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Francisco Arceo 2025-08-21 21:42:26 -06:00 • committed by GitHub
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12
.github/dependabot.yml vendored
View file

@ -9,6 +9,7 @@ updates:
day: "saturday"
commit-message:
prefix: chore(github-deps)
- package-ecosystem: "uv"
directory: "/"
schedule:
@ -19,3 +20,14 @@ updates:
- python
commit-message:
prefix: chore(python-deps)
- package-ecosystem: npm
directory: "/llama_stack/ui"
schedule:
interval: "weekly"
day: "saturday"
labels:
- type/dependencies
- javascript
commit-message:
prefix: chore(ui-deps)

2
.github/workflows/changelog.yml vendored
View file

@ -17,7 +17,7 @@ jobs:
pull-requests: write # for peter-evans/create-pull-request to create a PR
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
ref: main
fetch-depth: 0

4
.github/workflows/install-script-ci.yml vendored
View file

@ -16,14 +16,14 @@ jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # 5.0.0
- name: Run ShellCheck on install.sh
run: shellcheck scripts/install.sh
smoke-test-on-dev:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

4
.github/workflows/integration-auth-tests.yml vendored
View file

@ -18,7 +18,7 @@ on:
- '.github/workflows/integration-auth-tests.yml' # This workflow
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -31,7 +31,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

4
.github/workflows/integration-sql-store-tests.yml vendored
View file

@ -16,7 +16,7 @@ on:
- '.github/workflows/integration-sql-store-tests.yml' # This workflow
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -44,7 +44,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

2
.github/workflows/integration-tests.yml vendored
View file

@ -65,7 +65,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup test environment
uses: ./.github/actions/setup-test-environment

2
.github/workflows/integration-vector-io-tests.yml vendored
View file

@ -33,7 +33,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

4
.github/workflows/pre-commit.yml vendored
View file

@ -8,7 +8,7 @@ on:
branches: [main]
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -20,7 +20,7 @@ jobs:
steps:
- name: Checkout code
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
# For dependabot PRs, we need to checkout with a token that can push changes
token: ${{ github.actor == 'dependabot[bot]' && secrets.GITHUB_TOKEN || github.token }}

20
.github/workflows/providers-build.yml vendored
View file

@ -26,7 +26,7 @@ on:
- 'pyproject.toml'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -36,7 +36,7 @@ jobs:
distros: ${{ steps.set-matrix.outputs.distros }}
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Generate Distribution List
id: set-matrix
@ -55,7 +55,7 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner
@ -79,7 +79,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner
@ -92,7 +92,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner
@ -106,6 +106,10 @@ jobs:
- name: Inspect the container image entrypoint
run: |
IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
if [ -z "$IMAGE_ID" ]; then
echo "No image found"
exit 1
fi
entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
echo "Entrypoint: $entrypoint"
if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then
@ -117,7 +121,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner
@ -140,6 +144,10 @@ jobs:
- name: Inspect UBI9 image
run: |
IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
if [ -z "$IMAGE_ID" ]; then
echo "No image found"
exit 1
fi
entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
echo "Entrypoint: $entrypoint"
if [ "$entrypoint" != "[python -m llama_stack.core.server.server /app/run.yaml]" ]; then

4
.github/workflows/python-build-test.yml vendored
View file

@ -21,10 +21,10 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install uv
uses: astral-sh/setup-uv@e92bafb6253dcd438e0484186d7669ea7a8ca1cc # v6.4.3
uses: astral-sh/setup-uv@d9e0f98d3fc6adb07d1e3d37f3043649ddad06a1 # v6.5.0
with:
python-version: ${{ matrix.python-version }}
activate-environment: true

2
.github/workflows/record-integration-tests.yml vendored
View file

@ -46,7 +46,7 @@ jobs:
echo "::endgroup::"
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
fetch-depth: 0

2
.github/workflows/semantic-pr.yml vendored
View file

@ -22,6 +22,6 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Check PR Title's semantic conformance
uses: amannn/action-semantic-pull-request@0723387faaf9b38adef4775cd42cfd5155ed6017 # v5.5.3
uses: amannn/action-semantic-pull-request@7f33ba792281b034f64e96f4c0b5496782dd3b37 # v6.1.0
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

2
.github/workflows/test-external-provider-module.yml vendored
View file

@ -27,7 +27,7 @@ jobs:
# container and point 'uv pip install' to the correct path...
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

2
.github/workflows/test-external.yml vendored
View file

@ -27,7 +27,7 @@ jobs:
# container and point 'uv pip install' to the correct path...
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

6
.github/workflows/ui-unit-tests.yml vendored
View file

@ -13,7 +13,7 @@ on:
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -26,10 +26,10 @@ jobs:
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup Node.js
uses: actions/setup-node@39370e3970a6d050c480ffad4ff0ed4d3fdee5af # v4.1.0
uses: actions/setup-node@49933ea5288caeca8642d1e84afbd3f7d6820020 # v4.4.0
with:
node-version: ${{ matrix.node-version }}
cache: 'npm'

4
.github/workflows/unit-tests.yml vendored
View file

@ -18,7 +18,7 @@ on:
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -32,7 +32,7 @@ jobs:
- "3.13"
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

4
.github/workflows/update-readthedocs.yml vendored
View file

@ -27,7 +27,7 @@ on:
- '.github/workflows/update-readthedocs.yml'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.ref }}
cancel-in-progress: true
jobs:
@ -37,7 +37,7 @@ jobs:
TOKEN: ${{ secrets.READTHEDOCS_TOKEN }}
steps:
- name: Checkout repository
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Install dependencies
uses: ./.github/actions/setup-runner

132
docs/_static/llama-stack-spec.html vendored
View file

@ -4605,6 +4605,49 @@
}
}
},
"/v1/inference/rerank": {
"post": {
"responses": {
"200": {
"description": "RerankResponse with indices sorted by relevance score (descending).",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/RerankResponse"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"Inference"
],
"description": "Rerank a list of documents based on their relevance to a query.",
"parameters": [],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/RerankRequest"
}
}
},
"required": true
}
}
},
"/v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume": {
"post": {
"responses": {
@ -16587,6 +16630,95 @@
],
"title": "RegisterVectorDbRequest"
},
"RerankRequest": {
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "The identifier of the reranking model to use."
},
"query": {
"oneOf": [
{
"type": "string"
},
{
"$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam"
},
{
"$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam"
}
],
"description": "The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length."
},
"items": {
"type": "array",
"items": {
"oneOf": [
{
"type": "string"
},
{
"$ref": "#/components/schemas/OpenAIChatCompletionContentPartTextParam"
},
{
"$ref": "#/components/schemas/OpenAIChatCompletionContentPartImageParam"
}
]
},
"description": "List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length."
},
"max_num_results": {
"type": "integer",
"description": "(Optional) Maximum number of results to return. Default: returns all."
}
},
"additionalProperties": false,
"required": [
"model",
"query",
"items"
],
"title": "RerankRequest"
},
"RerankData": {
"type": "object",
"properties": {
"index": {
"type": "integer",
"description": "The original index of the document in the input list"
},
"relevance_score": {
"type": "number",
"description": "The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance."
}
},
"additionalProperties": false,
"required": [
"index",
"relevance_score"
],
"title": "RerankData",
"description": "A single rerank result from a reranking response."
},
"RerankResponse": {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"$ref": "#/components/schemas/RerankData"
},
"description": "List of rerank result objects, sorted by relevance score (descending)"
}
},
"additionalProperties": false,
"required": [
"data"
],
"title": "RerankResponse",
"description": "Response from a reranking request."
},
"ResumeAgentTurnRequest": {
"type": "object",
"properties": {

101
docs/_static/llama-stack-spec.yaml vendored
View file

@ -3264,6 +3264,37 @@ paths:
schema:
$ref: '#/components/schemas/QueryTracesRequest'
required: true
/v1/inference/rerank:
post:
responses:
'200':
description: >-
RerankResponse with indices sorted by relevance score (descending).
content:
application/json:
schema:
$ref: '#/components/schemas/RerankResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- Inference
description: >-
Rerank a list of documents based on their relevance to a query.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/RerankRequest'
required: true
/v1/agents/{agent_id}/session/{session_id}/turn/{turn_id}/resume:
post:
responses:
@ -12337,6 +12368,76 @@ components:
- vector_db_id
- embedding_model
title: RegisterVectorDbRequest
RerankRequest:
type: object
properties:
model:
type: string
description: >-
The identifier of the reranking model to use.
query:
oneOf:
- type: string
- $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam'
- $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam'
description: >-
The search query to rank items against. Can be a string, text content
part, or image content part. The input must not exceed the model's max
input token length.
items:
type: array
items:
oneOf:
- type: string
- $ref: '#/components/schemas/OpenAIChatCompletionContentPartTextParam'
- $ref: '#/components/schemas/OpenAIChatCompletionContentPartImageParam'
description: >-
List of items to rerank. Each item can be a string, text content part,
or image content part. Each input must not exceed the model's max input
token length.
max_num_results:
type: integer
description: >-
(Optional) Maximum number of results to return. Default: returns all.
additionalProperties: false
required:
- model
- query
- items
title: RerankRequest
RerankData:
type: object
properties:
index:
type: integer
description: >-
The original index of the document in the input list
relevance_score:
type: number
description: >-
The relevance score from the model output. Values are inverted when applicable
so that higher scores indicate greater relevance.
additionalProperties: false
required:
- index
- relevance_score
title: RerankData
description: >-
A single rerank result from a reranking response.
RerankResponse:
type: object
properties:
data:
type: array
items:
$ref: '#/components/schemas/RerankData'
description: >-
List of rerank result objects, sorted by relevance score (descending)
additionalProperties: false
required:
- data
title: RerankResponse
description: Response from a reranking request.
ResumeAgentTurnRequest:
type: object
properties:

