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feat: Updating files/content response to return additional fields

Browse source 
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-08-06 16:55:14 -04:00
parent e12524af85
commit a19c16428f
143 changed files with 6907 additions and 15104 deletions
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2
.github/workflows/integration-tests.yml vendored
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@ -53,7 +53,7 @@ jobs:
# Get test directories dynamically, excluding non-test directories
# NOTE: we are excluding post_training since the tests take too long
TEST_TYPES=$(find tests/integration -maxdepth 1 -mindepth 1 -type d -printf "%f\n" |
grep -Ev "^(__pycache__|fixtures|test_cases|recordings|post_training)$" |
grep -Ev "^(__pycache__|fixtures|test_cases|recordings|non_ci|post_training)$" |
sort | jq -R -s -c 'split("\n")[:-1]')
echo "test-types=$TEST_TYPES" >> $GITHUB_OUTPUT

1
CONTRIBUTING.md
View file

@ -157,6 +157,7 @@ uv sync
that describes the configuration. These descriptions will be used to generate the provider
documentation.
* When possible, use keyword arguments only when calling functions.
* Llama Stack utilizes [custom Exception classes](llama_stack/apis/common/errors.py) for certain Resources that should be used where applicable.
## Common Tasks

209
docs/_static/llama-stack-spec.html vendored
View file

@ -4734,6 +4734,49 @@
}
}
},
"/v1/openai/v1/moderations": {
"post": {
"responses": {
"200": {
"description": "A moderation object.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/ModerationObject"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"Safety"
],
"description": "Classifies if text and/or image inputs are potentially harmful.",
"parameters": [],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/RunModerationRequest"
}
}
},
"required": true
}
}
},
"/v1/safety/run-shield": {
"post": {
"responses": {
@ -14778,6 +14821,47 @@
"text": {
"type": "string",
"description": "The actual text content"
},
"embedding": {
"type": "array",
"items": {
"type": "number"
},
"description": "(Optional) Embedding vector for the content, if available"
},
"created_timestamp": {
"type": "integer",
"description": "(Optional) Timestamp when the content was created"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
},
"description": "(Optional) Metadata associated with the content, such as source, author, etc."
},
"chunk_metadata": {
"$ref": "#/components/schemas/ChunkMetadata",
"description": "(Optional) Metadata associated with the chunk, such as document ID, source, etc."
}
},
"additionalProperties": false,
@ -16401,6 +16485,131 @@
],
"title": "RunEvalRequest"
},
"RunModerationRequest": {
"type": "object",
"properties": {
"input": {
"oneOf": [
{
"type": "string"
},
{
"type": "array",
"items": {
"type": "string"
}
}
],
"description": "Input (or inputs) to classify. Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models."
},
"model": {
"type": "string",
"description": "The content moderation model you would like to use."
}
},
"additionalProperties": false,
"required": [
"input",
"model"
],
"title": "RunModerationRequest"
},
"ModerationObject": {
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "The unique identifier for the moderation request."
},
"model": {
"type": "string",
"description": "The model used to generate the moderation results."
},
"results": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ModerationObjectResults"
},
"description": "A list of moderation objects"
}
},
"additionalProperties": false,
"required": [
"id",
"model",
"results"
],
"title": "ModerationObject",
"description": "A moderation object."
},
"ModerationObjectResults": {
"type": "object",
"properties": {
"flagged": {
"type": "boolean",
"description": "Whether any of the below categories are flagged."
},
"categories": {
"type": "object",
"additionalProperties": {
"type": "boolean"
},
"description": "A list of the categories, and whether they are flagged or not."
},
"category_applied_input_types": {
"type": "object",
"additionalProperties": {
"type": "array",
"items": {
"type": "string"
}
},
"description": "A list of the categories along with the input type(s) that the score applies to."
},
"category_scores": {
"type": "object",
"additionalProperties": {
"type": "number"
},
"description": "A list of the categories along with their scores as predicted by model. Required set of categories that need to be in response - violence - violence/graphic - harassment - harassment/threatening - hate - hate/threatening - illicit - illicit/violent - sexual - sexual/minors - self-harm - self-harm/intent - self-harm/instructions"
},
"user_message": {
"type": "string"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
}
},
"additionalProperties": false,
"required": [
"flagged",
"metadata"
],
"title": "ModerationObjectResults",
"description": "A moderation object."
},
"RunShieldRequest": {
"type": "object",
"properties": {

152
docs/_static/llama-stack-spec.yaml vendored
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@ -3358,6 +3358,36 @@ paths:
schema:
$ref: '#/components/schemas/RunEvalRequest'
required: true
/v1/openai/v1/moderations:
post:
responses:
'200':
description: A moderation object.
content:
application/json:
schema:
$ref: '#/components/schemas/ModerationObject'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- Safety
description: >-
Classifies if text and/or image inputs are potentially harmful.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/RunModerationRequest'
required: true
/v1/safety/run-shield:
post:
responses:
@ -10966,6 +10996,34 @@ components:
text:
type: string
description: The actual text content
embedding:
type: array
items:
type: number
description: >-
(Optional) Embedding vector for the content, if available
created_timestamp:
type: integer
description: >-
(Optional) Timestamp when the content was created
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
(Optional) Metadata associated with the content, such as source, author,
etc.
chunk_metadata:
$ref: '#/components/schemas/ChunkMetadata'
description: >-
(Optional) Metadata associated with the chunk, such as document ID, source,
etc.
additionalProperties: false
required:
- type
@ -12184,6 +12242,100 @@ components:
required:
- benchmark_config
title: RunEvalRequest
RunModerationRequest:
type: object
properties:
input:
oneOf:
- type: string
- type: array
items:
type: string
description: >-
Input (or inputs) to classify. Can be a single string, an array of strings,
or an array of multi-modal input objects similar to other models.
model:
type: string
description: >-
The content moderation model you would like to use.
additionalProperties: false
required:
- input
- model
title: RunModerationRequest
ModerationObject:
type: object
properties:
id:
type: string
description: >-
The unique identifier for the moderation request.
model:
type: string
description: >-
The model used to generate the moderation results.
results:
type: array
items:
$ref: '#/components/schemas/ModerationObjectResults'
description: A list of moderation objects
additionalProperties: false
required:
- id
- model
- results
title: ModerationObject
description: A moderation object.
ModerationObjectResults:
type: object
properties:
flagged:
type: boolean
description: >-
Whether any of the below categories are flagged.
categories:
type: object
additionalProperties:
type: boolean
description: >-
A list of the categories, and whether they are flagged or not.
category_applied_input_types:
type: object
additionalProperties:
type: array
items:
type: string
description: >-
A list of the categories along with the input type(s) that the score applies
to.
category_scores:
type: object
additionalProperties:
type: number
description: >-
A list of the categories along with their scores as predicted by model.
Required set of categories that need to be in response - violence - violence/graphic
- harassment - harassment/threatening - hate - hate/threatening - illicit
- illicit/violent - sexual - sexual/minors - self-harm - self-harm/intent
- self-harm/instructions
user_message:
type: string
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
additionalProperties: false
required:
- flagged
- metadata
title: ModerationObjectResults
description: A moderation object.
RunShieldRequest:
type: object
properties:

23
docs/source/distributions/building_distro.md
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@ -53,24 +53,31 @@ The main points to consider are:
```
llama stack build -h
usage: llama stack build [-h] [--config CONFIG] [--template TEMPLATE] [--list-templates] [--image-type {container,venv}] [--image-name IMAGE_NAME] [--print-deps-only] [--run]
usage: llama stack build [-h] [--config CONFIG] [--template TEMPLATE] [--distro DISTRIBUTION] [--list-distros] [--image-type {container,venv}] [--image-name IMAGE_NAME] [--print-deps-only]
[--run] [--providers PROVIDERS]
Build a Llama stack container
options:
-h, --help show this help message and exit
--config CONFIG Path to a config file to use for the build. You can find example configs in llama_stack.cores/**/build.yaml. If this argument is not provided, you will
be prompted to enter information interactively (default: None)
--template TEMPLATE Name of the example template config to use for build. You may use `llama stack build --list-templates` to check out the available templates (default: None)
--list-templates Show the available templates for building a Llama Stack distribution (default: False)
--config CONFIG Path to a config file to use for the build. You can find example configs in llama_stack.cores/**/build.yaml. If this argument is not provided, you will be prompted to
enter information interactively (default: None)
--template TEMPLATE (deprecated) Name of the example template config to use for build. You may use `llama stack build --list-distros` to check out the available distributions (default:
None)
--distro DISTRIBUTION, --distribution DISTRIBUTION
Name of the distribution to use for build. You may use `llama stack build --list-distros` to check out the available distributions (default: None)
--list-distros, --list-distributions
Show the available distributions for building a Llama Stack distribution (default: False)
--image-type {container,venv}
Image Type to use for the build. If not specified, will use the image type from the template config. (default: None)
--image-name IMAGE_NAME
[for image-type=container|venv] Name of the virtual environment to use for the build. If not specified, currently active environment will be used if
found. (default: None)
[for image-type=container|venv] Name of the virtual environment to use for the build. If not specified, currently active environment will be used if found. (default:
None)
--print-deps-only Print the dependencies for the stack only, without building the stack (default: False)
--run Run the stack after building using the same image type, name, and other applicable arguments (default: False)
--providers PROVIDERS
Build a config for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple providers per
API. (default: None)
```
After this step is complete, a file named `<name>-build.yaml` and template file `<name>-run.yaml` will be generated and saved at the output file path specified at the end of the command.

8
docs/source/distributions/ondevice_distro/android_sdk.md
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@ -56,12 +56,12 @@ Breaking down the demo app, this section will show the core pieces that are used
### Setup Remote Inferencing
Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution:
```
python -m venv stack-fireworks
source stack-fireworks/bin/activate # On Windows: stack-fireworks\Scripts\activate
uv venv starter --python 3.12
source starter/bin/activate # On Windows: starter\Scripts\activate
pip install --no-cache llama-stack==0.2.2
llama stack build --distro fireworks --image-type venv
llama stack build --distro starter --image-type venv
export FIREWORKS_API_KEY=<SOME_KEY>
llama stack run fireworks --port 5050
llama stack run starter --port 5050
```
Ensure the Llama Stack server version is the same as the Kotlin SDK Library for maximum compatibility.

2
docs/source/distributions/self_hosted_distro/nvidia.md
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@ -157,7 +157,7 @@ docker run \
If you've set up your local development environment, you can also build the image using your local virtual environment.
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
llama stack build --distro nvidia --image-type venv
llama stack run ./run.yaml \
--port 8321 \

7
docs/source/getting_started/demo_script.py
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@ -52,11 +52,16 @@ agent = Agent(
prompt = "How do you do great work?"
print("prompt>", prompt)
use_stream = True
response = agent.create_turn(
messages=[{"role": "user", "content": prompt}],
session_id=agent.create_session("rag_session"),
stream=True,
stream=use_stream,
)
# Only call `AgentEventLogger().log(response)` for streaming responses.
if use_stream:
for log in AgentEventLogger().log(response):
log.print()
else:
print(response)

8
docs/source/getting_started/detailed_tutorial.md
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@ -150,13 +150,7 @@ pip install llama-stack-client
```
:::
:::{tab-item} Install with `venv`
```bash
python -m venv stack-client
source stack-client/bin/activate # On Windows: stack-client\Scripts\activate
pip install llama-stack-client
```
:::
::::
Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference.md) to check the

2
docs/source/providers/files/inline_localfs.md
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@ -8,7 +8,7 @@ Local filesystem-based file storage provider for managing files and documents lo
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `storage_dir` | `<class 'str'>` | No | PydanticUndefined | Directory to store uploaded files |
| `storage_dir` | `<class 'str'>` | No | | Directory to store uploaded files |
| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
| `ttl_secs` | `<class 'int'>` | No | 31536000 | |

2
docs/source/providers/inference/remote_hf_endpoint.md
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@ -8,7 +8,7 @@ HuggingFace Inference Endpoints provider for dedicated model serving.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `endpoint_name` | `<class 'str'>` | No | PydanticUndefined | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
| `endpoint_name` | `<class 'str'>` | No | | The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided. |
| `api_token` | `pydantic.types.SecretStr \| None` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
## Sample Configuration

2
docs/source/providers/inference/remote_hf_serverless.md
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@ -8,7 +8,7 @@ HuggingFace Inference API serverless provider for on-demand model inference.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `huggingface_repo` | `<class 'str'>` | No | PydanticUndefined | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
| `huggingface_repo` | `<class 'str'>` | No | | The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct') |
| `api_token` | `pydantic.types.SecretStr \| None` | No | | Your Hugging Face user access token (will default to locally saved token if not provided) |
## Sample Configuration

2
docs/source/providers/inference/remote_tgi.md
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@ -8,7 +8,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `<class 'str'>` | No | PydanticUndefined | The URL for the TGI serving endpoint |
| `url` | `<class 'str'>` | No | | The URL for the TGI serving endpoint |
## Sample Configuration

3
docs/source/providers/post_training/inline_huggingface.md
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@ -27,7 +27,7 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
| `dpo_beta` | `<class 'float'>` | No | 0.1 | |
| `use_reference_model` | `<class 'bool'>` | No | True | |
| `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid | |
| `dpo_output_dir` | `<class 'str'>` | No | ./checkpoints/dpo | |
| `dpo_output_dir` | `<class 'str'>` | No | | |
## Sample Configuration
@ -35,6 +35,7 @@ HuggingFace-based post-training provider for fine-tuning models using the Huggin
checkpoint_format: huggingface
distributed_backend: null
device: cpu
dpo_output_dir: ~/.llama/dummy/dpo_output
```

2
docs/source/providers/vector_io/inline_chromadb.md
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@ -41,7 +41,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `db_path` | `<class 'str'>` | No | PydanticUndefined | |
| `db_path` | `<class 'str'>` | No | | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
## Sample Configuration

2
docs/source/providers/vector_io/inline_milvus.md
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@ -10,7 +10,7 @@ Please refer to the remote provider documentation.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `db_path` | `<class 'str'>` | No | PydanticUndefined | |
| `db_path` | `<class 'str'>` | No | | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
| `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |

2
docs/source/providers/vector_io/inline_qdrant.md
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@ -50,7 +50,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `path` | `<class 'str'>` | No | PydanticUndefined | |
| `path` | `<class 'str'>` | No | | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | |
## Sample Configuration

2
docs/source/providers/vector_io/inline_sqlite-vec.md
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@ -205,7 +205,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
| `db_path` | `<class 'str'>` | No | | Path to the SQLite database file |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
## Sample Configuration

2
docs/source/providers/vector_io/inline_sqlite_vec.md
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@ -10,7 +10,7 @@ Please refer to the sqlite-vec provider documentation.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
| `db_path` | `<class 'str'>` | No | | Path to the SQLite database file |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
## Sample Configuration

2
docs/source/providers/vector_io/remote_chromadb.md
View file

@ -40,7 +40,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `url` | `str \| None` | No | PydanticUndefined | |
| `url` | `str \| None` | No | | |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
## Sample Configuration

4
docs/source/providers/vector_io/remote_milvus.md
View file

@ -111,8 +111,8 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `uri` | `<class 'str'>` | No | PydanticUndefined | The URI of the Milvus server |
| `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
| `uri` | `<class 'str'>` | No | | The URI of the Milvus server |
| `token` | `str \| None` | No | | The token of the Milvus server |
| `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend |
| `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |

2
docs/source/references/llama_cli_reference/download_models.md
View file

@ -19,7 +19,7 @@ You have two ways to install Llama Stack:
cd ~/local
git clone git@github.com:meta-llama/llama-stack.git
python -m venv myenv
uv venv myenv --python 3.12
source myenv/bin/activate # On Windows: myenv\Scripts\activate
cd llama-stack

2
docs/source/references/llama_cli_reference/index.md
View file

@ -19,7 +19,7 @@ You have two ways to install Llama Stack:
cd ~/local
git clone git@github.com:meta-llama/llama-stack.git
python -m venv myenv
uv venv myenv --python 3.12
source myenv/bin/activate # On Windows: myenv\Scripts\activate
cd llama-stack

