|
@runtime_checkable
|
||||||
@telemetry_traceable
|
@telemetry_traceable
|
||||||
class ToolGroups(Protocol):
|
class ToolGroups(Protocol):
|
||||||
@webmethod(route="/toolgroups", method="POST", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/toolgroups", method="POST", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def register_tool_group(
|
async def register_tool_group(
|
||||||
self,
|
self,
|
||||||
toolgroup_id: str,
|
toolgroup_id: str,
|
||||||
|
|
@ -167,7 +167,7 @@ class ToolGroups(Protocol):
|
||||||
"""
|
"""
|
||||||
...
|
...
|
||||||
|
|
||||||
@webmethod(route="/toolgroups/{toolgroup_id:path}", method="DELETE", level=LLAMA_STACK_API_V1)
|
@webmethod(route="/toolgroups/{toolgroup_id:path}", method="DELETE", level=LLAMA_STACK_API_V1, deprecated=True)
|
||||||
async def unregister_toolgroup(
|
async def unregister_toolgroup(
|
||||||
self,
|
self,
|
||||||
toolgroup_id: str,
|
toolgroup_id: str,
|
||||||
|
|
|
||||||
|
|
@ -15,7 +15,6 @@ from llama_stack.apis.inspect import (
|
||||||
RouteInfo,
|
RouteInfo,
|
||||||
VersionInfo,
|
VersionInfo,
|
||||||
)
|
)
|
||||||
from llama_stack.apis.version import LLAMA_STACK_API_V1
|
|
||||||
from llama_stack.core.datatypes import StackRunConfig
|
from llama_stack.core.datatypes import StackRunConfig
|
||||||
from llama_stack.core.external import load_external_apis
|
from llama_stack.core.external import load_external_apis
|
||||||
from llama_stack.core.server.routes import get_all_api_routes
|
from llama_stack.core.server.routes import get_all_api_routes
|
||||||
|
|
@ -46,8 +45,8 @@ class DistributionInspectImpl(Inspect):
|
||||||
# Helper function to determine if a route should be included based on api_filter
|
# Helper function to determine if a route should be included based on api_filter
|
||||||
def should_include_route(webmethod) -> bool:
|
def should_include_route(webmethod) -> bool:
|
||||||
if api_filter is None:
|
if api_filter is None:
|
||||||
# Default: only non-deprecated v1 APIs
|
# Default: only non-deprecated APIs
|
||||||
return not webmethod.deprecated and webmethod.level == LLAMA_STACK_API_V1
|
return not webmethod.deprecated
|
||||||
elif api_filter == "deprecated":
|
elif api_filter == "deprecated":
|
||||||
# Special filter: show deprecated routes regardless of their actual level
|
# Special filter: show deprecated routes regardless of their actual level
|
||||||
return bool(webmethod.deprecated)
|
return bool(webmethod.deprecated)
|
||||||
|
|
|
||||||
|
|
@ -18,6 +18,8 @@ from typing import Any, TypeVar, Union, get_args, get_origin
|
||||||
import httpx
|
import httpx
|
||||||
import yaml
|
import yaml
|
||||||
from fastapi import Response as FastAPIResponse
|
from fastapi import Response as FastAPIResponse
|
||||||
|
|
||||||
|
try:
|
||||||
from llama_stack_client import (
|
from llama_stack_client import (
|
||||||
NOT_GIVEN,
|
NOT_GIVEN,
|
||||||
APIResponse,
|
APIResponse,
|
||||||
|
|
@ -26,6 +28,11 @@ from llama_stack_client import (
|
||||||
AsyncStream,
|
AsyncStream,
|
||||||
LlamaStackClient,
|
LlamaStackClient,
|
||||||
)
|
)
|
||||||
|
except ImportError as e:
|
||||||
|
raise ImportError(
|
||||||
|
"llama-stack-client is not installed. Please install it with `uv pip install llama-stack[client]`."
|
||||||
|
) from e
|
||||||
|
|
||||||
from pydantic import BaseModel, TypeAdapter
|
from pydantic import BaseModel, TypeAdapter
|
||||||
from rich.console import Console
|
from rich.console import Console
|
||||||
from termcolor import cprint
|
from termcolor import cprint
|
||||||
|
|
|
||||||
|
|
@ -6,7 +6,7 @@
|
||||||
|
|
||||||
from typing import Any
|
from typing import Any
|
||||||
|
|
||||||
from llama_stack.apis.inference import Message
|
from llama_stack.apis.inference import OpenAIMessageParam
|
||||||
from llama_stack.apis.safety import RunShieldResponse, Safety
|
from llama_stack.apis.safety import RunShieldResponse, Safety
|
||||||
from llama_stack.apis.safety.safety import ModerationObject
|
from llama_stack.apis.safety.safety import ModerationObject
|
||||||
from llama_stack.apis.shields import Shield
|
from llama_stack.apis.shields import Shield
|
||||||
|
|
@ -52,7 +52,7 @@ class SafetyRouter(Safety):
|
||||||
async def run_shield(
|
async def run_shield(
|
||||||
self,
|
self,
|
||||||
shield_id: str,
|
shield_id: str,
|
||||||
messages: list[Message],
|
messages: list[OpenAIMessageParam],
|
||||||
params: dict[str, Any] = None,
|
params: dict[str, Any] = None,
|
||||||
) -> RunShieldResponse:
|
) -> RunShieldResponse:
|
||||||
logger.debug(f"SafetyRouter.run_shield: {shield_id}")
|
logger.debug(f"SafetyRouter.run_shield: {shield_id}")
|
||||||
|
|
|
||||||
7
src/llama_stack/distributions/oci/__init__.py
Normal file
7
src/llama_stack/distributions/oci/__init__.py
Normal file
|
|
@ -0,0 +1,7 @@
|
||||||
|
# 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 .oci import get_distribution_template # noqa: F401
|
||||||
35
src/llama_stack/distributions/oci/build.yaml
Normal file
35
src/llama_stack/distributions/oci/build.yaml
Normal file
|
|
@ -0,0 +1,35 @@
|
||||||
|
version: 2
|
||||||
|
distribution_spec:
|
||||||
|
description: Use Oracle Cloud Infrastructure (OCI) Generative AI for running LLM
|
||||||
|
inference with scalable cloud services
|
||||||
|
providers:
|
||||||
|
inference:
|
||||||
|
- provider_type: remote::oci
|
||||||
|
vector_io:
|
||||||
|
- provider_type: inline::faiss
|
||||||
|
- provider_type: remote::chromadb
|
||||||
|
- provider_type: remote::pgvector
|
||||||
|
safety:
|
||||||
|
- provider_type: inline::llama-guard
|
||||||
|
agents:
|
||||||
|
- provider_type: inline::meta-reference
|
||||||
|
eval:
|
||||||
|
- provider_type: inline::meta-reference
|
||||||
|
datasetio:
|
||||||
|
- provider_type: remote::huggingface
|
||||||
|
- provider_type: inline::localfs
|
||||||
|
scoring:
|
||||||
|
- provider_type: inline::basic
|
||||||
|
- provider_type: inline::llm-as-judge
|
||||||
|
- provider_type: inline::braintrust
|
||||||
|
tool_runtime:
|
||||||
|
- provider_type: remote::brave-search
|
||||||
|
- provider_type: remote::tavily-search
|
||||||
|
- provider_type: inline::rag-runtime
|
||||||
|
- provider_type: remote::model-context-protocol
|
||||||
|
files:
|
||||||
|
- provider_type: inline::localfs
|
||||||
|
image_type: venv
|
||||||
|
additional_pip_packages:
|
||||||
|
- aiosqlite
|
||||||
|
- sqlalchemy[asyncio]
|
||||||
140
src/llama_stack/distributions/oci/doc_template.md
Normal file
140
src/llama_stack/distributions/oci/doc_template.md
Normal file
|
|
@ -0,0 +1,140 @@
|
||||||
|
---
|
||||||
|
orphan: true
|
||||||
|
---
|
||||||
|
# OCI Distribution
|
||||||
|
|
||||||
|
The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations.
|
||||||
|
|
||||||
|
{{ providers_table }}
|
||||||
|
|
||||||
|
{% if run_config_env_vars %}
|
||||||
|
### Environment Variables
|
||||||
|
|
||||||
|
The following environment variables can be configured:
|
||||||
|
|
||||||
|
{% for var, (default_value, description) in run_config_env_vars.items() %}
|
||||||
|
- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
|
||||||
|
{% endfor %}
|
||||||
|
{% endif %}
|
||||||
|
|
||||||
|
{% if default_models %}
|
||||||
|
### Models
|
||||||
|
|
||||||
|
The following models are available by default:
|
||||||
|
|
||||||
|
{% for model in default_models %}
|
||||||
|
- `{{ model.model_id }} {{ model.doc_string }}`
|
||||||
|
{% endfor %}
|
||||||
|
{% endif %}
|
||||||
|
|
||||||
|
## Prerequisites
|
||||||
|
### Oracle Cloud Infrastructure Setup
|
||||||
|
|
||||||
|
Before using the OCI Generative AI distribution, ensure you have:
|
||||||
|
|
||||||
|
1. **Oracle Cloud Infrastructure Account**: Sign up at [Oracle Cloud Infrastructure](https://cloud.oracle.com/)
|
||||||
|
2. **Generative AI Service Access**: Enable the Generative AI service in your OCI tenancy
|
||||||
|
3. **Compartment**: Create or identify a compartment where you'll deploy Generative AI models
|
||||||
|
4. **Authentication**: Configure authentication using either:
|
||||||
|
- **Instance Principal** (recommended for cloud-hosted deployments)
|
||||||
|
- **API Key** (for on-premises or development environments)
|
||||||
|
|
||||||
|
### Authentication Methods
|
||||||
|
|
||||||
|
#### Instance Principal Authentication (Recommended)
|
||||||
|
Instance Principal authentication allows OCI resources to authenticate using the identity of the compute instance they're running on. This is the most secure method for production deployments.
|
||||||
|
|
||||||
|
Requirements:
|
||||||
|
- Instance must be running in an Oracle Cloud Infrastructure compartment
|
||||||
|
- Instance must have appropriate IAM policies to access Generative AI services
|
||||||
|
|
||||||
|
#### API Key Authentication
|
||||||
|
For development or on-premises deployments, follow [this doc](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm) to learn how to create your API signing key for your config file.
|
||||||
|
|
||||||
|
### Required IAM Policies
|
||||||
|
|
||||||
|
Ensure your OCI user or instance has the following policy statements:
|
||||||
|
|
||||||
|
```
|
||||||
|
Allow group <group_name> to use generative-ai-inference-endpoints in compartment <compartment_name>
|
||||||
|
Allow group <group_name> to manage generative-ai-inference-endpoints in compartment <compartment_name>
|
||||||
|
```
|
||||||
|
|
||||||
|
## Supported Services
|
||||||
|
|
||||||
|
### Inference: OCI Generative AI
|
||||||
|
Oracle Cloud Infrastructure Generative AI provides access to high-performance AI models through OCI's Platform-as-a-Service offering. The service supports:
|
||||||
|
|
||||||
|
- **Chat Completions**: Conversational AI with context awareness
|
||||||
|
- **Text Generation**: Complete prompts and generate text content
|
||||||
|
|
||||||
|
#### Available Models
|
||||||
|
Common OCI Generative AI models include access to Meta, Cohere, OpenAI, Grok, and more models.
|
||||||
|
|
||||||
|
### Safety: Llama Guard
|
||||||
|
For content safety and moderation, this distribution uses Meta's LlamaGuard model through the OCI Generative AI service to provide:
|
||||||
|
- Content filtering and moderation
|
||||||
|
- Policy compliance checking
|
||||||
|
- Harmful content detection
|
||||||
|
|
||||||
|
### Vector Storage: Multiple Options
|
||||||
|
The distribution supports several vector storage providers:
|
||||||
|
- **FAISS**: Local in-memory vector search
|
||||||
|
- **ChromaDB**: Distributed vector database
|
||||||
|
- **PGVector**: PostgreSQL with vector extensions
|
||||||
|
|
||||||
|
### Additional Services
|
||||||
|
- **Dataset I/O**: Local filesystem and Hugging Face integration
|
||||||
|
- **Tool Runtime**: Web search (Brave, Tavily) and RAG capabilities
|
||||||
|
- **Evaluation**: Meta reference evaluation framework
|
||||||
|
|
||||||
|
## Running Llama Stack with OCI
|
||||||
|
|
||||||
|
You can run the OCI distribution via Docker or local virtual environment.
|
||||||
|
|
||||||
|
### Via venv
|
||||||
|
|
||||||
|
If you've set up your local development environment, you can also build the image using your local virtual environment.
|
||||||
|
|
||||||
|
```bash
|
||||||
|
OCI_AUTH=$OCI_AUTH_TYPE OCI_REGION=$OCI_REGION OCI_COMPARTMENT_OCID=$OCI_COMPARTMENT_OCID llama stack run --port 8321 oci
|
||||||
|
```
|
||||||
|
|
||||||
|
### Configuration Examples
|
||||||
|
|
||||||
|
#### Using Instance Principal (Recommended for Production)
|
||||||
|
```bash
|
||||||
|
export OCI_AUTH_TYPE=instance_principal
|
||||||
|
export OCI_REGION=us-chicago-1
|
||||||
|
export OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..<your-compartment-id>
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Using API Key Authentication (Development)
|
||||||
|
```bash
|
||||||
|
export OCI_AUTH_TYPE=config_file
|
||||||
|
export OCI_CONFIG_FILE_PATH=~/.oci/config
|
||||||
|
export OCI_CLI_PROFILE=DEFAULT
|
||||||
|
export OCI_REGION=us-chicago-1
|
||||||
|
export OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..your-compartment-id
|
||||||
|
```
|
||||||
|
|
||||||
|
## Regional Endpoints
|
||||||
|
|
||||||
|
OCI Generative AI is available in multiple regions. The service automatically routes to the appropriate regional endpoint based on your configuration. For a full list of regional model availability, visit:
|
||||||
|
|
||||||
|
https://docs.oracle.com/en-us/iaas/Content/generative-ai/overview.htm#regions
|
||||||
|
|
||||||
|
## Troubleshooting
|
||||||
|
|
||||||
|
### Common Issues
|
||||||
|
|
||||||
|
1. **Authentication Errors**: Verify your OCI credentials and IAM policies
|
||||||
|
2. **Model Not Found**: Ensure the model OCID is correct and the model is available in your region
|
||||||
|
3. **Permission Denied**: Check compartment permissions and Generative AI service access
|
||||||
|
4. **Region Unavailable**: Verify the specified region supports Generative AI services
|
||||||
|
|
||||||
|
### Getting Help
|
||||||
|
|
||||||
|
For additional support:
|
||||||
|
- [OCI Generative AI Documentation](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)
|
||||||
|
- [Llama Stack Issues](https://github.com/meta-llama/llama-stack/issues)
|
||||||
108
src/llama_stack/distributions/oci/oci.py
Normal file
108
src/llama_stack/distributions/oci/oci.py
Normal file
|
|
@ -0,0 +1,108 @@
|
||||||
|
# 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 pathlib import Path
|
||||||
|
|
||||||
|
from llama_stack.core.datatypes import BuildProvider, Provider, ToolGroupInput
|
||||||
|
from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings
|
||||||
|
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
|
||||||
|
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||||
|
from llama_stack.providers.remote.inference.oci.config import OCIConfig
|
||||||
|
|
||||||
|
|
||||||
|
def get_distribution_template(name: str = "oci") -> DistributionTemplate:
|
||||||
|
providers = {
|
||||||
|
"inference": [BuildProvider(provider_type="remote::oci")],
|
||||||
|
"vector_io": [
|
||||||
|
BuildProvider(provider_type="inline::faiss"),
|
||||||
|
BuildProvider(provider_type="remote::chromadb"),
|
||||||
|
BuildProvider(provider_type="remote::pgvector"),
|
||||||
|
],
|
||||||
|
"safety": [BuildProvider(provider_type="inline::llama-guard")],
|
||||||
|
"agents": [BuildProvider(provider_type="inline::meta-reference")],
|
||||||
|
"eval": [BuildProvider(provider_type="inline::meta-reference")],
|
||||||
|
"datasetio": [
|
||||||
|
BuildProvider(provider_type="remote::huggingface"),
|
||||||
|
BuildProvider(provider_type="inline::localfs"),
|
||||||
|
],
|
||||||
|
"scoring": [
|
||||||
|
BuildProvider(provider_type="inline::basic"),
|
||||||
|
BuildProvider(provider_type="inline::llm-as-judge"),
|
||||||
|
BuildProvider(provider_type="inline::braintrust"),
|
||||||
|
],
|
||||||
|
"tool_runtime": [
|
||||||
|
BuildProvider(provider_type="remote::brave-search"),
|
||||||
|
BuildProvider(provider_type="remote::tavily-search"),
|
||||||
|
BuildProvider(provider_type="inline::rag-runtime"),
|
||||||
|
BuildProvider(provider_type="remote::model-context-protocol"),
|
||||||
|
],
|
||||||
|
"files": [BuildProvider(provider_type="inline::localfs")],
|
||||||
|
}
|
||||||
|
|
||||||
|
inference_provider = Provider(
|
||||||
|
provider_id="oci",
|
||||||
|
provider_type="remote::oci",
|
||||||
|
config=OCIConfig.sample_run_config(),
|
||||||
|
)
|
||||||
|
|
||||||
|
vector_io_provider = Provider(
|
||||||
|
provider_id="faiss",
|
||||||
|
provider_type="inline::faiss",
|
||||||
|