72
docs/source/distributions/configuration.md
View file

@ -225,8 +225,32 @@ server:
port: 8321 # Port to listen on (default: 8321)
tls_certfile: "/path/to/cert.pem" # Optional: Path to TLS certificate for HTTPS
tls_keyfile: "/path/to/key.pem" # Optional: Path to TLS key for HTTPS
cors: true # Optional: Enable CORS (dev mode) or full config object
```
### CORS Configuration
CORS (Cross-Origin Resource Sharing) can be configured in two ways:
**Local development** (allows localhost origins only):
```yaml
server:
cors: true
```
**Explicit configuration** (custom origins and settings):
```yaml
server:
cors:
allow_origins: ["https://myapp.com", "https://app.example.com"]
allow_methods: ["GET", "POST", "PUT", "DELETE"]
allow_headers: ["Content-Type", "Authorization"]
allow_credentials: true
max_age: 3600
```
When `cors: true`, the server enables secure localhost-only access for local development. For production, specify exact origins to maintain security.
### Authentication Configuration
> **Breaking Change (v0.2.14)**: The authentication configuration structure has changed. The previous format with `provider_type` and `config` fields has been replaced with a unified `provider_config` field that includes the `type` field. Update your configuration files accordingly.
@ -618,6 +642,54 @@ Content-Type: application/json
}
```
### CORS Configuration
Configure CORS to allow web browsers to make requests from different domains. Disabled by default.
#### Quick Setup
For development, use the simple boolean flag:
```yaml
server:
cors: true # Auto-enables localhost with any port
```
This automatically allows `http://localhost:*` and `https://localhost:*` with secure defaults.
#### Custom Configuration
For specific origins and full control:
```yaml
server:
cors:
allow_origins: ["https://myapp.com", "https://staging.myapp.com"]
allow_credentials: true
allow_methods: ["GET", "POST", "PUT", "DELETE"]
allow_headers: ["Content-Type", "Authorization"]
allow_origin_regex: "https://.*\\.example\\.com" # Optional regex pattern
expose_headers: ["X-Total-Count"]
max_age: 86400
```
#### Configuration Options
| Field | Description | Default |
| -------------------- | ---------------------------------------------- | ------- |
| `allow_origins` | List of allowed origins. Use `["*"]` for any. | `["*"]` |
| `allow_origin_regex` | Regex pattern for allowed origins (optional). | `None` |
| `allow_methods` | Allowed HTTP methods. | `["*"]` |
| `allow_headers` | Allowed headers. | `["*"]` |
| `allow_credentials` | Allow credentials (cookies, auth headers). | `false` |
| `expose_headers` | Headers exposed to browser. | `[]` |
| `max_age` | Preflight cache time (seconds). | `600` |
**Security Notes**:
- `allow_credentials: true` requires explicit origins (no wildcards)
- `cors: true` enables localhost access only (secure for development)
- For public APIs, always specify exact allowed origins
## Extending to handle Safety
Configuring Safety can be a little involved so it is instructive to go through an example.