32
llama_stack/apis/common/errors.py
View file

@ -10,6 +10,16 @@
# 3. All classes should propogate the inherited __init__ function otherwise via 'super().__init__(message)'
class ResourceNotFoundError(ValueError):
"""generic exception for a missing Llama Stack resource"""
def __init__(self, resource_name: str, resource_type: str, client_list: str) -> None:
message = (
f"{resource_type} '{resource_name}' not found. Use '{client_list}' to list available {resource_type}s."
)
super().__init__(message)
class UnsupportedModelError(ValueError):
"""raised when model is not present in the list of supported models"""
@ -18,38 +28,32 @@ class UnsupportedModelError(ValueError):
super().__init__(message)
class ModelNotFoundError(ValueError):
class ModelNotFoundError(ResourceNotFoundError):
"""raised when Llama Stack cannot find a referenced model"""
def __init__(self, model_name: str) -> None:
message = f"Model '{model_name}' not found. Use client.models.list() to list available models."
super().__init__(message)
super().__init__(model_name, "Model", "client.models.list()")
class VectorStoreNotFoundError(ValueError):
class VectorStoreNotFoundError(ResourceNotFoundError):
"""raised when Llama Stack cannot find a referenced vector store"""
def __init__(self, vector_store_name: str) -> None:
message = f"Vector store '{vector_store_name}' not found. Use client.vector_dbs.list() to list available vector stores."
super().__init__(message)
super().__init__(vector_store_name, "Vector Store", "client.vector_dbs.list()")
class DatasetNotFoundError(ValueError):
class DatasetNotFoundError(ResourceNotFoundError):
"""raised when Llama Stack cannot find a referenced dataset"""
def __init__(self, dataset_name: str) -> None:
message = f"Dataset '{dataset_name}' not found. Use client.datasets.list() to list available datasets."
super().__init__(message)
super().__init__(dataset_name, "Dataset", "client.datasets.list()")
class ToolGroupNotFoundError(ValueError):
class ToolGroupNotFoundError(ResourceNotFoundError):
"""raised when Llama Stack cannot find a referenced tool group"""
def __init__(self, toolgroup_name: str) -> None:
message = (
f"Tool group '{toolgroup_name}' not found. Use client.toolgroups.list() to list available tool groups."
)
super().__init__(message)
super().__init__(toolgroup_name, "Tool Group", "client.toolgroups.list()")
class SessionNotFoundError(ValueError):

77
llama_stack/apis/safety/safety.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from enum import Enum
from enum import Enum, StrEnum
from typing import Any, Protocol, runtime_checkable
from pydantic import BaseModel, Field
@ -15,6 +15,71 @@ from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
from llama_stack.schema_utils import json_schema_type, webmethod
# OpenAI Categories to return in the response
class OpenAICategories(StrEnum):
"""
Required set of categories in moderations api response
"""
VIOLENCE = "violence"
VIOLENCE_GRAPHIC = "violence/graphic"
HARRASMENT = "harassment"
HARRASMENT_THREATENING = "harassment/threatening"
HATE = "hate"
HATE_THREATENING = "hate/threatening"
ILLICIT = "illicit"
ILLICIT_VIOLENT = "illicit/violent"
SEXUAL = "sexual"
SEXUAL_MINORS = "sexual/minors"
SELF_HARM = "self-harm"
SELF_HARM_INTENT = "self-harm/intent"
SELF_HARM_INSTRUCTIONS = "self-harm/instructions"
@json_schema_type
class ModerationObjectResults(BaseModel):
"""A moderation object.
:param flagged: Whether any of the below categories are flagged.
:param categories: A list of the categories, and whether they are flagged or not.
:param category_applied_input_types: A list of the categories along with the input type(s) that the score applies to.
:param category_scores: A list of the categories along with their scores as predicted by model.
Required set of categories that need to be in response
- violence
- violence/graphic
- harassment
- harassment/threatening
- hate
- hate/threatening
- illicit
- illicit/violent
- sexual
- sexual/minors
- self-harm
- self-harm/intent
- self-harm/instructions
"""
flagged: bool
categories: dict[str, bool] | None = None
category_applied_input_types: dict[str, list[str]] | None = None
category_scores: dict[str, float] | None = None
user_message: str | None = None
metadata: dict[str, Any] = Field(default_factory=dict)
@json_schema_type
class ModerationObject(BaseModel):
"""A moderation object.
:param id: The unique identifier for the moderation request.
:param model: The model used to generate the moderation results.
:param results: A list of moderation objects
"""
id: str
model: str
results: list[ModerationObjectResults]
@json_schema_type
class ViolationLevel(Enum):
"""Severity level of a safety violation.
@ -82,3 +147,13 @@ class Safety(Protocol):
:returns: A RunShieldResponse.
"""
...
@webmethod(route="/openai/v1/moderations", method="POST")
async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
"""Classifies if text and/or image inputs are potentially harmful.
:param input: Input (or inputs) to classify.
Can be a single string, an array of strings, or an array of multi-modal input objects similar to other models.
:param model: The content moderation model you would like to use.
:returns: A moderation object.
"""
...

8
llama_stack/apis/vector_io/vector_io.py
View file

@ -226,10 +226,18 @@ class VectorStoreContent(BaseModel):
:param type: Content type, currently only "text" is supported
:param text: The actual text content
:param embedding: (Optional) Embedding vector for the content, if available
:param created_timestamp: (Optional) Timestamp when the content was created
:param metadata: (Optional) Metadata associated with the content, such as source, author, etc.
:param chunk_metadata: (Optional) Metadata associated with the chunk, such as document ID, source, etc.
"""
type: Literal["text"]
text: str
embedding: list[float] | None = None
created_timestamp: int | None = None
metadata: dict[str, Any] | None = None
chunk_metadata: ChunkMetadata | None = None
@json_schema_type

397
llama_stack/core/routers/inference.py
View file

@ -7,6 +7,7 @@
import asyncio
import time
from collections.abc import AsyncGenerator, AsyncIterator
from datetime import UTC, datetime
from typing import Annotated, Any
from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam
@ -25,14 +26,21 @@ from llama_stack.apis.inference import (
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
CompletionMessage,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
ListOpenAIChatCompletionResponse,
LogProbConfig,
Message,
OpenAIAssistantMessageParam,
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAIChatCompletionToolCall,
OpenAIChatCompletionToolCallFunction,
OpenAIChoice,
OpenAIChoiceLogprobs,
OpenAICompletion,
OpenAICompletionWithInputMessages,
OpenAIEmbeddingsResponse,
@ -55,7 +63,6 @@ from llama_stack.models.llama.llama3.chat_format import ChatFormat
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
from llama_stack.providers.utils.inference.inference_store import InferenceStore
from llama_stack.providers.utils.inference.stream_utils import stream_and_store_openai_completion
from llama_stack.providers.utils.telemetry.tracing import get_current_span
logger = get_logger(name=__name__, category="core")
@ -119,6 +126,7 @@ class InferenceRouter(Inference):
if span is None:
logger.warning("No span found for token usage metrics")
return []
metrics = [
("prompt_tokens", prompt_tokens),
("completion_tokens", completion_tokens),
@ -132,7 +140,7 @@ class InferenceRouter(Inference):
span_id=span.span_id,
metric=metric_name,
value=value,
timestamp=time.time(),
timestamp=datetime.now(UTC),
unit="tokens",
attributes={
"model_id": model.model_id,
@ -234,48 +242,25 @@ class InferenceRouter(Inference):
prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)
if stream:
response_stream = await provider.chat_completion(**params)
return self.stream_tokens_and_compute_metrics(
response=response_stream,
prompt_tokens=prompt_tokens,
model=model,
tool_prompt_format=tool_config.tool_prompt_format,
)
async def stream_generator():
completion_text = ""
async for chunk in await provider.chat_completion(**params):
if chunk.event.event_type == ChatCompletionResponseEventType.progress:
if chunk.event.delta.type == "text":
completion_text += chunk.event.delta.text
if chunk.event.event_type == ChatCompletionResponseEventType.complete:
completion_tokens = await self._count_tokens(
[
CompletionMessage(
content=completion_text,
stop_reason=StopReason.end_of_turn,
)
],
tool_config.tool_prompt_format,
)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
metrics = await self._compute_and_log_token_usage(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
)
chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
yield chunk
return stream_generator()
else:
response = await provider.chat_completion(**params)
completion_tokens = await self._count_tokens(
[response.completion_message],
tool_config.tool_prompt_format,
metrics = await self.count_tokens_and_compute_metrics(
response=response,
prompt_tokens=prompt_tokens,
model=model,
tool_prompt_format=tool_config.tool_prompt_format,
)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
metrics = await self._compute_and_log_token_usage(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
# these metrics will show up in the client response.
response.metrics = (
metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
)
response.metrics = metrics if response.metrics is None else response.metrics + metrics
return response
async def batch_chat_completion(
@ -332,38 +317,19 @@ class InferenceRouter(Inference):
)
prompt_tokens = await self._count_tokens(content)
if stream:
async def stream_generator():
completion_text = ""
async for chunk in await provider.completion(**params):
if hasattr(chunk, "delta"):
completion_text += chunk.delta
if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
completion_tokens = await self._count_tokens(completion_text)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
metrics = await self._compute_and_log_token_usage(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
)
chunk.metrics = metrics if chunk.metrics is None else chunk.metrics + metrics
yield chunk
return stream_generator()
else:
response = await provider.completion(**params)
completion_tokens = await self._count_tokens(response.content)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
metrics = await self._compute_and_log_token_usage(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
if stream:
return self.stream_tokens_and_compute_metrics(
response=response,
prompt_tokens=prompt_tokens,
model=model,
)
metrics = await self.count_tokens_and_compute_metrics(
response=response, prompt_tokens=prompt_tokens, model=model
)
response.metrics = metrics if response.metrics is None else response.metrics + metrics
return response
async def batch_completion(
@ -457,9 +423,29 @@ class InferenceRouter(Inference):
prompt_logprobs=prompt_logprobs,
suffix=suffix,
)
provider = await self.routing_table.get_provider_impl(model_obj.identifier)
if stream:
return await provider.openai_completion(**params)
# TODO: Metrics do NOT work with openai_completion stream=True due to the fact
# that we do not return an AsyncIterator, our tests expect a stream of chunks we cannot intercept currently.
# response_stream = await provider.openai_completion(**params)
response = await provider.openai_completion(**params)
if self.telemetry:
metrics = self._construct_metrics(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
# these metrics will show up in the client response.
response.metrics = (
metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
)
return response
async def openai_chat_completion(
self,
@ -537,17 +523,37 @@ class InferenceRouter(Inference):
top_p=top_p,
user=user,
)
provider = await self.routing_table.get_provider_impl(model_obj.identifier)
if stream:
response_stream = await provider.openai_chat_completion(**params)
if self.store:
return stream_and_store_openai_completion(response_stream, model, self.store, messages)
return response_stream
else:
# For streaming, the provider returns AsyncIterator[OpenAIChatCompletionChunk]
# We need to add metrics to each chunk and store the final completion
return self.stream_tokens_and_compute_metrics_openai_chat(
response=response_stream,
model=model_obj,
messages=messages,
)
response = await self._nonstream_openai_chat_completion(provider, params)
# Store the response with the ID that will be returned to the client
if self.store:
await self.store.store_chat_completion(response, messages)
if self.telemetry:
metrics = self._construct_metrics(
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
total_tokens=response.usage.total_tokens,
model=model_obj,
)
for metric in metrics:
await self.telemetry.log_event(metric)
# these metrics will show up in the client response.
response.metrics = (
metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
)
return response
async def openai_embeddings(
@ -625,3 +631,244 @@ class InferenceRouter(Inference):
status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}"
)
return health_statuses
async def stream_tokens_and_compute_metrics(
self,
response,
prompt_tokens,
model,
tool_prompt_format: ToolPromptFormat | None = None,
) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None] | AsyncGenerator[CompletionResponseStreamChunk, None]:
completion_text = ""
async for chunk in response:
complete = False
if hasattr(chunk, "event"): # only ChatCompletions have .event
if chunk.event.event_type == ChatCompletionResponseEventType.progress:
if chunk.event.delta.type == "text":
completion_text += chunk.event.delta.text
if chunk.event.event_type == ChatCompletionResponseEventType.complete:
complete = True
completion_tokens = await self._count_tokens(
[
CompletionMessage(
content=completion_text,
stop_reason=StopReason.end_of_turn,
)
],
tool_prompt_format=tool_prompt_format,
)
else:
if hasattr(chunk, "delta"):
completion_text += chunk.delta
if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry:
complete = True
completion_tokens = await self._count_tokens(completion_text)
# if we are done receiving tokens
if complete:
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
# Create a separate span for streaming completion metrics
if self.telemetry:
# Log metrics in the new span context
completion_metrics = self._construct_metrics(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
model=model,
)
for metric in completion_metrics:
if metric.metric in [
"completion_tokens",
"total_tokens",
]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
# Return metrics in response
async_metrics = [
MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics
]
chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics
else:
# Fallback if no telemetry
completion_metrics = self._construct_metrics(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
)
async_metrics = [
MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics
]
chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics
yield chunk
async def count_tokens_and_compute_metrics(
self,
response: ChatCompletionResponse | CompletionResponse,
prompt_tokens,
model,
tool_prompt_format: ToolPromptFormat | None = None,
):
if isinstance(response, ChatCompletionResponse):
content = [response.completion_message]
else:
content = response.content
completion_tokens = await self._count_tokens(messages=content, tool_prompt_format=tool_prompt_format)
total_tokens = (prompt_tokens or 0) + (completion_tokens or 0)
# Create a separate span for completion metrics
if self.telemetry:
# Log metrics in the new span context
completion_metrics = self._construct_metrics(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
model=model,
)
for metric in completion_metrics:
if metric.metric in ["completion_tokens", "total_tokens"]: # Only log completion and total tokens
await self.telemetry.log_event(metric)
# Return metrics in response
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics]
# Fallback if no telemetry
metrics = self._construct_metrics(
prompt_tokens or 0,
completion_tokens or 0,
total_tokens,
model,
)
return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics]
async def stream_tokens_and_compute_metrics_openai_chat(
self,
response: AsyncIterator[OpenAIChatCompletionChunk],
model: Model,
messages: list[OpenAIMessageParam] | None = None,
) -> AsyncIterator[OpenAIChatCompletionChunk]:
"""Stream OpenAI chat completion chunks, compute metrics, and store the final completion."""
id = None
created = None
choices_data: dict[int, dict[str, Any]] = {}
try:
async for chunk in response:
# Skip None chunks
if chunk is None:
continue
# Capture ID and created timestamp from first chunk
if id is None and chunk.id:
id = chunk.id
if created is None and chunk.created:
created = chunk.created
# Accumulate choice data for final assembly
if chunk.choices:
for choice_delta in chunk.choices:
idx = choice_delta.index
if idx not in choices_data:
choices_data[idx] = {
"content_parts": [],
"tool_calls_builder": {},
"finish_reason": None,
"logprobs_content_parts": [],
}
current_choice_data = choices_data[idx]
if choice_delta.delta:
delta = choice_delta.delta
if delta.content:
current_choice_data["content_parts"].append(delta.content)
if delta.tool_calls:
for tool_call_delta in delta.tool_calls:
tc_idx = tool_call_delta.index
if tc_idx not in current_choice_data["tool_calls_builder"]:
current_choice_data["tool_calls_builder"][tc_idx] = {
"id": None,
"type": "function",
"function_name_parts": [],
"function_arguments_parts": [],
}
builder = current_choice_data["tool_calls_builder"][tc_idx]
if tool_call_delta.id:
builder["id"] = tool_call_delta.id
if tool_call_delta.type:
builder["type"] = tool_call_delta.type
if tool_call_delta.function:
if tool_call_delta.function.name:
builder["function_name_parts"].append(tool_call_delta.function.name)
if tool_call_delta.function.arguments:
builder["function_arguments_parts"].append(
tool_call_delta.function.arguments
)
if choice_delta.finish_reason:
current_choice_data["finish_reason"] = choice_delta.finish_reason
if choice_delta.logprobs and choice_delta.logprobs.content:
current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content)
# Compute metrics on final chunk
if chunk.choices and chunk.choices[0].finish_reason:
completion_text = ""
for choice_data in choices_data.values():
completion_text += "".join(choice_data["content_parts"])
# Add metrics to the chunk
if self.telemetry and chunk.usage:
metrics = self._construct_metrics(
prompt_tokens=chunk.usage.prompt_tokens,
completion_tokens=chunk.usage.completion_tokens,
total_tokens=chunk.usage.total_tokens,
model=model,
)
for metric in metrics:
await self.telemetry.log_event(metric)
yield chunk
finally:
# Store the final assembled completion
if id and self.store and messages:
assembled_choices: list[OpenAIChoice] = []
for choice_idx, choice_data in choices_data.items():
content_str = "".join(choice_data["content_parts"])
assembled_tool_calls: list[OpenAIChatCompletionToolCall] = []
if choice_data["tool_calls_builder"]:
for tc_build_data in choice_data["tool_calls_builder"].values():
if tc_build_data["id"]:
func_name = "".join(tc_build_data["function_name_parts"])
func_args = "".join(tc_build_data["function_arguments_parts"])
assembled_tool_calls.append(
OpenAIChatCompletionToolCall(
id=tc_build_data["id"],
type=tc_build_data["type"],
function=OpenAIChatCompletionToolCallFunction(
name=func_name, arguments=func_args
),
)
)
message = OpenAIAssistantMessageParam(
role="assistant",
content=content_str if content_str else None,
tool_calls=assembled_tool_calls if assembled_tool_calls else None,
)
logprobs_content = choice_data["logprobs_content_parts"]
final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None
assembled_choices.append(
OpenAIChoice(
finish_reason=choice_data["finish_reason"],
index=choice_idx,
message=message,
logprobs=final_logprobs,
)
)
final_response = OpenAIChatCompletion(
id=id,
choices=assembled_choices,
created=created or int(time.time()),
model=model.identifier,
object="chat.completion",
)
await self.store.store_chat_completion(final_response, messages)