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||||
|
)
|
||||||
|
|
||||||
|
files_provider = Provider(
|
||||||
|
provider_id="meta-reference-files",
|
||||||
|
provider_type="inline::localfs",
|
||||||
|
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
|
||||||
|
)
|
||||||
|
default_tool_groups = [
|
||||||
|
ToolGroupInput(
|
||||||
|
toolgroup_id="builtin::websearch",
|
||||||
|
provider_id="tavily-search",
|
||||||
|
),
|
||||||
|
]
|
||||||
|
|
||||||
|
return DistributionTemplate(
|
||||||
|
name=name,
|
||||||
|
distro_type="remote_hosted",
|
||||||
|
description="Use Oracle Cloud Infrastructure (OCI) Generative AI for running LLM inference with scalable cloud services",
|
||||||
|
container_image=None,
|
||||||
|
template_path=Path(__file__).parent / "doc_template.md",
|
||||||
|
providers=providers,
|
||||||
|
run_configs={
|
||||||
|
"run.yaml": RunConfigSettings(
|
||||||
|
provider_overrides={
|
||||||
|
"inference": [inference_provider],
|
||||||
|
"vector_io": [vector_io_provider],
|
||||||
|
"files": [files_provider],
|
||||||
|
},
|
||||||
|
default_tool_groups=default_tool_groups,
|
||||||
|
),
|
||||||
|
},
|
||||||
|
run_config_env_vars={
|
||||||
|
"OCI_AUTH_TYPE": (
|
||||||
|
"instance_principal",
|
||||||
|
"OCI authentication type (instance_principal or config_file)",
|
||||||
|
),
|
||||||
|
"OCI_REGION": (
|
||||||
|
"",
|
||||||
|
"OCI region (e.g., us-ashburn-1, us-chicago-1, us-phoenix-1, eu-frankfurt-1)",
|
||||||
|
),
|
||||||
|
"OCI_COMPARTMENT_OCID": (
|
||||||
|
"",
|
||||||
|
"OCI compartment ID for the Generative AI service",
|
||||||
|
),
|
||||||
|
"OCI_CONFIG_FILE_PATH": (
|
||||||
|
"~/.oci/config",
|
||||||
|
"OCI config file path (required if OCI_AUTH_TYPE is config_file)",
|
||||||
|
),
|
||||||
|
"OCI_CLI_PROFILE": (
|
||||||
|
"DEFAULT",
|
||||||
|
"OCI CLI profile name to use from config file",
|
||||||
|
),
|
||||||
|
},
|
||||||
|
)
|
||||||
136
src/llama_stack/distributions/oci/run.yaml
Normal file
136
src/llama_stack/distributions/oci/run.yaml
Normal file
|
|
@ -0,0 +1,136 @@
|
||||||
|
version: 2
|
||||||
|
image_name: oci
|
||||||
|
apis:
|
||||||
|
- agents
|
||||||
|
- datasetio
|
||||||
|
- eval
|
||||||
|
- files
|
||||||
|
- inference
|
||||||
|
- safety
|
||||||
|
- scoring
|
||||||
|
- tool_runtime
|
||||||
|
- vector_io
|
||||||
|
providers:
|
||||||
|
inference:
|
||||||
|
- provider_id: oci
|
||||||
|
provider_type: remote::oci
|
||||||
|
config:
|
||||||
|
oci_auth_type: ${env.OCI_AUTH_TYPE:=instance_principal}
|
||||||
|
oci_config_file_path: ${env.OCI_CONFIG_FILE_PATH:=~/.oci/config}
|
||||||
|
oci_config_profile: ${env.OCI_CLI_PROFILE:=DEFAULT}
|
||||||
|
oci_region: ${env.OCI_REGION:=us-ashburn-1}
|
||||||
|
oci_compartment_id: ${env.OCI_COMPARTMENT_OCID:=}
|
||||||
|
vector_io:
|
||||||
|
- provider_id: faiss
|
||||||
|
provider_type: inline::faiss
|
||||||
|
config:
|
||||||
|
persistence:
|
||||||
|
namespace: vector_io::faiss
|
||||||
|
backend: kv_default
|
||||||
|
safety:
|
||||||
|
- provider_id: llama-guard
|
||||||
|
provider_type: inline::llama-guard
|
||||||
|
config:
|
||||||
|
excluded_categories: []
|
||||||
|
agents:
|
||||||
|
- provider_id: meta-reference
|
||||||
|
provider_type: inline::meta-reference
|
||||||
|
config:
|
||||||
|
persistence:
|
||||||
|
agent_state:
|
||||||
|
namespace: agents
|
||||||
|
backend: kv_default
|
||||||
|
responses:
|
||||||
|
table_name: responses
|
||||||
|
backend: sql_default
|
||||||
|
max_write_queue_size: 10000
|
||||||
|
num_writers: 4
|
||||||
|
eval:
|
||||||
|
- provider_id: meta-reference
|
||||||
|
provider_type: inline::meta-reference
|
||||||
|
config:
|
||||||
|
kvstore:
|
||||||
|
namespace: eval
|
||||||
|
backend: kv_default
|
||||||
|
datasetio:
|
||||||
|
- provider_id: huggingface
|
||||||
|
provider_type: remote::huggingface
|
||||||
|
config:
|
||||||
|
kvstore:
|
||||||
|
namespace: datasetio::huggingface
|
||||||
|
backend: kv_default
|
||||||
|
- provider_id: localfs
|
||||||
|
provider_type: inline::localfs
|
||||||
|
config:
|
||||||
|
kvstore:
|
||||||
|
namespace: datasetio::localfs
|
||||||
|
backend: kv_default
|
||||||
|
scoring:
|
||||||
|
- provider_id: basic
|
||||||
|
provider_type: inline::basic
|
||||||
|
- provider_id: llm-as-judge
|
||||||
|
provider_type: inline::llm-as-judge
|
||||||
|
- provider_id: braintrust
|
||||||
|
provider_type: inline::braintrust
|
||||||
|
config:
|
||||||
|
openai_api_key: ${env.OPENAI_API_KEY:=}
|
||||||
|
tool_runtime:
|
||||||
|
- provider_id: brave-search
|
||||||
|
provider_type: remote::brave-search
|
||||||
|
config:
|
||||||
|
api_key: ${env.BRAVE_SEARCH_API_KEY:=}
|
||||||
|
max_results: 3
|
||||||
|
- provider_id: tavily-search
|
||||||
|
provider_type: remote::tavily-search
|
||||||
|
config:
|
||||||
|
api_key: ${env.TAVILY_SEARCH_API_KEY:=}
|
||||||
|
max_results: 3
|
||||||
|
- provider_id: rag-runtime
|
||||||
|
provider_type: inline::rag-runtime
|
||||||
|
- provider_id: model-context-protocol
|
||||||
|
provider_type: remote::model-context-protocol
|
||||||
|
files:
|
||||||
|
- provider_id: meta-reference-files
|
||||||
|
provider_type: inline::localfs
|
||||||
|
config:
|
||||||
|
storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/oci/files}
|
||||||
|
metadata_store:
|
||||||
|
table_name: files_metadata
|
||||||
|
backend: sql_default
|
||||||
|
storage:
|
||||||
|
backends:
|
||||||
|
kv_default:
|
||||||
|
type: kv_sqlite
|
||||||
|
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/oci}/kvstore.db
|
||||||
|
sql_default:
|
||||||
|
type: sql_sqlite
|
||||||
|
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/oci}/sql_store.db
|
||||||
|
stores:
|
||||||
|
metadata:
|
||||||
|
namespace: registry
|
||||||
|
backend: kv_default
|
||||||
|
inference:
|
||||||
|
table_name: inference_store
|
||||||
|
backend: sql_default
|
||||||
|
max_write_queue_size: 10000
|
||||||
|
num_writers: 4
|
||||||
|
conversations:
|
||||||
|
table_name: openai_conversations
|
||||||
|
backend: sql_default
|
||||||
|
prompts:
|
||||||
|
namespace: prompts
|
||||||
|
backend: kv_default
|
||||||
|
registered_resources:
|
||||||
|
models: []
|
||||||
|
shields: []
|
||||||
|
vector_dbs: []
|
||||||
|
datasets: []
|
||||||
|
scoring_fns: []
|
||||||
|
benchmarks: []
|
||||||
|
tool_groups:
|
||||||
|
- toolgroup_id: builtin::websearch
|
||||||
|
provider_id: tavily-search
|
||||||
|
server:
|
||||||
|
port: 8321
|
||||||
|
telemetry:
|
||||||
|
enabled: true
|
||||||
|
|
@ -26,8 +26,10 @@ from fairscale.nn.model_parallel.initialize import (
|
||||||
)
|
)
|
||||||
from termcolor import cprint
|
from termcolor import cprint
|
||||||
|
|
||||||
|
from llama_stack.models.llama.datatypes import ToolPromptFormat
|
||||||
|
|
||||||
from ..checkpoint import maybe_reshard_state_dict
|
from ..checkpoint import maybe_reshard_state_dict
|
||||||
from ..datatypes import GenerationResult, QuantizationMode, RawContent, RawMessage, ToolPromptFormat
|
from ..datatypes import GenerationResult, QuantizationMode, RawContent, RawMessage
|
||||||
from .args import ModelArgs
|
from .args import ModelArgs
|
||||||
from .chat_format import ChatFormat, LLMInput
|
from .chat_format import ChatFormat, LLMInput
|
||||||
from .model import Transformer
|
from .model import Transformer
|
||||||
|
|
|
||||||
|
|
@ -15,13 +15,10 @@ from pathlib import Path
|
||||||
|
|
||||||
from termcolor import colored
|
from termcolor import colored
|
||||||
|
|
||||||
|
from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall, ToolDefinition, ToolPromptFormat
|
||||||
|
|
||||||
from ..datatypes import (
|
from ..datatypes import (
|
||||||
BuiltinTool,
|
|
||||||
RawMessage,
|
RawMessage,
|
||||||
StopReason,
|
|
||||||
ToolCall,
|
|
||||||
ToolDefinition,
|
|
||||||
ToolPromptFormat,
|
|
||||||
)
|
)
|
||||||
from . import template_data
|
from . import template_data
|
||||||
from .chat_format import ChatFormat
|
from .chat_format import ChatFormat
|
||||||
|
|
|
||||||
|
|
@ -15,7 +15,7 @@ import textwrap
|
||||||
from datetime import datetime
|
from datetime import datetime
|
||||||
from typing import Any
|
from typing import Any
|
||||||
|
|
||||||
from llama_stack.apis.inference import (
|
from llama_stack.models.llama.datatypes import (
|
||||||
BuiltinTool,
|
BuiltinTool,
|
||||||
ToolDefinition,
|
ToolDefinition,
|
||||||
)
|
)
|
||||||
|
|
|
||||||
|
|
@ -8,8 +8,9 @@ import json
|
||||||
import re
|
import re
|
||||||
|
|
||||||
from llama_stack.log import get_logger
|
from llama_stack.log import get_logger
|
||||||
|
from llama_stack.models.llama.datatypes import BuiltinTool, ToolCall, ToolPromptFormat
|
||||||
|
|
||||||
from ..datatypes import BuiltinTool, RecursiveType, ToolCall, ToolPromptFormat
|
from ..datatypes import RecursiveType
|
||||||
|
|
||||||
logger = get_logger(name=__name__, category="models::llama")
|
logger = get_logger(name=__name__, category="models::llama")
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -13,7 +13,7 @@
|
||||||
|
|
||||||
import textwrap
|
import textwrap
|
||||||
|
|
||||||
from llama_stack.apis.inference import ToolDefinition
|
from llama_stack.models.llama.datatypes import ToolDefinition
|
||||||
from llama_stack.models.llama.llama3.prompt_templates.base import (
|
from llama_stack.models.llama.llama3.prompt_templates.base import (
|
||||||
PromptTemplate,
|
PromptTemplate,
|
||||||
PromptTemplateGeneratorBase,
|
PromptTemplateGeneratorBase,
|
||||||
|
|
|
||||||
|
|
@ -102,6 +102,7 @@ class MetaReferenceAgentsImpl(Agents):
|
||||||
include: list[str] | None = None,
|
include: list[str] | None = None,
|
||||||
max_infer_iters: int | None = 10,
|
max_infer_iters: int | None = 10,
|
||||||
guardrails: list[ResponseGuardrail] | None = None,
|
guardrails: list[ResponseGuardrail] | None = None,
|
||||||
|
max_tool_calls: int | None = None,
|
||||||
) -> OpenAIResponseObject:
|
) -> OpenAIResponseObject:
|
||||||
assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
|
assert self.openai_responses_impl is not None, "OpenAI responses not initialized"
|
||||||
result = await self.openai_responses_impl.create_openai_response(
|
result = await self.openai_responses_impl.create_openai_response(
|
||||||
|
|
@ -119,6 +120,7 @@ class MetaReferenceAgentsImpl(Agents):
|
||||||
include,
|
include,
|
||||||
max_infer_iters,
|
max_infer_iters,
|
||||||
guardrails,
|
guardrails,
|
||||||
|
max_tool_calls,
|
||||||
)
|
)
|
||||||
return result # type: ignore[no-any-return]
|
return result # type: ignore[no-any-return]
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -255,6 +255,7 @@ class OpenAIResponsesImpl:
|
||||||
include: list[str] | None = None,
|
include: list[str] | None = None,
|
||||||
max_infer_iters: int | None = 10,
|
max_infer_iters: int | None = 10,
|
||||||
guardrails: list[str | ResponseGuardrailSpec] | None = None,
|
guardrails: list[str | ResponseGuardrailSpec] | None = None,
|
||||||
|
max_tool_calls: int | None = None,
|
||||||
):
|
):
|
||||||
stream = bool(stream)
|
stream = bool(stream)
|
||||||
text = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) if text is None else text
|
text = OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")) if text is None else text
|
||||||
|
|
@ -270,6 +271,9 @@ class OpenAIResponsesImpl:
|
||||||
if not conversation.startswith("conv_"):
|
if not conversation.startswith("conv_"):
|
||||||
raise InvalidConversationIdError(conversation)
|
raise InvalidConversationIdError(conversation)
|
||||||
|
|
||||||
|
if max_tool_calls is not None and max_tool_calls < 1:
|
||||||
|
raise ValueError(f"Invalid {max_tool_calls=}; should be >= 1")
|
||||||
|
|
||||||
stream_gen = self._create_streaming_response(
|
stream_gen = self._create_streaming_response(
|
||||||
input=input,
|
input=input,
|
||||||
conversation=conversation,
|
conversation=conversation,
|
||||||
|
|
@ -282,6 +286,7 @@ class OpenAIResponsesImpl:
|
||||||
tools=tools,
|
tools=tools,
|
||||||
max_infer_iters=max_infer_iters,
|
max_infer_iters=max_infer_iters,
|
||||||
guardrail_ids=guardrail_ids,
|
guardrail_ids=guardrail_ids,
|
||||||
|
max_tool_calls=max_tool_calls,
|
||||||
)
|
)
|
||||||
|
|
||||||
if stream:
|
if stream:
|
||||||
|
|
@ -331,6 +336,7 @@ class OpenAIResponsesImpl:
|
||||||
tools: list[OpenAIResponseInputTool] | None = None,
|
tools: list[OpenAIResponseInputTool] | None = None,
|
||||||
max_infer_iters: int | None = 10,
|
max_infer_iters: int | None = 10,
|
||||||
guardrail_ids: list[str] | None = None,
|
guardrail_ids: list[str] | None = None,
|
||||||
|
max_tool_calls: int | None = None,
|
||||||
) -> AsyncIterator[OpenAIResponseObjectStream]:
|
) -> AsyncIterator[OpenAIResponseObjectStream]:
|
||||||
# These should never be None when called from create_openai_response (which sets defaults)
|
# These should never be None when called from create_openai_response (which sets defaults)
|
||||||
# but we assert here to help mypy understand the types
|
# but we assert here to help mypy understand the types
|
||||||
|
|
@ -373,6 +379,7 @@ class OpenAIResponsesImpl:
|
||||||
safety_api=self.safety_api,
|
safety_api=self.safety_api,
|
||||||
guardrail_ids=guardrail_ids,
|
guardrail_ids=guardrail_ids,
|
||||||
instructions=instructions,
|
instructions=instructions,
|
||||||
|
max_tool_calls=max_tool_calls,
|
||||||
)
|
)
|
||||||
|
|
||||||
# Stream the response
|
# Stream the response
|
||||||
|
|
|
||||||
|
|
@ -115,6 +115,7 @@ class StreamingResponseOrchestrator:
|
||||||
safety_api,
|
safety_api,
|
||||||
guardrail_ids: list[str] | None = None,
|
guardrail_ids: list[str] | None = None,
|
||||||
prompt: OpenAIResponsePrompt | None = None,
|
prompt: OpenAIResponsePrompt | None = None,
|
||||||
|
max_tool_calls: int | None = None,
|
||||||
):
|
):
|
||||||
self.inference_api = inference_api
|
self.inference_api = inference_api
|
||||||
self.ctx = ctx
|
self.ctx = ctx
|
||||||
|
|
@ -126,6 +127,10 @@ class StreamingResponseOrchestrator:
|
||||||
self.safety_api = safety_api
|
self.safety_api = safety_api
|
||||||
self.guardrail_ids = guardrail_ids or []
|
self.guardrail_ids = guardrail_ids or []
|
||||||
self.prompt = prompt
|
self.prompt = prompt
|
||||||
|
# System message that is inserted into the model's context
|
||||||
|
self.instructions = instructions
|
||||||
|
# Max number of total calls to built-in tools that can be processed in a response
|
||||||
|
self.max_tool_calls = max_tool_calls
|
||||||
self.sequence_number = 0
|
self.sequence_number = 0
|
||||||
# Store MCP tool mapping that gets built during tool processing
|
# Store MCP tool mapping that gets built during tool processing
|
||||||
self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = (
|
self.mcp_tool_to_server: dict[str, OpenAIResponseInputToolMCP] = (
|
||||||
|
|
@ -139,8 +144,8 @@ class StreamingResponseOrchestrator:
|
||||||
self.accumulated_usage: OpenAIResponseUsage | None = None
|
self.accumulated_usage: OpenAIResponseUsage | None = None
|
||||||
# Track if we've sent a refusal response
|
# Track if we've sent a refusal response
|
||||||
self.violation_detected = False
|
self.violation_detected = False
|
||||||
# system message that is inserted into the model's context
|
# Track total calls made to built-in tools
|
||||||
self.instructions = instructions
|
self.accumulated_builtin_tool_calls = 0
|
||||||
|
|
||||||
async def _create_refusal_response(self, violation_message: str) -> OpenAIResponseObjectStream:
|
async def _create_refusal_response(self, violation_message: str) -> OpenAIResponseObjectStream:
|
||||||
"""Create a refusal response to replace streaming content."""