2
docs/source/distributions/importing_as_library.md
View file

@ -17,7 +17,6 @@ client = LlamaStackAsLibraryClient(
# provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
provider_data={"tavily_search_api_key": os.environ["TAVILY_SEARCH_API_KEY"]},
)
client.initialize()
```
This will parse your config and set up any inline implementations and remote clients needed for your implementation.
@ -32,5 +31,4 @@ If you've created a [custom distribution](https://llama-stack.readthedocs.io/en/
```python
client = LlamaStackAsLibraryClient(config_path)
client.initialize()
```

40
llama_stack/apis/inference/inference.py
View file

@ -473,6 +473,28 @@ class EmbeddingsResponse(BaseModel):
embeddings: list[list[float]]
@json_schema_type
class RerankData(BaseModel):
"""A single rerank result from a reranking response.
:param index: The original index of the document in the input list
:param relevance_score: The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance.
"""
index: int
relevance_score: float
@json_schema_type
class RerankResponse(BaseModel):
"""Response from a reranking request.
:param data: List of rerank result objects, sorted by relevance score (descending)
"""
data: list[RerankData]
@json_schema_type
class OpenAIChatCompletionContentPartTextParam(BaseModel):
"""Text content part for OpenAI-compatible chat completion messages.
@ -1131,6 +1153,24 @@ class InferenceProvider(Protocol):
"""
...
@webmethod(route="/inference/rerank", method="POST", experimental=True)
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
"""Rerank a list of documents based on their relevance to a query.
:param model: The identifier of the reranking model to use.
:param query: The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.
:param items: List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.
:param max_num_results: (Optional) Maximum number of results to return. Default: returns all.
:returns: RerankResponse with indices sorted by relevance score (descending).
"""
raise NotImplementedError("Reranking is not implemented")
@webmethod(route="/openai/v1/completions", method="POST")
async def openai_completion(
self,

2
llama_stack/cli/stack/run.py
View file

@ -15,7 +15,7 @@ from llama_stack.log import get_logger
REPO_ROOT = Path(__file__).parent.parent.parent.parent
logger = get_logger(name=__name__, category="server")
logger = get_logger(name=__name__, category="cli")
class StackRun(Subcommand):

41
llama_stack/core/datatypes.py
View file

@ -318,6 +318,41 @@ class QuotaConfig(BaseModel):
period: QuotaPeriod = Field(default=QuotaPeriod.DAY, description="Quota period to set")
class CORSConfig(BaseModel):
allow_origins: list[str] = Field(default_factory=list)
allow_origin_regex: str | None = Field(default=None)
allow_methods: list[str] = Field(default=["OPTIONS"])
allow_headers: list[str] = Field(default_factory=list)
allow_credentials: bool = Field(default=False)
expose_headers: list[str] = Field(default_factory=list)
max_age: int = Field(default=600, ge=0)
@model_validator(mode="after")
def validate_credentials_config(self) -> Self:
if self.allow_credentials and (self.allow_origins == ["*"] or "*" in self.allow_origins):
raise ValueError("Cannot use wildcard origins with credentials enabled")
return self
def process_cors_config(cors_config: bool | CORSConfig | None) -> CORSConfig | None:
if cors_config is False or cors_config is None:
return None
if cors_config is True:
# dev mode: allow localhost on any port
return CORSConfig(
allow_origins=[],
allow_origin_regex=r"https?://localhost:\d+",
allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
allow_headers=["Content-Type", "Authorization", "X-Requested-With"],
)
if isinstance(cors_config, CORSConfig):
return cors_config
raise ValueError(f"Expected bool or CORSConfig, got {type(cors_config).__name__}")
class ServerConfig(BaseModel):
port: int = Field(
default=8321,
@ -349,6 +384,12 @@ class ServerConfig(BaseModel):
default=None,
description="Per client quota request configuration",
)
cors: bool | CORSConfig | None = Field(
default=None,
description="CORS configuration for cross-origin requests. Can be:\n"
"- true: Enable localhost CORS for development\n"
"- {allow_origins: [...], allow_methods: [...], ...}: Full configuration",
)
class StackRunConfig(BaseModel):

48
llama_stack/core/library_client.py
View file

@ -146,39 +146,26 @@ class LlamaStackAsLibraryClient(LlamaStackClient):
):
super().__init__()
self.async_client = AsyncLlamaStackAsLibraryClient(
config_path_or_distro_name, custom_provider_registry, provider_data
config_path_or_distro_name, custom_provider_registry, provider_data, skip_logger_removal
)
self.pool_executor = ThreadPoolExecutor(max_workers=4)
self.skip_logger_removal = skip_logger_removal
self.provider_data = provider_data
self.loop = asyncio.new_event_loop()
def initialize(self):
if in_notebook():
import nest_asyncio
nest_asyncio.apply()
if not self.skip_logger_removal:
self._remove_root_logger_handlers()
# use a new event loop to avoid interfering with the main event loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
return loop.run_until_complete(self.async_client.initialize())
loop.run_until_complete(self.async_client.initialize())
finally:
asyncio.set_event_loop(None)
def _remove_root_logger_handlers(self):
def initialize(self):
"""
Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
Deprecated method for backward compatibility.
"""
root_logger = logging.getLogger()
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
pass
def request(self, *args, **kwargs):
loop = self.loop
@ -216,6 +203,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
config_path_or_distro_name: str,
custom_provider_registry: ProviderRegistry | None = None,
provider_data: dict[str, Any] | None = None,
skip_logger_removal: bool = False,
):
super().__init__()
# when using the library client, we should not log to console since many
@ -223,6 +211,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
if in_notebook():
import nest_asyncio
nest_asyncio.apply()
if not skip_logger_removal:
self._remove_root_logger_handlers()
if config_path_or_distro_name.endswith(".yaml"):
config_path = Path(config_path_or_distro_name)
if not config_path.exists():
@ -239,7 +234,24 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
self.provider_data = provider_data
self.route_impls: RouteImpls | None = None # Initialize to None to prevent AttributeError
def _remove_root_logger_handlers(self):
"""
Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
"""
root_logger = logging.getLogger()
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
async def initialize(self) -> bool:
"""
Initialize the async client.
Returns:
bool: True if initialization was successful
"""
try:
self.route_impls = None
self.impls = await construct_stack(self.config, self.custom_provider_registry)

2
llama_stack/core/routers/datasets.py
View file

@ -12,7 +12,7 @@ from llama_stack.apis.datasets import DatasetPurpose, DataSource
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import RoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routers")
class DatasetIORouter(DatasetIO):

2
llama_stack/core/routers/eval_scoring.py
View file

@ -16,7 +16,7 @@ from llama_stack.apis.scoring import (
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import RoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routers")
class ScoringRouter(Scoring):

2
llama_stack/core/routers/inference.py
View file

@ -65,7 +65,7 @@ from llama_stack.providers.datatypes import HealthResponse, HealthStatus, Routin
from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.telemetry.tracing import get_current_span
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="core::routers")
class InferenceRouter(Inference):

2
llama_stack/core/routers/safety.py
View file

@ -13,7 +13,7 @@ from llama_stack.apis.shields import Shield
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import RoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routers")
class SafetyRouter(Safety):

2
llama_stack/core/routers/tool_runtime.py
View file

@ -22,7 +22,7 @@ from llama_stack.log import get_logger
from ..routing_tables.toolgroups import ToolGroupsRoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routers")
class ToolRuntimeRouter(ToolRuntime):

2
llama_stack/core/routers/vector_io.py
View file

@ -30,7 +30,7 @@ from llama_stack.apis.vector_io import (
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routers")
class VectorIORouter(VectorIO):

2
llama_stack/core/routing_tables/benchmarks.py
View file

@ -14,7 +14,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):

2
llama_stack/core/routing_tables/common.py
View file

@ -23,7 +23,7 @@ from llama_stack.core.store import DistributionRegistry
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api, RoutingTable
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
def get_impl_api(p: Any) -> Api:

2
llama_stack/core/routing_tables/datasets.py
View file

@ -26,7 +26,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):

2
llama_stack/core/routing_tables/models.py
View file

@ -17,7 +17,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl, lookup_model
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class ModelsRoutingTable(CommonRoutingTableImpl, Models):

2
llama_stack/core/routing_tables/scoring_functions.py
View file

@ -19,7 +19,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):

2
llama_stack/core/routing_tables/shields.py
View file

@ -15,7 +15,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):

2
llama_stack/core/routing_tables/toolgroups.py
View file

@ -14,7 +14,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
def parse_toolgroup_from_toolgroup_name_pair(toolgroup_name_with_maybe_tool_name: str) -> str | None:

2
llama_stack/core/routing_tables/vector_dbs.py
View file

@ -30,7 +30,7 @@ from llama_stack.log import get_logger
from .common import CommonRoutingTableImpl, lookup_model
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="core::routing_tables")
class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):

2
llama_stack/core/server/auth.py
View file

@ -15,7 +15,7 @@ from llama_stack.core.server.auth_providers import create_auth_provider
from llama_stack.core.server.routes import find_matching_route, initialize_route_impls
from llama_stack.log import get_logger
logger = get_logger(name=__name__, category="auth")
logger = get_logger(name=__name__, category="core::auth")
class AuthenticationMiddleware:

2
llama_stack/core/server/auth_providers.py
View file

@ -23,7 +23,7 @@ from llama_stack.core.datatypes import (
)
from llama_stack.log import get_logger
logger = get_logger(name=__name__, category="auth")
logger = get_logger(name=__name__, category="core::auth")
class AuthResponse(BaseModel):

2
llama_stack/core/server/quota.py
View file

@ -15,7 +15,7 @@ from llama_stack.providers.utils.kvstore.api import KVStore
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
logger = get_logger(name=__name__, category="quota")
logger = get_logger(name=__name__, category="core::server")
class QuotaMiddleware:

12
llama_stack/core/server/server.py
View file

@ -28,6 +28,7 @@ from aiohttp import hdrs
from fastapi import Body, FastAPI, HTTPException, Request, Response
from fastapi import Path as FastapiPath
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, StreamingResponse
from openai import BadRequestError
from pydantic import BaseModel, ValidationError
@ -40,6 +41,7 @@ from llama_stack.core.datatypes import (
AuthenticationRequiredError,
LoggingConfig,
StackRunConfig,
process_cors_config,
)
from llama_stack.core.distribution import builtin_automatically_routed_apis
from llama_stack.core.external import ExternalApiSpec, load_external_apis
@ -82,7 +84,7 @@ from .quota import QuotaMiddleware
REPO_ROOT = Path(__file__).parent.parent.parent.parent
logger = get_logger(name=__name__, category="server")
logger = get_logger(name=__name__, category="core::server")
def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
@ -413,7 +415,7 @@ def main(args: argparse.Namespace | None = None):
config_contents = yaml.safe_load(fp)
if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
logger_config = LoggingConfig(**cfg)
logger = get_logger(name=__name__, category="server", config=logger_config)
logger = get_logger(name=__name__, category="core::server", config=logger_config)
if args.env:
for env_pair in args.env:
try:
@ -483,6 +485,12 @@ def main(args: argparse.Namespace | None = None):
window_seconds=window_seconds,
)
if config.server.cors:
logger.info("Enabling CORS")
cors_config = process_cors_config(config.server.cors)
if cors_config:
app.add_middleware(CORSMiddleware, **cors_config.model_dump())
if Api.telemetry in impls:
setup_logger(impls[Api.telemetry])
else:

2
llama_stack/core/store/registry.py
View file

@ -16,7 +16,7 @@ from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
logger = get_logger(__name__, category="core")
logger = get_logger(__name__, category="core::registry")
class DistributionRegistry(Protocol):

2
llama_stack/core/utils/config_resolution.py
View file

@ -10,7 +10,7 @@ from pathlib import Path
from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.log import get_logger
logger = get_logger(name=__name__, category="config_resolution")
logger = get_logger(name=__name__, category="core")
DISTRO_DIR = Path(__file__).parent.parent.parent.parent / "llama_stack" / "distributions"

2
llama_stack/models/llama/llama3/multimodal/model.py
View file

@ -36,7 +36,7 @@ from .utils import get_negative_inf_value, to_2tuple
MP_SCALE = 8
logger = get_logger(name=__name__, category="models")
logger = get_logger(name=__name__, category="models::llama")
def reduce_from_tensor_model_parallel_region(input_):

2
llama_stack/models/llama/llama3/tool_utils.py
View file

@ -11,7 +11,7 @@ from llama_stack.log import get_logger
from ..datatypes import BuiltinTool, RecursiveType, ToolCall, ToolPromptFormat
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="models::llama")
BUILTIN_TOOL_PATTERN = r'\b(?P<tool_name>\w+)\.call\(query="(?P<query>[^"]*)"\)'
CUSTOM_TOOL_CALL_PATTERN = re.compile(r"<function=(?P<function_name>[^}]+)>(?P<args>{.*?})")

2
llama_stack/models/llama/llama4/quantization/loader.py
View file

@ -18,7 +18,7 @@ from ...datatypes import QuantizationMode
from ..model import Transformer, TransformerBlock
from ..moe import MoE
log = get_logger(name=__name__, category="models")
log = get_logger(name=__name__, category="models::llama")
def swiglu_wrapper_no_reduce(

2
llama_stack/models/llama/quantize_impls.py
View file

@ -9,7 +9,7 @@ import collections
from llama_stack.log import get_logger
log = get_logger(name=__name__, category="llama")
log = get_logger(name=__name__, category="models::llama")
try:
import fbgemm_gpu.experimental.gen_ai # noqa: F401

2
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -84,7 +84,7 @@ MEMORY_QUERY_TOOL = "knowledge_search"
WEB_SEARCH_TOOL = "web_search"
RAG_TOOL_GROUP = "builtin::rag"
logger = get_logger(name=__name__, category="agents")
logger = get_logger(name=__name__, category="agents::meta_reference")
class ChatAgent(ShieldRunnerMixin):

2
llama_stack/providers/inline/agents/meta_reference/agents.py
View file

@ -51,7 +51,7 @@ from .config import MetaReferenceAgentsImplConfig
from .persistence import AgentInfo
from .responses.openai_responses import OpenAIResponsesImpl
logger = get_logger(name=__name__, category="agents")
logger = get_logger(name=__name__, category="agents::meta_reference")
class MetaReferenceAgentsImpl(Agents):

2
llama_stack/providers/inline/agents/meta_reference/persistence.py
View file

@ -17,7 +17,7 @@ from llama_stack.core.request_headers import get_authenticated_user
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore import KVStore
log = get_logger(name=__name__, category="agents")
log = get_logger(name=__name__, category="agents::meta_reference")
class AgentSessionInfo(Session):

2
llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py
View file

@ -41,7 +41,7 @@ from .utils import (
convert_response_text_to_chat_response_format,
)
logger = get_logger(name=__name__, category="responses")
logger = get_logger(name=__name__, category="openai::responses")
class OpenAIResponsePreviousResponseWithInputItems(BaseModel):

2
llama_stack/providers/inline/agents/meta_reference/responses/streaming.py
View file

@ -47,7 +47,7 @@ from llama_stack.log import get_logger
from .types import ChatCompletionContext, ChatCompletionResult
from .utils import convert_chat_choice_to_response_message, is_function_tool_call
logger = get_logger(name=__name__, category="responses")
logger = get_logger(name=__name__, category="agents::meta_reference")
class StreamingResponseOrchestrator:

2
llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py
View file

@ -38,7 +38,7 @@ from llama_stack.log import get_logger
from .types import ChatCompletionContext, ToolExecutionResult
logger = get_logger(name=__name__, category="responses")
logger = get_logger(name=__name__, category="agents::meta_reference")
class ToolExecutor:

21
llama_stack/providers/inline/agents/meta_reference/responses/utils.py
View file

@ -17,6 +17,8 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseOutputMessageContent,
OpenAIResponseOutputMessageContentOutputText,
OpenAIResponseOutputMessageFunctionToolCall,
OpenAIResponseOutputMessageMCPCall,
OpenAIResponseOutputMessageMCPListTools,
OpenAIResponseText,
)
from llama_stack.apis.inference import (
@ -117,6 +119,25 @@ async def convert_response_input_to_chat_messages(
),
)
messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
elif isinstance(input_item, OpenAIResponseOutputMessageMCPCall):
tool_call = OpenAIChatCompletionToolCall(
index=0,
id=input_item.id,
function=OpenAIChatCompletionToolCallFunction(
name=input_item.name,
arguments=input_item.arguments,
),
)
messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
messages.append(
OpenAIToolMessageParam(
content=input_item.output,
tool_call_id=input_item.id,
)
)
elif isinstance(input_item, OpenAIResponseOutputMessageMCPListTools):
# the tool list will be handled separately
pass
else:
content = await convert_response_content_to_chat_content(input_item.content)
message_type = await get_message_type_by_role(input_item.role)

2
llama_stack/providers/inline/agents/meta_reference/safety.py
View file

@ -11,7 +11,7 @@ from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel
from llama_stack.log import get_logger
from llama_stack.providers.utils.telemetry import tracing
log = get_logger(name=__name__, category="agents")
log = get_logger(name=__name__, category="agents::meta_reference")
class SafetyException(Exception): # noqa: N818

12
llama_stack/providers/inline/inference/meta_reference/inference.py
View file

@ -33,6 +33,9 @@ from llama_stack.apis.inference import (
InterleavedContent,
LogProbConfig,
Message,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
RerankResponse,
ResponseFormat,
SamplingParams,
StopReason,
@ -442,6 +445,15 @@ class MetaReferenceInferenceImpl(
results = await self._nonstream_chat_completion(request_batch)
return BatchChatCompletionResponse(batch=results)
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
raise NotImplementedError("Reranking is not supported for Meta Reference")
async def _nonstream_chat_completion(
self, request_batch: list[ChatCompletionRequest]
) -> list[ChatCompletionResponse]:

12
llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py
View file

@ -12,6 +12,9 @@ from llama_stack.apis.inference import (
InterleavedContent,
LogProbConfig,
Message,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
RerankResponse,
ResponseFormat,
SamplingParams,
ToolChoice,
@ -122,3 +125,12 @@ class SentenceTransformersInferenceImpl(
logprobs: LogProbConfig | None = None,
):
raise NotImplementedError("Batch chat completion is not supported for Sentence Transformers")
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
raise NotImplementedError("Reranking is not supported for Sentence Transformers")

2
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -65,7 +65,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import FireworksImplConfig
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::fireworks")
class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

16
llama_stack/providers/remote/inference/llama_openai_compat/llama.py
View file

@ -3,6 +3,11 @@
#
# 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 (
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
RerankResponse,
)
from llama_stack.log import get_logger
from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
@ -10,7 +15,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::llama_openai_compat")
class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
@ -54,3 +59,12 @@ class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
async def shutdown(self):
await super().shutdown()
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
raise NotImplementedError("Reranking is not supported for Llama OpenAI Compat")

72
llama_stack/providers/remote/inference/nvidia/NVIDIA.md
View file

@ -41,6 +41,11 @@ client.initialize()
### Create Completion
> Note on Completion API
>
> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does.
```python
response = client.inference.completion(
model_id="meta-llama/Llama-3.1-8B-Instruct",
@ -76,6 +81,73 @@ response = client.inference.chat_completion(
print(f"Response: {response.completion_message.content}")
```
### Tool Calling Example ###
```python
from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
tool_definition = ToolDefinition(
tool_name="get_weather",
description="Get current weather information for a location",
parameters={
"location": ToolParamDefinition(
param_type="string",
description="The city and state, e.g. San Francisco, CA",
required=True,
),
"unit": ToolParamDefinition(
param_type="string",
description="Temperature unit (celsius or fahrenheit)",
required=False,
default="celsius",
),
},
)
tool_response = client.inference.chat_completion(
model_id="meta-llama/Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
tools=[tool_definition],
)
print(f"Tool Response: {tool_response.completion_message.content}")
if tool_response.completion_message.tool_calls:
for tool_call in tool_response.completion_message.tool_calls:
print(f"Tool Called: {tool_call.tool_name}")
print(f"Arguments: {tool_call.arguments}")
```
### Structured Output Example
```python
from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType
person_schema = {
"type": "object",
"properties": {
"name": {"type": "string"},
"age": {"type": "integer"},
"occupation": {"type": "string"},
},
"required": ["name", "age", "occupation"],
}
response_format = JsonSchemaResponseFormat(
type=ResponseFormatType.json_schema, json_schema=person_schema
)
structured_response = client.inference.chat_completion(
model_id="meta-llama/Llama-3.1-8B-Instruct",
messages=[
{
"role": "user",
"content": "Create a profile for a fictional person named Alice who is 30 years old and is a software engineer. ",
}
],
response_format=response_format,
)
print(f"Structured Response: {structured_response.completion_message.content}")
```
### Create Embeddings
> Note on OpenAI embeddings compatibility
>

18
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -7,7 +7,7 @@
import warnings
from collections.abc import AsyncIterator
from openai import NOT_GIVEN, APIConnectionError, BadRequestError
from openai import NOT_GIVEN, APIConnectionError
from llama_stack.apis.common.content_types import (
InterleavedContent,
@ -57,7 +57,7 @@ from .openai_utils import (
)
from .utils import _is_nvidia_hosted
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::nvidia")
class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
@ -197,15 +197,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
}
extra_body["input_type"] = task_type_options[task_type]
try:
response = await self.client.embeddings.create(
model=provider_model_id,
input=input,
extra_body=extra_body,
)
except BadRequestError as e:
raise ValueError(f"Failed to get embeddings: {e}") from e
response = await self.client.embeddings.create(
model=provider_model_id,
input=input,
extra_body=extra_body,
)
#
# OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
# ->

2
llama_stack/providers/remote/inference/nvidia/utils.py
View file

@ -10,7 +10,7 @@ from llama_stack.log import get_logger
from . import NVIDIAConfig
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::nvidia")
def _is_nvidia_hosted(config: NVIDIAConfig) -> bool:

14
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -37,11 +37,14 @@ from llama_stack.apis.inference import (
Message,
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
OpenAICompletion,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
OpenAIMessageParam,
OpenAIResponseFormatParam,
RerankResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
@ -85,7 +88,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::ollama")
class OllamaInferenceAdapter(
@ -641,6 +644,15 @@ class OllamaInferenceAdapter(
):
raise NotImplementedError("Batch chat completion is not supported for Ollama")
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
raise NotImplementedError("Reranking is not supported for Ollama")
async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
async def _convert_content(content) -> dict:

2
llama_stack/providers/remote/inference/openai/openai.py
View file

@ -11,7 +11,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from .config import OpenAIConfig
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::openai")
#

2
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
log = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="inference::tgi")
def build_hf_repo_model_entries():

2
llama_stack/providers/remote/inference/together/together.py
View file

@ -61,7 +61,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import TogetherImplConfig
from .models import MODEL_ENTRIES
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="inference::together")
class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

16
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -39,12 +39,15 @@ from llama_stack.apis.inference import (
Message,
ModelStore,
OpenAIChatCompletion,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
OpenAICompletion,
OpenAIEmbeddingData,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
OpenAIMessageParam,
OpenAIResponseFormatParam,
RerankResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
@ -85,7 +88,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
from .config import VLLMInferenceAdapterConfig
log = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="inference::vllm")
def build_hf_repo_model_entries():
@ -732,4 +735,13 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
):
raise NotImplementedError("Batch chat completion is not supported for Ollama")
raise NotImplementedError("Batch chat completion is not supported for vLLM")
async def rerank(
self,
model: str,
query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
max_num_results: int | None = None,
) -> RerankResponse:
raise NotImplementedError("Reranking is not supported for vLLM")

2
llama_stack/providers/remote/post_training/nvidia/utils.py
View file

@ -15,7 +15,7 @@ from llama_stack.providers.remote.post_training.nvidia.config import SFTLoRADefa
from .config import NvidiaPostTrainingConfig
logger = get_logger(name=__name__, category="integration")
logger = get_logger(name=__name__, category="post_training::nvidia")
def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None:

2
llama_stack/providers/remote/safety/bedrock/bedrock.py
View file

@ -21,7 +21,7 @@ from llama_stack.providers.utils.bedrock.client import create_bedrock_client
from .config import BedrockSafetyConfig
logger = get_logger(name=__name__, category="safety")
logger = get_logger(name=__name__, category="safety::bedrock")
class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):

7
llama_stack/providers/remote/safety/nvidia/nvidia.py
View file

@ -9,7 +9,7 @@ from typing import Any
import requests
from llama_stack.apis.inference import Message
from llama_stack.apis.safety import RunShieldResponse, Safety, SafetyViolation, ViolationLevel
from llama_stack.apis.safety import ModerationObject, RunShieldResponse, Safety, SafetyViolation, ViolationLevel
from llama_stack.apis.shields import Shield
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
@ -17,7 +17,7 @@ from llama_stack.providers.utils.inference.openai_compat import convert_message_
from .config import NVIDIASafetyConfig
logger = get_logger(name=__name__, category="safety")
logger = get_logger(name=__name__, category="safety::nvidia")
class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
@ -67,6 +67,9 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
self.shield = NeMoGuardrails(self.config, shield.shield_id)
return await self.shield.run(messages)
async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
raise NotImplementedError("NVIDIA safety provider currently does not implement run_moderation")
class NeMoGuardrails:
"""

2
llama_stack/providers/remote/safety/sambanova/sambanova.py
View file

@ -25,7 +25,7 @@ from llama_stack.providers.utils.inference.openai_compat import convert_message_
from .config import SambaNovaSafetyConfig
logger = get_logger(name=__name__, category="safety")
logger = get_logger(name=__name__, category="safety::sambanova")
CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"

2
llama_stack/providers/remote/vector_io/chroma/chroma.py
View file

@ -33,7 +33,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
log = get_logger(name=__name__, category="vector_io")
log = get_logger(name=__name__, category="vector_io::chroma")
ChromaClientType = chromadb.api.AsyncClientAPI | chromadb.api.ClientAPI

2
llama_stack/providers/remote/vector_io/milvus/milvus.py
View file

@ -36,7 +36,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
logger = get_logger(name=__name__, category="vector_io")
logger = get_logger(name=__name__, category="vector_io::milvus")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"

2
llama_stack/providers/remote/vector_io/pgvector/pgvector.py
View file

@ -34,7 +34,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import PGVectorVectorIOConfig
log = get_logger(name=__name__, category="vector_io")
log = get_logger(name=__name__, category="vector_io::pgvector")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::"

2
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -36,7 +36,7 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
log = get_logger(name=__name__, category="vector_io")
log = get_logger(name=__name__, category="vector_io::qdrant")
CHUNK_ID_KEY = "_chunk_id"
# KV store prefixes for vector databases

2
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
View file

@ -34,7 +34,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
from .config import WeaviateVectorIOConfig
log = get_logger(name=__name__, category="vector_io")
log = get_logger(name=__name__, category="vector_io::weaviate")
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::"

2
llama_stack/providers/utils/inference/embedding_mixin.py
View file

@ -28,7 +28,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import interleaved_con
EMBEDDING_MODELS = {}
log = get_logger(name=__name__, category="inference")
log = get_logger(name=__name__, category="providers::utils")
class SentenceTransformerEmbeddingMixin:

2
llama_stack/providers/utils/inference/litellm_openai_mixin.py
View file

@ -54,7 +54,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
interleaved_content_as_str,
)
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="providers::utils")
class LiteLLMOpenAIMixin(

2
llama_stack/providers/utils/inference/model_registry.py
View file

@ -17,7 +17,7 @@ from llama_stack.providers.utils.inference import (
ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
)
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="providers::utils")
class RemoteInferenceProviderConfig(BaseModel):

2
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -134,7 +134,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
decode_assistant_message,
)
logger = get_logger(name=__name__, category="inference")
logger = get_logger(name=__name__, category="providers::utils")
class OpenAICompatCompletionChoiceDelta(BaseModel):

2
llama_stack/providers/utils/inference/openai_mixin.py
View file

@ -25,7 +25,7 @@ from llama_stack.apis.inference import (
from llama_stack.log import get_logger
from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
logger = get_logger(name=__name__, category="core")
logger = get_logger(name=__name__, category="providers::utils")
class OpenAIMixin(ABC):

2
llama_stack/providers/utils/inference/prompt_adapter.py
View file

@ -58,7 +58,7 @@ from llama_stack.models.llama.sku_list import resolve_model
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="inference")
log = get_logger(name=__name__, category="providers::utils")
class ChatCompletionRequestWithRawContent(ChatCompletionRequest):

2
llama_stack/providers/utils/kvstore/mongodb/mongodb.py
View file

@ -13,7 +13,7 @@ from llama_stack.providers.utils.kvstore import KVStore
from ..config import MongoDBKVStoreConfig
log = get_logger(name=__name__, category="kvstore")
log = get_logger(name=__name__, category="providers::utils")
class MongoDBKVStoreImpl(KVStore):

2
llama_stack/providers/utils/kvstore/postgres/postgres.py
View file

@ -14,7 +14,7 @@ from llama_stack.log import get_logger
from ..api import KVStore
from ..config import PostgresKVStoreConfig
log = get_logger(name=__name__, category="kvstore")
log = get_logger(name=__name__, category="providers::utils")
class PostgresKVStoreImpl(KVStore):

2
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -44,7 +44,7 @@ from llama_stack.providers.utils.memory.vector_store import (
make_overlapped_chunks,
)
logger = get_logger(name=__name__, category="memory")
logger = get_logger(name=__name__, category="providers::utils")
# Constants for OpenAI vector stores
CHUNK_MULTIPLIER = 5

2
llama_stack/providers/utils/memory/vector_store.py
View file

@ -33,7 +33,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
)
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
log = get_logger(name=__name__, category="memory")
log = get_logger(name=__name__, category="providers::utils")
class ChunkForDeletion(BaseModel):

2
llama_stack/providers/utils/scheduler.py
View file

@ -17,7 +17,7 @@ from pydantic import BaseModel
from llama_stack.log import get_logger
logger = get_logger(name=__name__, category="scheduler")
logger = get_logger(name=__name__, category="providers::utils")
# TODO: revisit the list of possible statuses when defining a more coherent

2
llama_stack/providers/utils/sqlstore/authorized_sqlstore.py
View file

@ -17,7 +17,7 @@ from llama_stack.log import get_logger
from .api import ColumnDefinition, ColumnType, PaginatedResponse, SqlStore
from .sqlstore import SqlStoreType
logger = get_logger(name=__name__, category="authorized_sqlstore")
logger = get_logger(name=__name__, category="providers::utils")
# Hardcoded copy of the default policy that our SQL filtering implements
# WARNING: If default_policy() changes, this constant must be updated accordingly

12
llama_stack/providers/utils/sqlstore/sqlalchemy_sqlstore.py
View file

@ -22,6 +22,7 @@ from sqlalchemy import (
text,
)
from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
from sqlalchemy.ext.asyncio.engine import AsyncEngine
from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.log import get_logger
@ -29,7 +30,7 @@ from llama_stack.log import get_logger
from .api import ColumnDefinition, ColumnType, SqlStore
from .sqlstore import SqlAlchemySqlStoreConfig
logger = get_logger(name=__name__, category="sqlstore")
logger = get_logger(name=__name__, category="providers::utils")
TYPE_MAPPING: dict[ColumnType, Any] = {
ColumnType.INTEGER: Integer,
@ -45,9 +46,12 @@ TYPE_MAPPING: dict[ColumnType, Any] = {
class SqlAlchemySqlStoreImpl(SqlStore):
def __init__(self, config: SqlAlchemySqlStoreConfig):
self.config = config
self.async_session = async_sessionmaker(create_async_engine(config.engine_str))
self.async_session = async_sessionmaker(self.create_engine())
self.metadata = MetaData()
def create_engine(self) -> AsyncEngine:
return create_async_engine(self.config.engine_str, pool_pre_ping=True)
async def create_table(
self,
table: str,
@ -83,7 +87,7 @@ class SqlAlchemySqlStoreImpl(SqlStore):
else:
sqlalchemy_table = self.metadata.tables[table]
engine = create_async_engine(self.config.engine_str)
engine = self.create_engine()
async with engine.begin() as conn:
await conn.run_sync(self.metadata.create_all, tables=[sqlalchemy_table], checkfirst=True)
@ -241,7 +245,7 @@ class SqlAlchemySqlStoreImpl(SqlStore):
nullable: bool = True,
) -> None:
"""Add a column to an existing table if the column doesn't already exist."""
engine = create_async_engine(self.config.engine_str)
engine = self.create_engine()
try:
async with engine.begin() as conn:

587
llama_stack/ui/app/chat-playground/page.test.tsx Normal file
View file

@ -0,0 +1,587 @@
import React from "react";
import {
render,
screen,
fireEvent,
waitFor,
act,