39
llama_stack/core/routers/safety.py
View file

@ -10,6 +10,7 @@ from llama_stack.apis.inference import (
Message,
)
from llama_stack.apis.safety import RunShieldResponse, Safety
from llama_stack.apis.safety.safety import ModerationObject, OpenAICategories
from llama_stack.apis.shields import Shield
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import RoutingTable
@ -60,3 +61,41 @@ class SafetyRouter(Safety):
messages=messages,
params=params,
)
async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
async def get_shield_id(self, model: str) -> str:
"""Get Shield id from model (provider_resource_id) of shield."""
list_shields_response = await self.routing_table.list_shields()
matches = [s.identifier for s in list_shields_response.data if model == s.provider_resource_id]
if not matches:
raise ValueError(f"No shield associated with provider_resource id {model}")
if len(matches) > 1:
raise ValueError(f"Multiple shields associated with provider_resource id {model}")
return matches[0]
shield_id = await get_shield_id(self, model)
logger.debug(f"SafetyRouter.run_moderation: {shield_id}")
provider = await self.routing_table.get_provider_impl(shield_id)
response = await provider.run_moderation(
input=input,
model=model,
)
self._validate_required_categories_exist(response)
return response
def _validate_required_categories_exist(self, response: ModerationObject) -> None:
"""Validate the ProviderImpl response contains the required Open AI moderations categories."""
required_categories = list(map(str, OpenAICategories))
categories = response.results[0].categories
category_applied_input_types = response.results[0].category_applied_input_types
category_scores = response.results[0].category_scores
for i in [categories, category_applied_input_types, category_scores]:
if not set(required_categories).issubset(set(i.keys())):
raise ValueError(
f"ProviderImpl response is missing required categories: {set(required_categories) - set(i.keys())}"
)

1
llama_stack/distributions/ci-tests/run.yaml
View file

@ -154,6 +154,7 @@ providers:
checkpoint_format: huggingface
distributed_backend: null
device: cpu
dpo_output_dir: ~/.llama/distributions/ci-tests/dpo_output
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference

2
llama_stack/distributions/nvidia/doc_template.md
View file

@ -129,7 +129,7 @@ docker run \
If you've set up your local development environment, you can also build the image using your local virtual environment.
```bash
INFERENCE_MODEL=meta-llama/Llama-3.1-8b-Instruct
INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct
llama stack build --distro nvidia --image-type venv
llama stack run ./run.yaml \
--port 8321 \

2
llama_stack/distributions/postgres-demo/postgres_demo.py
View file

@ -123,7 +123,7 @@ def get_distribution_template() -> DistributionTemplate:
config=dict(
service_name="${env.OTEL_SERVICE_NAME:=\u200b}",
sinks="${env.TELEMETRY_SINKS:=console,otel_trace}",
otel_trace_endpoint="${env.OTEL_TRACE_ENDPOINT:=http://localhost:4318/v1/traces}",
otel_exporter_otlp_endpoint="${env.OTEL_EXPORTER_OTLP_ENDPOINT:=http://localhost:4318/v1/traces}",
),
)
],

2
llama_stack/distributions/postgres-demo/run.yaml
View file

@ -55,7 +55,7 @@ providers:
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,otel_trace}
otel_trace_endpoint: ${env.OTEL_TRACE_ENDPOINT:=http://localhost:4318/v1/traces}
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=http://localhost:4318/v1/traces}
tool_runtime:
- provider_id: brave-search
provider_type: remote::brave-search

1
llama_stack/distributions/starter/run.yaml
View file

@ -154,6 +154,7 @@ providers:
checkpoint_format: huggingface
distributed_backend: null
device: cpu
dpo_output_dir: ~/.llama/distributions/starter/dpo_output
eval:
- provider_id: meta-reference
provider_type: inline::meta-reference

6
llama_stack/providers/inline/datasetio/localfs/datasetio.py
View file

@ -5,8 +5,6 @@
# the root directory of this source tree.
from typing import Any
import pandas
from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Dataset
@ -44,6 +42,8 @@ class PandasDataframeDataset:
if self.dataset_def.source.type == "uri":
self.df = await get_dataframe_from_uri(self.dataset_def.source.uri)
elif self.dataset_def.source.type == "rows":
import pandas
self.df = pandas.DataFrame(self.dataset_def.source.rows)
else:
raise ValueError(f"Unsupported dataset source type: {self.dataset_def.source.type}")
@ -103,6 +103,8 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
return paginate_records(records, start_index, limit)
async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
import pandas
dataset_def = self.dataset_infos[dataset_id]
dataset_impl = PandasDataframeDataset(dataset_def)
await dataset_impl.load()

9
llama_stack/providers/inline/post_training/huggingface/config.py
View file

@ -71,8 +71,13 @@ class HuggingFacePostTrainingConfig(BaseModel):
dpo_beta: float = 0.1
use_reference_model: bool = True
dpo_loss_type: Literal["sigmoid", "hinge", "ipo", "kto_pair"] = "sigmoid"
dpo_output_dir: str = "./checkpoints/dpo"
dpo_output_dir: str
@classmethod
def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
return {"checkpoint_format": "huggingface", "distributed_backend": None, "device": "cpu"}
return {
"checkpoint_format": "huggingface",
"distributed_backend": None,
"device": "cpu",
"dpo_output_dir": __distro_dir__ + "/dpo_output",
}

19
llama_stack/providers/inline/post_training/huggingface/post_training.py
View file

@ -22,15 +22,8 @@ from llama_stack.apis.post_training import (
from llama_stack.providers.inline.post_training.huggingface.config import (
HuggingFacePostTrainingConfig,
)
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
HFFinetuningSingleDevice,
)
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
HFDPOAlignmentSingleDevice,
)
from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
from llama_stack.schema_utils import webmethod
class TrainingArtifactType(Enum):
@ -85,6 +78,10 @@ class HuggingFacePostTrainingImpl:
algorithm_config: AlgorithmConfig | None = None,
) -> PostTrainingJob:
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device import (
HFFinetuningSingleDevice,
)
on_log_message_cb("Starting HF finetuning")
recipe = HFFinetuningSingleDevice(
@ -124,6 +121,10 @@ class HuggingFacePostTrainingImpl:
logger_config: dict[str, Any],
) -> PostTrainingJob:
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.huggingface.recipes.finetune_single_device_dpo import (
HFDPOAlignmentSingleDevice,
)
on_log_message_cb("Starting HF DPO alignment")
recipe = HFDPOAlignmentSingleDevice(
@ -168,7 +169,6 @@ class HuggingFacePostTrainingImpl:
data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
return data[0] if data else None
@webmethod(route="/post-training/job/status")
async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
job = self._scheduler.get_job(job_uuid)
@ -195,16 +195,13 @@ class HuggingFacePostTrainingImpl:
resources_allocated=self._get_resources_allocated(job),
)
@webmethod(route="/post-training/job/cancel")
async def cancel_training_job(self, job_uuid: str) -> None:
self._scheduler.cancel(job_uuid)
@webmethod(route="/post-training/job/artifacts")
async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
job = self._scheduler.get_job(job_uuid)
return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))
@webmethod(route="/post-training/jobs", method="GET")
async def get_training_jobs(self) -> ListPostTrainingJobsResponse:
return ListPostTrainingJobsResponse(
data=[PostTrainingJob(job_uuid=job.id) for job in self._scheduler.get_jobs()]

11
llama_stack/providers/inline/post_training/torchtune/post_training.py
View file

@ -23,12 +23,8 @@ from llama_stack.apis.post_training import (
from llama_stack.providers.inline.post_training.torchtune.config import (
TorchtunePostTrainingConfig,
)
from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
LoraFinetuningSingleDevice,
)
from llama_stack.providers.utils.scheduler import JobArtifact, Scheduler
from llama_stack.providers.utils.scheduler import JobStatus as SchedulerJobStatus
from llama_stack.schema_utils import webmethod
class TrainingArtifactType(Enum):
@ -84,6 +80,10 @@ class TorchtunePostTrainingImpl:
if isinstance(algorithm_config, LoraFinetuningConfig):
async def handler(on_log_message_cb, on_status_change_cb, on_artifact_collected_cb):
from llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device import (
LoraFinetuningSingleDevice,
)
on_log_message_cb("Starting Lora finetuning")
recipe = LoraFinetuningSingleDevice(
@ -144,7 +144,6 @@ class TorchtunePostTrainingImpl:
data = cls._get_artifacts_metadata_by_type(job, TrainingArtifactType.RESOURCES_STATS.value)
return data[0] if data else None
@webmethod(route="/post-training/job/status")
async def get_training_job_status(self, job_uuid: str) -> PostTrainingJobStatusResponse | None:
job = self._scheduler.get_job(job_uuid)
@ -171,11 +170,9 @@ class TorchtunePostTrainingImpl:
resources_allocated=self._get_resources_allocated(job),
)
@webmethod(route="/post-training/job/cancel")
async def cancel_training_job(self, job_uuid: str) -> None:
self._scheduler.cancel(job_uuid)
@webmethod(route="/post-training/job/artifacts")
async def get_training_job_artifacts(self, job_uuid: str) -> PostTrainingJobArtifactsResponse | None:
job = self._scheduler.get_job(job_uuid)
return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=self._get_checkpoints(job))

170
llama_stack/providers/inline/safety/llama_guard/llama_guard.py
View file

@ -4,7 +4,9 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import re
import uuid
from string import Template
from typing import Any
@ -20,6 +22,7 @@ from llama_stack.apis.safety import (
SafetyViolation,
ViolationLevel,
)
from llama_stack.apis.safety.safety import ModerationObject, ModerationObjectResults, OpenAICategories
from llama_stack.apis.shields import Shield
from llama_stack.core.datatypes import Api
from llama_stack.models.llama.datatypes import Role
@ -67,6 +70,31 @@ SAFETY_CATEGORIES_TO_CODE_MAP = {
CAT_ELECTIONS: "S13",
CAT_CODE_INTERPRETER_ABUSE: "S14",
}
SAFETY_CODE_TO_CATEGORIES_MAP = {v: k for k, v in SAFETY_CATEGORIES_TO_CODE_MAP.items()}
OPENAI_TO_LLAMA_CATEGORIES_MAP = {
OpenAICategories.VIOLENCE: [CAT_VIOLENT_CRIMES],
OpenAICategories.VIOLENCE_GRAPHIC: [CAT_VIOLENT_CRIMES],
OpenAICategories.HARRASMENT: [CAT_CHILD_EXPLOITATION],
OpenAICategories.HARRASMENT_THREATENING: [CAT_VIOLENT_CRIMES, CAT_CHILD_EXPLOITATION],
OpenAICategories.HATE: [CAT_HATE],
OpenAICategories.HATE_THREATENING: [CAT_HATE, CAT_VIOLENT_CRIMES],
OpenAICategories.ILLICIT: [CAT_NON_VIOLENT_CRIMES],
OpenAICategories.ILLICIT_VIOLENT: [CAT_VIOLENT_CRIMES, CAT_INDISCRIMINATE_WEAPONS],
OpenAICategories.SEXUAL: [CAT_SEX_CRIMES, CAT_SEXUAL_CONTENT],
OpenAICategories.SEXUAL_MINORS: [CAT_CHILD_EXPLOITATION],
OpenAICategories.SELF_HARM: [CAT_SELF_HARM],
OpenAICategories.SELF_HARM_INTENT: [CAT_SELF_HARM],
OpenAICategories.SELF_HARM_INSTRUCTIONS: [CAT_SELF_HARM, CAT_SPECIALIZED_ADVICE],
# These are custom categories that are not in the OpenAI moderation categories
"custom/defamation": [CAT_DEFAMATION],
"custom/specialized_advice": [CAT_SPECIALIZED_ADVICE],
"custom/privacy_violation": [CAT_PRIVACY],
"custom/intellectual_property": [CAT_INTELLECTUAL_PROPERTY],
"custom/weapons": [CAT_INDISCRIMINATE_WEAPONS],
"custom/elections": [CAT_ELECTIONS],
"custom/code_interpreter_abuse": [CAT_CODE_INTERPRETER_ABUSE],
}
DEFAULT_LG_V3_SAFETY_CATEGORIES = [
@ -194,6 +222,34 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
return await impl.run(messages)
async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
if isinstance(input, list):
messages = input.copy()
else:
messages = [input]
# convert to user messages format with role
messages = [UserMessage(content=m) for m in messages]
# Determine safety categories based on the model type
# For known Llama Guard models, use specific categories
if model in LLAMA_GUARD_MODEL_IDS:
# Use the mapped model for categories but the original model_id for inference
mapped_model = LLAMA_GUARD_MODEL_IDS[model]
safety_categories = MODEL_TO_SAFETY_CATEGORIES_MAP.get(mapped_model, DEFAULT_LG_V3_SAFETY_CATEGORIES)
else:
# For unknown models, use default Llama Guard 3 8B categories
safety_categories = DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
impl = LlamaGuardShield(
model=model,
inference_api=self.inference_api,
excluded_categories=self.config.excluded_categories,
safety_categories=safety_categories,
)
return await impl.run_moderation(messages)
class LlamaGuardShield:
def __init__(
@ -340,3 +396,117 @@ class LlamaGuardShield:
)
raise ValueError(f"Unexpected response: {response}")
async def run_moderation(self, messages: list[Message]) -> ModerationObject:
if not messages:
return self.create_moderation_object(self.model)
# TODO: Add Image based support for OpenAI Moderations
shield_input_message = self.build_text_shield_input(messages)
response = await self.inference_api.openai_chat_completion(
model=self.model,
messages=[shield_input_message],
stream=False,
)
content = response.choices[0].message.content
content = content.strip()
return self.get_moderation_object(content)
def create_moderation_object(self, model: str, unsafe_code: str | None = None) -> ModerationObject:
"""Create a ModerationObject for either safe or unsafe content.
Args:
model: The model name
unsafe_code: Optional comma-separated list of safety codes. If None, creates safe object.
Returns:
ModerationObject with appropriate configuration
"""
# Set default values for safe case
categories = dict.fromkeys(OPENAI_TO_LLAMA_CATEGORIES_MAP.keys(), False)
category_scores = dict.fromkeys(OPENAI_TO_LLAMA_CATEGORIES_MAP.keys(), 1.0)
category_applied_input_types = {key: [] for key in OPENAI_TO_LLAMA_CATEGORIES_MAP.keys()}
flagged = False
user_message = None
metadata = {}
# Handle unsafe case
if unsafe_code:
unsafe_code_list = [code.strip() for code in unsafe_code.split(",")]
invalid_codes = [code for code in unsafe_code_list if code not in SAFETY_CODE_TO_CATEGORIES_MAP]
if invalid_codes:
logging.warning(f"Invalid safety codes returned: {invalid_codes}")
# just returning safe object, as we don't know what the invalid codes can map to
return ModerationObject(
id=f"modr-{uuid.uuid4()}",
model=model,
results=[
ModerationObjectResults(
flagged=flagged,
categories=categories,
category_applied_input_types=category_applied_input_types,
category_scores=category_scores,
user_message=user_message,
metadata=metadata,
)
],
)
# Get OpenAI categories for the unsafe codes
openai_categories = []
for code in unsafe_code_list:
llama_guard_category = SAFETY_CODE_TO_CATEGORIES_MAP[code]
openai_categories.extend(
k for k, v_l in OPENAI_TO_LLAMA_CATEGORIES_MAP.items() if llama_guard_category in v_l
)
# Update categories for unsafe content
categories = {k: k in openai_categories for k in OPENAI_TO_LLAMA_CATEGORIES_MAP}
category_scores = {k: 1.0 if k in openai_categories else 0.0 for k in OPENAI_TO_LLAMA_CATEGORIES_MAP}
category_applied_input_types = {
k: ["text"] if k in openai_categories else [] for k in OPENAI_TO_LLAMA_CATEGORIES_MAP
}
flagged = True
user_message = CANNED_RESPONSE_TEXT
metadata = {"violation_type": unsafe_code_list}
return ModerationObject(
id=f"modr-{uuid.uuid4()}",
model=model,
results=[
ModerationObjectResults(
flagged=flagged,
categories=categories,
category_applied_input_types=category_applied_input_types,
category_scores=category_scores,
user_message=user_message,
metadata=metadata,
)
],
)
def is_content_safe(self, response: str, unsafe_code: str | None = None) -> bool:
"""Check if content is safe based on response and unsafe code."""
if response.strip() == SAFE_RESPONSE:
return True
if unsafe_code:
unsafe_code_list = unsafe_code.split(",")
if set(unsafe_code_list).issubset(set(self.excluded_categories)):
return True
return False
def get_moderation_object(self, response: str) -> ModerationObject:
response = response.strip()
if self.is_content_safe(response):
return self.create_moderation_object(self.model)
unsafe_code = self.check_unsafe_response(response)
if not unsafe_code:
raise ValueError(f"Unexpected response: {response}")
if self.is_content_safe(response, unsafe_code):
return self.create_moderation_object(self.model)
else:
return self.create_moderation_object(self.model, unsafe_code)

5
llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py
View file

@ -28,9 +28,6 @@ class ConsoleSpanProcessor(SpanProcessor):
logger.info(f"[dim]{timestamp}[/dim] [bold magenta][START][/bold magenta] [dim]{span.name}[/dim]")
def on_end(self, span: ReadableSpan) -> None:
if span.attributes and span.attributes.get("__autotraced__"):
return
timestamp = datetime.fromtimestamp(span.end_time / 1e9, tz=UTC).strftime("%H:%M:%S.%f")[:-3]
span_context = f"[dim]{timestamp}[/dim] [bold magenta][END][/bold magenta] [dim]{span.name}[/dim]"
if span.status.status_code == StatusCode.ERROR:
@ -67,7 +64,7 @@ class ConsoleSpanProcessor(SpanProcessor):
for key, value in event.attributes.items():
if key.startswith("__") or key in ["message", "severity"]:
continue
logger.info(f"/r[dim]{key}[/dim]: {value}")
logger.info(f"[dim]{key}[/dim]: {value}")
def shutdown(self) -> None:
"""Shutdown the processor."""

39
llama_stack/providers/inline/telemetry/meta_reference/telemetry.py
View file

@ -4,10 +4,13 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import threading
from typing import Any
from opentelemetry import metrics, trace
logger = logging.getLogger(__name__)
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.metrics import MeterProvider
@ -110,7 +113,7 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
if TelemetrySink.SQLITE in self.config.sinks:
trace.get_tracer_provider().add_span_processor(SQLiteSpanProcessor(self.config.sqlite_db_path))
if TelemetrySink.CONSOLE in self.config.sinks:
trace.get_tracer_provider().add_span_processor(ConsoleSpanProcessor())
trace.get_tracer_provider().add_span_processor(ConsoleSpanProcessor(print_attributes=True))
if TelemetrySink.OTEL_METRIC in self.config.sinks:
self.meter = metrics.get_meter(__name__)
@ -126,9 +129,11 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
trace.get_tracer_provider().force_flush()
async def log_event(self, event: Event, ttl_seconds: int = 604800) -> None:
logger.debug(f"DEBUG: log_event called with event type: {type(event).__name__}")
if isinstance(event, UnstructuredLogEvent):
self._log_unstructured(event, ttl_seconds)
elif isinstance(event, MetricEvent):
logger.debug("DEBUG: Routing MetricEvent to _log_metric")
self._log_metric(event)
elif isinstance(event, StructuredLogEvent):
self._log_structured(event, ttl_seconds)
@ -188,6 +193,38 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
return _GLOBAL_STORAGE["gauges"][name]
def _log_metric(self, event: MetricEvent) -> None:
# Always log to console if console sink is enabled (debug)
if TelemetrySink.CONSOLE in self.config.sinks:
logger.debug(f"METRIC: {event.metric}={event.value} {event.unit} {event.attributes}")
# Add metric as an event to the current span
try:
with self._lock:
# Only try to add to span if we have a valid span_id
if event.span_id:
try:
span_id = int(event.span_id, 16)
span = _GLOBAL_STORAGE["active_spans"].get(span_id)
if span:
timestamp_ns = int(event.timestamp.timestamp() * 1e9)
span.add_event(
name=f"metric.{event.metric}",
attributes={
"value": event.value,
"unit": event.unit,
**(event.attributes or {}),
},
timestamp=timestamp_ns,
)
except (ValueError, KeyError):
# Invalid span_id or span not found, but we already logged to console above
pass
except Exception:
# Lock acquisition failed
logger.debug("Failed to acquire lock to add metric to span")
# Log to OpenTelemetry meter if available
if self.meter is None:
return
if isinstance(event.value, int):

6
llama_stack/providers/remote/datasetio/huggingface/huggingface.py
View file

@ -6,8 +6,6 @@
from typing import Any
from urllib.parse import parse_qs, urlparse
import datasets as hf_datasets
from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Dataset
@ -73,6 +71,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
start_index: int | None = None,
limit: int | None = None,
) -> PaginatedResponse:
import datasets as hf_datasets
dataset_def = self.dataset_infos[dataset_id]
path, params = parse_hf_params(dataset_def)
loaded_dataset = hf_datasets.load_dataset(path, **params)