|
"""Create a refusal response to replace streaming content."""
|
||||||
|
|
@ -186,6 +191,7 @@ class StreamingResponseOrchestrator:
|
||||||
usage=self.accumulated_usage,
|
usage=self.accumulated_usage,
|
||||||
instructions=self.instructions,
|
instructions=self.instructions,
|
||||||
prompt=self.prompt,
|
prompt=self.prompt,
|
||||||
|
max_tool_calls=self.max_tool_calls,
|
||||||
)
|
)
|
||||||
|
|
||||||
async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
|
async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
|
||||||
|
|
@ -894,6 +900,11 @@ class StreamingResponseOrchestrator:
|
||||||
"""Coordinate execution of both function and non-function tool calls."""
|
"""Coordinate execution of both function and non-function tool calls."""
|
||||||
# Execute non-function tool calls
|
# Execute non-function tool calls
|
||||||
for tool_call in non_function_tool_calls:
|
for tool_call in non_function_tool_calls:
|
||||||
|
# Check if total calls made to built-in and mcp tools exceed max_tool_calls
|
||||||
|
if self.max_tool_calls is not None and self.accumulated_builtin_tool_calls >= self.max_tool_calls:
|
||||||
|
logger.info(f"Ignoring built-in and mcp tool call since reached the limit of {self.max_tool_calls=}.")
|
||||||
|
break
|
||||||
|
|
||||||
# Find the item_id for this tool call
|
# Find the item_id for this tool call
|
||||||
matching_item_id = None
|
matching_item_id = None
|
||||||
for index, item_id in completion_result_data.tool_call_item_ids.items():
|
for index, item_id in completion_result_data.tool_call_item_ids.items():
|
||||||
|
|
@ -974,6 +985,9 @@ class StreamingResponseOrchestrator:
|
||||||
if tool_response_message:
|
if tool_response_message:
|
||||||
next_turn_messages.append(tool_response_message)
|
next_turn_messages.append(tool_response_message)
|
||||||
|
|
||||||
|
# Track number of calls made to built-in and mcp tools
|
||||||
|
self.accumulated_builtin_tool_calls += 1
|
||||||
|
|
||||||
# Execute function tool calls (client-side)
|
# Execute function tool calls (client-side)
|
||||||
for tool_call in function_tool_calls:
|
for tool_call in function_tool_calls:
|
||||||
# Find the item_id for this tool call from our tracking dictionary
|
# Find the item_id for this tool call from our tracking dictionary
|
||||||
|
|
|
||||||
|
|
@ -5,7 +5,6 @@
|
||||||
# the root directory of this source tree.
|
# the root directory of this source tree.
|
||||||
|
|
||||||
import math
|
import math
|
||||||
from collections.abc import Generator
|
|
||||||
from typing import Optional
|
from typing import Optional
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
|
|
@ -14,21 +13,19 @@ from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerToken
|
||||||
from llama_stack.apis.inference import (
|
from llama_stack.apis.inference import (
|
||||||
GreedySamplingStrategy,
|
GreedySamplingStrategy,
|
||||||
JsonSchemaResponseFormat,
|
JsonSchemaResponseFormat,
|
||||||
|
OpenAIChatCompletionRequestWithExtraBody,
|
||||||
|
OpenAIResponseFormatJSONSchema,
|
||||||
ResponseFormat,
|
ResponseFormat,
|
||||||
|
ResponseFormatType,
|
||||||
SamplingParams,
|
SamplingParams,
|
||||||
TopPSamplingStrategy,
|
TopPSamplingStrategy,
|
||||||
)
|
)
|
||||||
from llama_stack.models.llama.datatypes import QuantizationMode
|
from llama_stack.models.llama.datatypes import QuantizationMode, ToolPromptFormat
|
||||||
from llama_stack.models.llama.llama3.generation import Llama3
|
from llama_stack.models.llama.llama3.generation import Llama3
|
||||||
from llama_stack.models.llama.llama3.tokenizer import Tokenizer as Llama3Tokenizer
|
from llama_stack.models.llama.llama3.tokenizer import Tokenizer as Llama3Tokenizer
|
||||||
from llama_stack.models.llama.llama4.generation import Llama4
|
from llama_stack.models.llama.llama4.generation import Llama4
|
||||||
from llama_stack.models.llama.llama4.tokenizer import Tokenizer as Llama4Tokenizer
|
from llama_stack.models.llama.llama4.tokenizer import Tokenizer as Llama4Tokenizer
|
||||||
from llama_stack.models.llama.sku_types import Model, ModelFamily
|
from llama_stack.models.llama.sku_types import Model, ModelFamily
|
||||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
|
||||||
ChatCompletionRequestWithRawContent,
|
|
||||||
CompletionRequestWithRawContent,
|
|
||||||
get_default_tool_prompt_format,
|
|
||||||
)
|
|
||||||
|
|
||||||
from .common import model_checkpoint_dir
|
from .common import model_checkpoint_dir
|
||||||
from .config import MetaReferenceInferenceConfig
|
from .config import MetaReferenceInferenceConfig
|
||||||
|
|
@ -106,14 +103,6 @@ def _infer_sampling_params(sampling_params: SamplingParams):
|
||||||
return temperature, top_p
|
return temperature, top_p
|
||||||
|
|
||||||
|
|
||||||
def _infer_tool_prompt_format(request: ChatCompletionRequestWithRawContent):
|
|
||||||
tool_config = request.tool_config
|
|
||||||
if tool_config is not None and tool_config.tool_prompt_format is not None:
|
|
||||||
return tool_config.tool_prompt_format
|
|
||||||
else:
|
|
||||||
return get_default_tool_prompt_format(request.model)
|
|
||||||
|
|
||||||
|
|
||||||
class LlamaGenerator:
|
class LlamaGenerator:
|
||||||
def __init__(
|
def __init__(
|
||||||
self,
|
self,
|
||||||
|
|
@ -157,55 +146,56 @@ class LlamaGenerator:
|
||||||
self.args = self.inner_generator.args
|
self.args = self.inner_generator.args
|
||||||
self.formatter = self.inner_generator.formatter
|
self.formatter = self.inner_generator.formatter
|
||||||
|
|
||||||
def completion(
|
|
||||||
self,
|
|
||||||
request_batch: list[CompletionRequestWithRawContent],
|
|
||||||
) -> Generator:
|
|
||||||
first_request = request_batch[0]
|
|
||||||
sampling_params = first_request.sampling_params or SamplingParams()
|
|
||||||
max_gen_len = sampling_params.max_tokens
|
|
||||||
if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
|
|
||||||
max_gen_len = self.args.max_seq_len - 1
|
|
||||||
|
|
||||||
temperature, top_p = _infer_sampling_params(sampling_params)
|
|
||||||
yield from self.inner_generator.generate(
|
|
||||||
llm_inputs=[self.formatter.encode_content(request.content) for request in request_batch],
|
|
||||||
max_gen_len=max_gen_len,
|
|
||||||
temperature=temperature,
|
|
||||||
top_p=top_p,
|
|
||||||
logprobs=bool(first_request.logprobs),
|
|
||||||
echo=False,
|
|
||||||
logits_processor=get_logits_processor(
|
|
||||||
self.tokenizer,
|
|
||||||
self.args.vocab_size,
|
|
||||||
first_request.response_format,
|
|
||||||
),
|
|
||||||
)
|
|
||||||
|
|
||||||
def chat_completion(
|
def chat_completion(
|
||||||
self,
|
self,
|
||||||
request_batch: list[ChatCompletionRequestWithRawContent],
|
request: OpenAIChatCompletionRequestWithExtraBody,
|
||||||
) -> Generator:
|
raw_messages: list,
|
||||||
first_request = request_batch[0]
|
):
|
||||||
sampling_params = first_request.sampling_params or SamplingParams()
|
"""Generate chat completion using OpenAI request format.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
request: OpenAI chat completion request
|
||||||
|
raw_messages: Pre-converted list of RawMessage objects
|
||||||
|
"""
|
||||||
|
|
||||||
|
# Determine tool prompt format
|
||||||
|
tool_prompt_format = ToolPromptFormat.json if request.tools else ToolPromptFormat.json
|
||||||
|
|
||||||
|
# Prepare sampling params
|
||||||
|
sampling_params = SamplingParams()
|
||||||
|
if request.temperature is not None or request.top_p is not None:
|
||||||
|
sampling_params.strategy = TopPSamplingStrategy(
|
||||||
|
temperature=request.temperature if request.temperature is not None else 1.0,
|
||||||
|
top_p=request.top_p if request.top_p is not None else 1.0,
|
||||||
|
)
|
||||||
|
if request.max_tokens:
|
||||||
|
sampling_params.max_tokens = request.max_tokens
|
||||||
|
|
||||||
max_gen_len = sampling_params.max_tokens
|
max_gen_len = sampling_params.max_tokens
|
||||||
if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
|
if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
|
||||||
max_gen_len = self.args.max_seq_len - 1
|
max_gen_len = self.args.max_seq_len - 1
|
||||||
|
|
||||||
temperature, top_p = _infer_sampling_params(sampling_params)
|
temperature, top_p = _infer_sampling_params(sampling_params)
|
||||||
|
|
||||||
|
# Get logits processor for response format
|
||||||
|
logits_processor = None
|
||||||
|
if request.response_format:
|
||||||
|
if isinstance(request.response_format, OpenAIResponseFormatJSONSchema):
|
||||||
|
# Extract the actual schema from OpenAIJSONSchema TypedDict
|
||||||
|
schema_dict = request.response_format.json_schema.get("schema") or {}
|
||||||
|
json_schema_format = JsonSchemaResponseFormat(
|
||||||
|
type=ResponseFormatType.json_schema,
|
||||||
|
json_schema=schema_dict,
|
||||||
|
)
|
||||||
|
logits_processor = get_logits_processor(self.tokenizer, self.args.vocab_size, json_schema_format)
|
||||||
|
|
||||||
|
# Generate
|
||||||
yield from self.inner_generator.generate(
|
yield from self.inner_generator.generate(
|
||||||
llm_inputs=[
|
llm_inputs=[self.formatter.encode_dialog_prompt(raw_messages, tool_prompt_format)],
|
||||||
self.formatter.encode_dialog_prompt(request.messages, _infer_tool_prompt_format(request))
|
|
||||||
for request in request_batch
|
|
||||||
],
|
|
||||||
max_gen_len=max_gen_len,
|
max_gen_len=max_gen_len,
|
||||||
temperature=temperature,
|
temperature=temperature,
|
||||||
top_p=top_p,
|
top_p=top_p,
|
||||||
logprobs=bool(first_request.logprobs),
|
logprobs=False,
|
||||||
echo=False,
|
echo=False,
|
||||||
logits_processor=get_logits_processor(
|
logits_processor=logits_processor,
|
||||||
self.tokenizer,
|
|
||||||
self.args.vocab_size,
|
|
||||||
first_request.response_format,
|
|
||||||
),
|
|
||||||
)
|
)
|
||||||
|
|
|
||||||
|
|
@ -5,12 +5,19 @@
|
||||||
# the root directory of this source tree.
|
# the root directory of this source tree.
|
||||||
|
|
||||||
import asyncio
|
import asyncio
|
||||||
|
import time
|
||||||
|
import uuid
|
||||||
from collections.abc import AsyncIterator
|
from collections.abc import AsyncIterator
|
||||||
|
|
||||||
from llama_stack.apis.inference import (
|
from llama_stack.apis.inference import (
|
||||||
InferenceProvider,
|
InferenceProvider,
|
||||||
|
OpenAIAssistantMessageParam,
|
||||||
OpenAIChatCompletionRequestWithExtraBody,
|
OpenAIChatCompletionRequestWithExtraBody,
|
||||||
|
OpenAIChatCompletionUsage,
|
||||||
|
OpenAIChoice,
|
||||||
OpenAICompletionRequestWithExtraBody,
|
OpenAICompletionRequestWithExtraBody,
|
||||||
|
OpenAIUserMessageParam,
|
||||||
|
ToolChoice,
|
||||||
)
|
)
|
||||||
from llama_stack.apis.inference.inference import (
|
from llama_stack.apis.inference.inference import (
|
||||||
OpenAIChatCompletion,
|
OpenAIChatCompletion,
|
||||||
|
|
@ -19,12 +26,20 @@ from llama_stack.apis.inference.inference import (
|
||||||
)
|
)
|
||||||
from llama_stack.apis.models import Model, ModelType
|
from llama_stack.apis.models import Model, ModelType
|
||||||
from llama_stack.log import get_logger
|
from llama_stack.log import get_logger
|
||||||
|
from llama_stack.models.llama.datatypes import RawMessage, RawTextItem, ToolDefinition
|
||||||
from llama_stack.models.llama.llama3.chat_format import ChatFormat as Llama3ChatFormat
|
from llama_stack.models.llama.llama3.chat_format import ChatFormat as Llama3ChatFormat
|
||||||
|
from llama_stack.models.llama.llama3.prompt_templates import (
|
||||||
|
JsonCustomToolGenerator,
|
||||||
|
SystemDefaultGenerator,
|
||||||
|
)
|
||||||
from llama_stack.models.llama.llama3.tokenizer import Tokenizer as Llama3Tokenizer
|
from llama_stack.models.llama.llama3.tokenizer import Tokenizer as Llama3Tokenizer
|
||||||
from llama_stack.models.llama.llama4.chat_format import ChatFormat as Llama4ChatFormat
|
from llama_stack.models.llama.llama4.chat_format import ChatFormat as Llama4ChatFormat
|
||||||
|
from llama_stack.models.llama.llama4.prompt_templates.system_prompts import (
|
||||||
|
PythonListCustomToolGenerator as PythonListCustomToolGeneratorLlama4,
|
||||||
|
)
|
||||||
from llama_stack.models.llama.llama4.tokenizer import Tokenizer as Llama4Tokenizer
|
from llama_stack.models.llama.llama4.tokenizer import Tokenizer as Llama4Tokenizer
|
||||||
from llama_stack.models.llama.sku_list import resolve_model
|
from llama_stack.models.llama.sku_list import resolve_model
|
||||||
from llama_stack.models.llama.sku_types import ModelFamily
|
from llama_stack.models.llama.sku_types import ModelFamily, is_multimodal
|
||||||
from llama_stack.providers.datatypes import ModelsProtocolPrivate
|
from llama_stack.providers.datatypes import ModelsProtocolPrivate
|
||||||
from llama_stack.providers.utils.inference.embedding_mixin import (
|
from llama_stack.providers.utils.inference.embedding_mixin import (
|
||||||
SentenceTransformerEmbeddingMixin,
|
SentenceTransformerEmbeddingMixin,
|
||||||
|
|
@ -44,6 +59,170 @@ log = get_logger(__name__, category="inference")
|
||||||
SEMAPHORE = asyncio.Semaphore(1)
|
SEMAPHORE = asyncio.Semaphore(1)
|
||||||
|
|
||||||
|
|
||||||
|
def _convert_openai_tool_to_tool_definition(tool) -> ToolDefinition:
|
||||||
|
"""Convert OpenAI tool format to ToolDefinition format."""
|
||||||
|
# OpenAI tools have function.name and function.parameters
|
||||||
|
return ToolDefinition(
|
||||||
|
tool_name=tool.function.name,
|
||||||
|
description=tool.function.description or "",
|
||||||
|
parameters=tool.function.parameters or {},
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def _get_tool_choice_prompt(tool_choice, tools) -> str:
|
||||||
|
"""Generate prompt text for tool_choice behavior."""
|
||||||
|
if not tool_choice or tool_choice == ToolChoice.auto or tool_choice == "auto":
|
||||||
|
return ""
|
||||||
|
elif tool_choice == ToolChoice.required or tool_choice == "required":
|
||||||
|
return "You MUST use one of the provided functions/tools to answer the user query."
|
||||||
|
elif tool_choice == ToolChoice.none or tool_choice == "none":
|
||||||
|
return ""
|
||||||
|
else:
|
||||||
|
# Specific tool specified
|
||||||
|
return f"You MUST use the tool `{tool_choice}` to answer the user query."
|
||||||
|
|
||||||
|
|
||||||
|
def _raw_content_as_str(content) -> str:
|
||||||
|
"""Convert RawContent to string for system messages."""
|
||||||
|
if isinstance(content, str):
|
||||||
|
return content
|
||||||
|
elif isinstance(content, RawTextItem):
|
||||||
|
return content.text
|
||||||
|
elif isinstance(content, list):
|
||||||
|
return "\n".join(_raw_content_as_str(c) for c in content)
|
||||||
|
else:
|
||||||
|
return "<media>"
|
||||||
|
|
||||||
|
|
||||||
|
def _augment_raw_messages_for_tools_llama_3_1(
|
||||||
|
raw_messages: list[RawMessage],
|
||||||
|
tools: list,
|
||||||
|
tool_choice,
|
||||||
|
) -> list[RawMessage]:
|
||||||
|
"""Augment raw messages with tool definitions for Llama 3.1 style models."""