} from "@testing-library/react";
import "@testing-library/jest-dom";
import ChatPlaygroundPage from "./page";
const mockClient = {
agents: {
list: jest.fn(),
create: jest.fn(),
retrieve: jest.fn(),
delete: jest.fn(),
session: {
list: jest.fn(),
create: jest.fn(),
delete: jest.fn(),
retrieve: jest.fn(),
},
turn: {
create: jest.fn(),
},
},
models: {
list: jest.fn(),
},
toolgroups: {
list: jest.fn(),
},
};
jest.mock("@/hooks/use-auth-client", () => ({
useAuthClient: jest.fn(() => mockClient),
}));
jest.mock("@/components/chat-playground/chat", () => ({
Chat: jest.fn(
({
className,
messages,
handleSubmit,
input,
handleInputChange,
isGenerating,
append,
suggestions,
}) => (
<div data-testid="chat-component" className={className}>
<div data-testid="messages-count">{messages.length}</div>
<input
data-testid="chat-input"
value={input}
onChange={handleInputChange}
disabled={isGenerating}
/>
<button data-testid="submit-button" onClick={handleSubmit}>
Submit
</button>
{suggestions?.map((suggestion: string, index: number) => (
<button
key={index}
data-testid={`suggestion-${index}`}
onClick={() => append({ role: "user", content: suggestion })}
>
{suggestion}
</button>
))}
</div>
)
),
}));
jest.mock("@/components/chat-playground/conversations", () => ({
SessionManager: jest.fn(({ selectedAgentId, onNewSession }) => (
<div data-testid="session-manager">
{selectedAgentId && (
<>
<div data-testid="selected-agent">{selectedAgentId}</div>
<button data-testid="new-session-button" onClick={onNewSession}>
New Session
</button>
</>
)}
</div>
)),
SessionUtils: {
saveCurrentSessionId: jest.fn(),
loadCurrentSessionId: jest.fn(),
loadCurrentAgentId: jest.fn(),
saveCurrentAgentId: jest.fn(),
clearCurrentSession: jest.fn(),
saveSessionData: jest.fn(),
loadSessionData: jest.fn(),
saveAgentConfig: jest.fn(),
loadAgentConfig: jest.fn(),
clearAgentCache: jest.fn(),
createDefaultSession: jest.fn(() => ({
id: "test-session-123",
name: "Default Session",
messages: [],
selectedModel: "",
systemMessage: "You are a helpful assistant.",
agentId: "test-agent-123",
createdAt: Date.now(),
updatedAt: Date.now(),
})),
},
}));
const mockAgents = [
{
agent_id: "agent_123",
agent_config: {
name: "Test Agent",
instructions: "You are a test assistant.",
},
},
{
agent_id: "agent_456",
agent_config: {
agent_name: "Another Agent",
instructions: "You are another assistant.",
},
},
];
const mockModels = [
{
identifier: "test-model-1",
model_type: "llm",
},
{
identifier: "test-model-2",
model_type: "llm",
},
];
const mockToolgroups = [
{
identifier: "builtin::rag",
provider_id: "test-provider",
type: "tool_group",
provider_resource_id: "test-resource",
},
];
describe("ChatPlaygroundPage", () => {
beforeEach(() => {
jest.clearAllMocks();
Element.prototype.scrollIntoView = jest.fn();
mockClient.agents.list.mockResolvedValue({ data: mockAgents });
mockClient.models.list.mockResolvedValue(mockModels);
mockClient.toolgroups.list.mockResolvedValue(mockToolgroups);
mockClient.agents.session.create.mockResolvedValue({
session_id: "new-session-123",
});
mockClient.agents.session.list.mockResolvedValue({ data: [] });
mockClient.agents.session.retrieve.mockResolvedValue({
session_id: "test-session",
session_name: "Test Session",
started_at: new Date().toISOString(),
turns: [],
}); // No turns by default
mockClient.agents.retrieve.mockResolvedValue({
agent_id: "test-agent",
agent_config: {
toolgroups: ["builtin::rag"],
instructions: "Test instructions",
model: "test-model",
},
});
mockClient.agents.delete.mockResolvedValue(undefined);
});
describe("Agent Selector Rendering", () => {
test("shows agent selector when agents are available", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(screen.getByText("Agent Session:")).toBeInTheDocument();
expect(screen.getAllByRole("combobox")).toHaveLength(2);
expect(screen.getByText("+ New Agent")).toBeInTheDocument();
expect(screen.getByText("Clear Chat")).toBeInTheDocument();
});
});
test("does not show agent selector when no agents are available", async () => {
mockClient.agents.list.mockResolvedValue({ data: [] });
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(screen.queryByText("Agent Session:")).not.toBeInTheDocument();
expect(screen.getAllByRole("combobox")).toHaveLength(1);
expect(screen.getByText("+ New Agent")).toBeInTheDocument();
expect(screen.queryByText("Clear Chat")).not.toBeInTheDocument();
});
});
test("does not show agent selector while loading", async () => {
mockClient.agents.list.mockImplementation(() => new Promise(() => {}));
await act(async () => {
render(<ChatPlaygroundPage />);
});
expect(screen.queryByText("Agent Session:")).not.toBeInTheDocument();
expect(screen.getAllByRole("combobox")).toHaveLength(1);
expect(screen.getByText("+ New Agent")).toBeInTheDocument();
expect(screen.queryByText("Clear Chat")).not.toBeInTheDocument();
});
test("shows agent options in selector", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
const agentCombobox = screen.getAllByRole("combobox").find(element => {
return (
element.textContent?.includes("Test Agent") ||
element.textContent?.includes("Select Agent")
);
});
expect(agentCombobox).toBeDefined();
fireEvent.click(agentCombobox!);
});
await waitFor(() => {
expect(screen.getAllByText("Test Agent")).toHaveLength(2);
expect(screen.getByText("Another Agent")).toBeInTheDocument();
});
});
test("displays agent ID when no name is available", async () => {
const agentWithoutName = {
agent_id: "agent_789",
agent_config: {
instructions: "You are an agent without a name.",
},
};
mockClient.agents.list.mockResolvedValue({ data: [agentWithoutName] });
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
const agentCombobox = screen.getAllByRole("combobox").find(element => {
return (
element.textContent?.includes("Agent agent_78") ||
element.textContent?.includes("Select Agent")
);
});
expect(agentCombobox).toBeDefined();
fireEvent.click(agentCombobox!);
});
await waitFor(() => {
expect(screen.getAllByText("Agent agent_78...")).toHaveLength(2);
});
});
});
describe("Agent Creation Modal", () => {
test("opens agent creation modal when + New Agent is clicked", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
const newAgentButton = screen.getByText("+ New Agent");
fireEvent.click(newAgentButton);
expect(screen.getByText("Create New Agent")).toBeInTheDocument();
expect(screen.getByText("Agent Name (optional)")).toBeInTheDocument();
expect(screen.getAllByText("Model")).toHaveLength(2);
expect(screen.getByText("System Instructions")).toBeInTheDocument();
expect(screen.getByText("Tools (optional)")).toBeInTheDocument();
});
test("closes modal when Cancel is clicked", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
const newAgentButton = screen.getByText("+ New Agent");
fireEvent.click(newAgentButton);
const cancelButton = screen.getByText("Cancel");
fireEvent.click(cancelButton);
expect(screen.queryByText("Create New Agent")).not.toBeInTheDocument();
});
test("creates agent when Create Agent is clicked", async () => {
mockClient.agents.create.mockResolvedValue({ agent_id: "new-agent-123" });
mockClient.agents.list
.mockResolvedValueOnce({ data: mockAgents })
.mockResolvedValueOnce({
data: [
...mockAgents,
{ agent_id: "new-agent-123", agent_config: { name: "New Agent" } },
],
});
await act(async () => {
render(<ChatPlaygroundPage />);
});
const newAgentButton = screen.getByText("+ New Agent");
await act(async () => {
fireEvent.click(newAgentButton);
});
await waitFor(() => {
expect(screen.getByText("Create New Agent")).toBeInTheDocument();
});
const nameInput = screen.getByPlaceholderText("My Custom Agent");
await act(async () => {
fireEvent.change(nameInput, { target: { value: "Test Agent Name" } });
});
const instructionsTextarea = screen.getByDisplayValue(
"You are a helpful assistant."
);
await act(async () => {
fireEvent.change(instructionsTextarea, {
target: { value: "Custom instructions" },
});
});
await waitFor(() => {
const modalModelSelectors = screen
.getAllByRole("combobox")
.filter(el => {
return (
el.textContent?.includes("Select Model") ||
el.closest('[class*="modal"]') ||
el.closest('[class*="card"]')
);
});
expect(modalModelSelectors.length).toBeGreaterThan(0);
});
const modalModelSelectors = screen.getAllByRole("combobox").filter(el => {
return (
el.textContent?.includes("Select Model") ||
el.closest('[class*="modal"]') ||
el.closest('[class*="card"]')
);
});
await act(async () => {
fireEvent.click(modalModelSelectors[0]);
});
await waitFor(() => {
const modelOptions = screen.getAllByText("test-model-1");
expect(modelOptions.length).toBeGreaterThan(0);
});
const modelOptions = screen.getAllByText("test-model-1");
const dropdownOption = modelOptions.find(
option =>
option.closest('[role="option"]') ||
option.id?.includes("radix") ||
option.getAttribute("aria-selected") !== null
);
await act(async () => {
fireEvent.click(
dropdownOption || modelOptions[modelOptions.length - 1]
);
});
await waitFor(() => {
const createButton = screen.getByText("Create Agent");
expect(createButton).not.toBeDisabled();
});
const createButton = screen.getByText("Create Agent");
await act(async () => {
fireEvent.click(createButton);
});
await waitFor(() => {
expect(mockClient.agents.create).toHaveBeenCalledWith({
agent_config: {
model: expect.any(String),
instructions: "Custom instructions",
name: "Test Agent Name",
enable_session_persistence: true,
},
});
});
await waitFor(() => {
expect(screen.queryByText("Create New Agent")).not.toBeInTheDocument();
});
});
});
describe("Agent Selection", () => {
test("creates default session when agent is selected", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
// first agent should be auto-selected
expect(mockClient.agents.session.create).toHaveBeenCalledWith(
"agent_123",
{ session_name: "Default Session" }
);
});
});
test("switches agent when different agent is selected", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
const agentCombobox = screen.getAllByRole("combobox").find(element => {
return (
element.textContent?.includes("Test Agent") ||
element.textContent?.includes("Select Agent")
);
});
expect(agentCombobox).toBeDefined();
fireEvent.click(agentCombobox!);
});
await waitFor(() => {
const anotherAgentOption = screen.getByText("Another Agent");
fireEvent.click(anotherAgentOption);
});
expect(mockClient.agents.session.create).toHaveBeenCalledWith(
"agent_456",
{ session_name: "Default Session" }
);
});
});
describe("Agent Deletion", () => {
test("shows delete button when multiple agents exist", async () => {
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(screen.getByTitle("Delete current agent")).toBeInTheDocument();
});
});
test("hides delete button when only one agent exists", async () => {
mockClient.agents.list.mockResolvedValue({
data: [mockAgents[0]],
});
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(
screen.queryByTitle("Delete current agent")
).not.toBeInTheDocument();
});
});
test("deletes agent and switches to another when confirmed", async () => {
global.confirm = jest.fn(() => true);
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(screen.getByTitle("Delete current agent")).toBeInTheDocument();
});
mockClient.agents.delete.mockResolvedValue(undefined);
mockClient.agents.list.mockResolvedValueOnce({ data: mockAgents });
mockClient.agents.list.mockResolvedValueOnce({
data: [mockAgents[1]],
});
const deleteButton = screen.getByTitle("Delete current agent");
await act(async () => {
deleteButton.click();
});
await waitFor(() => {
expect(mockClient.agents.delete).toHaveBeenCalledWith("agent_123");
expect(global.confirm).toHaveBeenCalledWith(
"Are you sure you want to delete this agent? This action cannot be undone and will delete all associated sessions."
);
});
(global.confirm as jest.Mock).mockRestore();
});
test("does not delete agent when cancelled", async () => {
global.confirm = jest.fn(() => false);
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(screen.getByTitle("Delete current agent")).toBeInTheDocument();
});
const deleteButton = screen.getByTitle("Delete current agent");
await act(async () => {
deleteButton.click();
});
await waitFor(() => {
expect(global.confirm).toHaveBeenCalled();
expect(mockClient.agents.delete).not.toHaveBeenCalled();
});
(global.confirm as jest.Mock).mockRestore();
});
});
describe("Error Handling", () => {
test("handles agent loading errors gracefully", async () => {
mockClient.agents.list.mockRejectedValue(