@ -81,6 +81,8 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
return paginate_records(records, start_index, limit)
async def append_rows(self, dataset_id: str, rows: list[dict[str, Any]]) -> None:
import datasets as hf_datasets
dataset_def = self.dataset_infos[dataset_id]
path, params = parse_hf_params(dataset_def)
loaded_dataset = hf_datasets.load_dataset(path, **params)

2
llama_stack/providers/remote/inference/gemini/models.py
View file

@ -13,7 +13,9 @@ LLM_MODEL_IDS = [
"gemini-1.5-flash",
"gemini-1.5-pro",
"gemini-2.0-flash",
"gemini-2.0-flash-lite",
"gemini-2.5-flash",
"gemini-2.5-flash-lite",
"gemini-2.5-pro",
]

14
llama_stack/providers/remote/inference/nvidia/NVIDIA.md
View file

@ -42,8 +42,8 @@ client.initialize()
### Create Completion
```python
response = client.completion(
model_id="meta-llama/Llama-3.1-8b-Instruct",
response = client.inference.completion(
model_id="meta-llama/Llama-3.1-8B-Instruct",
content="Complete the sentence using one word: Roses are red, violets are :",
stream=False,
sampling_params={
@ -56,8 +56,8 @@ print(f"Response: {response.content}")
### Create Chat Completion
```python
response = client.chat_completion(
model_id="meta-llama/Llama-3.1-8b-Instruct",
response = client.inference.chat_completion(
model_id="meta-llama/Llama-3.1-8B-Instruct",
messages=[
{
"role": "system",
@ -78,8 +78,10 @@ print(f"Response: {response.completion_message.content}")
### Create Embeddings
```python
response = client.embeddings(
model_id="meta-llama/Llama-3.1-8b-Instruct", contents=["foo", "bar", "baz"]
response = client.inference.embeddings(
model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",
contents=["What is the capital of France?"],
task_type="query",
)
print(f"Embeddings: {response.embeddings}")
```

3
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -112,7 +112,8 @@ class OllamaInferenceAdapter(
@property
def openai_client(self) -> AsyncOpenAI:
if self._openai_client is None:
self._openai_client = AsyncOpenAI(base_url=f"{self.config.url}/v1", api_key="ollama")
url = self.config.url.rstrip("/")
self._openai_client = AsyncOpenAI(base_url=f"{url}/v1", api_key="ollama")
return self._openai_client
async def initialize(self) -> None:

51
llama_stack/providers/remote/vector_io/milvus/milvus.py
View file

@ -10,7 +10,7 @@ import os
from typing import Any
from numpy.typing import NDArray
from pymilvus import DataType, Function, FunctionType, MilvusClient
from pymilvus import AnnSearchRequest, DataType, Function, FunctionType, MilvusClient, RRFRanker, WeightedRanker
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files.files import Files
@ -27,6 +27,7 @@ from llama_stack.providers.utils.kvstore import kvstore_impl
from llama_stack.providers.utils.kvstore.api import KVStore
from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
from llama_stack.providers.utils.memory.vector_store import (
RERANKER_TYPE_WEIGHTED,
EmbeddingIndex,
VectorDBWithIndex,
)
@ -238,7 +239,53 @@ class MilvusIndex(EmbeddingIndex):
reranker_type: str,
reranker_params: dict[str, Any] | None = None,
) -> QueryChunksResponse:
raise NotImplementedError("Hybrid search is not supported in Milvus")
"""
Hybrid search using Milvus's native hybrid search capabilities.
This implementation uses Milvus's hybrid_search method which combines
vector search and BM25 search with configurable reranking strategies.
"""
search_requests = []
# nprobe: Controls search accuracy vs performance trade-off
# 10 balances these trade-offs for RAG applications
search_requests.append(
AnnSearchRequest(data=[embedding.tolist()], anns_field="vector", param={"nprobe": 10}, limit=k)
)
# drop_ratio_search: Filters low-importance terms to improve search performance
# 0.2 balances noise reduction with recall
search_requests.append(
AnnSearchRequest(data=[query_string], anns_field="sparse", param={"drop_ratio_search": 0.2}, limit=k)
)
if reranker_type == RERANKER_TYPE_WEIGHTED:
alpha = (reranker_params or {}).get("alpha", 0.5)
rerank = WeightedRanker(alpha, 1 - alpha)
else:
impact_factor = (reranker_params or {}).get("impact_factor", 60.0)
rerank = RRFRanker(impact_factor)
search_res = await asyncio.to_thread(
self.client.hybrid_search,
collection_name=self.collection_name,
reqs=search_requests,
ranker=rerank,
limit=k,
output_fields=["chunk_content"],
)
chunks = []
scores = []
for res in search_res[0]:
chunk = Chunk(**res["entity"]["chunk_content"])
chunks.append(chunk)
scores.append(res["distance"])
filtered_chunks = [chunk for chunk, score in zip(chunks, scores, strict=False) if score >= score_threshold]
filtered_scores = [score for score in scores if score >= score_threshold]
return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
async def delete_chunk(self, chunk_id: str) -> None:
"""Remove a chunk from the Milvus collection."""

4
llama_stack/providers/utils/datasetio/url_utils.py
View file

@ -9,12 +9,12 @@ import base64
import io
from urllib.parse import unquote
import pandas
from llama_stack.providers.utils.memory.vector_store import parse_data_url
async def get_dataframe_from_uri(uri: str):
import pandas
df = None
if uri.endswith(".csv"):
# Moving to its own thread to avoid io from blocking the eventloop

129
llama_stack/providers/utils/inference/stream_utils.py
View file

@ -1,129 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from collections.abc import AsyncIterator
from datetime import UTC, datetime
from typing import Any
from llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIChatCompletion,
OpenAIChatCompletionChunk,
OpenAIChatCompletionToolCall,
OpenAIChatCompletionToolCallFunction,
OpenAIChoice,
OpenAIChoiceLogprobs,
OpenAIMessageParam,
)
from llama_stack.providers.utils.inference.inference_store import InferenceStore
async def stream_and_store_openai_completion(
provider_stream: AsyncIterator[OpenAIChatCompletionChunk],
model: str,
store: InferenceStore,
input_messages: list[OpenAIMessageParam],
) -> AsyncIterator[OpenAIChatCompletionChunk]:
"""
Wraps a provider's stream, yields chunks, and stores the full completion at the end.
"""
id = None
created = None
choices_data: dict[int, dict[str, Any]] = {}
try:
async for chunk in provider_stream:
if id is None and chunk.id:
id = chunk.id
if created is None and chunk.created:
created = chunk.created
if chunk.choices:
for choice_delta in chunk.choices:
idx = choice_delta.index
if idx not in choices_data:
choices_data[idx] = {
"content_parts": [],
"tool_calls_builder": {},
"finish_reason": None,
"logprobs_content_parts": [],
}
current_choice_data = choices_data[idx]
if choice_delta.delta:
delta = choice_delta.delta
if delta.content:
current_choice_data["content_parts"].append(delta.content)
if delta.tool_calls:
for tool_call_delta in delta.tool_calls:
tc_idx = tool_call_delta.index
if tc_idx not in current_choice_data["tool_calls_builder"]:
# Initialize with correct structure for _ToolCallBuilderData
current_choice_data["tool_calls_builder"][tc_idx] = {
"id": None,
"type": "function",
"function_name_parts": [],
"function_arguments_parts": [],
}
builder = current_choice_data["tool_calls_builder"][tc_idx]
if tool_call_delta.id:
builder["id"] = tool_call_delta.id
if tool_call_delta.type:
builder["type"] = tool_call_delta.type
if tool_call_delta.function:
if tool_call_delta.function.name:
builder["function_name_parts"].append(tool_call_delta.function.name)
if tool_call_delta.function.arguments:
builder["function_arguments_parts"].append(tool_call_delta.function.arguments)
if choice_delta.finish_reason:
current_choice_data["finish_reason"] = choice_delta.finish_reason
if choice_delta.logprobs and choice_delta.logprobs.content:
# Ensure that we are extending with the correct type
current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content)
yield chunk
finally:
if id:
assembled_choices: list[OpenAIChoice] = []
for choice_idx, choice_data in choices_data.items():
content_str = "".join(choice_data["content_parts"])
assembled_tool_calls: list[OpenAIChatCompletionToolCall] = []
if choice_data["tool_calls_builder"]:
for tc_build_data in choice_data["tool_calls_builder"].values():
if tc_build_data["id"]:
func_name = "".join(tc_build_data["function_name_parts"])
func_args = "".join(tc_build_data["function_arguments_parts"])
assembled_tool_calls.append(
OpenAIChatCompletionToolCall(
id=tc_build_data["id"],
type=tc_build_data["type"], # No or "function" needed, already set
function=OpenAIChatCompletionToolCallFunction(name=func_name, arguments=func_args),
)
)
message = OpenAIAssistantMessageParam(
role="assistant",
content=content_str if content_str else None,
tool_calls=assembled_tool_calls if assembled_tool_calls else None,
)
logprobs_content = choice_data["logprobs_content_parts"]
final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None
assembled_choices.append(
OpenAIChoice(
finish_reason=choice_data["finish_reason"],
index=choice_idx,
message=message,
logprobs=final_logprobs,
)
)
final_response = OpenAIChatCompletion(
id=id,
choices=assembled_choices,
created=created or int(datetime.now(UTC).timestamp()),
model=model,
object="chat.completion",
)
await store.store_chat_completion(final_response, input_messages)

52
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -18,6 +18,7 @@ from llama_stack.apis.files import Files, OpenAIFileObject
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
ChunkMetadata,
QueryChunksResponse,
SearchRankingOptions,
VectorStoreChunkingStrategy,
@ -516,31 +517,68 @@ class OpenAIVectorStoreMixin(ABC):
raise ValueError(f"Unsupported filter type: {filter_type}")
def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
created_ts = None
if chunk.chunk_metadata is not None:
created_ts = getattr(chunk.chunk_metadata, "created_timestamp", None)
metadata_dict = {}
if chunk.chunk_metadata:
if hasattr(chunk.chunk_metadata, "model_dump"):
metadata_dict = chunk.chunk_metadata.model_dump()
else:
metadata_dict = vars(chunk.chunk_metadata)
user_metadata = chunk.metadata or {}
base_meta = {**metadata_dict, **user_metadata}
# content is InterleavedContent
if isinstance(chunk.content, str):
content = [
VectorStoreContent(
type="text",
text=chunk.content,
embedding=chunk.embedding,
created_timestamp=created_ts,
metadata=user_metadata,
chunk_metadata=ChunkMetadata(**base_meta) if base_meta else None,
)
]
elif isinstance(chunk.content, list):
# TODO: Add support for other types of content
content = [
content = []
for item in chunk.content:
if hasattr(item, "type") and item.type == "text":
item_meta = {**base_meta}
item_user_meta = getattr(item, "metadata", {}) or {}
if item_user_meta:
item_meta.update(item_user_meta)
content.append(
VectorStoreContent(
type="text",
text=item.text,
embedding=getattr(item, "embedding", None),
created_timestamp=created_ts,
metadata=item_user_meta,
chunk_metadata=ChunkMetadata(**item_meta) if item_meta else None,
)
)
for item in chunk.content
if item.type == "text"
]
else:
if chunk.content.type != "text":
raise ValueError(f"Unsupported content type: {chunk.content.type}")
content_item = chunk.content
if content_item.type != "text":
raise ValueError(f"Unsupported content type: {content_item.type}")
item_user_meta = getattr(content_item, "metadata", {}) or {}
combined_meta = {**base_meta, **item_user_meta}
content = [
VectorStoreContent(
type="text",
text=chunk.content.text,
text=content_item.text,
embedding=getattr(content_item, "embedding", None),
created_timestamp=created_ts,
metadata=item_user_meta,
chunk_metadata=ChunkMetadata(**combined_meta) if combined_meta else None,
)
]
return content

16
llama_stack/providers/utils/memory/vector_store.py
View file

@ -302,23 +302,25 @@ class VectorDBWithIndex:
mode = params.get("mode")
score_threshold = params.get("score_threshold", 0.0)
# Get ranker configuration
ranker = params.get("ranker")
if ranker is None:
# Default to RRF with impact_factor=60.0
reranker_type = RERANKER_TYPE_RRF
reranker_params = {"impact_factor": 60.0}
else:
reranker_type = ranker.type
reranker_params = (
{"impact_factor": ranker.impact_factor} if ranker.type == RERANKER_TYPE_RRF else {"alpha": ranker.alpha}
)
strategy = ranker.get("strategy", "rrf")
if strategy == "weighted":
weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
reranker_type = RERANKER_TYPE_WEIGHTED
reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
else:
reranker_type = RERANKER_TYPE_RRF
k_value = ranker.get("params", {}).get("k", 60.0)
reranker_params = {"impact_factor": k_value}
query_string = interleaved_content_as_str(query)
if mode == "keyword":
return await self.index.query_keyword(query_string, k, score_threshold)
# Calculate embeddings for both vector and hybrid modes
embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
if mode == "hybrid":

31
llama_stack/providers/utils/telemetry/tracing.py
View file

@ -9,7 +9,9 @@ import contextvars
import logging
import queue
import random
import sys
import threading
import time
from collections.abc import Callable
from datetime import UTC, datetime
from functools import wraps
@ -30,6 +32,16 @@ from llama_stack.providers.utils.telemetry.trace_protocol import serialize_value
logger = get_logger(__name__, category="core")
# Fallback logger that does NOT propagate to TelemetryHandler to avoid recursion
_fallback_logger = logging.getLogger("llama_stack.telemetry.background")
if not _fallback_logger.handlers:
_fallback_logger.propagate = False
_fallback_logger.setLevel(logging.ERROR)
_fallback_handler = logging.StreamHandler(sys.stderr)
_fallback_handler.setLevel(logging.ERROR)
_fallback_handler.setFormatter(logging.Formatter("%(asctime)s [%(levelname)s] %(name)s: %(message)s"))
_fallback_logger.addHandler(_fallback_handler)
INVALID_SPAN_ID = 0x0000000000000000
INVALID_TRACE_ID = 0x00000000000000000000000000000000
@ -79,19 +91,32 @@ def generate_trace_id() -> str:
CURRENT_TRACE_CONTEXT = contextvars.ContextVar("trace_context", default=None)
BACKGROUND_LOGGER = None
LOG_QUEUE_FULL_LOG_INTERVAL_SECONDS = 60.0
class BackgroundLogger:
def __init__(self, api: Telemetry, capacity: int = 1000):
def __init__(self, api: Telemetry, capacity: int = 100000):
self.api = api
self.log_queue = queue.Queue(maxsize=capacity)
self.log_queue: queue.Queue[Any] = queue.Queue(maxsize=capacity)
self.worker_thread = threading.Thread(target=self._process_logs, daemon=True)
self.worker_thread.start()
self._last_queue_full_log_time: float = 0.0
self._dropped_since_last_notice: int = 0
def log_event(self, event):
try:
self.log_queue.put_nowait(event)
except queue.Full:
logger.error("Log queue is full, dropping event")
# Aggregate drops and emit at most once per interval via fallback logger
self._dropped_since_last_notice += 1
current_time = time.time()
if current_time - self._last_queue_full_log_time >= LOG_QUEUE_FULL_LOG_INTERVAL_SECONDS:
_fallback_logger.error(
"Log queue is full; dropped %d events since last notice",
self._dropped_since_last_notice,
)
self._last_queue_full_log_time = current_time
self._dropped_since_last_notice = 0
def _process_logs(self):
while True:

383
llama_stack/ui/app/logs/vector-stores/[id]/files/[fileId]/contents/[contentId]/page.tsx Normal file
View file

@ -0,0 +1,383 @@
"use client";
import { useEffect, useState } from "react";
import { useParams, useRouter } from "next/navigation";
import { useAuthClient } from "@/hooks/use-auth-client";
import { ContentsAPI, VectorStoreContentItem } from "@/lib/contents-api";
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Button } from "@/components/ui/button";
import { Input } from "@/components/ui/input";
import { Edit, Save, X, Trash2 } from "lucide-react";
import {
DetailLoadingView,
DetailErrorView,
DetailNotFoundView,
DetailLayout,
PropertiesCard,
PropertyItem,
} from "@/components/layout/detail-layout";
import { PageBreadcrumb, BreadcrumbSegment } from "@/components/layout/page-breadcrumb";
export default function ContentDetailPage() {
const params = useParams();
const router = useRouter();
const vectorStoreId = params.id as string;
const fileId = params.fileId as string;
const contentId = params.contentId as string;
const client = useAuthClient();
const getTextFromContent = (content: any): string => {
if (typeof content === 'string') {
return content;
} else if (content && content.type === 'text') {
return content.text;
}
return '';
};
const [store, setStore] = useState<VectorStore | null>(null);
const [file, setFile] = useState<VectorStoreFile | null>(null);
const [content, setContent] = useState<VectorStoreContentItem | null>(null);
const [isLoading, setIsLoading] = useState(true);
const [error, setError] = useState<Error | null>(null);
const [isEditing, setIsEditing] = useState(false);
const [editedContent, setEditedContent] = useState("");
const [editedMetadata, setEditedMetadata] = useState<Record<string, any>>({});
const [isEditingEmbedding, setIsEditingEmbedding] = useState(false);
const [editedEmbedding, setEditedEmbedding] = useState<number[]>([]);
useEffect(() => {
if (!vectorStoreId || !fileId || !contentId) return;
const fetchData = async () => {
setIsLoading(true);
setError(null);
try {
const [storeResponse, fileResponse] = await Promise.all([
client.vectorStores.retrieve(vectorStoreId),
client.vectorStores.files.retrieve(vectorStoreId, fileId),
]);
setStore(storeResponse as VectorStore);
setFile(fileResponse as VectorStoreFile);
const contentsAPI = new ContentsAPI(client);
const contentsResponse = await contentsAPI.listContents(vectorStoreId, fileId);
const targetContent = contentsResponse.data.find(c => c.id === contentId);
if (targetContent) {
setContent(targetContent);
setEditedContent(getTextFromContent(targetContent.content));
setEditedMetadata({ ...targetContent.metadata });
setEditedEmbedding(targetContent.embedding || []);
} else {
throw new Error(`Content ${contentId} not found`);
}
} catch (err) {
setError(err instanceof Error ? err : new Error("Failed to load content."));
} finally {
setIsLoading(false);
}
};
fetchData();
}, [vectorStoreId, fileId, contentId, client]);
const handleSave = async () => {
if (!content) return;
try {
const updates: { content?: string; metadata?: Record<string, any> } = {};