|
||||||
|
messages = raw_messages.copy()
|
||||||
|
existing_system_message = None
|
||||||
|
if messages and messages[0].role == "system":
|
||||||
|
existing_system_message = messages.pop(0)
|
||||||
|
|
||||||
|
sys_content = ""
|
||||||
|
|
||||||
|
# Add tool definitions first (if present)
|
||||||
|
if tools:
|
||||||
|
# Convert OpenAI tools to ToolDefinitions
|
||||||
|
tool_definitions = [_convert_openai_tool_to_tool_definition(t) for t in tools]
|
||||||
|
|
||||||
|
# For OpenAI format, all tools are custom (have string names)
|
||||||
|
tool_gen = JsonCustomToolGenerator()
|
||||||
|
tool_template = tool_gen.gen(tool_definitions)
|
||||||
|
sys_content += tool_template.render()
|
||||||
|
sys_content += "\n"
|
||||||
|
|
||||||
|
# Add default system prompt
|
||||||
|
default_gen = SystemDefaultGenerator()
|
||||||
|
default_template = default_gen.gen()
|
||||||
|
sys_content += default_template.render()
|
||||||
|
|
||||||
|
# Add existing system message if present
|
||||||
|
if existing_system_message:
|
||||||
|
sys_content += "\n" + _raw_content_as_str(existing_system_message.content)
|
||||||
|
|
||||||
|
# Add tool choice prompt if needed
|
||||||
|
if tool_choice_prompt := _get_tool_choice_prompt(tool_choice, tools):
|
||||||
|
sys_content += "\n" + tool_choice_prompt
|
||||||
|
|
||||||
|
# Create new system message
|
||||||
|
new_system_message = RawMessage(
|
||||||
|
role="system",
|
||||||
|
content=[RawTextItem(text=sys_content.strip())],
|
||||||
|
)
|
||||||
|
|
||||||
|
return [new_system_message] + messages
|
||||||
|
|
||||||
|
|
||||||
|
def _augment_raw_messages_for_tools_llama_4(
|
||||||
|
raw_messages: list[RawMessage],
|
||||||
|
tools: list,
|
||||||
|
tool_choice,
|
||||||
|
) -> list[RawMessage]:
|
||||||
|
"""Augment raw messages with tool definitions for Llama 4/3.2/3.3 style models."""
|
||||||
|
messages = raw_messages.copy()
|
||||||
|
existing_system_message = None
|
||||||
|
if messages and messages[0].role == "system":
|
||||||
|
existing_system_message = messages.pop(0)
|
||||||
|
|
||||||
|
sys_content = ""
|
||||||
|
|
||||||
|
# Add tool definitions if present
|
||||||
|
if tools:
|
||||||
|
# Convert OpenAI tools to ToolDefinitions
|
||||||
|
tool_definitions = [_convert_openai_tool_to_tool_definition(t) for t in tools]
|
||||||
|
|
||||||
|
# Use python_list format for Llama 4
|
||||||
|
tool_gen = PythonListCustomToolGeneratorLlama4()
|
||||||
|
system_prompt = None
|
||||||
|
if existing_system_message:
|
||||||
|
system_prompt = _raw_content_as_str(existing_system_message.content)
|
||||||
|
|
||||||
|
tool_template = tool_gen.gen(tool_definitions, system_prompt)
|
||||||
|
sys_content = tool_template.render()
|
||||||
|
elif existing_system_message:
|
||||||
|
# No tools, just use existing system message
|
||||||
|
sys_content = _raw_content_as_str(existing_system_message.content)
|
||||||
|
|
||||||
|
# Add tool choice prompt if needed
|
||||||
|
if tool_choice_prompt := _get_tool_choice_prompt(tool_choice, tools):
|
||||||
|
sys_content += "\n" + tool_choice_prompt
|
||||||
|
|
||||||
|
if sys_content:
|
||||||
|
new_system_message = RawMessage(
|
||||||
|
role="system",
|
||||||
|
content=[RawTextItem(text=sys_content.strip())],
|
||||||
|
)
|
||||||
|
return [new_system_message] + messages
|
||||||
|
|
||||||
|
return messages
|
||||||
|
|
||||||
|
|
||||||
|
def augment_raw_messages_for_tools(
|
||||||
|
raw_messages: list[RawMessage],
|
||||||
|
params: OpenAIChatCompletionRequestWithExtraBody,
|
||||||
|
llama_model,
|
||||||
|
) -> list[RawMessage]:
|
||||||
|
"""Augment raw messages with tool definitions based on model family."""
|
||||||
|
if not params.tools:
|
||||||
|
return raw_messages
|
||||||
|
|
||||||
|
# Determine augmentation strategy based on model family
|
||||||
|
if llama_model.model_family == ModelFamily.llama3_1 or (
|
||||||
|
llama_model.model_family == ModelFamily.llama3_2 and is_multimodal(llama_model.core_model_id)
|
||||||
|
):
|
||||||
|
# Llama 3.1 and Llama 3.2 multimodal use JSON format
|
||||||
|
return _augment_raw_messages_for_tools_llama_3_1(
|
||||||
|
raw_messages,
|
||||||
|
params.tools,
|
||||||
|
params.tool_choice,
|
||||||
|
)
|
||||||
|
elif llama_model.model_family in (
|
||||||
|
ModelFamily.llama3_2,
|
||||||
|
ModelFamily.llama3_3,
|
||||||
|
ModelFamily.llama4,
|
||||||
|
):
|
||||||
|
# Llama 3.2/3.3/4 use python_list format
|
||||||
|
return _augment_raw_messages_for_tools_llama_4(
|
||||||
|
raw_messages,
|
||||||
|
params.tools,
|
||||||
|
params.tool_choice,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
# Default to Llama 3.1 style
|
||||||
|
return _augment_raw_messages_for_tools_llama_3_1(
|
||||||
|
raw_messages,
|
||||||
|
params.tools,
|
||||||
|
params.tool_choice,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_model: Model) -> LlamaGenerator:
|
def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_model: Model) -> LlamaGenerator:
|
||||||
return LlamaGenerator(config, model_id, llama_model)
|
return LlamaGenerator(config, model_id, llama_model)
|
||||||
|
|
||||||
|
|
@ -136,17 +315,20 @@ class MetaReferenceInferenceImpl(
|
||||||
self.llama_model = llama_model
|
self.llama_model = llama_model
|
||||||
|
|
||||||
log.info("Warming up...")
|
log.info("Warming up...")
|
||||||
|
|
||||||
await self.openai_chat_completion(
|
await self.openai_chat_completion(
|
||||||
|
params=OpenAIChatCompletionRequestWithExtraBody(
|
||||||
model=model_id,
|
model=model_id,
|
||||||
messages=[{"role": "user", "content": "Hi how are you?"}],
|
messages=[OpenAIUserMessageParam(role="user", content="Hi how are you?")],
|
||||||
max_tokens=20,
|
max_tokens=20,
|
||||||
)
|
)
|
||||||
|
)
|
||||||
log.info("Warmed up!")
|
log.info("Warmed up!")
|
||||||
|
|
||||||
def check_model(self, request) -> None:
|
def check_model(self, request) -> None:
|
||||||
if self.model_id is None or self.llama_model is None:
|
if self.model_id is None or self.llama_model is None:
|
||||||
raise RuntimeError(
|
raise RuntimeError(
|
||||||
"No avaible model yet, please register your requested model or add your model in the resouces first"
|
"No available model yet, please register your requested model or add your model in the resources first"
|
||||||
)
|
)
|
||||||
elif request.model != self.model_id:
|
elif request.model != self.model_id:
|
||||||
raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")
|
raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")
|
||||||
|
|
@ -155,4 +337,207 @@ class MetaReferenceInferenceImpl(
|
||||||
self,
|
self,
|
||||||
params: OpenAIChatCompletionRequestWithExtraBody,
|
params: OpenAIChatCompletionRequestWithExtraBody,
|
||||||
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
|
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
|
||||||
raise NotImplementedError("OpenAI chat completion not supported by meta-reference inference provider")
|
self.check_model(params)
|
||||||
|
|
||||||
|
# Convert OpenAI messages to RawMessages
|
||||||
|
from llama_stack.models.llama.datatypes import StopReason
|
||||||
|
from llama_stack.providers.utils.inference.prompt_adapter import (
|
||||||
|
convert_openai_message_to_raw_message,
|
||||||
|
decode_assistant_message,
|
||||||
|
)
|
||||||
|
|
||||||
|
raw_messages = [await convert_openai_message_to_raw_message(msg) for msg in params.messages]
|
||||||
|
|
||||||
|
# Augment messages with tool definitions if tools are present
|
||||||
|
raw_messages = augment_raw_messages_for_tools(raw_messages, params, self.llama_model)
|
||||||
|
|
||||||
|
# Call generator's chat_completion method (works for both single-GPU and model-parallel)
|
||||||
|
if isinstance(self.generator, LlamaGenerator):
|
||||||
|
generator = self.generator.chat_completion(params, raw_messages)
|
||||||
|
else:
|
||||||
|
# Model parallel: submit task to process group
|
||||||
|
generator = self.generator.group.run_inference(("chat_completion", [params, raw_messages]))
|
||||||
|
|
||||||
|
# Check if streaming is requested
|
||||||
|
if params.stream:
|
||||||
|
return self._stream_chat_completion(generator, params)
|
||||||
|
|
||||||
|
# Non-streaming: collect all generated text
|
||||||
|
generated_text = ""
|
||||||
|
for result_batch in generator:
|
||||||
|
for result in result_batch:
|
||||||
|
if not result.ignore_token and result.source == "output":
|
||||||
|
generated_text += result.text
|
||||||
|
|
||||||
|
# Decode assistant message to extract tool calls and determine stop_reason
|
||||||
|
# Default to end_of_turn if generation completed normally
|
||||||
|
decoded_message = decode_assistant_message(generated_text, StopReason.end_of_turn)
|
||||||
|
|
||||||
|
# Convert tool calls to OpenAI format
|
||||||
|
openai_tool_calls = None
|
||||||
|
if decoded_message.tool_calls:
|
||||||
|
from llama_stack.apis.inference import (
|
||||||
|
OpenAIChatCompletionToolCall,
|
||||||
|
OpenAIChatCompletionToolCallFunction,
|
||||||
|
)
|
||||||
|
|
||||||
|
openai_tool_calls = [
|
||||||
|
OpenAIChatCompletionToolCall(
|
||||||
|
# generate a uuid for the call id. This is the only inline provider that does this, so need to get creative.
|
||||||
|
id=f"call_{uuid.uuid4().hex[:24]}",
|
||||||
|
type="function",
|
||||||
|
function=OpenAIChatCompletionToolCallFunction(
|
||||||
|
name=str(tc.tool_name),
|
||||||
|
arguments=tc.arguments,
|
||||||
|
),
|
||||||
|
)
|
||||||
|
for tc in decoded_message.tool_calls
|
||||||
|
]
|
||||||
|
|
||||||
|
# Determine finish_reason based on whether tool calls are present
|
||||||
|
finish_reason = "tool_calls" if openai_tool_calls else "stop"
|
||||||
|
|
||||||
|
# Extract content from decoded message
|
||||||
|
content = ""
|
||||||
|
if isinstance(decoded_message.content, str):
|
||||||
|
content = decoded_message.content
|
||||||
|
elif isinstance(decoded_message.content, list):
|
||||||
|
for item in decoded_message.content:
|
||||||
|
if isinstance(item, RawTextItem):
|
||||||
|
content += item.text
|
||||||
|
|
||||||
|
# Create OpenAI response
|
||||||
|
# generate a uuid for the call id. This is the only inline provider that does this, so need to get creative.
|
||||||
|
response_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
|
||||||
|
created = int(time.time())
|
||||||
|
|
||||||
|
return OpenAIChatCompletion(
|
||||||
|
id=response_id,
|
||||||
|
object="chat.completion",
|
||||||
|
created=created,
|
||||||
|
model=params.model,
|
||||||
|
choices=[
|
||||||
|
OpenAIChoice(
|
||||||
|
index=0,
|
||||||
|
message=OpenAIAssistantMessageParam(
|
||||||
|
role="assistant",
|
||||||
|
content=content,
|
||||||
|
tool_calls=openai_tool_calls,
|
||||||
|
),
|
||||||
|
finish_reason=finish_reason,
|
||||||
|
logprobs=None,
|
||||||
|
)
|
||||||
|
],
|
||||||
|
usage=OpenAIChatCompletionUsage(
|
||||||
|
prompt_tokens=0, # TODO: calculate properly
|
||||||
|
completion_tokens=0, # TODO: calculate properly
|
||||||
|
total_tokens=0, # TODO: calculate properly
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
async def _stream_chat_completion(
|
||||||
|
self,
|
||||||
|
generator,
|
||||||
|
params: OpenAIChatCompletionRequestWithExtraBody,
|
||||||
|
) -> AsyncIterator[OpenAIChatCompletionChunk]:
|
||||||
|
"""Stream chat completion chunks as they're generated."""
|
||||||
|
from llama_stack.apis.inference import (
|
||||||
|
OpenAIChatCompletionChunk,
|
||||||
|
OpenAIChatCompletionToolCall,
|
||||||
|
OpenAIChatCompletionToolCallFunction,
|
||||||
|
OpenAIChoiceDelta,
|
||||||
|
OpenAIChunkChoice,
|
||||||
|
)
|
||||||
|
from llama_stack.models.llama.datatypes import StopReason
|
||||||
|
from llama_stack.providers.utils.inference.prompt_adapter import decode_assistant_message
|
||||||
|
|
||||||
|
response_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
|
||||||
|
created = int(time.time())
|
||||||
|
generated_text = ""
|
||||||
|
|
||||||
|
# Yield chunks as tokens are generated
|
||||||
|
for result_batch in generator:
|
||||||
|
for result in result_batch:
|
||||||
|
if result.ignore_token or result.source != "output":
|
||||||
|
continue
|
||||||
|
|
||||||
|
generated_text += result.text
|
||||||
|
|
||||||
|
# Yield delta chunk with the new text
|
||||||
|
chunk = OpenAIChatCompletionChunk(
|
||||||
|
id=response_id,
|
||||||
|
object="chat.completion.chunk",
|
||||||
|
created=created,
|
||||||
|
model=params.model,
|
||||||
|
choices=[
|
||||||
|
OpenAIChunkChoice(
|
||||||
|
index=0,
|
||||||
|
delta=OpenAIChoiceDelta(
|
||||||
|
role="assistant",
|
||||||
|
content=result.text,
|
||||||
|
),
|
||||||
|
finish_reason="",
|
||||||
|
logprobs=None,
|
||||||
|
)
|
||||||
|
],
|
||||||
|
)
|
||||||
|
yield chunk
|
||||||
|
|
||||||
|
# After generation completes, decode the full message to extract tool calls
|
||||||
|
decoded_message = decode_assistant_message(generated_text, StopReason.end_of_turn)
|
||||||
|
|
||||||
|
# If tool calls are present, yield a final chunk with tool_calls
|
||||||
|
if decoded_message.tool_calls:
|
||||||
|
openai_tool_calls = [
|
||||||
|
OpenAIChatCompletionToolCall(
|
||||||
|
# generate a uuid for the call id. This is the only inline provider that does this, so need to get creative.
|
||||||
|
id=f"call_{uuid.uuid4().hex[:24]}",
|
||||||
|
type="function",
|
||||||
|
function=OpenAIChatCompletionToolCallFunction(
|
||||||
|
name=str(tc.tool_name),
|
||||||
|
arguments=tc.arguments,
|
||||||
|
),
|
||||||
|
)
|
||||||
|
for tc in decoded_message.tool_calls
|
||||||
|
]
|
||||||
|
|
||||||
|
# Yield chunk with tool_calls
|
||||||
|
chunk = OpenAIChatCompletionChunk(
|
||||||
|
id=response_id,
|
||||||
|
object="chat.completion.chunk",
|
||||||
|
created=created,
|
||||||
|
model=params.model,
|
||||||
|
choices=[
|
||||||
|
OpenAIChunkChoice(
|
||||||
|
index=0,
|
||||||
|
delta=OpenAIChoiceDelta(
|
||||||
|
role="assistant",
|
||||||
|
tool_calls=openai_tool_calls,
|
||||||
|
),
|
||||||
|
finish_reason="",
|
||||||
|
logprobs=None,
|
||||||
|
)
|
||||||
|
],
|
||||||
|
)
|
||||||
|
yield chunk
|
||||||
|
|
||||||
|
finish_reason = "tool_calls"
|
||||||
|
else:
|
||||||
|
finish_reason = "stop"
|
||||||
|
|
||||||
|
# Yield final chunk with finish_reason
|
||||||
|
final_chunk = OpenAIChatCompletionChunk(
|
||||||
|
id=response_id,
|
||||||
|
object="chat.completion.chunk",
|
||||||
|
created=created,
|
||||||
|
model=params.model,
|
||||||
|
choices=[
|
||||||
|
OpenAIChunkChoice(
|
||||||
|
index=0,
|
||||||
|
delta=OpenAIChoiceDelta(),
|
||||||
|
finish_reason=finish_reason,
|
||||||
|
logprobs=None,
|
||||||
|
)
|
||||||
|
],
|
||||||
|
)