new Error("Failed to load agents")
);
const consoleSpy = jest
.spyOn(console, "error")
.mockImplementation(() => {});
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(consoleSpy).toHaveBeenCalledWith(
"Error fetching agents:",
expect.any(Error)
);
});
expect(screen.getByText("+ New Agent")).toBeInTheDocument();
consoleSpy.mockRestore();
});
test("handles model loading errors gracefully", async () => {
mockClient.models.list.mockRejectedValue(
new Error("Failed to load models")
);
const consoleSpy = jest
.spyOn(console, "error")
.mockImplementation(() => {});
await act(async () => {
render(<ChatPlaygroundPage />);
});
await waitFor(() => {
expect(consoleSpy).toHaveBeenCalledWith(
"Error fetching models:",
expect.any(Error)
);
});
consoleSpy.mockRestore();
});
});
});

1229
llama_stack/ui/app/chat-playground/page.tsx
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41
llama_stack/ui/app/globals.css
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@ -120,3 +120,44 @@
@apply bg-background text-foreground;
}
}
@layer utilities {
.animate-typing-dot-1 {
animation: typing-dot-bounce-1 0.8s cubic-bezier(0.4, 0, 0.6, 1) infinite;
}
.animate-typing-dot-2 {
animation: typing-dot-bounce-2 0.8s cubic-bezier(0.4, 0, 0.6, 1) infinite;
}
.animate-typing-dot-3 {
animation: typing-dot-bounce-3 0.8s cubic-bezier(0.4, 0, 0.6, 1) infinite;
}
@keyframes typing-dot-bounce-1 {
0%, 15%, 85%, 100% {
transform: translateY(0);
}
7.5% {
transform: translateY(-6px);
}
}
@keyframes typing-dot-bounce-2 {
0%, 15%, 35%, 85%, 100% {
transform: translateY(0);
}
25% {
transform: translateY(-6px);
}
}
@keyframes typing-dot-bounce-3 {
0%, 35%, 55%, 85%, 100% {
transform: translateY(0);
}
45% {
transform: translateY(-6px);
}
}
}

3
llama_stack/ui/app/layout.tsx
View file

@ -18,6 +18,9 @@ const geistMono = Geist_Mono({
export const metadata: Metadata = {
title: "Llama Stack",
description: "Llama Stack UI",
icons: {
icon: "/favicon.ico",
},
};
import { SidebarProvider, SidebarTrigger } from "@/components/ui/sidebar";

16
llama_stack/ui/components/chat-playground/chat-message.tsx
View file

@ -161,10 +161,12 @@ export const ChatMessage: React.FC<ChatMessageProps> = ({
const isUser = role === "user";
const formattedTime = createdAt?.toLocaleTimeString("en-US", {
hour: "2-digit",
minute: "2-digit",
});
const formattedTime = createdAt
? new Date(createdAt).toLocaleTimeString("en-US", {
hour: "2-digit",
minute: "2-digit",
})
: undefined;
if (isUser) {
return (
@ -185,7 +187,7 @@ export const ChatMessage: React.FC<ChatMessageProps> = ({
{showTimeStamp && createdAt ? (
<time
dateTime={createdAt.toISOString()}
dateTime={new Date(createdAt).toISOString()}
className={cn(
"mt-1 block px-1 text-xs opacity-50",
animation !== "none" && "duration-500 animate-in fade-in-0"
@ -220,7 +222,7 @@ export const ChatMessage: React.FC<ChatMessageProps> = ({
{showTimeStamp && createdAt ? (
<time
dateTime={createdAt.toISOString()}
dateTime={new Date(createdAt).toISOString()}
className={cn(
"mt-1 block px-1 text-xs opacity-50",
animation !== "none" && "duration-500 animate-in fade-in-0"
@ -262,7 +264,7 @@ export const ChatMessage: React.FC<ChatMessageProps> = ({
{showTimeStamp && createdAt ? (
<time
dateTime={createdAt.toISOString()}
dateTime={new Date(createdAt).toISOString()}
className={cn(
"mt-1 block px-1 text-xs opacity-50",
animation !== "none" && "duration-500 animate-in fade-in-0"

345
llama_stack/ui/components/chat-playground/conversations.test.tsx Normal file
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@ -0,0 +1,345 @@
import React from "react";
import { render, screen, waitFor, act } from "@testing-library/react";
import "@testing-library/jest-dom";
import { Conversations, SessionUtils } from "./conversations";
import type { Message } from "@/components/chat-playground/chat-message";
interface ChatSession {
id: string;
name: string;
messages: Message[];
selectedModel: string;
systemMessage: string;
agentId: string;
createdAt: number;
updatedAt: number;
}
const mockOnSessionChange = jest.fn();
const mockOnNewSession = jest.fn();
// Mock the auth client
const mockClient = {
agents: {
session: {
list: jest.fn(),
create: jest.fn(),
delete: jest.fn(),
retrieve: jest.fn(),
},
},
};
// Mock the useAuthClient hook
jest.mock("@/hooks/use-auth-client", () => ({
useAuthClient: jest.fn(() => mockClient),
}));
// Mock additional SessionUtils methods that are now being used
jest.mock("./conversations", () => {
const actual = jest.requireActual("./conversations");
return {
...actual,
SessionUtils: {
...actual.SessionUtils,
saveSessionData: jest.fn(),
loadSessionData: jest.fn(),
saveAgentConfig: jest.fn(),
loadAgentConfig: jest.fn(),
clearAgentCache: jest.fn(),
},
};
});
const localStorageMock = {
getItem: jest.fn(),
setItem: jest.fn(),
removeItem: jest.fn(),
clear: jest.fn(),
};
Object.defineProperty(window, "localStorage", {
value: localStorageMock,
writable: true,
});
// Mock crypto.randomUUID for test environment
let uuidCounter = 0;
Object.defineProperty(globalThis, "crypto", {
value: {
randomUUID: jest.fn(() => `test-uuid-${++uuidCounter}`),
},
writable: true,
});
describe("SessionManager", () => {
const mockSession: ChatSession = {
id: "session_123",
name: "Test Session",
messages: [
{
id: "msg_1",
role: "user",
content: "Hello",
createdAt: new Date(),
},
],
selectedModel: "test-model",
systemMessage: "You are a helpful assistant.",
agentId: "agent_123",
createdAt: 1710000000,
updatedAt: 1710001000,
};
const mockAgentSessions = [
{
session_id: "session_123",
session_name: "Test Session",
started_at: "2024-01-01T00:00:00Z",
turns: [],
},
{
session_id: "session_456",
session_name: "Another Session",
started_at: "2024-01-01T01:00:00Z",
turns: [],
},
];
beforeEach(() => {
jest.clearAllMocks();
localStorageMock.getItem.mockReturnValue(null);
localStorageMock.setItem.mockImplementation(() => {});
mockClient.agents.session.list.mockResolvedValue({
data: mockAgentSessions,
});
mockClient.agents.session.create.mockResolvedValue({
session_id: "new_session_123",
});
mockClient.agents.session.delete.mockResolvedValue(undefined);
mockClient.agents.session.retrieve.mockResolvedValue({
session_id: "test-session",
session_name: "Test Session",
started_at: new Date().toISOString(),
turns: [],
});
uuidCounter = 0; // Reset UUID counter for consistent test behavior
});
describe("Component Rendering", () => {
test("does not render when no agent is selected", async () => {
const { container } = await act(async () => {
return render(
<Conversations
selectedAgentId=""
currentSession={null}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
expect(container.firstChild).toBeNull();
});
test("renders loading state initially", async () => {
mockClient.agents.session.list.mockImplementation(
() => new Promise(() => {}) // Never resolves to simulate loading
);
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={null}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
expect(screen.getByText("Select Session")).toBeInTheDocument();
// When loading, the "+ New" button should be disabled
expect(screen.getByText("+ New")).toBeDisabled();
});
test("renders session selector when agent sessions are loaded", async () => {
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={null}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
await waitFor(() => {
expect(screen.getByText("Select Session")).toBeInTheDocument();
});
});
test("renders current session name when session is selected", async () => {
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={mockSession}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
await waitFor(() => {
expect(screen.getByText("Test Session")).toBeInTheDocument();
});
});
});
describe("Agent API Integration", () => {
test("loads sessions from agent API on mount", async () => {
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={mockSession}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
await waitFor(() => {
expect(mockClient.agents.session.list).toHaveBeenCalledWith(
"agent_123"
);
});
});
test("handles API errors gracefully", async () => {
mockClient.agents.session.list.mockRejectedValue(new Error("API Error"));
const consoleSpy = jest
.spyOn(console, "error")
.mockImplementation(() => {});
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={mockSession}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
await waitFor(() => {
expect(consoleSpy).toHaveBeenCalledWith(
"Error loading agent sessions:",
expect.any(Error)
);
});
consoleSpy.mockRestore();
});
});
describe("Error Handling", () => {
test("component renders without crashing when API is unavailable", async () => {
mockClient.agents.session.list.mockRejectedValue(
new Error("Network Error")
);
const consoleSpy = jest
.spyOn(console, "error")
.mockImplementation(() => {});
await act(async () => {
render(
<Conversations
selectedAgentId="agent_123"
currentSession={mockSession}
onSessionChange={mockOnSessionChange}
onNewSession={mockOnNewSession}
/>
);
});
// Should still render the session manager with the select trigger
expect(screen.getByRole("combobox")).toBeInTheDocument();
expect(screen.getByText("+ New")).toBeInTheDocument();
consoleSpy.mockRestore();
});
});
});
describe("SessionUtils", () => {
beforeEach(() => {
jest.clearAllMocks();
localStorageMock.getItem.mockReturnValue(null);
localStorageMock.setItem.mockImplementation(() => {});
});
describe("saveCurrentSessionId", () => {
test("saves session ID to localStorage", () => {
SessionUtils.saveCurrentSessionId("test-session-id");
expect(localStorageMock.setItem).toHaveBeenCalledWith(
"chat-playground-current-session",
"test-session-id"
);
});
});
describe("createDefaultSession", () => {
test("creates default session with agent ID", () => {
const result = SessionUtils.createDefaultSession("agent_123");
expect(result).toEqual(
expect.objectContaining({
name: "Default Session",
messages: [],
selectedModel: "",
systemMessage: "You are a helpful assistant.",
agentId: "agent_123",
})
);
expect(result.id).toBeTruthy();
expect(result.createdAt).toBeTruthy();
expect(result.updatedAt).toBeTruthy();
});
test("creates default session with inherited model", () => {
const result = SessionUtils.createDefaultSession(
"agent_123",
"inherited-model"
);
expect(result.selectedModel).toBe("inherited-model");
expect(result.agentId).toBe("agent_123");
});
test("creates unique session IDs", () => {
const originalNow = Date.now;
let mockTime = 1710005000;
Date.now = jest.fn(() => ++mockTime);
const session1 = SessionUtils.createDefaultSession("agent_123");
const session2 = SessionUtils.createDefaultSession("agent_123");
expect(session1.id).not.toBe(session2.id);
Date.now = originalNow;
});
test("sets creation and update timestamps", () => {
const result = SessionUtils.createDefaultSession("agent_123");
expect(result.createdAt).toBeTruthy();
expect(result.updatedAt).toBeTruthy();
expect(typeof result.createdAt).toBe("number");
expect(typeof result.updatedAt).toBe("number");
});
});
});

568
llama_stack/ui/components/chat-playground/conversations.tsx Normal file
View file

@ -0,0 +1,568 @@
"use client";
import { useState, useEffect, useCallback } from "react";
import { Button } from "@/components/ui/button";
import {
Select,
SelectContent,
SelectItem,
SelectTrigger,
SelectValue,
} from "@/components/ui/select";
import { Input } from "@/components/ui/input";
import { Card } from "@/components/ui/card";
import { Trash2 } from "lucide-react";
import type { Message } from "@/components/chat-playground/chat-message";
import { useAuthClient } from "@/hooks/use-auth-client";
import type {
Session,
SessionCreateParams,
} from "llama-stack-client/resources/agents";
export interface ChatSession {
id: string;
name: string;
messages: Message[];
selectedModel: string;
systemMessage: string;
agentId: string;
session?: Session;
createdAt: number;
updatedAt: number;
}
interface SessionManagerProps {
currentSession: ChatSession | null;
onSessionChange: (session: ChatSession) => void;
onNewSession: () => void;
selectedAgentId: string;
}
const CURRENT_SESSION_KEY = "chat-playground-current-session";
// ensures this only happens client side
const safeLocalStorage = {
getItem: (key: string): string | null => {
if (typeof window === "undefined") return null;
try {
return localStorage.getItem(key);
} catch (err) {
console.error("Error accessing localStorage:", err);
return null;
}
},
setItem: (key: string, value: string): void => {