if (editedContent !== getTextFromContent(content.content)) {
updates.content = editedContent;
}
if (JSON.stringify(editedMetadata) !== JSON.stringify(content.metadata)) {
updates.metadata = editedMetadata;
}
if (Object.keys(updates).length > 0) {
const contentsAPI = new ContentsAPI(client);
const updatedContent = await contentsAPI.updateContent(vectorStoreId, fileId, contentId, updates);
setContent(updatedContent);
}
setIsEditing(false);
} catch (err) {
console.error('Failed to update content:', err);
}
};
const handleDelete = async () => {
if (!confirm('Are you sure you want to delete this content?')) return;
try {
const contentsAPI = new ContentsAPI(client);
await contentsAPI.deleteContent(vectorStoreId, fileId, contentId);
router.push(`/logs/vector-stores/${vectorStoreId}/files/${fileId}/contents`);
} catch (err) {
console.error('Failed to delete content:', err);
}
};
const handleCancel = () => {
setEditedContent(content ? getTextFromContent(content.content) : "");
setEditedMetadata({ ...content?.metadata });
setEditedEmbedding(content?.embedding || []);
setIsEditing(false);
setIsEditingEmbedding(false);
};
const title = `Content: ${contentId}`;
const breadcrumbSegments: BreadcrumbSegment[] = [
{ label: "Vector Stores", href: "/logs/vector-stores" },
{ label: store?.name || vectorStoreId, href: `/logs/vector-stores/${vectorStoreId}` },
{ label: "Files", href: `/logs/vector-stores/${vectorStoreId}` },
{ label: fileId, href: `/logs/vector-stores/${vectorStoreId}/files/${fileId}` },
{ label: "Contents", href: `/logs/vector-stores/${vectorStoreId}/files/${fileId}/contents` },
{ label: contentId },
];
if (error) {
return <DetailErrorView title={title} id={contentId} error={error} />;
}
if (isLoading) {
return <DetailLoadingView title={title} />;
}
if (!content) {
return <DetailNotFoundView title={title} id={contentId} />;
}
const mainContent = (
<>
<Card>
<CardHeader className="flex flex-row items-center justify-between">
<CardTitle>Content</CardTitle>
<div className="flex gap-2">
{isEditing ? (
<>
<Button size="sm" onClick={handleSave}>
<Save className="h-4 w-4 mr-1" />
Save
</Button>
<Button size="sm" variant="outline" onClick={handleCancel}>
<X className="h-4 w-4 mr-1" />
Cancel
</Button>
</>
) : (
<>
<Button size="sm" onClick={() => setIsEditing(true)}>
<Edit className="h-4 w-4 mr-1" />
Edit
</Button>
<Button size="sm" variant="destructive" onClick={handleDelete}>
<Trash2 className="h-4 w-4 mr-1" />
Delete
</Button>
</>
)}
</div>
</CardHeader>
<CardContent>
{isEditing ? (
<textarea
value={editedContent}
onChange={(e) => setEditedContent(e.target.value)}
className="w-full h-64 p-3 border rounded-md resize-none font-mono text-sm"
placeholder="Enter content..."
/>
) : (
<div className="p-3 bg-gray-50 dark:bg-gray-800 rounded-md">
<pre className="whitespace-pre-wrap font-mono text-sm text-gray-900 dark:text-gray-100">
{getTextFromContent(content.content)}
</pre>
</div>
)}
</CardContent>
</Card>
<Card>
<CardHeader className="flex flex-row items-center justify-between">
<CardTitle>Content Embedding</CardTitle>
<div className="flex gap-2">
{isEditingEmbedding ? (
<>
<Button size="sm" onClick={() => {
setIsEditingEmbedding(false);
}}>
<Save className="h-4 w-4 mr-1" />
Save
</Button>
<Button size="sm" variant="outline" onClick={() => {
setEditedEmbedding(content?.embedding || []);
setIsEditingEmbedding(false);
}}>
<X className="h-4 w-4 mr-1" />
Cancel
</Button>
</>
) : (
<Button size="sm" onClick={() => setIsEditingEmbedding(true)}>
<Edit className="h-4 w-4 mr-1" />
Edit
</Button>
)}
</div>
</CardHeader>
<CardContent>
{content?.embedding && content.embedding.length > 0 ? (
isEditingEmbedding ? (
<div className="space-y-2">
<p className="text-sm text-gray-600 dark:text-gray-400">
Embedding ({editedEmbedding.length}D vector):
</p>
<textarea
value={JSON.stringify(editedEmbedding, null, 2)}
onChange={(e) => {
try {
const parsed = JSON.parse(e.target.value);
if (Array.isArray(parsed) && parsed.every(v => typeof v === 'number')) {
setEditedEmbedding(parsed);
}
} catch {
}
}}
className="w-full h-32 p-3 border rounded-md resize-none font-mono text-xs"
placeholder="Enter embedding as JSON array..."
/>
</div>
) : (
<div className="space-y-2">
<div className="flex items-center gap-2">
<span className="font-mono text-xs bg-gray-100 dark:bg-gray-800 rounded px-2 py-1">
{content.embedding.length}D vector
</span>
</div>
<div className="p-3 bg-gray-50 dark:bg-gray-800 rounded-md max-h-32 overflow-y-auto">
<pre className="whitespace-pre-wrap font-mono text-xs text-gray-900 dark:text-gray-100">
[{content.embedding.slice(0, 20).map(v => v.toFixed(6)).join(', ')}
{content.embedding.length > 20 ? `\n... and ${content.embedding.length - 20} more values` : ''}]
</pre>
</div>
</div>
)
) : (
<p className="text-gray-500 italic text-sm">
No embedding available for this content.
</p>
)}
</CardContent>
</Card>
<Card>
<CardHeader>
<CardTitle>Metadata</CardTitle>
</CardHeader>
<CardContent>
{isEditing ? (
<div className="space-y-2">
{Object.entries(editedMetadata).map(([key, value]) => (
<div key={key} className="flex gap-2">
<Input
value={key}
onChange={(e) => {
const newMetadata = { ...editedMetadata };
delete newMetadata[key];
newMetadata[e.target.value] = value;
setEditedMetadata(newMetadata);
}}
placeholder="Key"
className="flex-1"
/>
<Input
value={typeof value === 'string' ? value : JSON.stringify(value)}
onChange={(e) => {
setEditedMetadata({
...editedMetadata,
[key]: e.target.value
});
}}
placeholder="Value"
className="flex-1"
/>
</div>
))}
<Button
size="sm"
variant="outline"
onClick={() => {
setEditedMetadata({
...editedMetadata,
['']: ''
});
}}
>
Add Field
</Button>
</div>
) : (
<div className="space-y-2">
{Object.entries(content.metadata).map(([key, value]) => (
<div key={key} className="flex justify-between py-1">
<span className="font-medium text-gray-600">{key}:</span>
<span className="font-mono text-sm">
{typeof value === 'string' ? value : JSON.stringify(value)}
</span>
</div>
))}
</div>
)}
</CardContent>
</Card>
</>
);
const sidebar = (
<PropertiesCard>
<PropertyItem label="Content ID" value={contentId} />
<PropertyItem label="File ID" value={fileId} />
<PropertyItem label="Vector Store ID" value={vectorStoreId} />
<PropertyItem label="Object Type" value={content.object} />
<PropertyItem
label="Created"
value={new Date(content.created_timestamp * 1000).toLocaleString()}
/>
<PropertyItem
label="Content Length"
value={`${getTextFromContent(content.content).length} chars`}
/>
{content.metadata.chunk_window && (
<PropertyItem
label="Position"
value={content.metadata.chunk_window}
/>
)}
{file && (
<>
<PropertyItem label="File Status" value={file.status} />
<PropertyItem label="File Usage" value={`${file.usage_bytes} bytes`} />
</>
)}
{store && (
<>
<PropertyItem label="Store Name" value={store.name || ""} />
<PropertyItem
label="Provider ID"
value={(store.metadata.provider_id as string) || ""}
/>
</>
)}
</PropertiesCard>
);
return (
<>
<PageBreadcrumb segments={breadcrumbSegments} />
<DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
</>
);
}

297
llama_stack/ui/app/logs/vector-stores/[id]/files/[fileId]/contents/page.tsx Normal file
View file

@ -0,0 +1,297 @@
"use client";
import { useEffect, useState } from "react";
import { useParams, useRouter } from "next/navigation";
import { useAuthClient } from "@/hooks/use-auth-client";
import { ContentsAPI, VectorStoreContentItem } from "@/lib/contents-api";
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Skeleton } from "@/components/ui/skeleton";
import { Button } from "@/components/ui/button";
import { Edit, Trash2, Eye } from "lucide-react";
import {
DetailLoadingView,
DetailErrorView,
DetailNotFoundView,
DetailLayout,
PropertiesCard,
PropertyItem,
} from "@/components/layout/detail-layout";
import { PageBreadcrumb, BreadcrumbSegment } from "@/components/layout/page-breadcrumb";
import {
Table,
TableBody,
TableCaption,
TableCell,
TableHead,
TableHeader,
TableRow,
} from "@/components/ui/table";
export default function ContentsListPage() {
const params = useParams();
const router = useRouter();
const vectorStoreId = params.id as string;
const fileId = params.fileId as string;
const client = useAuthClient();
const getTextFromContent = (content: any): string => {
if (typeof content === 'string') {
return content;
} else if (content && content.type === 'text') {
return content.text;
}
return '';
};
const [store, setStore] = useState<VectorStore | null>(null);
const [file, setFile] = useState<VectorStoreFile | null>(null);
const [contents, setContents] = useState<VectorStoreContentItem[]>([]);
const [isLoadingStore, setIsLoadingStore] = useState(true);
const [isLoadingFile, setIsLoadingFile] = useState(true);
const [isLoadingContents, setIsLoadingContents] = useState(true);
const [errorStore, setErrorStore] = useState<Error | null>(null);
const [errorFile, setErrorFile] = useState<Error | null>(null);
const [errorContents, setErrorContents] = useState<Error | null>(null);
useEffect(() => {
if (!vectorStoreId) return;
const fetchStore = async () => {
setIsLoadingStore(true);
setErrorStore(null);
try {
const response = await client.vectorStores.retrieve(vectorStoreId);
setStore(response as VectorStore);
} catch (err) {
setErrorStore(err instanceof Error ? err : new Error("Failed to load vector store."));
} finally {
setIsLoadingStore(false);
}
};
fetchStore();
}, [vectorStoreId, client]);
useEffect(() => {
if (!vectorStoreId || !fileId) return;
const fetchFile = async () => {
setIsLoadingFile(true);
setErrorFile(null);
try {
const response = await client.vectorStores.files.retrieve(vectorStoreId, fileId);
setFile(response as VectorStoreFile);
} catch (err) {
setErrorFile(err instanceof Error ? err : new Error("Failed to load file."));
} finally {
setIsLoadingFile(false);
}
};
fetchFile();
}, [vectorStoreId, fileId, client]);
useEffect(() => {
if (!vectorStoreId || !fileId) return;
const fetchContents = async () => {
setIsLoadingContents(true);
setErrorContents(null);
try {
const contentsAPI = new ContentsAPI(client);
const contentsResponse = await contentsAPI.listContents(vectorStoreId, fileId, { limit: 100 });
setContents(contentsResponse.data);
} catch (err) {
setErrorContents(err instanceof Error ? err : new Error("Failed to load contents."));
} finally {
setIsLoadingContents(false);
}
};
fetchContents();
}, [vectorStoreId, fileId, client]);
const handleDeleteContent = async (contentId: string) => {
try {
const contentsAPI = new ContentsAPI(client);
await contentsAPI.deleteContent(vectorStoreId, fileId, contentId);
setContents(contents.filter(content => content.id !== contentId));
} catch (err) {
console.error('Failed to delete content:', err);
}
};
const handleViewContent = (contentId: string) => {
router.push(`/logs/vector-stores/${vectorStoreId}/files/${fileId}/contents/${contentId}`);
};
const title = `Contents in File: ${fileId}`;
const breadcrumbSegments: BreadcrumbSegment[] = [
{ label: "Vector Stores", href: "/logs/vector-stores" },
{ label: store?.name || vectorStoreId, href: `/logs/vector-stores/${vectorStoreId}` },
{ label: "Files", href: `/logs/vector-stores/${vectorStoreId}` },
{ label: fileId, href: `/logs/vector-stores/${vectorStoreId}/files/${fileId}` },
{ label: "Contents" },
];
if (errorStore) {
return <DetailErrorView title={title} id={vectorStoreId} error={errorStore} />;
}
if (isLoadingStore) {
return <DetailLoadingView title={title} />;
}
if (!store) {
return <DetailNotFoundView title={title} id={vectorStoreId} />;
}
const mainContent = (
<>
<Card>
<CardHeader>
<CardTitle>Content Chunks ({contents.length})</CardTitle>
</CardHeader>
<CardContent>
{isLoadingContents ? (
<div className="space-y-2">
<Skeleton className="h-4 w-full" />
<Skeleton className="h-4 w-3/4" />
<Skeleton className="h-4 w-1/2" />
</div>
) : errorContents ? (
<div className="text-destructive text-sm">
Error loading contents: {errorContents.message}
</div>
) : contents.length > 0 ? (
<Table>
<TableCaption>Contents in this file</TableCaption>
<TableHeader>
<TableRow>
<TableHead>Content ID</TableHead>
<TableHead>Content Preview</TableHead>
<TableHead>Embedding</TableHead>
<TableHead>Position</TableHead>
<TableHead>Created</TableHead>
<TableHead>Actions</TableHead>
</TableRow>
</TableHeader>
<TableBody>
{contents.map((content) => (
<TableRow key={content.id}>
<TableCell className="font-mono text-xs">
<Button
variant="link"
className="p-0 h-auto font-mono text-xs text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
onClick={() => handleViewContent(content.id)}
title={content.id}
>
{content.id.substring(0, 10)}...
</Button>
</TableCell>
<TableCell>
<div className="max-w-md">
<p className="text-sm truncate" title={getTextFromContent(content.content)}>
{getTextFromContent(content.content)}
</p>
</div>
</TableCell>
<TableCell className="text-xs text-gray-500">
{content.embedding && content.embedding.length > 0 ? (
<div className="max-w-xs">
<span className="font-mono text-xs bg-gray-100 dark:bg-gray-800 rounded px-1 py-0.5" title={`${content.embedding.length}D vector: [${content.embedding.slice(0, 3).map(v => v.toFixed(3)).join(', ')}...]`}>
[{content.embedding.slice(0, 3).map(v => v.toFixed(3)).join(', ')}...] ({content.embedding.length}D)
</span>
</div>
) : (
<span className="text-gray-400 dark:text-gray-500 italic">No embedding</span>
)}
</TableCell>
<TableCell className="text-xs text-gray-500">
{content.metadata.chunk_window
? content.metadata.chunk_window
: `${content.metadata.content_length || 0} chars`}
</TableCell>
<TableCell className="text-xs">
{new Date(content.created_timestamp * 1000).toLocaleString()}
</TableCell>
<TableCell>
<div className="flex gap-1">
<Button
variant="ghost"
size="sm"
className="h-6 w-6 p-0"
title="View content details"
onClick={() => handleViewContent(content.id)}
>
<Eye className="h-3 w-3" />
</Button>
<Button
variant="ghost"
size="sm"
className="h-6 w-6 p-0"
title="Edit content"
onClick={() => handleViewContent(content.id)}
>
<Edit className="h-3 w-3" />
</Button>
<Button
variant="ghost"
size="sm"
className="h-6 w-6 p-0 text-destructive hover:text-destructive"
title="Delete content"
onClick={() => handleDeleteContent(content.id)}
>
<Trash2 className="h-3 w-3" />
</Button>
</div>
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
) : (
<p className="text-gray-500 italic text-sm">
No contents found for this file.
</p>
)}
</CardContent>
</Card>
</>
);
const sidebar = (
<PropertiesCard>
<PropertyItem label="File ID" value={fileId} />
<PropertyItem label="Vector Store ID" value={vectorStoreId} />
{file && (
<>
<PropertyItem label="Status" value={file.status} />
<PropertyItem
label="Created"
value={new Date(file.created_at * 1000).toLocaleString()}
/>
<PropertyItem label="Usage Bytes" value={file.usage_bytes} />
<PropertyItem
label="Chunking Strategy"
value={file.chunking_strategy.type}
/>
</>
)}
{store && (
<>
<PropertyItem label="Store Name" value={store.name || ""} />
<PropertyItem
label="Provider ID"
value={(store.metadata.provider_id as string) || ""}
/>
</>
)}
</PropertiesCard>
);
return (
<>
<PageBreadcrumb segments={breadcrumbSegments} />
<DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
</>
);
}

258
llama_stack/ui/app/logs/vector-stores/[id]/files/[fileId]/page.tsx Normal file
View file

@ -0,0 +1,258 @@
"use client";
import { useEffect, useState } from "react";
import { useParams, useRouter } from "next/navigation";
import { useAuthClient } from "@/hooks/use-auth-client";
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
import type { VectorStoreFile, FileContentResponse } from "llama-stack-client/resources/vector-stores/files";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Skeleton } from '@/components/ui/skeleton';
import { Button } from "@/components/ui/button";
import { List } from "lucide-react";
import {
DetailLoadingView,
DetailErrorView,
DetailNotFoundView,
DetailLayout,
PropertiesCard,
PropertyItem,
} from "@/components/layout/detail-layout";
import { PageBreadcrumb, BreadcrumbSegment } from "@/components/layout/page-breadcrumb";
export default function FileDetailPage() {
const params = useParams();
const router = useRouter();
const vectorStoreId = params.id as string;
const fileId = params.fileId as string;
const client = useAuthClient();
const [store, setStore] = useState<VectorStore | null>(null);
const [file, setFile] = useState<VectorStoreFile | null>(null);
const [contents, setContents] = useState<FileContentResponse | null>(null);
const [isLoadingStore, setIsLoadingStore] = useState(true);
const [isLoadingFile, setIsLoadingFile] = useState(true);
const [isLoadingContents, setIsLoadingContents] = useState(true);
const [errorStore, setErrorStore] = useState<Error | null>(null);
const [errorFile, setErrorFile] = useState<Error | null>(null);
const [errorContents, setErrorContents] = useState<Error | null>(null);
useEffect(() => {
if (!vectorStoreId) return;
const fetchStore = async () => {
setIsLoadingStore(true);
setErrorStore(null);
try {
const response = await client.vectorStores.retrieve(vectorStoreId);
setStore(response as VectorStore);
} catch (err) {
setErrorStore(err instanceof Error ? err : new Error("Failed to load vector store."));
} finally {
setIsLoadingStore(false);
}
};
fetchStore();
}, [vectorStoreId, client]);
useEffect(() => {
if (!vectorStoreId || !fileId) return;
const fetchFile = async () => {
setIsLoadingFile(true);
setErrorFile(null);
try {
const response = await client.vectorStores.files.retrieve(vectorStoreId, fileId);
setFile(response as VectorStoreFile);
} catch (err) {
setErrorFile(err instanceof Error ? err : new Error("Failed to load file."));
} finally {
setIsLoadingFile(false);
}
};
fetchFile();
}, [vectorStoreId, fileId, client]);
useEffect(() => {
if (!vectorStoreId || !fileId) return;
const fetchContents = async () => {
setIsLoadingContents(true);
setErrorContents(null);
try {
const response = await client.vectorStores.files.content(vectorStoreId, fileId);
setContents(response);
} catch (err) {
setErrorContents(err instanceof Error ? err : new Error("Failed to load contents."));
} finally {
setIsLoadingContents(false);