|
||||||
|
yield final_chunk
|
||||||
|
|
|
||||||
|
|
@ -4,17 +4,12 @@
|
||||||
# This source code is licensed under the terms described in the LICENSE file in
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
# the root directory of this source tree.
|
# the root directory of this source tree.
|
||||||
|
|
||||||
from collections.abc import Callable, Generator
|
from collections.abc import Callable
|
||||||
from copy import deepcopy
|
|
||||||
from functools import partial
|
from functools import partial
|
||||||
from typing import Any
|
from typing import Any
|
||||||
|
|
||||||
from llama_stack.models.llama.llama3.chat_format import ChatFormat as Llama3ChatFormat
|
from llama_stack.models.llama.llama3.chat_format import ChatFormat as Llama3ChatFormat
|
||||||
from llama_stack.models.llama.llama4.chat_format import ChatFormat as Llama4ChatFormat
|
from llama_stack.models.llama.llama4.chat_format import ChatFormat as Llama4ChatFormat
|
||||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
|
||||||
ChatCompletionRequestWithRawContent,
|
|
||||||
CompletionRequestWithRawContent,
|
|
||||||
)
|
|
||||||
|
|
||||||
from .parallel_utils import ModelParallelProcessGroup
|
from .parallel_utils import ModelParallelProcessGroup
|
||||||
|
|
||||||
|
|
@ -23,12 +18,14 @@ class ModelRunner:
|
||||||
def __init__(self, llama):
|
def __init__(self, llama):
|
||||||
self.llama = llama
|
self.llama = llama
|
||||||
|
|
||||||
# the `task` object is the same that is sent to `ModelParallelProcessGroup.run_inference()`
|
|
||||||
def __call__(self, task: Any):
|
def __call__(self, task: Any):
|
||||||
if task[0] == "chat_completion":
|
task_type = task[0]
|
||||||
return self.llama.chat_completion(task[1])
|
if task_type == "chat_completion":
|
||||||
|
# task[1] is [params, raw_messages]
|
||||||
|
params, raw_messages = task[1]
|
||||||
|
return self.llama.chat_completion(params, raw_messages)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f"Unexpected task type {task[0]}")
|
raise ValueError(f"Unexpected task type {task_type}")
|
||||||
|
|
||||||
|
|
||||||
def init_model_cb(
|
def init_model_cb(
|
||||||
|
|
@ -78,19 +75,3 @@ class LlamaModelParallelGenerator:
|
||||||
|
|
||||||
def __exit__(self, exc_type, exc_value, exc_traceback):
|
def __exit__(self, exc_type, exc_value, exc_traceback):
|
||||||
self.group.stop()
|
self.group.stop()
|
||||||
|
|
||||||
def completion(
|
|
||||||
self,
|
|
||||||
request_batch: list[CompletionRequestWithRawContent],
|
|
||||||
) -> Generator:
|
|
||||||
req_obj = deepcopy(request_batch)
|
|
||||||
gen = self.group.run_inference(("completion", req_obj))
|
|
||||||
yield from gen
|
|
||||||
|
|
||||||
def chat_completion(
|
|
||||||
self,
|
|
||||||
request_batch: list[ChatCompletionRequestWithRawContent],
|
|
||||||
) -> Generator:
|
|
||||||
req_obj = deepcopy(request_batch)
|
|
||||||
gen = self.group.run_inference(("chat_completion", req_obj))
|
|
||||||
yield from gen
|
|
||||||
|
|
|
||||||
|
|
@ -33,10 +33,6 @@ from torch.distributed.launcher.api import LaunchConfig, elastic_launch
|
||||||
|
|
||||||
from llama_stack.log import get_logger
|
from llama_stack.log import get_logger
|
||||||
from llama_stack.models.llama.datatypes import GenerationResult
|
from llama_stack.models.llama.datatypes import GenerationResult
|
||||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
|
||||||
ChatCompletionRequestWithRawContent,
|
|
||||||
CompletionRequestWithRawContent,
|
|
||||||
)
|
|
||||||
|
|
||||||
log = get_logger(name=__name__, category="inference")
|
log = get_logger(name=__name__, category="inference")
|
||||||
|
|
||||||
|
|
@ -69,10 +65,7 @@ class CancelSentinel(BaseModel):
|
||||||
|
|
||||||
class TaskRequest(BaseModel):
|
class TaskRequest(BaseModel):
|
||||||
type: Literal[ProcessingMessageName.task_request] = ProcessingMessageName.task_request
|
type: Literal[ProcessingMessageName.task_request] = ProcessingMessageName.task_request
|
||||||
task: tuple[
|
task: tuple[str, list]
|
||||||
str,
|
|
||||||
list[CompletionRequestWithRawContent] | list[ChatCompletionRequestWithRawContent],
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
class TaskResponse(BaseModel):
|
class TaskResponse(BaseModel):
|
||||||
|
|
@ -328,10 +321,7 @@ class ModelParallelProcessGroup:
|
||||||
|
|
||||||
def run_inference(
|
def run_inference(
|
||||||
self,
|
self,
|
||||||
req: tuple[
|
req: tuple[str, list],
|
||||||
str,
|
|
||||||
list[CompletionRequestWithRawContent] | list[ChatCompletionRequestWithRawContent],
|
|
||||||
],
|
|
||||||
) -> Generator:
|
) -> Generator:
|
||||||
assert not self.running, "inference already running"
|
assert not self.running, "inference already running"
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -22,9 +22,6 @@ from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
|
||||||
from llama_stack.providers.utils.inference.embedding_mixin import (
|
from llama_stack.providers.utils.inference.embedding_mixin import (
|
||||||
SentenceTransformerEmbeddingMixin,
|
SentenceTransformerEmbeddingMixin,
|
||||||
)
|
)
|
||||||
from llama_stack.providers.utils.inference.openai_compat import (
|
|
||||||
OpenAIChatCompletionToLlamaStackMixin,
|
|
||||||
)
|
|
||||||
|
|
||||||
from .config import SentenceTransformersInferenceConfig
|
from .config import SentenceTransformersInferenceConfig
|
||||||
|
|
||||||
|
|
@ -32,7 +29,6 @@ log = get_logger(name=__name__, category="inference")
|
||||||
|
|
||||||
|
|
||||||
class SentenceTransformersInferenceImpl(
|
class SentenceTransformersInferenceImpl(
|
||||||
OpenAIChatCompletionToLlamaStackMixin,
|
|
||||||
SentenceTransformerEmbeddingMixin,
|
SentenceTransformerEmbeddingMixin,
|
||||||
InferenceProvider,
|
InferenceProvider,
|
||||||
ModelsProtocolPrivate,
|
ModelsProtocolPrivate,
|
||||||
|
|
|
||||||
|
|
@ -91,7 +91,7 @@ class TorchtuneCheckpointer:
|
||||||
if checkpoint_format == "meta" or checkpoint_format is None:
|
if checkpoint_format == "meta" or checkpoint_format is None:
|
||||||
self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
|
self._save_meta_format_checkpoint(model_file_path, state_dict, adapter_only)
|
||||||
elif checkpoint_format == "huggingface":
|
elif checkpoint_format == "huggingface":
|
||||||
# Note: for saving hugging face format checkpoints, we only suppport saving adapter weights now
|
# Note: for saving hugging face format checkpoints, we only support saving adapter weights now
|
||||||
self._save_hf_format_checkpoint(model_file_path, state_dict)
|
self._save_hf_format_checkpoint(model_file_path, state_dict)
|
||||||
else:
|
else:
|
||||||
raise ValueError(f"Unsupported checkpoint format: {format}")
|
raise ValueError(f"Unsupported checkpoint format: {format}")
|
||||||
|
|
|
||||||
|
|
@ -25,7 +25,7 @@ def llama_stack_instruct_to_torchtune_instruct(
|
||||||
)
|
)
|
||||||
input_messages = json.loads(sample[ColumnName.chat_completion_input.value])
|
input_messages = json.loads(sample[ColumnName.chat_completion_input.value])
|
||||||
|
|
||||||
assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message"
|
assert len(input_messages) == 1, "llama stack instruct dataset format only supports 1 user message"
|
||||||
input_message = input_messages[0]
|
input_message = input_messages[0]
|
||||||
|
|
||||||
assert "content" in input_message, "content not found in input message"
|
assert "content" in input_message, "content not found in input message"
|
||||||
|
|
|
||||||
|
|
@ -223,7 +223,8 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
|
||||||
return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
|
return HealthResponse(status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}")
|
||||||
|
|
||||||
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before registering vector stores.")
|
||||||
|
|
||||||
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
||||||
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
||||||
|
|
@ -239,7 +240,8 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
|
||||||
return [i.vector_store for i in self.cache.values()]
|
return [i.vector_store for i in self.cache.values()]
|
||||||
|
|
||||||
async def unregister_vector_store(self, vector_store_id: str) -> None:
|
async def unregister_vector_store(self, vector_store_id: str) -> None:
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before unregistering vector stores.")
|
||||||
|
|
||||||
if vector_store_id not in self.cache:
|
if vector_store_id not in self.cache:
|
||||||
return
|
return
|
||||||
|
|
@ -248,6 +250,27 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoco
|
||||||
del self.cache[vector_store_id]
|
del self.cache[vector_store_id]
|
||||||
await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
||||||
|
|
||||||
|
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:
|
||||||
|
if vector_store_id in self.cache:
|
||||||
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
|
|
||||||
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
|
index = VectorStoreWithIndex(
|
||||||
|
vector_store=vector_store,
|
||||||
|
index=await FaissIndex.create(vector_store.embedding_dimension, self.kvstore, vector_store.identifier),
|
||||||
|
inference_api=self.inference_api,
|
||||||
|
)
|
||||||
|
self.cache[vector_store_id] = index
|
||||||
|
return index
|
||||||
|
|
||||||
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||||
index = self.cache.get(vector_store_id)
|
index = self.cache.get(vector_store_id)
|
||||||
if index is None:
|
if index is None:
|
||||||
|
|
|
||||||
|
|
@ -412,6 +412,14 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
|
||||||
return [v.vector_store for v in self.cache.values()]
|
return [v.vector_store for v in self.cache.values()]
|
||||||
|
|
||||||
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
||||||
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before registering vector stores.")
|
||||||
|
|
||||||
|
# Save to kvstore for persistence
|
||||||
|
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
||||||
|
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
||||||
|
|
||||||
|
# Create and cache the index
|
||||||
index = await SQLiteVecIndex.create(
|
index = await SQLiteVecIndex.create(
|
||||||
vector_store.embedding_dimension, self.config.db_path, vector_store.identifier
|
vector_store.embedding_dimension, self.config.db_path, vector_store.identifier
|
||||||
)
|
)
|
||||||
|
|
@ -421,13 +429,16 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresPro
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
if self.vector_store_table is None:
|
# Try to load from kvstore
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
vector_store = self.vector_store_table.get_vector_store(vector_store_id)
|
|
||||||
if not vector_store:
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
index = VectorStoreWithIndex(
|
index = VectorStoreWithIndex(
|
||||||
vector_store=vector_store,
|
vector_store=vector_store,
|
||||||
index=SQLiteVecIndex(
|
index=SQLiteVecIndex(
|
||||||
|
|
|
||||||
|
|
@ -296,6 +296,20 @@ Available Models:
|
||||||
Azure OpenAI inference provider for accessing GPT models and other Azure services.
|
Azure OpenAI inference provider for accessing GPT models and other Azure services.
|
||||||
Provider documentation
|
Provider documentation
|
||||||
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
|
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
|
||||||
|
""",
|
||||||
|
),
|
||||||
|
RemoteProviderSpec(
|
||||||
|
api=Api.inference,
|
||||||
|
provider_type="remote::oci",
|
||||||
|
adapter_type="oci",
|
||||||
|
pip_packages=["oci"],
|
||||||
|
module="llama_stack.providers.remote.inference.oci",
|
||||||
|
config_class="llama_stack.providers.remote.inference.oci.config.OCIConfig",
|
||||||
|
provider_data_validator="llama_stack.providers.remote.inference.oci.config.OCIProviderDataValidator",
|
||||||
|
description="""
|
||||||
|
Oracle Cloud Infrastructure (OCI) Generative AI inference provider for accessing OCI's Generative AI Platform-as-a-Service models.
|
||||||
|
Provider documentation
|
||||||
|
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
|
||||||
""",
|
""",
|
||||||
),
|
),
|
||||||
]
|
]
|
||||||
|
|
|
||||||
|
|
@ -20,6 +20,7 @@ This provider enables dataset management using NVIDIA's NeMo Customizer service.
|
||||||
Build the NVIDIA environment:
|
Build the NVIDIA environment:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
|
uv pip install llama-stack-client
|
||||||
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -18,6 +18,7 @@ This provider enables running inference using NVIDIA NIM.
|
||||||
Build the NVIDIA environment:
|
Build the NVIDIA environment:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
|
uv pip install llama-stack-client
|
||||||
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
||||||
17
src/llama_stack/providers/remote/inference/oci/__init__.py
Normal file
17
src/llama_stack/providers/remote/inference/oci/__init__.py
Normal file
|
|
@ -0,0 +1,17 @@
|
||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
|
# the root directory of this source tree.
|
||||||
|
|
||||||
|
from llama_stack.apis.inference import InferenceProvider
|
||||||
|
|
||||||
|
from .config import OCIConfig
|
||||||
|
|
||||||
|
|
||||||
|
async def get_adapter_impl(config: OCIConfig, _deps) -> InferenceProvider:
|
||||||
|
from .oci import OCIInferenceAdapter
|
||||||
|
|
||||||
|
adapter = OCIInferenceAdapter(config=config)
|
||||||
|
await adapter.initialize()
|
||||||
|
return adapter
|
||||||
79
src/llama_stack/providers/remote/inference/oci/auth.py
Normal file
79
src/llama_stack/providers/remote/inference/oci/auth.py
Normal file
|
|
@ -0,0 +1,79 @@
|
||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
|
# the root directory of this source tree.
|
||||||
|
|
||||||
|
from collections.abc import Generator, Mapping
|
||||||
|
from typing import Any, override
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
import oci
|
||||||
|
import requests
|
||||||
|
from oci.config import DEFAULT_LOCATION, DEFAULT_PROFILE
|
||||||
|
|
||||||
|
OciAuthSigner = type[oci.signer.AbstractBaseSigner]
|
||||||
|
|
||||||
|
|
||||||
|
class HttpxOciAuth(httpx.Auth):
|
||||||
|
"""
|
||||||
|
Custom HTTPX authentication class that implements OCI request signing.
|
||||||
|
|
||||||
|
This class handles the authentication flow for HTTPX requests by signing them
|
||||||
|
using the OCI Signer, which adds the necessary authentication headers for
|
||||||
|
OCI API calls.
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
signer (oci.signer.Signer): The OCI signer instance used for request signing
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, signer: OciAuthSigner):
|
||||||
|
self.signer = signer
|
||||||
|
|
||||||
|
@override
|
||||||
|
def auth_flow(self, request: httpx.Request) -> Generator[httpx.Request, httpx.Response, None]:
|
||||||
|
# Read the request content to handle streaming requests properly
|
||||||
|
try:
|
||||||
|
content = request.content
|
||||||
|
except httpx.RequestNotRead:
|
||||||
|
# For streaming requests, we need to read the content first
|
||||||
|
content = request.read()
|
||||||
|
|
||||||
|
req = requests.Request(
|
||||||
|
method=request.method,
|
||||||
|
url=str(request.url),
|
||||||
|
headers=dict(request.headers),
|
||||||
|
data=content,
|
||||||
|
)
|
||||||
|
prepared_request = req.prepare()
|
||||||
|
|
||||||
|
# Sign the request using the OCI Signer
|
||||||
|
self.signer.do_request_sign(prepared_request) # type: ignore
|
||||||
|
|
||||||
|
# Update the original HTTPX request with the signed headers
|
||||||
|
request.headers.update(prepared_request.headers)
|
||||||
|
|
||||||
|
yield request
|
||||||
|
|
||||||
|
|
||||||
|
class OciInstancePrincipalAuth(HttpxOciAuth):
|
||||||
|
def __init__(self, **kwargs: Mapping[str, Any]):
|
||||||
|
self.signer = oci.auth.signers.InstancePrincipalsSecurityTokenSigner(**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
class OciUserPrincipalAuth(HttpxOciAuth):
|
||||||
|
def __init__(self, config_file: str = DEFAULT_LOCATION, profile_name: str = DEFAULT_PROFILE):
|
||||||
|
config = oci.config.from_file(config_file, profile_name)
|
||||||
|
oci.config.validate_config(config) # type: ignore
|
||||||
|
key_content = ""
|
||||||
|
with open(config["key_file"]) as f:
|
||||||
|
key_content = f.read()
|
||||||
|
|
||||||
|
self.signer = oci.signer.Signer(
|
||||||
|
tenancy=config["tenancy"],
|
||||||
|
user=config["user"],
|
||||||
|
fingerprint=config["fingerprint"],
|
||||||
|
private_key_file_location=config.get("key_file"),
|
||||||
|
pass_phrase="none", # type: ignore
|
||||||
|
private_key_content=key_content,
|
||||||
|
)
|
||||||
75
src/llama_stack/providers/remote/inference/oci/config.py
Normal file
75
src/llama_stack/providers/remote/inference/oci/config.py
Normal file
|
|
@ -0,0 +1,75 @@
|
||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
|
# the root directory of this source tree.
|
||||||
|
|
||||||
|
import os
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
from pydantic import BaseModel, Field
|
||||||
|
|
||||||
|
from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
|
||||||
|
from llama_stack.schema_utils import json_schema_type
|
||||||
|
|
||||||
|
|
||||||
|
class OCIProviderDataValidator(BaseModel):
|
||||||
|
oci_auth_type: str = Field(
|
||||||
|
description="OCI authentication type (must be one of: instance_principal, config_file)",
|
||||||
|
)
|
||||||
|
oci_region: str = Field(
|
||||||
|
description="OCI region (e.g., us-ashburn-1)",
|
||||||
|
)
|
||||||
|
oci_compartment_id: str = Field(
|
||||||
|
description="OCI compartment ID for the Generative AI service",
|
||||||
|
)
|
||||||
|
oci_config_file_path: str | None = Field(
|
||||||
|
default="~/.oci/config",
|
||||||
|
description="OCI config file path (required if oci_auth_type is config_file)",
|
||||||
|
)
|
||||||
|
oci_config_profile: str | None = Field(
|
||||||
|
default="DEFAULT",
|
||||||
|
description="OCI config profile (required if oci_auth_type is config_file)",
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@json_schema_type
|
||||||
|
class OCIConfig(RemoteInferenceProviderConfig):
|
||||||
|
oci_auth_type: str = Field(
|
||||||
|
description="OCI authentication type (must be one of: instance_principal, config_file)",
|
||||||
|
default_factory=lambda: os.getenv("OCI_AUTH_TYPE", "instance_principal"),
|
||||||
|
)
|
||||||
|
oci_region: str = Field(
|
||||||
|
default_factory=lambda: os.getenv("OCI_REGION", "us-ashburn-1"),
|
||||||
|
description="OCI region (e.g., us-ashburn-1)",
|
||||||
|
)
|
||||||
|
oci_compartment_id: str = Field(
|
||||||
|
default_factory=lambda: os.getenv("OCI_COMPARTMENT_OCID", ""),
|
||||||
|
description="OCI compartment ID for the Generative AI service",
|
||||||
|
)
|
||||||
|
oci_config_file_path: str = Field(
|
||||||
|
default_factory=lambda: os.getenv("OCI_CONFIG_FILE_PATH", "~/.oci/config"),
|
||||||
|
description="OCI config file path (required if oci_auth_type is config_file)",
|
||||||
|
)
|
||||||
|
oci_config_profile: str = Field(
|
||||||
|
default_factory=lambda: os.getenv("OCI_CLI_PROFILE", "DEFAULT"),
|
||||||
|
description="OCI config profile (required if oci_auth_type is config_file)",
|
||||||
|
)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def sample_run_config(
|
||||||
|
cls,
|
||||||
|
oci_auth_type: str = "${env.OCI_AUTH_TYPE:=instance_principal}",
|
||||||
|
oci_config_file_path: str = "${env.OCI_CONFIG_FILE_PATH:=~/.oci/config}",
|
||||||
|
oci_config_profile: str = "${env.OCI_CLI_PROFILE:=DEFAULT}",
|
||||||
|
oci_region: str = "${env.OCI_REGION:=us-ashburn-1}",
|
||||||
|
oci_compartment_id: str = "${env.OCI_COMPARTMENT_OCID:=}",
|
||||||
|
**kwargs,
|
||||||
|
) -> dict[str, Any]:
|
||||||
|
return {
|
||||||
|
"oci_auth_type": oci_auth_type,
|
||||||
|
"oci_config_file_path": oci_config_file_path,
|
||||||
|
"oci_config_profile": oci_config_profile,
|
||||||
|
"oci_region": oci_region,
|
||||||
|
"oci_compartment_id": oci_compartment_id,
|
||||||
|
}
|
||||||
140
src/llama_stack/providers/remote/inference/oci/oci.py
Normal file
140
src/llama_stack/providers/remote/inference/oci/oci.py
Normal file
|
|
@ -0,0 +1,140 @@
|
||||||
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||||
|
# All rights reserved.
|
||||||
|
#
|
||||||
|
# This source code is licensed under the terms described in the LICENSE file in
|
||||||
|
# the root directory of this source tree.
|
||||||
|
|
||||||
|
|
||||||
|
from collections.abc import Iterable
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
import httpx
|
||||||
|
import oci
|
||||||
|
from oci.generative_ai.generative_ai_client import GenerativeAiClient
|
||||||
|
from oci.generative_ai.models import ModelCollection
|
||||||
|
from openai._base_client import DefaultAsyncHttpxClient
|
||||||
|
|
||||||
|
from llama_stack.apis.inference.inference import (
|
||||||
|
OpenAIEmbeddingsRequestWithExtraBody,
|
||||||
|
OpenAIEmbeddingsResponse,
|
||||||
|
)
|
||||||
|
from llama_stack.apis.models import ModelType
|
||||||
|
from llama_stack.log import get_logger
|
||||||
|
from llama_stack.providers.remote.inference.oci.auth import OciInstancePrincipalAuth, OciUserPrincipalAuth
|
||||||
|
from llama_stack.providers.remote.inference.oci.config import OCIConfig
|
||||||
|
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
||||||
|
|
||||||
|
logger = get_logger(name=__name__, category="inference::oci")
|
||||||
|
|
||||||
|
OCI_AUTH_TYPE_INSTANCE_PRINCIPAL = "instance_principal"
|
||||||
|
OCI_AUTH_TYPE_CONFIG_FILE = "config_file"
|
||||||
|
VALID_OCI_AUTH_TYPES = [OCI_AUTH_TYPE_INSTANCE_PRINCIPAL, OCI_AUTH_TYPE_CONFIG_FILE]
|
||||||
|
DEFAULT_OCI_REGION = "us-ashburn-1"
|
||||||
|
|
||||||
|
MODEL_CAPABILITIES = ["TEXT_GENERATION", "TEXT_SUMMARIZATION", "TEXT_EMBEDDINGS", "CHAT"]
|
||||||
|
|
||||||
|
|
||||||
|
class OCIInferenceAdapter(OpenAIMixin):
|
||||||
|
config: OCIConfig
|
||||||
|
|
||||||
|
async def initialize(self) -> None:
|
||||||
|
"""Initialize and validate OCI configuration."""
|
||||||
|
if self.config.oci_auth_type not in VALID_OCI_AUTH_TYPES:
|
||||||
|
raise ValueError(
|
||||||
|
f"Invalid OCI authentication type: {self.config.oci_auth_type}."
|
||||||
|
f"Valid types are one of: {VALID_OCI_AUTH_TYPES}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if not self.config.oci_compartment_id:
|
||||||
|
raise ValueError("OCI_COMPARTMENT_OCID is a required parameter. Either set in env variable or config.")
|
||||||
|
|
||||||
|
def get_base_url(self) -> str:
|
||||||
|
region = self.config.oci_region or DEFAULT_OCI_REGION
|
||||||
|
return f"https://inference.generativeai.{region}.oci.oraclecloud.com/20231130/actions/v1"
|
||||||
|
|
||||||
|
def get_api_key(self) -> str | None:
|
||||||
|
# OCI doesn't use API keys, it uses request signing
|
||||||
|
return "<NOTUSED>"
|
||||||
|
|
||||||
|
def get_extra_client_params(self) -> dict[str, Any]:
|
||||||
|
"""
|
||||||
|
Get extra parameters for the AsyncOpenAI client, including OCI-specific auth and headers.
|
||||||
|
"""
|
||||||
|
auth = self._get_auth()
|
||||||
|
compartment_id = self.config.oci_compartment_id or ""
|
||||||
|
|
||||||
|
return {
|
||||||
|
"http_client": DefaultAsyncHttpxClient(
|
||||||
|
auth=auth,
|
||||||
|
headers={
|
||||||
|
"CompartmentId": compartment_id,
|
||||||
|
},
|
||||||
|
),
|
||||||
|
}
|
||||||
|
|
||||||
|
def _get_oci_signer(self) -> oci.signer.AbstractBaseSigner | None:
|
||||||
|
if self.config.oci_auth_type == OCI_AUTH_TYPE_INSTANCE_PRINCIPAL:
|
||||||
|
return oci.auth.signers.InstancePrincipalsSecurityTokenSigner()
|
||||||
|
return None
|
||||||
|
|
||||||
|
def _get_oci_config(self) -> dict:
|
||||||
|
if self.config.oci_auth_type == OCI_AUTH_TYPE_INSTANCE_PRINCIPAL:
|
||||||
|
config = {"region": self.config.oci_region}
|
||||||
|
elif self.config.oci_auth_type == OCI_AUTH_TYPE_CONFIG_FILE:
|
||||||
|
config = oci.config.from_file(self.config.oci_config_file_path, self.config.oci_config_profile)
|
||||||
|
if not config.get("region"):
|
||||||
|
raise ValueError(
|
||||||
|
"Region not specified in config. Please specify in config or with OCI_REGION env variable."
|
||||||
|
)
|
||||||
|
|
||||||
|
return config
|
||||||
|
|
||||||
|
def _get_auth(self) -> httpx.Auth:
|
||||||
|
if self.config.oci_auth_type == OCI_AUTH_TYPE_INSTANCE_PRINCIPAL:
|
||||||
|
return OciInstancePrincipalAuth()
|
||||||
|
elif self.config.oci_auth_type == OCI_AUTH_TYPE_CONFIG_FILE:
|
||||||
|
return OciUserPrincipalAuth(
|
||||||
|
config_file=self.config.oci_config_file_path, profile_name=self.config.oci_config_profile
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Invalid OCI authentication type: {self.config.oci_auth_type}")
|
||||||
|
|
||||||
|
async def list_provider_model_ids(self) -> Iterable[str]:
|
||||||
|
"""
|
||||||
|
List available models from OCI Generative AI service.
|
||||||
|
"""
|
||||||
|
oci_config = self._get_oci_config()
|
||||||
|
oci_signer = self._get_oci_signer()
|
||||||
|
compartment_id = self.config.oci_compartment_id or ""
|
||||||
|
|
||||||
|
if oci_signer is None:
|
||||||
|
client = GenerativeAiClient(config=oci_config)
|
||||||
|
else:
|
||||||
|
client = GenerativeAiClient(config=oci_config, signer=oci_signer)
|
||||||
|
|
||||||
|
models: ModelCollection = client.list_models(
|
||||||
|
compartment_id=compartment_id, capability=MODEL_CAPABILITIES, lifecycle_state="ACTIVE"
|
||||||
|
).data
|
||||||
|
|
||||||
|
seen_models = set()
|
||||||
|
model_ids = []
|
||||||
|
for model in models.items:
|
||||||
|
if model.time_deprecated or model.time_on_demand_retired:
|
||||||
|
continue
|
||||||
|
|
||||||
|
if "CHAT" not in model.capabilities or "FINE_TUNE" in model.capabilities:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# Use display_name + model_type as the key to avoid conflicts
|
||||||
|
model_key = (model.display_name, ModelType.llm)
|
||||||
|
if model_key in seen_models:
|
||||||
|
continue
|
||||||
|
|
||||||
|
seen_models.add(model_key)
|
||||||
|
model_ids.append(model.display_name)
|
||||||
|
|
||||||
|
return model_ids
|
||||||
|
|
||||||
|
async def openai_embeddings(self, params: OpenAIEmbeddingsRequestWithExtraBody) -> OpenAIEmbeddingsResponse:
|
||||||
|
# The constructed url is a mask that hits OCI's "chat" action, which is not supported for embeddings.
|
||||||
|
raise NotImplementedError("OCI Provider does not (currently) support embeddings")
|
||||||
|
|
@ -22,6 +22,7 @@ This provider enables fine-tuning of LLMs using NVIDIA's NeMo Customizer service
|
||||||
Build the NVIDIA environment:
|
Build the NVIDIA environment:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
|
uv pip install llama-stack-client
|
||||||
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -19,6 +19,7 @@ This provider enables safety checks and guardrails for LLM interactions using NV
|
||||||
Build the NVIDIA environment:
|
Build the NVIDIA environment:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
|
uv pip install llama-stack-client
|
||||||
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
uv run llama stack list-deps nvidia | xargs -L1 uv pip install
|
||||||
```
|
```
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -131,7 +131,6 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
|
||||||
|
|
||||||
async def initialize(self) -> None:
|
async def initialize(self) -> None:
|
||||||
self.kvstore = await kvstore_impl(self.config.persistence)
|
self.kvstore = await kvstore_impl(self.config.persistence)
|
||||||
self.vector_store_table = self.kvstore
|
|
||||||
|
|
||||||
if isinstance(self.config, RemoteChromaVectorIOConfig):
|
if isinstance(self.config, RemoteChromaVectorIOConfig):
|
||||||
log.info(f"Connecting to Chroma server at: {self.config.url}")
|
log.info(f"Connecting to Chroma server at: {self.config.url}")
|
||||||
|
|
@ -190,9 +189,16 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
|
# Try to load from kvstore
|
||||||
if not vector_store:
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
|
|
||||||
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise ValueError(f"Vector DB {vector_store_id} not found in Llama Stack")
|
raise ValueError(f"Vector DB {vector_store_id} not found in Llama Stack")
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
collection = await maybe_await(self.client.get_collection(vector_store_id))
|
collection = await maybe_await(self.client.get_collection(vector_store_id))
|
||||||
if not collection:
|
if not collection:
|
||||||
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
|
raise ValueError(f"Vector DB {vector_store_id} not found in Chroma")
|
||||||
|
|
|
||||||
|
|
@ -328,13 +328,16 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
if self.vector_store_table is None:
|
# Try to load from kvstore
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
|
|
||||||
if not vector_store:
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
index = VectorStoreWithIndex(
|
index = VectorStoreWithIndex(
|
||||||
vector_store=vector_store,
|
vector_store=vector_store,
|
||||||
index=MilvusIndex(client=self.client, collection_name=vector_store.identifier, kvstore=self.kvstore),
|
index=MilvusIndex(client=self.client, collection_name=vector_store.identifier, kvstore=self.kvstore),
|
||||||
|
|
|
||||||
|
|
@ -368,6 +368,22 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
|
||||||
log.exception("Could not connect to PGVector database server")
|
log.exception("Could not connect to PGVector database server")
|
||||||
raise RuntimeError("Could not connect to PGVector database server") from e
|
raise RuntimeError("Could not connect to PGVector database server") from e
|
||||||
|
|
||||||
|
# Load existing vector stores from KV store into cache
|
||||||
|
start_key = VECTOR_DBS_PREFIX
|
||||||
|
end_key = f"{VECTOR_DBS_PREFIX}\xff"
|
||||||
|
stored_vector_stores = await self.kvstore.values_in_range(start_key, end_key)
|
||||||
|
for vector_store_data in stored_vector_stores:
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
|
pgvector_index = PGVectorIndex(
|
||||||
|
vector_store=vector_store,
|
||||||
|
dimension=vector_store.embedding_dimension,
|
||||||
|
conn=self.conn,
|
||||||
|
kvstore=self.kvstore,
|
||||||
|
)
|
||||||
|
await pgvector_index.initialize()
|
||||||
|
index = VectorStoreWithIndex(vector_store, index=pgvector_index, inference_api=self.inference_api)
|
||||||
|
self.cache[vector_store.identifier] = index
|
||||||
|
|
||||||
async def shutdown(self) -> None:
|
async def shutdown(self) -> None:
|
||||||
if self.conn is not None:
|
if self.conn is not None:
|
||||||
self.conn.close()
|
self.conn.close()
|
||||||
|
|
@ -377,7 +393,13 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
|
||||||
|
|
||||||
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
||||||
# Persist vector DB metadata in the KV store
|
# Persist vector DB metadata in the KV store
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before registering vector stores.")
|
||||||
|
|
||||||
|
# Save to kvstore for persistence
|
||||||
|
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
||||||
|
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
||||||
|
|
||||||
# Upsert model metadata in Postgres
|
# Upsert model metadata in Postgres
|
||||||
upsert_models(self.conn, [(vector_store.identifier, vector_store)])
|
upsert_models(self.conn, [(vector_store.identifier, vector_store)])
|
||||||
|
|
||||||
|
|
@ -396,7 +418,8 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
|
||||||
del self.cache[vector_store_id]
|
del self.cache[vector_store_id]
|
||||||
|
|
||||||
# Delete vector DB metadata from KV store
|
# Delete vector DB metadata from KV store
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before unregistering vector stores.")
|
||||||
await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
||||||
|
|
||||||
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
async def insert_chunks(self, vector_store_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
|
||||||
|
|
@ -413,13 +436,16 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProt
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
if self.vector_store_table is None:
|
# Try to load from kvstore
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
|
|
||||||
if not vector_store:
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
index = PGVectorIndex(vector_store, vector_store.embedding_dimension, self.conn)
|
index = PGVectorIndex(vector_store, vector_store.embedding_dimension, self.conn)
|
||||||
await index.initialize()
|
await index.initialize()
|
||||||
self.cache[vector_store_id] = VectorStoreWithIndex(vector_store, index, self.inference_api)
|
self.cache[vector_store_id] = VectorStoreWithIndex(vector_store, index, self.inference_api)
|
||||||
|
|
|
||||||
|
|
@ -183,7 +183,8 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
|
||||||
await super().shutdown()
|
await super().shutdown()
|
||||||
|
|
||||||
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
async def register_vector_store(self, vector_store: VectorStore) -> None:
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before registering vector stores.")
|
||||||
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
key = f"{VECTOR_DBS_PREFIX}{vector_store.identifier}"
|
||||||
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
await self.kvstore.set(key=key, value=vector_store.model_dump_json())
|
||||||
|
|
||||||
|
|
@ -200,20 +201,24 @@ class QdrantVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
|
||||||
await self.cache[vector_store_id].index.delete()
|
await self.cache[vector_store_id].index.delete()
|
||||||
del self.cache[vector_store_id]
|
del self.cache[vector_store_id]
|
||||||
|
|
||||||
assert self.kvstore is not None
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
await self.kvstore.delete(f"{VECTOR_DBS_PREFIX}{vector_store_id}")
|
||||||
|
|
||||||
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:
|
async def _get_and_cache_vector_store_index(self, vector_store_id: str) -> VectorStoreWithIndex | None:
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
if self.vector_store_table is None:
|
# Try to load from kvstore
|
||||||
raise ValueError(f"Vector DB not found {vector_store_id}")
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
|
|
||||||
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
if not vector_store:
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
index = VectorStoreWithIndex(
|
index = VectorStoreWithIndex(
|
||||||
vector_store=vector_store,
|
vector_store=vector_store,
|
||||||
index=QdrantIndex(client=self.client, collection_name=vector_store.identifier),
|
index=QdrantIndex(client=self.client, collection_name=vector_store.identifier),
|
||||||
|
|
|
||||||
|
|
@ -346,13 +346,16 @@ class WeaviateVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, NeedsRequestProv
|
||||||
if vector_store_id in self.cache:
|
if vector_store_id in self.cache:
|
||||||
return self.cache[vector_store_id]
|
return self.cache[vector_store_id]
|
||||||
|
|
||||||
if self.vector_store_table is None:
|
# Try to load from kvstore
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
if self.kvstore is None:
|
||||||
|
raise RuntimeError("KVStore not initialized. Call initialize() before using vector stores.")