if (typeof window === "undefined") return;
try {
localStorage.setItem(key, value);
} catch (err) {
console.error("Error writing to localStorage:", err);
}
},
removeItem: (key: string): void => {
if (typeof window === "undefined") return;
try {
localStorage.removeItem(key);
} catch (err) {
console.error("Error removing from localStorage:", err);
}
},
};
const generateSessionId = (): string => {
return globalThis.crypto.randomUUID();
};
export function Conversations({
currentSession,
onSessionChange,
selectedAgentId,
}: SessionManagerProps) {
const [sessions, setSessions] = useState<ChatSession[]>([]);
const [showCreateForm, setShowCreateForm] = useState(false);
const [newSessionName, setNewSessionName] = useState("");
const [loading, setLoading] = useState(false);
const client = useAuthClient();
const loadAgentSessions = useCallback(async () => {
if (!selectedAgentId) return;
setLoading(true);
try {
const response = await client.agents.session.list(selectedAgentId);
console.log("Sessions response:", response);
if (!response.data || !Array.isArray(response.data)) {
console.warn("Invalid sessions response, starting fresh");
setSessions([]);
return;
}
const agentSessions: ChatSession[] = response.data
.filter(sessionData => {
const isValid =
sessionData &&
typeof sessionData === "object" &&
sessionData.session_id &&
sessionData.session_name;
if (!isValid) {
console.warn("Filtering out invalid session:", sessionData);
}
return isValid;
})
.map(sessionData => ({
id: sessionData.session_id,
name: sessionData.session_name,
messages: [],
selectedModel: currentSession?.selectedModel || "",
systemMessage:
currentSession?.systemMessage || "You are a helpful assistant.",
agentId: selectedAgentId,
session: sessionData,
createdAt: sessionData.started_at
? new Date(sessionData.started_at).getTime()
: Date.now(),
updatedAt: sessionData.started_at
? new Date(sessionData.started_at).getTime()
: Date.now(),
}));
setSessions(agentSessions);
} catch (error) {
console.error("Error loading agent sessions:", error);
setSessions([]);
} finally {
setLoading(false);
}
}, [
selectedAgentId,
client,
currentSession?.selectedModel,
currentSession?.systemMessage,
]);
useEffect(() => {
if (selectedAgentId) {
loadAgentSessions();
}
}, [selectedAgentId, loadAgentSessions]);
const createNewSession = async () => {
if (!selectedAgentId) return;
const sessionName =
newSessionName.trim() || `Session ${sessions.length + 1}`;
setLoading(true);
try {
const response = await client.agents.session.create(selectedAgentId, {
session_name: sessionName,
} as SessionCreateParams);
const newSession: ChatSession = {
id: response.session_id,
name: sessionName,
messages: [],
selectedModel: currentSession?.selectedModel || "",
systemMessage:
currentSession?.systemMessage || "You are a helpful assistant.",
agentId: selectedAgentId,
createdAt: Date.now(),
updatedAt: Date.now(),
};
setSessions(prev => [...prev, newSession]);
SessionUtils.saveCurrentSessionId(newSession.id, selectedAgentId);
onSessionChange(newSession);
setNewSessionName("");
setShowCreateForm(false);
} catch (error) {
console.error("Error creating session:", error);
} finally {
setLoading(false);
}
};
const loadSessionMessages = useCallback(
async (agentId: string, sessionId: string): Promise<Message[]> => {
try {
const session = await client.agents.session.retrieve(
agentId,
sessionId
);
if (!session || !session.turns || !Array.isArray(session.turns)) {
return [];
}
const messages: Message[] = [];
for (const turn of session.turns) {
// Add user messages from input_messages
if (turn.input_messages && Array.isArray(turn.input_messages)) {
for (const input of turn.input_messages) {
if (input.role === "user" && input.content) {
messages.push({
id: `${turn.turn_id}-user-${messages.length}`,
role: "user",
content:
typeof input.content === "string"
? input.content
: JSON.stringify(input.content),
createdAt: new Date(turn.started_at || Date.now()),
});
}
}
}
// Add assistant message from output_message
if (turn.output_message && turn.output_message.content) {
messages.push({
id: `${turn.turn_id}-assistant-${messages.length}`,
role: "assistant",
content:
typeof turn.output_message.content === "string"
? turn.output_message.content
: JSON.stringify(turn.output_message.content),
createdAt: new Date(
turn.completed_at || turn.started_at || Date.now()
),
});
}
}
return messages;
} catch (error) {
console.error("Error loading session messages:", error);
return [];
}
},
[client]
);
const switchToSession = useCallback(
async (sessionId: string) => {
const session = sessions.find(s => s.id === sessionId);
if (session) {
setLoading(true);
try {
// Load messages for this session
const messages = await loadSessionMessages(
selectedAgentId,
sessionId
);
const sessionWithMessages = {
...session,
messages,
};
SessionUtils.saveCurrentSessionId(sessionId, selectedAgentId);
onSessionChange(sessionWithMessages);
} catch (error) {
console.error("Error switching to session:", error);
// Fallback to session without messages
SessionUtils.saveCurrentSessionId(sessionId, selectedAgentId);
onSessionChange(session);
} finally {
setLoading(false);
}
}
},
[sessions, selectedAgentId, loadSessionMessages, onSessionChange]
);
const deleteSession = async (sessionId: string) => {
if (sessions.length <= 1 || !selectedAgentId) {
return;
}
if (
confirm(
"Are you sure you want to delete this session? This action cannot be undone."
)
) {
setLoading(true);
try {
await client.agents.session.delete(selectedAgentId, sessionId);
const updatedSessions = sessions.filter(s => s.id !== sessionId);
setSessions(updatedSessions);
if (currentSession?.id === sessionId) {
const newCurrentSession = updatedSessions[0] || null;
if (newCurrentSession) {
SessionUtils.saveCurrentSessionId(
newCurrentSession.id,
selectedAgentId
);
onSessionChange(newCurrentSession);
} else {
SessionUtils.clearCurrentSession(selectedAgentId);
onNewSession();
}
}
} catch (error) {
console.error("Error deleting session:", error);
} finally {
setLoading(false);
}
}
};
useEffect(() => {
if (currentSession) {
setSessions(prevSessions => {
const updatedSessions = prevSessions.map(session =>
session.id === currentSession.id ? currentSession : session
);
if (!prevSessions.find(s => s.id === currentSession.id)) {
updatedSessions.push(currentSession);
}
return updatedSessions;
});
}
}, [currentSession]);
// Don't render if no agent is selected
if (!selectedAgentId) {
return null;
}
return (
<div className="relative">
<div className="flex items-center gap-2">
<Select
value={currentSession?.id || ""}
onValueChange={switchToSession}
>
<SelectTrigger className="w-[200px]">
<SelectValue placeholder="Select Session" />
</SelectTrigger>
<SelectContent>
{sessions.map(session => (
<SelectItem key={session.id} value={session.id}>
{session.name}
</SelectItem>
))}
</SelectContent>
</Select>
<Button
onClick={() => setShowCreateForm(true)}
variant="outline"
size="sm"
disabled={loading || !selectedAgentId}
>
+ New
</Button>
{currentSession && sessions.length > 1 && (
<Button
onClick={() => deleteSession(currentSession.id)}
variant="outline"
size="sm"
className="text-destructive hover:text-destructive hover:bg-destructive/10"
title="Delete current session"
>
<Trash2 className="h-3 w-3" />
</Button>
)}
</div>
{showCreateForm && (
<Card className="absolute top-full left-0 mt-2 p-4 space-y-3 w-80 z-50 bg-background border shadow-lg">
<h3 className="text-md font-semibold">Create New Session</h3>
<Input
value={newSessionName}
onChange={e => setNewSessionName(e.target.value)}
placeholder="Session name (optional)"
onKeyDown={e => {
if (e.key === "Enter") {
createNewSession();
} else if (e.key === "Escape") {
setShowCreateForm(false);
setNewSessionName("");
}
}}
/>
<div className="flex gap-2">
<Button
onClick={createNewSession}
className="flex-1"
disabled={loading}
>
{loading ? "Creating..." : "Create"}
</Button>
<Button
variant="outline"
onClick={() => {
setShowCreateForm(false);
setNewSessionName("");
}}
className="flex-1"
>
Cancel
</Button>
</div>
</Card>
)}
{currentSession && sessions.length > 1 && (
<div className="absolute top-full left-0 mt-1 text-xs text-gray-500 whitespace-nowrap">
{sessions.length} sessions • Current: {currentSession.name}
{currentSession.messages.length > 0 &&
` • ${currentSession.messages.length} messages`}
</div>
)}
</div>
);
}
export const SessionUtils = {
loadCurrentSessionId: (agentId?: string): string | null => {
const key = agentId
? `${CURRENT_SESSION_KEY}-${agentId}`
: CURRENT_SESSION_KEY;
return safeLocalStorage.getItem(key);
},
saveCurrentSessionId: (sessionId: string, agentId?: string) => {
const key = agentId
? `${CURRENT_SESSION_KEY}-${agentId}`
: CURRENT_SESSION_KEY;
safeLocalStorage.setItem(key, sessionId);
},
createDefaultSession: (
agentId: string,
inheritModel?: string
): ChatSession => ({
id: generateSessionId(),
name: "Default Session",
messages: [],
selectedModel: inheritModel || "",
systemMessage: "You are a helpful assistant.",
agentId,
createdAt: Date.now(),
updatedAt: Date.now(),
}),
clearCurrentSession: (agentId?: string) => {
const key = agentId
? `${CURRENT_SESSION_KEY}-${agentId}`
: CURRENT_SESSION_KEY;
safeLocalStorage.removeItem(key);
},
loadCurrentAgentId: (): string | null => {
return safeLocalStorage.getItem("chat-playground-current-agent");
},
saveCurrentAgentId: (agentId: string) => {
safeLocalStorage.setItem("chat-playground-current-agent", agentId);
},
// Comprehensive session caching
saveSessionData: (agentId: string, sessionData: ChatSession) => {
const key = `chat-playground-session-data-${agentId}-${sessionData.id}`;
safeLocalStorage.setItem(
key,
JSON.stringify({
...sessionData,
cachedAt: Date.now(),
})
);
},
loadSessionData: (agentId: string, sessionId: string): ChatSession | null => {
const key = `chat-playground-session-data-${agentId}-${sessionId}`;
const cached = safeLocalStorage.getItem(key);
if (!cached) return null;
try {
const data = JSON.parse(cached);
// Check if cache is fresh (less than 1 hour old)
const cacheAge = Date.now() - (data.cachedAt || 0);
if (cacheAge > 60 * 60 * 1000) {
safeLocalStorage.removeItem(key);
return null;
}
// Convert date strings back to Date objects
return {
...data,
messages: data.messages.map(
(msg: { createdAt: string; [key: string]: unknown }) => ({
...msg,
createdAt: new Date(msg.createdAt),
})
),
};
} catch (error) {
console.error("Error parsing cached session data:", error);
safeLocalStorage.removeItem(key);
return null;
}
},
// Agent config caching
saveAgentConfig: (
agentId: string,
config: {
toolgroups?: Array<
string | { name: string; args: Record<string, unknown> }
>;
[key: string]: unknown;
}
) => {
const key = `chat-playground-agent-config-${agentId}`;
safeLocalStorage.setItem(
key,
JSON.stringify({
config,
cachedAt: Date.now(),
})
);
},
loadAgentConfig: (
agentId: string
): {
toolgroups?: Array<
string | { name: string; args: Record<string, unknown> }
>;
[key: string]: unknown;
} | null => {
const key = `chat-playground-agent-config-${agentId}`;
const cached = safeLocalStorage.getItem(key);
if (!cached) return null;
try {
const data = JSON.parse(cached);
// Check if cache is fresh (less than 30 minutes old)
const cacheAge = Date.now() - (data.cachedAt || 0);
if (cacheAge > 30 * 60 * 1000) {
safeLocalStorage.removeItem(key);
return null;
}
return data.config;
} catch (error) {
console.error("Error parsing cached agent config:", error);
safeLocalStorage.removeItem(key);
return null;
}
},
// Clear all cached data for an agent
clearAgentCache: (agentId: string) => {
const keys = Object.keys(localStorage).filter(
key =>
key.includes(`chat-playground-session-data-${agentId}`) ||
key.includes(`chat-playground-agent-config-${agentId}`)
);
keys.forEach(key => safeLocalStorage.removeItem(key));
},
};

6
llama_stack/ui/components/chat-playground/typing-indicator.tsx
View file

@ -5,9 +5,9 @@ export function TypingIndicator() {
<div className="justify-left flex space-x-1">
<div className="rounded-lg bg-muted p-3">
<div className="flex -space-x-2.5">
<Dot className="h-5 w-5 animate-typing-dot-bounce" />
<Dot className="h-5 w-5 animate-typing-dot-bounce [animation-delay:90ms]" />
<Dot className="h-5 w-5 animate-typing-dot-bounce [animation-delay:180ms]" />
<Dot className="h-5 w-5 animate-typing-dot-1" />
<Dot className="h-5 w-5 animate-typing-dot-2" />
<Dot className="h-5 w-5 animate-typing-dot-3" />
</div>
</div>
</div>

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