}
};
fetchContents();
}, [vectorStoreId, fileId, client]);
const handleViewContents = () => {
router.push(`/logs/vector-stores/${vectorStoreId}/files/${fileId}/contents`);
};
const title = `File: ${fileId}`;
const breadcrumbSegments: BreadcrumbSegment[] = [
{ label: "Vector Stores", href: "/logs/vector-stores" },
{ label: store?.name || vectorStoreId, href: `/logs/vector-stores/${vectorStoreId}` },
{ label: "Files", href: `/logs/vector-stores/${vectorStoreId}` },
{ label: fileId },
];
if (errorStore) {
return <DetailErrorView title={title} id={vectorStoreId} error={errorStore} />;
}
if (isLoadingStore) {
return <DetailLoadingView title={title} />;
}
if (!store) {
return <DetailNotFoundView title={title} id={vectorStoreId} />;
}
const mainContent = (
<>
<Card>
<CardHeader>
<CardTitle>File Information</CardTitle>
</CardHeader>
<CardContent>
{isLoadingFile ? (
<div className="space-y-2">
<Skeleton className="h-4 w-full" />
<Skeleton className="h-4 w-3/4" />
<Skeleton className="h-4 w-1/2" />
</div>
) : errorFile ? (
<div className="text-destructive text-sm">
Error loading file: {errorFile.message}
</div>
) : file ? (
<div className="space-y-4">
<div>
<h3 className="text-lg font-medium mb-2">File Details</h3>
<div className="grid grid-cols-2 gap-4 text-sm">
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Status:</span>
<span className="ml-2">{file.status}</span>
</div>
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Size:</span>
<span className="ml-2">{file.usage_bytes} bytes</span>
</div>
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Created:</span>
<span className="ml-2">{new Date(file.created_at * 1000).toLocaleString()}</span>
</div>
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Content Strategy:</span>
<span className="ml-2">{file.chunking_strategy.type}</span>
</div>
</div>
</div>
<div className="border-t pt-4">
<h3 className="text-lg font-medium mb-3">Actions</h3>
<Button
onClick={handleViewContents}
className="flex items-center gap-2 hover:bg-primary/90 dark:hover:bg-primary/80 hover:scale-105 transition-all duration-200"
>
<List className="h-4 w-4" />
View Contents
</Button>
</div>
</div>
) : (
<p className="text-gray-500 italic text-sm">
File not found.
</p>
)}
</CardContent>
</Card>
<Card>
<CardHeader>
<CardTitle>Content Summary</CardTitle>
</CardHeader>
<CardContent>
{isLoadingContents ? (
<div className="space-y-2">
<Skeleton className="h-4 w-full" />
<Skeleton className="h-4 w-3/4" />
<Skeleton className="h-4 w-1/2" />
</div>
) : errorContents ? (
<div className="text-destructive text-sm">
Error loading content summary: {errorContents.message}
</div>
) : contents && contents.content.length > 0 ? (
<div className="space-y-3">
<div className="grid grid-cols-2 gap-4 text-sm">
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Content Items:</span>
<span className="ml-2">{contents.content.length}</span>
</div>
<div>
<span className="font-medium text-gray-600 dark:text-gray-400">Total Characters:</span>
<span className="ml-2">{contents.content.reduce((total, item) => total + item.text.length, 0)}</span>
</div>
</div>
<div className="pt-2">
<span className="text-sm font-medium text-gray-600 dark:text-gray-400">Preview:</span>
<div className="mt-1 bg-gray-50 dark:bg-gray-800 rounded-md p-3">
<p className="text-sm text-gray-900 dark:text-gray-100 line-clamp-3">
{contents.content[0]?.text.substring(0, 200)}...
</p>
</div>
</div>
</div>
) : (
<p className="text-gray-500 italic text-sm">
No contents found for this file.
</p>
)}
</CardContent>
</Card>
</>
);
const sidebar = (
<PropertiesCard>
<PropertyItem label="File ID" value={fileId} />
<PropertyItem label="Vector Store ID" value={vectorStoreId} />
{file && (
<>
<PropertyItem label="Status" value={file.status} />
<PropertyItem
label="Created"
value={new Date(file.created_at * 1000).toLocaleString()}
/>
<PropertyItem label="Usage Bytes" value={file.usage_bytes} />
<PropertyItem
label="Content Strategy"
value={file.chunking_strategy.type}
/>
</>
)}
{store && (
<>
<PropertyItem label="Store Name" value={store.name || ""} />
<PropertyItem
label="Provider ID"
value={(store.metadata.provider_id as string) || ""}
/>
</>
)}
</PropertiesCard>
);
return (
<>
<PageBreadcrumb segments={breadcrumbSegments} />
<DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
</>
);
}

25
llama_stack/ui/app/logs/vector-stores/layout.tsx
View file

@ -1,16 +1,31 @@
"use client";
import React from "react";
import LogsLayout from "@/components/layout/logs-layout";
import { useParams, usePathname } from "next/navigation";
import {
PageBreadcrumb,
BreadcrumbSegment,
} from "@/components/layout/page-breadcrumb";
export default function VectorStoresLayout({
export default function VectorStoreDetailLayout({
children,
}: {
children: React.ReactNode;
}) {
const params = useParams();
const pathname = usePathname();
const vectorStoreId = params.id as string;
const breadcrumbSegments: BreadcrumbSegment[] = [
{ label: "Vector Stores", href: "/logs/vector-stores" },
{ label: `Details (${vectorStoreId})` },
];
const isBaseDetailPage = pathname === `/logs/vector-stores/${vectorStoreId}`;
return (
<LogsLayout sectionLabel="Vector Stores" basePath="/logs/vector-stores">
<div className="space-y-4">
{isBaseDetailPage && <PageBreadcrumb segments={breadcrumbSegments} />}
{children}
</LogsLayout>
</div>
);
}

22
llama_stack/ui/app/logs/vector-stores/page.tsx
View file

@ -8,6 +8,7 @@ import type {
} from "llama-stack-client/resources/vector-stores/vector-stores";
import { useRouter } from "next/navigation";
import { usePagination } from "@/hooks/use-pagination";
import { Button } from "@/components/ui/button";
import {
Table,
TableBody,
@ -49,6 +50,7 @@ export default function VectorStoresPage() {
}
}, [status, hasMore, loadMore]);
const renderContent = () => {
if (status === "loading") {
return (
<div className="space-y-2">
@ -98,7 +100,17 @@ export default function VectorStoresPage() {
onClick={() => router.push(`/logs/vector-stores/${store.id}`)}
className="cursor-pointer hover:bg-muted/50"
>
<TableCell>{store.id}</TableCell>
<TableCell>
<Button
variant="link"
className="p-0 h-auto font-mono text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
onClick={() =>
router.push(`/logs/vector-stores/${store.id}`)
}
>
{store.id}
</Button>
</TableCell>
<TableCell>{store.name}</TableCell>
<TableCell>
{new Date(store.created_at * 1000).toLocaleString()}
@ -118,4 +130,12 @@ export default function VectorStoresPage() {
</Table>
</div>
);
};
return (
<div className="space-y-4">
<h1 className="text-2xl font-semibold">Vector Stores</h1>
{renderContent()}
</div>
);
}

17
llama_stack/ui/components/vector-stores/vector-store-detail.tsx
View file

@ -1,9 +1,11 @@
"use client";
import { useRouter } from "next/navigation";
import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Skeleton } from "@/components/ui/skeleton";
import { Button } from "@/components/ui/button";
import {
DetailLoadingView,
DetailErrorView,
@ -42,6 +44,11 @@ export function VectorStoreDetailView({
id,
}: VectorStoreDetailViewProps) {
const title = "Vector Store Details";
const router = useRouter();
const handleFileClick = (fileId: string) => {
router.push(`/logs/vector-stores/${id}/files/${fileId}`);
};
if (errorStore) {
return <DetailErrorView title={title} id={id} error={errorStore} />;
@ -80,7 +87,15 @@ export function VectorStoreDetailView({
<TableBody>
{files.map((file) => (
<TableRow key={file.id}>
<TableCell>{file.id}</TableCell>
<TableCell>
<Button
variant="link"
className="p-0 h-auto font-mono text-blue-600 hover:text-blue-800 dark:text-blue-400 dark:hover:text-blue-300"
onClick={() => handleFileClick(file.id)}
>
{file.id}
</Button>
</TableCell>
<TableCell>{file.status}</TableCell>
<TableCell>
{new Date(file.created_at * 1000).toLocaleString()}

112
llama_stack/ui/lib/contents-api.ts Normal file
View file

@ -0,0 +1,112 @@
import type { FileContentResponse } from "llama-stack-client/resources/vector-stores/files";
import type { LlamaStackClient } from "llama-stack-client";
export type VectorStoreContent = FileContentResponse.Content;
export type VectorStoreContentsResponse = FileContentResponse;
export interface VectorStoreContentItem {
id: string;
object: string;
created_timestamp: number;
vector_store_id: string;
file_id: string;
content: VectorStoreContent;
metadata: Record<string, any>;
embedding?: number[];
}
export interface VectorStoreContentDeleteResponse {
id: string;
object: string;
deleted: boolean;
}
export interface VectorStoreListContentsResponse {
object: string;
data: VectorStoreContentItem[];
first_id?: string;
last_id?: string;
has_more: boolean;
}
export class ContentsAPI {
constructor(private client: LlamaStackClient) {}
async getFileContents(vectorStoreId: string, fileId: string): Promise<VectorStoreContentsResponse> {
return this.client.vectorStores.files.content(vectorStoreId, fileId);
}
async getContent(vectorStoreId: string, fileId: string, contentId: string): Promise<VectorStoreContentItem> {
const contentsResponse = await this.listContents(vectorStoreId, fileId);
const targetContent = contentsResponse.data.find(c => c.id === contentId);
if (!targetContent) {
throw new Error(`Content ${contentId} not found`);
}
return targetContent;
}
async updateContent(
vectorStoreId: string,
fileId: string,
contentId: string,
updates: { content?: string; metadata?: Record<string, any> }
): Promise<VectorStoreContentItem> {
throw new Error("Individual content updates not yet implemented in API");
}
async deleteContent(vectorStoreId: string, fileId: string, contentId: string): Promise<VectorStoreContentDeleteResponse> {
throw new Error("Individual content deletion not yet implemented in API");
}
async listContents(
vectorStoreId: string,
fileId: string,
options?: {
limit?: number;
order?: string;
after?: string;
before?: string;
}
): Promise<VectorStoreListContentsResponse> {
const fileContents = await this.client.vectorStores.files.content(vectorStoreId, fileId);
const contentItems: VectorStoreContentItem[] = [];
fileContents.content.forEach((content, contentIndex) => {
const rawContent = content as any;
// Extract actual fields from the API response
const embedding = rawContent.embedding || undefined;
const created_timestamp = rawContent.created_timestamp || rawContent.created_at || Date.now() / 1000;
const chunkMetadata = rawContent.chunk_metadata || {};
const contentId = rawContent.chunk_metadata?.chunk_id || rawContent.id || `content_${fileId}_${contentIndex}`;
const objectType = rawContent.object || 'vector_store.file.content';
contentItems.push({
id: contentId,
object: objectType,
created_timestamp: created_timestamp,
vector_store_id: vectorStoreId,
file_id: fileId,
content: content,
embedding: embedding,
metadata: {
...chunkMetadata, // chunk_metadata fields from API
content_length: content.type === 'text' ? content.text.length : 0,
},
});
});
// apply pagination if needed
let filteredItems = contentItems;
if (options?.limit) {
filteredItems = filteredItems.slice(0, options.limit);
}
return {
object: 'list',
data: filteredItems,
has_more: contentItems.length > (options?.limit || contentItems.length),
};
}
}

10
llama_stack/ui/package-lock.json generated
View file

@ -18,7 +18,7 @@
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"framer-motion": "^11.18.2",
"llama-stack-client": "0.2.16",
"llama-stack-client": "0.2.17",
"lucide-react": "^0.510.0",
"next": "15.3.3",
"next-auth": "^4.24.11",
@ -9926,10 +9926,10 @@
"license": "MIT"
},
"node_modules/llama-stack-client": {
"version": "0.2.16",
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.16.tgz",
"integrity": "sha512-jM7sh1CB5wVumutYb3qfmYJpoTe3IRAa5lm3Us4qO7zVP4tbo3eCE7BOFNWyChpjo9efafUItwogNh28pum9PQ==",
"license": "Apache-2.0",
"version": "0.2.17",
"resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.17.tgz",
"integrity": "sha512-+/fEO8M7XPiVLjhH7ge18i1ijKp4+h3dOkE0C8g2cvGuDUtDYIJlf8NSyr9OMByjiWpCibWU7VOKL50LwGLS3Q==",
"license": "MIT",
"dependencies": {
"@types/node": "^18.11.18",
"@types/node-fetch": "^2.6.4",

2
llama_stack/ui/package.json
View file

@ -23,7 +23,7 @@
"class-variance-authority": "^0.7.1",
"clsx": "^2.1.1",
"framer-motion": "^11.18.2",
"llama-stack-client": ""0.2.17",
"llama-stack-client": "^0.2.17",
"lucide-react": "^0.510.0",
"next": "15.3.3",
"next-auth": "^4.24.11",

3
pyproject.toml
View file

@ -2,6 +2,9 @@
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[tool.uv]
required-version = ">=0.7.0"
[project]
name = "llama_stack"
version = "0.2.17"

5
scripts/provider_codegen.py
View file

@ -10,6 +10,7 @@ import sys
from pathlib import Path
from typing import Any
from pydantic_core import PydanticUndefined
from rich.progress import Progress, SpinnerColumn, TextColumn
from llama_stack.core.distribution import get_provider_registry
@ -59,6 +60,8 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
if hasattr(config_class, "model_fields"):
for field_name, field in config_class.model_fields.items():
field_type = str(field.annotation) if field.annotation else "Any"
# this string replace is ridiculous
field_type = field_type.replace("typing.", "").replace("Optional[", "").replace("]", "")
field_type = field_type.replace("Annotated[", "").replace("FieldInfo(", "").replace(")", "")
field_type = field_type.replace("llama_stack.apis.inference.inference.", "")
@ -77,7 +80,7 @@ def get_config_class_info(config_class_path: str) -> dict[str, Any]:
default_value = f"~/.llama/{path_part}"
except Exception:
default_value = ""
elif field.default is None:
elif field.default is None or field.default is PydanticUndefined:
default_value = ""
field_info = {

60
tests/client-sdk/post_training/test_supervied_fine_tuning.py
View file

@ -1,60 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import pytest
POST_TRAINING_PROVIDER_TYPES = ["remote::nvidia"]
@pytest.mark.integration
@pytest.fixture(scope="session")
def post_training_provider_available(llama_stack_client):
providers = llama_stack_client.providers.list()
post_training_providers = [p for p in providers if p.provider_type in POST_TRAINING_PROVIDER_TYPES]
return len(post_training_providers) > 0
@pytest.mark.integration
def test_post_training_provider_registration(llama_stack_client, post_training_provider_available):
"""Check if post_training is in the api list.
This is a sanity check to ensure the provider is registered."""
if not post_training_provider_available:
pytest.skip("post training provider not available")
providers = llama_stack_client.providers.list()
post_training_providers = [p for p in providers if p.provider_type in POST_TRAINING_PROVIDER_TYPES]
assert len(post_training_providers) > 0
@pytest.mark.integration
def test_get_training_jobs(llama_stack_client, post_training_provider_available):
"""Test listing all training jobs."""
if not post_training_provider_available:
pytest.skip("post training provider not available")
jobs = llama_stack_client.post_training.get_training_jobs()
assert isinstance(jobs, dict)
assert "data" in jobs
assert isinstance(jobs["data"], list)
@pytest.mark.integration
def test_get_training_job_status(llama_stack_client, post_training_provider_available):
"""Test getting status of a specific training job."""
if not post_training_provider_available:
pytest.skip("post training provider not available")
jobs = llama_stack_client.post_training.get_training_jobs()
if not jobs["data"]:
pytest.skip("No training jobs available to check status")
job_uuid = jobs["data"][0]["job_uuid"]
job_status = llama_stack_client.post_training.get_training_job_status(job_uuid=job_uuid)
assert job_status is not None
assert "job_uuid" in job_status
assert "status" in job_status
assert job_status["job_uuid"] == job_uuid

8
tests/integration/README.md
View file

@ -108,9 +108,7 @@ pytest -s -v tests/integration/inference/ \
Running Vector IO tests for a number of embedding models:
```bash
EMBEDDING_MODELS=all-MiniLM-L6-v2
pytest -s -v tests/integration/vector_io/ \
--stack-config=inference=sentence-transformers,vector_io=sqlite-vec \
--embedding-model=$EMBEDDING_MODELS
uv run pytest -sv --stack-config="inference=inline::sentence-transformers,vector_io=inline::sqlite-vec,files=localfs" \
tests/integration/vector_io --embedding-model \
sentence-transformers/all-MiniLM-L6-v2
```

37
tests/integration/agents/test_openai_responses.py
View file

@ -9,12 +9,6 @@ from openai import BadRequestError, OpenAI
from llama_stack.core.library_client import LlamaStackAsLibraryClient
@pytest.fixture
def openai_client(client_with_models):
base_url = f"{client_with_models.base_url}/v1/openai/v1"
return OpenAI(base_url=base_url, api_key="bar")
@pytest.mark.parametrize(
"stream",
[
@ -41,15 +35,14 @@ def openai_client(client_with_models):
],
],
)
def test_responses_store(openai_client, client_with_models, text_model_id, stream, tools):
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
def test_responses_store(compat_client, text_model_id, stream, tools):
if not isinstance(compat_client, OpenAI):
pytest.skip("OpenAI client is required until responses.delete() exists in llama-stack-client")
client = openai_client
message = "What's the weather in Tokyo?" + (
" YOU MUST USE THE get_weather function to get the weather." if tools else ""
)
response = client.responses.create(
response = compat_client.responses.create(
model=text_model_id,
input=[
{
@ -78,14 +71,8 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
if output_type == "message":
content = response.output[0].content[0].text
# list responses - use the underlying HTTP client for endpoints not in SDK
list_response = client._client.get("/responses")
assert list_response.status_code == 200
data = list_response.json()["data"]
assert response_id in [r["id"] for r in data]
# test retrieve response
retrieved_response = client.responses.retrieve(response_id)
retrieved_response = compat_client.responses.retrieve(response_id)
assert retrieved_response.id == response_id
assert retrieved_response.model == text_model_id
assert retrieved_response.output[0].type == output_type, retrieved_response