|
||||||
vector_store = await self.vector_store_table.get_vector_store(vector_store_id)
|
|
||||||
if not vector_store:
|
key = f"{VECTOR_DBS_PREFIX}{vector_store_id}"
|
||||||
|
vector_store_data = await self.kvstore.get(key)
|
||||||
|
if not vector_store_data:
|
||||||
raise VectorStoreNotFoundError(vector_store_id)
|
raise VectorStoreNotFoundError(vector_store_id)
|
||||||
|
|
||||||
|
vector_store = VectorStore.model_validate_json(vector_store_data)
|
||||||
client = self._get_client()
|
client = self._get_client()
|
||||||
sanitized_collection_name = sanitize_collection_name(vector_store.identifier, weaviate_format=True)
|
sanitized_collection_name = sanitize_collection_name(vector_store.identifier, weaviate_format=True)
|
||||||
if not client.collections.exists(sanitized_collection_name):
|
if not client.collections.exists(sanitized_collection_name):
|
||||||
|
|
|
||||||
|
|
@ -11,9 +11,7 @@ from collections.abc import AsyncIterator
|
||||||
import litellm
|
import litellm
|
||||||
|
|
||||||
from llama_stack.apis.inference import (
|
from llama_stack.apis.inference import (
|
||||||
ChatCompletionRequest,
|
|
||||||
InferenceProvider,
|
InferenceProvider,
|
||||||
JsonSchemaResponseFormat,
|
|
||||||
OpenAIChatCompletion,
|
OpenAIChatCompletion,
|
||||||
OpenAIChatCompletionChunk,
|
OpenAIChatCompletionChunk,
|
||||||
OpenAIChatCompletionRequestWithExtraBody,
|
OpenAIChatCompletionRequestWithExtraBody,
|
||||||
|
|
@ -23,15 +21,11 @@ from llama_stack.apis.inference import (
|
||||||
OpenAIEmbeddingsRequestWithExtraBody,
|
OpenAIEmbeddingsRequestWithExtraBody,
|
||||||
OpenAIEmbeddingsResponse,
|
OpenAIEmbeddingsResponse,
|
||||||
OpenAIEmbeddingUsage,
|
OpenAIEmbeddingUsage,
|
||||||
ToolChoice,
|
|
||||||
)
|
)
|
||||||
from llama_stack.core.request_headers import NeedsRequestProviderData
|
from llama_stack.core.request_headers import NeedsRequestProviderData
|
||||||
from llama_stack.log import get_logger
|
from llama_stack.log import get_logger
|
||||||
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry
|
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry
|
||||||
from llama_stack.providers.utils.inference.openai_compat import (
|
from llama_stack.providers.utils.inference.openai_compat import (
|
||||||
convert_message_to_openai_dict_new,
|
|
||||||
convert_tooldef_to_openai_tool,
|
|
||||||
get_sampling_options,
|
|
||||||
prepare_openai_completion_params,
|
prepare_openai_completion_params,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
@ -127,51 +121,6 @@ class LiteLLMOpenAIMixin(
|
||||||
|
|
||||||
return schema
|
return schema
|
||||||
|
|
||||||
async def _get_params(self, request: ChatCompletionRequest) -> dict:
|
|
||||||
from typing import Any
|
|
||||||
|
|
||||||
input_dict: dict[str, Any] = {}
|
|
||||||
|
|
||||||
input_dict["messages"] = [
|
|
||||||
await convert_message_to_openai_dict_new(m, download_images=self.download_images) for m in request.messages
|
|
||||||
]
|
|
||||||
if fmt := request.response_format:
|
|
||||||
if not isinstance(fmt, JsonSchemaResponseFormat):
|
|
||||||
raise ValueError(
|
|
||||||
f"Unsupported response format: {type(fmt)}. Only JsonSchemaResponseFormat is supported."
|
|
||||||
)
|
|
||||||
|
|
||||||
# Convert to dict for manipulation
|
|
||||||
fmt_dict = dict(fmt.json_schema)
|
|
||||||
name = fmt_dict["title"]
|
|
||||||
del fmt_dict["title"]
|
|
||||||
fmt_dict["additionalProperties"] = False
|
|
||||||
|
|
||||||
# Apply additionalProperties: False recursively to all objects
|
|
||||||
fmt_dict = self._add_additional_properties_recursive(fmt_dict)
|
|
||||||
|
|
||||||
input_dict["response_format"] = {
|
|
||||||
"type": "json_schema",
|
|
||||||
"json_schema": {
|
|
||||||
"name": name,
|
|
||||||
"schema": fmt_dict,
|
|
||||||
"strict": self.json_schema_strict,
|
|
||||||
},
|
|
||||||
}
|
|
||||||
if request.tools:
|
|
||||||
input_dict["tools"] = [convert_tooldef_to_openai_tool(tool) for tool in request.tools]
|
|
||||||
if request.tool_config and (tool_choice := request.tool_config.tool_choice):
|
|
||||||
input_dict["tool_choice"] = tool_choice.value if isinstance(tool_choice, ToolChoice) else tool_choice
|
|
||||||
|
|
||||||
return {
|
|
||||||
"model": request.model,
|
|
||||||
"api_key": self.get_api_key(),
|
|
||||||
"api_base": self.api_base,
|
|
||||||
**input_dict,
|
|
||||||
"stream": request.stream,
|
|
||||||
**get_sampling_options(request.sampling_params),
|
|
||||||
}
|
|
||||||
|
|
||||||
def get_api_key(self) -> str:
|
def get_api_key(self) -> str:
|
||||||
provider_data = self.get_request_provider_data()
|
provider_data = self.get_request_provider_data()
|
||||||
key_field = self.provider_data_api_key_field
|
key_field = self.provider_data_api_key_field
|
||||||
|
|
|
||||||
File diff suppressed because it is too large
Load diff
|
|
@ -21,19 +21,18 @@ from llama_stack.apis.common.content_types import (
|
||||||
TextContentItem,
|
TextContentItem,
|
||||||
)
|
)
|
||||||
from llama_stack.apis.inference import (
|
from llama_stack.apis.inference import (
|
||||||
ChatCompletionRequest,
|
|
||||||
CompletionRequest,
|
CompletionRequest,
|
||||||
Message,
|
OpenAIAssistantMessageParam,
|
||||||
OpenAIChatCompletionContentPartImageParam,
|
OpenAIChatCompletionContentPartImageParam,
|
||||||
OpenAIChatCompletionContentPartTextParam,
|
OpenAIChatCompletionContentPartTextParam,
|
||||||
OpenAIFile,
|
OpenAIFile,
|
||||||
|
OpenAIMessageParam,
|
||||||
|
OpenAISystemMessageParam,
|
||||||
|
OpenAIToolMessageParam,
|
||||||
|
OpenAIUserMessageParam,
|
||||||
ResponseFormat,
|
ResponseFormat,
|
||||||
ResponseFormatType,
|
ResponseFormatType,
|
||||||
SystemMessage,
|
|
||||||
SystemMessageBehavior,
|
|
||||||
ToolChoice,
|
ToolChoice,
|
||||||
ToolDefinition,
|
|
||||||
UserMessage,
|
|
||||||
)
|
)
|
||||||
from llama_stack.log import get_logger
|
from llama_stack.log import get_logger
|
||||||
from llama_stack.models.llama.datatypes import (
|
from llama_stack.models.llama.datatypes import (
|
||||||
|
|
@ -42,33 +41,19 @@ from llama_stack.models.llama.datatypes import (
|
||||||
RawMediaItem,
|
RawMediaItem,
|
||||||
RawMessage,
|
RawMessage,
|
||||||
RawTextItem,
|
RawTextItem,
|
||||||
Role,
|
|
||||||
StopReason,
|
StopReason,
|
||||||
|
ToolCall,
|
||||||
|
ToolDefinition,
|
||||||
ToolPromptFormat,
|
ToolPromptFormat,
|
||||||
)
|
)
|
||||||
from llama_stack.models.llama.llama3.chat_format import ChatFormat
|
from llama_stack.models.llama.llama3.chat_format import ChatFormat
|
||||||
from llama_stack.models.llama.llama3.prompt_templates import (
|
|
||||||
BuiltinToolGenerator,
|
|
||||||
FunctionTagCustomToolGenerator,
|
|
||||||
JsonCustomToolGenerator,
|
|
||||||
PythonListCustomToolGenerator,
|
|
||||||
SystemDefaultGenerator,
|
|
||||||
)
|
|
||||||
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
|
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
|
||||||
from llama_stack.models.llama.llama4.prompt_templates.system_prompts import (
|
|
||||||
PythonListCustomToolGenerator as PythonListCustomToolGeneratorLlama4,
|
|
||||||
)
|
|
||||||
from llama_stack.models.llama.sku_list import resolve_model
|
from llama_stack.models.llama.sku_list import resolve_model
|
||||||
from llama_stack.models.llama.sku_types import ModelFamily, is_multimodal
|
from llama_stack.models.llama.sku_types import ModelFamily, is_multimodal
|
||||||
from llama_stack.providers.utils.inference import supported_inference_models
|
|
||||||
|
|
||||||
log = get_logger(name=__name__, category="providers::utils")
|
log = get_logger(name=__name__, category="providers::utils")
|
||||||
|
|
||||||
|
|
||||||
class ChatCompletionRequestWithRawContent(ChatCompletionRequest):
|
|
||||||
messages: list[RawMessage]
|
|
||||||
|
|
||||||
|
|
||||||
class CompletionRequestWithRawContent(CompletionRequest):
|
class CompletionRequestWithRawContent(CompletionRequest):
|
||||||
content: RawContent
|
content: RawContent
|
||||||
|
|
||||||
|
|
@ -103,28 +88,6 @@ def interleaved_content_as_str(
|
||||||
return _process(content)
|
return _process(content)
|
||||||
|
|
||||||
|
|
||||||
async def convert_request_to_raw(
|
|
||||||
request: ChatCompletionRequest | CompletionRequest,
|
|
||||||
) -> ChatCompletionRequestWithRawContent | CompletionRequestWithRawContent:
|
|
||||||
if isinstance(request, ChatCompletionRequest):
|
|
||||||
messages = []
|
|
||||||
for m in request.messages:
|
|
||||||
content = await interleaved_content_convert_to_raw(m.content)
|
|
||||||
d = m.model_dump()
|
|
||||||
d["content"] = content
|
|
||||||
messages.append(RawMessage(**d))
|
|
||||||
|
|
||||||
d = request.model_dump()
|
|
||||||
d["messages"] = messages
|
|
||||||
request = ChatCompletionRequestWithRawContent(**d)
|
|
||||||
else:
|
|
||||||
d = request.model_dump()
|
|
||||||
d["content"] = await interleaved_content_convert_to_raw(request.content)
|
|
||||||
request = CompletionRequestWithRawContent(**d)
|
|
||||||
|
|
||||||
return request
|
|
||||||
|
|
||||||
|
|
||||||
async def interleaved_content_convert_to_raw(
|
async def interleaved_content_convert_to_raw(
|
||||||
content: InterleavedContent,
|
content: InterleavedContent,
|
||||||
) -> RawContent:
|
) -> RawContent:
|
||||||
|
|
@ -171,6 +134,36 @@ async def interleaved_content_convert_to_raw(
|
||||||
return await _localize_single(content)
|
return await _localize_single(content)
|
||||||
|
|
||||||
|
|
||||||
|
async def convert_openai_message_to_raw_message(message: OpenAIMessageParam) -> RawMessage:
|
||||||
|
"""Convert OpenAI message format to RawMessage format used by Llama formatters."""
|
||||||
|
if isinstance(message, OpenAIUserMessageParam):
|
||||||
|
content = await interleaved_content_convert_to_raw(message.content) # type: ignore[arg-type]
|
||||||
|
return RawMessage(role="user", content=content)
|
||||||
|
elif isinstance(message, OpenAISystemMessageParam):
|
||||||
|
content = await interleaved_content_convert_to_raw(message.content) # type: ignore[arg-type]
|
||||||
|
return RawMessage(role="system", content=content)
|
||||||
|
elif isinstance(message, OpenAIAssistantMessageParam):
|
||||||
|
content = await interleaved_content_convert_to_raw(message.content or "") # type: ignore[arg-type]
|
||||||
|
tool_calls = []
|
||||||
|
if message.tool_calls:
|
||||||
|
for tc in message.tool_calls:
|
||||||
|
if tc.function:
|
||||||
|
tool_calls.append(
|
||||||
|
ToolCall(
|
||||||
|
call_id=tc.id or "",
|
||||||
|
tool_name=tc.function.name or "",
|
||||||
|
arguments=tc.function.arguments or "{}",
|
||||||
|
)
|
||||||
|
)
|
||||||
|
return RawMessage(role="assistant", content=content, tool_calls=tool_calls)
|
||||||
|
elif isinstance(message, OpenAIToolMessageParam):
|
||||||
|
content = await interleaved_content_convert_to_raw(message.content) # type: ignore[arg-type]
|
||||||
|
return RawMessage(role="tool", content=content)
|
||||||
|
else:
|
||||||
|
# Handle OpenAIDeveloperMessageParam if needed
|
||||||
|
raise ValueError(f"Unsupported message type: {type(message)}")
|
||||||
|
|
||||||
|
|
||||||
def content_has_media(content: InterleavedContent):
|
def content_has_media(content: InterleavedContent):
|
||||||
def _has_media_content(c):
|
def _has_media_content(c):
|
||||||
return isinstance(c, ImageContentItem)
|
return isinstance(c, ImageContentItem)
|
||||||
|
|
@ -181,17 +174,6 @@ def content_has_media(content: InterleavedContent):
|
||||||
return _has_media_content(content)
|
return _has_media_content(content)
|
||||||
|
|
||||||
|
|
||||||
def messages_have_media(messages: list[Message]):
|
|
||||||
return any(content_has_media(m.content) for m in messages)
|
|
||||||
|
|
||||||
|
|
||||||
def request_has_media(request: ChatCompletionRequest | CompletionRequest):
|
|
||||||
if isinstance(request, ChatCompletionRequest):
|
|
||||||
return messages_have_media(request.messages)
|
|
||||||
else:
|
|
||||||
return content_has_media(request.content)
|
|
||||||
|
|
||||||
|
|
||||||
async def localize_image_content(uri: str) -> tuple[bytes, str] | None:
|
async def localize_image_content(uri: str) -> tuple[bytes, str] | None:
|
||||||
if uri.startswith("http"):
|
if uri.startswith("http"):
|
||||||
async with httpx.AsyncClient() as client:
|
async with httpx.AsyncClient() as client:
|
||||||
|
|
@ -253,79 +235,6 @@ def augment_content_with_response_format_prompt(response_format, content):
|
||||||
return content
|
return content
|
||||||
|
|
||||||
|
|
||||||
async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llama_model: str) -> str:
|
|
||||||
messages = chat_completion_request_to_messages(request, llama_model)
|
|
||||||
request.messages = messages
|
|
||||||
request = await convert_request_to_raw(request)
|
|
||||||
|
|
||||||
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
|
|
||||||
model_input = formatter.encode_dialog_prompt(
|
|
||||||
request.messages,
|
|
||||||
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
|
|
||||||
)
|
|
||||||
return formatter.tokenizer.decode(model_input.tokens)
|
|
||||||
|
|
||||||
|
|
||||||
async def chat_completion_request_to_model_input_info(
|
|
||||||
request: ChatCompletionRequest, llama_model: str
|
|
||||||
) -> tuple[str, int]:
|
|
||||||
messages = chat_completion_request_to_messages(request, llama_model)
|
|
||||||
request.messages = messages
|
|
||||||
request = await convert_request_to_raw(request)
|
|
||||||
|
|
||||||
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
|
|
||||||
model_input = formatter.encode_dialog_prompt(
|
|
||||||
request.messages,
|
|
||||||
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
|
|
||||||
)
|
|
||||||
return (
|
|
||||||
formatter.tokenizer.decode(model_input.tokens),
|
|
||||||
len(model_input.tokens),
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def chat_completion_request_to_messages(
|
|
||||||
request: ChatCompletionRequest,
|
|
||||||
llama_model: str,
|
|
||||||
) -> list[Message]:
|
|
||||||
"""Reads chat completion request and augments the messages to handle tools.
|
|
||||||
For eg. for llama_3_1, add system message with the appropriate tools or
|
|
||||||
add user messsage for custom tools, etc.
|
|
||||||
"""
|
|
||||||
assert llama_model is not None, "llama_model is required"
|
|
||||||
model = resolve_model(llama_model)
|
|
||||||
if model is None:
|
|
||||||
log.error(f"Could not resolve model {llama_model}")
|
|
||||||
return request.messages
|
|
||||||
|
|
||||||
allowed_models = supported_inference_models()
|
|
||||||
descriptors = [m.descriptor() for m in allowed_models]
|
|
||||||
if model.descriptor() not in descriptors:
|
|
||||||
log.error(f"Unsupported inference model? {model.descriptor()}")
|
|
||||||
return request.messages
|
|
||||||
|
|
||||||
if model.model_family == ModelFamily.llama3_1 or (
|
|
||||||
model.model_family == ModelFamily.llama3_2 and is_multimodal(model.core_model_id)
|
|
||||||
):
|
|
||||||
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
|
|
||||||
messages = augment_messages_for_tools_llama_3_1(request)
|
|
||||||
elif model.model_family in (
|
|
||||||
ModelFamily.llama3_2,
|
|
||||||
ModelFamily.llama3_3,
|
|
||||||
):
|
|
||||||
# llama3.2, llama3.3 follow the same tool prompt format
|
|
||||||
messages = augment_messages_for_tools_llama(request, PythonListCustomToolGenerator)
|
|
||||||
elif model.model_family == ModelFamily.llama4:
|
|
||||||
messages = augment_messages_for_tools_llama(request, PythonListCustomToolGeneratorLlama4)
|
|
||||||
else:
|
|
||||||
messages = request.messages
|
|
||||||
|
|
||||||
if fmt_prompt := response_format_prompt(request.response_format):
|
|
||||||
messages.append(UserMessage(content=fmt_prompt))
|
|
||||||
|
|
||||||
return messages
|
|
||||||
|
|
||||||
|
|
||||||
def response_format_prompt(fmt: ResponseFormat | None):
|
def response_format_prompt(fmt: ResponseFormat | None):
|
||||||
if not fmt:
|
if not fmt:
|
||||||
return None
|
return None
|
||||||
|
|
@ -338,128 +247,6 @@ def response_format_prompt(fmt: ResponseFormat | None):
|
||||||
raise ValueError(f"Unknown response format {fmt.type}")
|
raise ValueError(f"Unknown response format {fmt.type}")
|
||||||
|
|
||||||
|
|
||||||
def augment_messages_for_tools_llama_3_1(
|
|
||||||
request: ChatCompletionRequest,
|
|
||||||
) -> list[Message]:
|
|
||||||
existing_messages = request.messages
|
|
||||||
existing_system_message = None
|
|
||||||
if existing_messages[0].role == Role.system.value:
|
|
||||||
existing_system_message = existing_messages.pop(0)
|
|
||||||
|
|
||||||
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
|
|
||||||
|
|
||||||
messages = []
|
|
||||||
|
|
||||||
default_gen = SystemDefaultGenerator()
|
|
||||||
default_template = default_gen.gen()
|
|
||||||
|
|
||||||
sys_content = ""
|
|
||||||
|
|
||||||
tool_template = None
|
|
||||||
if request.tools:
|
|
||||||
tool_gen = BuiltinToolGenerator()
|
|
||||||
tool_template = tool_gen.gen(request.tools)
|
|
||||||
|
|
||||||
sys_content += tool_template.render()
|
|
||||||
sys_content += "\n"
|
|
||||||
|
|
||||||
sys_content += default_template.render()
|
|
||||||
|
|
||||||
if existing_system_message:
|
|
||||||
# TODO: this fn is needed in many places
|
|
||||||
def _process(c):
|
|
||||||
if isinstance(c, str):
|
|
||||||
return c
|
|
||||||
else:
|
|
||||||
return "<media>"
|
|
||||||
|
|
||||||
sys_content += "\n"
|
|
||||||
|
|
||||||
if isinstance(existing_system_message.content, str):
|
|
||||||
sys_content += _process(existing_system_message.content)
|
|
||||||
elif isinstance(existing_system_message.content, list):
|
|
||||||
sys_content += "\n".join([_process(c) for c in existing_system_message.content])
|
|
||||||
|
|
||||||
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
|
|
||||||
if tool_choice_prompt:
|
|
||||||
sys_content += "\n" + tool_choice_prompt
|
|
||||||
|
|
||||||
messages.append(SystemMessage(content=sys_content))
|
|
||||||
|
|
||||||
has_custom_tools = request.tools is not None and any(isinstance(dfn.tool_name, str) for dfn in request.tools)
|
|
||||||
if has_custom_tools:
|
|
||||||
fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.json
|
|
||||||
if fmt == ToolPromptFormat.json:
|
|
||||||
tool_gen = JsonCustomToolGenerator()
|
|
||||||
elif fmt == ToolPromptFormat.function_tag:
|
|
||||||
tool_gen = FunctionTagCustomToolGenerator()
|
|
||||||
else:
|
|
||||||
raise ValueError(f"Non supported ToolPromptFormat {fmt}")
|
|
||||||
|
|
||||||
custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
|
|
||||||
custom_template = tool_gen.gen(custom_tools)
|
|
||||||
messages.append(UserMessage(content=custom_template.render()))
|
|
||||||
|
|
||||||
# Add back existing messages from the request
|
|
||||||
messages += existing_messages
|
|
||||||
|
|
||||||
return messages
|
|
||||||
|
|
||||||
|
|
||||||
def augment_messages_for_tools_llama(
|
|
||||||
request: ChatCompletionRequest,
|
|
||||||
custom_tool_prompt_generator,
|
|
||||||
) -> list[Message]:
|
|
||||||
existing_messages = request.messages
|
|
||||||
existing_system_message = None
|
|
||||||
if existing_messages[0].role == Role.system.value:
|
|
||||||
existing_system_message = existing_messages.pop(0)
|
|
||||||
|
|
||||||
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
|
|
||||||
|
|
||||||
sys_content = ""
|
|
||||||
custom_tools, builtin_tools = [], []
|
|
||||||
for t in request.tools:
|
|
||||||
if isinstance(t.tool_name, str):
|
|
||||||
custom_tools.append(t)
|
|
||||||
else:
|
|
||||||
builtin_tools.append(t)
|
|
||||||
|
|
||||||
if builtin_tools:
|
|
||||||
tool_gen = BuiltinToolGenerator()
|
|
||||||
tool_template = tool_gen.gen(builtin_tools)
|
|
||||||
|
|
||||||
sys_content += tool_template.render()
|
|
||||||
sys_content += "\n"
|
|
||||||
|
|
||||||
custom_tools = [dfn for dfn in request.tools if isinstance(dfn.tool_name, str)]
|
|
||||||
if custom_tools:
|
|
||||||
fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.python_list
|
|
||||||
if fmt != ToolPromptFormat.python_list:
|
|
||||||
raise ValueError(f"Non supported ToolPromptFormat {request.tool_config.tool_prompt_format}")
|
|
||||||
|
|
||||||
system_prompt = None
|
|
||||||
if existing_system_message and request.tool_config.system_message_behavior == SystemMessageBehavior.replace:
|
|
||||||
system_prompt = existing_system_message.content
|
|
||||||
|
|
||||||
tool_template = custom_tool_prompt_generator().gen(custom_tools, system_prompt)
|
|
||||||
|
|
||||||
sys_content += tool_template.render()
|
|
||||||
sys_content += "\n"
|
|
||||||
|
|
||||||
if existing_system_message and (
|
|
||||||
request.tool_config.system_message_behavior == SystemMessageBehavior.append or not custom_tools
|
|
||||||
):
|
|
||||||
sys_content += interleaved_content_as_str(existing_system_message.content, sep="\n")
|
|
||||||
|
|
||||||
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
|
|
||||||
if tool_choice_prompt:
|
|
||||||
sys_content += "\n" + tool_choice_prompt
|
|
||||||
|
|
||||||
messages = [SystemMessage(content=sys_content.strip("\n")), *existing_messages]
|
|
||||||
return messages
|
|
||||||
|
|
||||||
|
|
||||||
def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: list[ToolDefinition]) -> str:
|
def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: list[ToolDefinition]) -> str:
|
||||||
if tool_choice == ToolChoice.auto:
|
if tool_choice == ToolChoice.auto:
|
||||||
return ""
|
return ""
|
||||||
|
|
|
||||||
20
src/llama_stack_ui/.dockerignore
Normal file
20
src/llama_stack_ui/.dockerignore
Normal file
|
|
@ -0,0 +1,20 @@
|
||||||
|
.git
|
||||||
|
.gitignore
|
||||||
|
.env.local
|
||||||
|
.env.*.local
|
||||||
|
.next
|
||||||
|
node_modules
|
||||||
|
npm-debug.log
|
||||||
|
*.md
|
||||||
|
.DS_Store
|
||||||
|
.vscode
|
||||||
|
.idea
|
||||||
|
playwright-report
|
||||||
|
e2e
|
||||||
|
jest.config.ts
|
||||||
|
jest.setup.ts
|
||||||
|
eslint.config.mjs
|
||||||
|
.prettierrc
|
||||||
|
.prettierignore
|
||||||
|
.nvmrc
|
||||||
|
playwright.config.ts
|
||||||
18
src/llama_stack_ui/Containerfile
Normal file
18
src/llama_stack_ui/Containerfile
Normal file
|
|
@ -0,0 +1,18 @@
|
||||||
|
FROM node:22.5.1-alpine
|
||||||
|
|
||||||
|
ENV NODE_ENV=production
|
||||||
|
|
||||||
|
# Install dumb-init for proper signal handling
|
||||||
|
RUN apk add --no-cache dumb-init
|
||||||
|
|
||||||
|
# Create non-root user for security
|
||||||
|
RUN addgroup --system --gid 1001 nodejs
|
||||||
|
RUN adduser --system --uid 1001 nextjs
|
||||||
|
|
||||||
|
# Install llama-stack-ui from npm
|
||||||
|
RUN npm install -g llama-stack-ui
|
||||||
|
|
||||||
|
USER nextjs
|
||||||
|
|
||||||
|
ENTRYPOINT ["dumb-init", "--"]
|
||||||
|
CMD ["llama-stack-ui"]
|
||||||
34
src/llama_stack_ui/bin/cli.js
Executable file
34
src/llama_stack_ui/bin/cli.js
Executable file
|
|
@ -0,0 +1,34 @@
|
||||||
|
#!/usr/bin/env node
|
||||||
|
|
||||||
|
const { spawn } = require('child_process');
|
||||||
|
const path = require('path');
|
||||||
|
|
||||||
|
const port = process.env.LLAMA_STACK_UI_PORT || 8322;
|
||||||
|
const uiDir = path.resolve(__dirname, '..');
|
||||||
|
const serverPath = path.join(uiDir, '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', 'server.js');
|
||||||
|
const serverDir = path.dirname(serverPath);
|
||||||
|
|
||||||
|
console.log(`Starting Llama Stack UI on http://localhost:${port}`);
|
||||||
|
|
||||||
|
const child = spawn(process.execPath, [serverPath], {
|
||||||
|
cwd: serverDir,
|
||||||
|
stdio: 'inherit',
|
||||||
|
env: {
|
||||||
|
...process.env,
|
||||||
|
PORT: port,
|
||||||
|
},
|
||||||
|
});
|
||||||
|
|
||||||
|
process.on('SIGINT', () => {
|
||||||
|
child.kill('SIGINT');
|
||||||
|
process.exit(0);
|
||||||
|
});
|
||||||
|
|
||||||
|
process.on('SIGTERM', () => {
|
||||||
|
child.kill('SIGTERM');
|
||||||
|
process.exit(0);
|
||||||
|
});
|
||||||
|
|
||||||
|
child.on('exit', (code) => {
|
||||||
|
process.exit(code);
|
||||||
|
});
|
||||||
|
|
@ -1,7 +1,13 @@
|
||||||
import type { NextConfig } from "next";
|
import type { NextConfig } from "next";
|
||||||
|
|
||||||
const nextConfig: NextConfig = {
|
const nextConfig: NextConfig = {
|
||||||
/* config options here */
|
typescript: {
|
||||||
|
ignoreBuildErrors: true,
|
||||||
|
},
|
||||||
|
output: "standalone",
|
||||||
|
images: {
|
||||||
|
unoptimized: true,
|
||||||
|
},
|
||||||
};
|
};
|
||||||
|
|
||||||
export default nextConfig;
|
export default nextConfig;
|
||||||
|
|
|
||||||
16
src/llama_stack_ui/package-lock.json
generated
16
src/llama_stack_ui/package-lock.json
generated
|
|
@ -1,12 +1,13 @@
|
||||||
{
|
{
|
||||||
"name": "ui",
|
"name": "llama-stack-ui",
|
||||||
"version": "0.1.0",
|
"version": "0.4.0-alpha.1",
|
||||||
"lockfileVersion": 3,
|
"lockfileVersion": 3,
|
||||||
"requires": true,
|
"requires": true,
|
||||||
"packages": {
|
"packages": {
|
||||||
"": {
|
"": {
|
||||||
"name": "ui",
|
"name": "llama-stack-ui",
|
||||||
"version": "0.1.0",
|
"version": "0.4.0-alpha.1",
|
||||||
|
"license": "MIT",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"@radix-ui/react-collapsible": "^1.1.12",
|
"@radix-ui/react-collapsible": "^1.1.12",
|
||||||
"@radix-ui/react-dialog": "^1.1.15",
|
"@radix-ui/react-dialog": "^1.1.15",
|
||||||
|
|
@ -20,7 +21,7 @@
|
||||||
"class-variance-authority": "^0.7.1",
|
"class-variance-authority": "^0.7.1",
|
||||||
"clsx": "^2.1.1",
|
"clsx": "^2.1.1",
|
||||||
"framer-motion": "^12.23.24",
|
"framer-motion": "^12.23.24",
|
||||||
"llama-stack-client": "github:llamastack/llama-stack-client-typescript",
|
"llama-stack-client": "^0.3.1",
|
||||||
"lucide-react": "^0.545.0",
|
"lucide-react": "^0.545.0",
|
||||||
"next": "15.5.4",
|
"next": "15.5.4",
|
||||||
"next-auth": "^4.24.11",
|
"next-auth": "^4.24.11",
|
||||||
|
|
@ -9684,8 +9685,9 @@
|
||||||
"license": "MIT"
|
"license": "MIT"
|
||||||
},
|
},
|
||||||
"node_modules/llama-stack-client": {
|
"node_modules/llama-stack-client": {
|
||||||
"version": "0.4.0-alpha.1",
|
"version": "0.3.1",
|
||||||
"resolved": "git+ssh://git@github.com/llamastack/llama-stack-client-typescript.git#78de4862c4b7d77939ac210fa9f9bde77a2c5c5f",
|
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.3.1.tgz",
|
||||||
|
"integrity": "sha512-4aYoF2aAQiBSfxyZEtczeQmJn8q9T22ePDqGhR+ej5RG6a8wvl5B3v7ZoKuFkft+vcP/kbJ58GQZEPLekxekZA==",
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"@types/node": "^18.11.18",
|
"@types/node": "^18.11.18",
|
||||||
|
|
|
||||||
|
|
@ -1,11 +1,31 @@
|
||||||
{
|
{
|
||||||
"name": "ui",
|
"name": "llama-stack-ui",
|
||||||
"version": "0.1.0",
|
"version": "0.4.0-alpha.4",
|
||||||
"private": true,
|
"description": "Web UI for Llama Stack",
|
||||||
|
"license": "MIT",
|
||||||
|
"author": "Llama Stack <llamastack@meta.com>",
|
||||||
|
"repository": {
|
||||||
|
"type": "git",
|
||||||
|
"url": "https://github.com/llamastack/llama-stack.git",
|
||||||
|
"directory": "llama_stack_ui"
|
||||||
|
},
|
||||||
|
"bin": {
|
||||||
|
"llama-stack-ui": "bin/cli.js"
|
||||||
|
},
|
||||||
|
"files": [
|
||||||
|
"bin",
|
||||||
|
".next",
|
||||||
|
"public",
|
||||||
|
"next.config.ts",
|
||||||
|
"instrumentation.ts",
|
||||||
|
"tsconfig.json",
|
||||||
|
"package.json"
|
||||||
|
],
|
||||||
"scripts": {
|
"scripts": {
|
||||||
"dev": "next dev --turbopack --port ${LLAMA_STACK_UI_PORT:-8322}",
|
"dev": "next dev --turbopack --port ${LLAMA_STACK_UI_PORT:-8322}",
|
||||||
"build": "next build",
|
"build": "next build && node scripts/postbuild.js",
|
||||||
"start": "next start",
|
"start": "next start",
|
||||||
|
"prepublishOnly": "npm run build",
|
||||||
"lint": "next lint",
|
"lint": "next lint",
|
||||||
"format": "prettier --write \"./**/*.{ts,tsx}\"",
|
"format": "prettier --write \"./**/*.{ts,tsx}\"",
|
||||||
"format:check": "prettier --check \"./**/*.{ts,tsx}\"",
|
"format:check": "prettier --check \"./**/*.{ts,tsx}\"",
|
||||||
|
|
@ -25,7 +45,7 @@
|
||||||
"class-variance-authority": "^0.7.1",
|
"class-variance-authority": "^0.7.1",
|
||||||
"clsx": "^2.1.1",
|
"clsx": "^2.1.1",
|
||||||
"framer-motion": "^12.23.24",
|
"framer-motion": "^12.23.24",
|
||||||
"llama-stack-client": "github:llamastack/llama-stack-client-typescript",
|
"llama-stack-client": "^0.3.1",
|
||||||
"lucide-react": "^0.545.0",
|
"lucide-react": "^0.545.0",
|
||||||
"next": "15.5.4",
|
"next": "15.5.4",
|
||||||
"next-auth": "^4.24.11",
|
"next-auth": "^4.24.11",
|
||||||
|
|
|
||||||
40
src/llama_stack_ui/scripts/postbuild.js
Normal file
40
src/llama_stack_ui/scripts/postbuild.js
Normal file
|
|
@ -0,0 +1,40 @@
|
||||||
|
const fs = require('fs');
|
||||||
|
const path = require('path');
|
||||||
|
|
||||||
|
// Copy public directory to standalone
|
||||||
|
const publicSrc = path.join(__dirname, '..', 'public');
|
||||||
|
const publicDest = path.join(__dirname, '..', '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', 'public');
|
||||||
|
|
||||||
|
if (fs.existsSync(publicSrc) && !fs.existsSync(publicDest)) {
|
||||||
|
console.log('Copying public directory to standalone...');
|
||||||
|
copyDir(publicSrc, publicDest);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Copy .next/static to standalone
|
||||||
|
const staticSrc = path.join(__dirname, '..', '.next', 'static');
|
||||||
|
const staticDest = path.join(__dirname, '..', '.next', 'standalone', 'ui', 'src', 'llama_stack_ui', '.next', 'static');
|
||||||
|
|
||||||
|
if (fs.existsSync(staticSrc) && !fs.existsSync(staticDest)) {
|
||||||
|
console.log('Copying .next/static to standalone...');
|
||||||
|
copyDir(staticSrc, staticDest);
|
||||||
|
}
|
||||||
|
|
||||||
|
function copyDir(src, dest) {
|
||||||
|
if (!fs.existsSync(dest)) {
|
||||||
|
fs.mkdirSync(dest, { recursive: true });
|
||||||
|
}
|
||||||
|
|
||||||
|
const files = fs.readdirSync(src);
|
||||||
|
files.forEach((file) => {
|
||||||
|
const srcFile = path.join(src, file);
|
||||||
|
const destFile = path.join(dest, file);
|
||||||
|
|
||||||
|
if (fs.statSync(srcFile).isDirectory()) {
|
||||||
|
copyDir(srcFile, destFile);
|
||||||
|
} else {
|
||||||
|
fs.copyFileSync(srcFile, destFile);
|
||||||
|
}
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
console.log('Postbuild complete!');
|
||||||
5
tests/integration/agents/__init__.py
Normal file
5
tests/integration/agents/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
||||||
|
# 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.
|
||||||
|
|
@ -0,0 +1,59 @@
|
||||||
|
{
|
||||||
|
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@ -0,0 +1,59 @@
|
||||||
|
{
|
||||||
|
"test_id": "tests/integration/agents/test_openai_responses.py::test_output_safety_guardrails_unsafe_content[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-False]",
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"messages": [
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": "Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Violent content refers to media or material that depicts or describes acts of violence, aggression, or harm towards individuals\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST User message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories."
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}
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}
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},
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"is_streaming": false
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},
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"id_normalization_mapping": {}
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}
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