@ -93,23 +80,19 @@ def test_responses_store(openai_client, client_with_models, text_model_id, strea
assert retrieved_response.output[0].content[0].text == content
# Delete the response
delete_response = client.responses.delete(response_id)
delete_response = compat_client.responses.delete(response_id)
assert delete_response is None
with pytest.raises(BadRequestError):
client.responses.retrieve(response_id)
compat_client.responses.retrieve(response_id)
def test_list_response_input_items(openai_client, client_with_models, text_model_id):
def test_list_response_input_items(compat_client, text_model_id):
"""Test the new list_openai_response_input_items endpoint."""
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
client = openai_client
message = "What is the capital of France?"
# Create a response first
response = client.responses.create(
response = compat_client.responses.create(
model=text_model_id,
input=[
{
@ -123,7 +106,7 @@ def test_list_response_input_items(openai_client, client_with_models, text_model
response_id = response.id
# Test the new list input items endpoint
input_items_response = client.responses.input_items.list(response_id=response_id)
input_items_response = compat_client.responses.input_items.list(response_id=response_id)
# Verify the structure follows OpenAI API spec
assert input_items_response.object == "list"

5
tests/integration/conftest.py
View file

@ -27,6 +27,11 @@ def pytest_runtest_makereport(item, call):
item.was_xfail = getattr(report, "wasxfail", False)
def pytest_sessionstart(session):
# stop macOS from complaining about duplicate OpenMP libraries
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
def pytest_runtest_teardown(item):
# Check if the test actually ran and passed or failed, but was not skipped or an expected failure (xfail)
outcome = getattr(item, "execution_outcome", None)

39
tests/integration/fixtures/common.py
View file

@ -82,8 +82,7 @@ def wait_for_server_ready(base_url: str, timeout: int = 30, process: subprocess.
return False
@pytest.fixture(scope="session")
def provider_data():
def get_provider_data():
# TODO: this needs to be generalized so each provider can have a sample provider data just
# like sample run config on which we can do replace_env_vars()
keymap = {
@ -178,8 +177,19 @@ def skip_if_no_model(request):
@pytest.fixture(scope="session")
def llama_stack_client(request, provider_data):
config = request.config.getoption("--stack-config")
def llama_stack_client(request):
# ideally, we could do this in session start given all the complex logs during initialization
# don't clobber the test one-liner outputs. however, this also means all tests in a sub-directory
# would be forced to use llama_stack_client, which is not what we want.
print("\ninstantiating llama_stack_client")
start_time = time.time()
client = instantiate_llama_stack_client(request.session)
print(f"llama_stack_client instantiated in {time.time() - start_time:.3f}s")
return client
def instantiate_llama_stack_client(session):
config = session.config.getoption("--stack-config")
if not config:
config = get_env_or_fail("LLAMA_STACK_CONFIG")
@ -212,13 +222,13 @@ def llama_stack_client(request, provider_data):
print(f"Server is ready at {base_url}")
# Store process for potential cleanup (pytest will handle termination at session end)
request.session._llama_stack_server_process = server_process
session._llama_stack_server_process = server_process
else:
print(f"Port {port} is already in use, assuming server is already running...")
return LlamaStackClient(
base_url=base_url,
provider_data=provider_data,
provider_data=get_provider_data(),
timeout=int(os.environ.get("LLAMA_STACK_CLIENT_TIMEOUT", "30")),
)
@ -228,7 +238,7 @@ def llama_stack_client(request, provider_data):
if parsed_url.scheme and parsed_url.netloc:
return LlamaStackClient(
base_url=config,
provider_data=provider_data,
provider_data=get_provider_data(),
)
except Exception:
# If URL parsing fails, treat as non-URL config
@ -243,7 +253,7 @@ def llama_stack_client(request, provider_data):
client = LlamaStackAsLibraryClient(
config,
provider_data=provider_data,
provider_data=get_provider_data(),
skip_logger_removal=True,
)
if not client.initialize():
@ -258,8 +268,17 @@ def openai_client(client_with_models):
return OpenAI(base_url=base_url, api_key="fake")
@pytest.fixture(params=["openai_client", "llama_stack_client"])
def compat_client(request):
@pytest.fixture(params=["openai_client", "client_with_models"])
def compat_client(request, client_with_models):
if isinstance(client_with_models, LlamaStackAsLibraryClient):
# OpenAI client expects a server, so unless we also rewrite OpenAI client's requests
# to go via the Stack library client (which itself rewrites requests to be served inline),
# we cannot do this.
#
# This means when we are using Stack as a library, we will test only via the Llama Stack client.
# When we are using a server setup, we can exercise both OpenAI and Llama Stack clients.
pytest.skip("(OpenAI) Compat client cannot be used with Stack library client")
return request.getfixturevalue(request.param)

17
tests/integration/inference/test_openai_completion.py
View file

@ -6,9 +6,6 @@
import pytest
from openai import OpenAI
from llama_stack.core.library_client import LlamaStackAsLibraryClient
from ..test_cases.test_case import TestCase
@ -59,9 +56,6 @@ def skip_if_model_doesnt_support_suffix(client_with_models, model_id):
def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, model_id):
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI chat completions are not supported when testing with library client yet.")
provider = provider_from_model(client_with_models, model_id)
if provider.provider_type in (
"inline::meta-reference",
@ -90,17 +84,6 @@ def skip_if_provider_isnt_openai(client_with_models, model_id):
)
@pytest.fixture
def openai_client(client_with_models):
base_url = f"{client_with_models.base_url}/v1/openai/v1"
return OpenAI(base_url=base_url, api_key="bar")
@pytest.fixture(params=["openai_client", "llama_stack_client"])
def compat_client(request):
return request.getfixturevalue(request.param)
@pytest.mark.parametrize(
"test_case",
[

0
tests/client-sdk/post_training/__init__.py → tests/integration/non_ci/responses/__init__.py
View file

0
tests/verifications/__init__.py → tests/integration/non_ci/responses/fixtures/__init__.py
View file

10
tests/verifications/openai_api/fixtures/fixtures.py → tests/integration/non_ci/responses/fixtures/fixtures.py
View file

@ -56,16 +56,6 @@ def case_id_generator(case):
return None
def should_skip_test(verification_config, provider, model, test_name_base):
"""Check if a test should be skipped based on config exclusions."""
provider_config = verification_config.get("providers", {}).get(provider)
if not provider_config:
return False # No config for provider, don't skip
exclusions = provider_config.get("test_exclusions", {}).get(model, [])
return test_name_base in exclusions
# Helper to get the base test name from the request object
def get_base_test_name(request):
return request.node.originalname

0
tests/verifications/openai_api/fixtures/images/vision_test_1.jpg → tests/integration/non_ci/responses/fixtures/images/vision_test_1.jpg
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0
tests/verifications/openai_api/fixtures/load.py → tests/integration/non_ci/responses/fixtures/load.py
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0
tests/verifications/openai_api/fixtures/pdfs/llama_stack_and_models.pdf → tests/integration/non_ci/responses/fixtures/pdfs/llama_stack_and_models.pdf
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0
tests/verifications/openai_api/fixtures/test_cases/chat_completion.yaml → tests/integration/non_ci/responses/fixtures/test_cases/chat_completion.yaml
View file

0
tests/verifications/openai_api/fixtures/test_cases/responses.yaml → tests/integration/non_ci/responses/fixtures/test_cases/responses.yaml
View file

252
tests/verifications/openai_api/test_responses.py → tests/integration/non_ci/responses/test_responses.py
View file

@ -15,12 +15,9 @@ import pytest
from llama_stack import LlamaStackAsLibraryClient
from llama_stack.core.datatypes import AuthenticationRequiredError
from tests.common.mcp import dependency_tools, make_mcp_server
from tests.verifications.openai_api.fixtures.fixtures import (
case_id_generator,
get_base_test_name,
should_skip_test,
)
from tests.verifications.openai_api.fixtures.load import load_test_cases
from .fixtures.fixtures import case_id_generator
from .fixtures.load import load_test_cases
responses_test_cases = load_test_cases("responses")
@ -55,13 +52,9 @@ def _upload_file(openai_client, name, file_path):
responses_test_cases["test_response_basic"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_basic(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
response = openai_client.responses.create(
model=model,
def test_response_non_streaming_basic(request, compat_client, text_model_id, case):
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
stream=False,
)
@ -69,11 +62,13 @@ def test_response_non_streaming_basic(request, openai_client, model, provider, v
assert len(output_text) > 0
assert case["output"].lower() in output_text
retrieved_response = openai_client.responses.retrieve(response_id=response.id)
retrieved_response = compat_client.responses.retrieve(response_id=response.id)
assert retrieved_response.output_text == response.output_text
next_response = openai_client.responses.create(
model=model, input="Repeat your previous response in all caps.", previous_response_id=response.id
next_response = compat_client.responses.create(
model=text_model_id,
input="Repeat your previous response in all caps.",
previous_response_id=response.id,
)
next_output_text = next_response.output_text.strip()
assert case["output"].upper() in next_output_text
@ -84,15 +79,11 @@ def test_response_non_streaming_basic(request, openai_client, model, provider, v
responses_test_cases["test_response_basic"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_streaming_basic(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
def test_response_streaming_basic(request, compat_client, text_model_id, case):
import time
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
stream=True,
)
@ -138,7 +129,7 @@ def test_response_streaming_basic(request, openai_client, model, provider, verif
assert created_index < completed_index, "response.created should come before response.completed"
# Verify stored response matches streamed response
retrieved_response = openai_client.responses.retrieve(response_id=response_id)
retrieved_response = compat_client.responses.retrieve(response_id=response_id)
final_event = events[-1]
assert retrieved_response.output_text == final_event.response.output_text
@ -148,16 +139,12 @@ def test_response_streaming_basic(request, openai_client, model, provider, verif
responses_test_cases["test_response_basic"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_streaming_incremental_content(request, openai_client, model, provider, verification_config, case):
def test_response_streaming_incremental_content(request, compat_client, text_model_id, case):
"""Test that streaming actually delivers content incrementally, not just at the end."""
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
import time
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
stream=True,
)
@ -241,15 +228,11 @@ def test_response_streaming_incremental_content(request, openai_client, model, p
responses_test_cases["test_response_multi_turn"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_multi_turn(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
def test_response_non_streaming_multi_turn(request, compat_client, text_model_id, case):
previous_response_id = None
for turn in case["turns"]:
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=turn["input"],
previous_response_id=previous_response_id,
tools=turn["tools"] if "tools" in turn else None,
@ -264,13 +247,9 @@ def test_response_non_streaming_multi_turn(request, openai_client, model, provid
responses_test_cases["test_response_web_search"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_web_search(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
response = openai_client.responses.create(
model=model,
def test_response_non_streaming_web_search(request, compat_client, text_model_id, case):
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=case["tools"],
stream=False,
@ -290,17 +269,11 @@ def test_response_non_streaming_web_search(request, openai_client, model, provid
responses_test_cases["test_response_file_search"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_file_search(
request, openai_client, model, provider, verification_config, tmp_path, case
):
if isinstance(openai_client, LlamaStackAsLibraryClient):
def test_response_non_streaming_file_search(request, compat_client, text_model_id, tmp_path, case):
if isinstance(compat_client, LlamaStackAsLibraryClient):
pytest.skip("Responses API file search is not yet supported in library client.")
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
vector_store = _new_vector_store(openai_client, "test_vector_store")
vector_store = _new_vector_store(compat_client, "test_vector_store")
if "file_content" in case:
file_name = "test_response_non_streaming_file_search.txt"
@ -312,10 +285,10 @@ def test_response_non_streaming_file_search(
else:
raise ValueError(f"No file content or path provided for case {case['case_id']}")
file_response = _upload_file(openai_client, file_name, file_path)
file_response = _upload_file(compat_client, file_name, file_path)
# Attach our file to the vector store
file_attach_response = openai_client.vector_stores.files.create(
file_attach_response = compat_client.vector_stores.files.create(
vector_store_id=vector_store.id,
file_id=file_response.id,
)
@ -323,7 +296,7 @@ def test_response_non_streaming_file_search(
# Wait for the file to be attached
while file_attach_response.status == "in_progress":
time.sleep(0.1)
file_attach_response = openai_client.vector_stores.files.retrieve(
file_attach_response = compat_client.vector_stores.files.retrieve(
vector_store_id=vector_store.id,
file_id=file_response.id,
)
@ -337,8 +310,8 @@ def test_response_non_streaming_file_search(
tool["vector_store_ids"] = [vector_store.id]
# Create the response request, which should query our vector store
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=tools,
stream=False,
@ -358,21 +331,15 @@ def test_response_non_streaming_file_search(
assert case["output"].lower() in response.output_text.lower().strip()
def test_response_non_streaming_file_search_empty_vector_store(
request, openai_client, model, provider, verification_config
):
if isinstance(openai_client, LlamaStackAsLibraryClient):
def test_response_non_streaming_file_search_empty_vector_store(request, compat_client, text_model_id):
if isinstance(compat_client, LlamaStackAsLibraryClient):
pytest.skip("Responses API file search is not yet supported in library client.")
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
vector_store = _new_vector_store(openai_client, "test_vector_store")
vector_store = _new_vector_store(compat_client, "test_vector_store")
# Create the response request, which should query our vector store
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="How many experts does the Llama 4 Maverick model have?",
tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
stream=False,
@ -395,19 +362,15 @@ def test_response_non_streaming_file_search_empty_vector_store(
responses_test_cases["test_response_mcp_tool"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_mcp_tool(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
def test_response_non_streaming_mcp_tool(request, compat_client, text_model_id, case):
with make_mcp_server() as mcp_server_info:
tools = case["tools"]
for tool in tools:
if tool["type"] == "mcp":
tool["server_url"] = mcp_server_info["server_url"]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=tools,
stream=False,
@ -418,7 +381,7 @@ def test_response_non_streaming_mcp_tool(request, openai_client, model, provider
assert list_tools.type == "mcp_list_tools"
assert list_tools.server_label == "localmcp"
assert len(list_tools.tools) == 2
assert {t["name"] for t in list_tools.tools} == {"get_boiling_point", "greet_everyone"}
assert {t.name for t in list_tools.tools} == {"get_boiling_point", "greet_everyone"}
call = response.output[1]
assert call.type == "mcp_call"
@ -440,12 +403,12 @@ def test_response_non_streaming_mcp_tool(request, openai_client, model, provider
exc_type = (
AuthenticationRequiredError
if isinstance(openai_client, LlamaStackAsLibraryClient)
if isinstance(compat_client, LlamaStackAsLibraryClient)
else (httpx.HTTPStatusError, openai.AuthenticationError)
)
with pytest.raises(exc_type):
openai_client.responses.create(
model=model,
compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=tools,
stream=False,
@ -456,8 +419,8 @@ def test_response_non_streaming_mcp_tool(request, openai_client, model, provider
tool["server_url"] = mcp_server_info["server_url"]
tool["headers"] = {"Authorization": "Bearer test-token"}
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=tools,
stream=False,
@ -470,13 +433,9 @@ def test_response_non_streaming_mcp_tool(request, openai_client, model, provider
responses_test_cases["test_response_custom_tool"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_custom_tool(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
response = openai_client.responses.create(
model=model,
def test_response_non_streaming_custom_tool(request, compat_client, text_model_id, case):
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
tools=case["tools"],
stream=False,
@ -492,13 +451,9 @@ def test_response_non_streaming_custom_tool(request, openai_client, model, provi
responses_test_cases["test_response_image"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_image(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
response = openai_client.responses.create(
model=model,
def test_response_non_streaming_image(request, compat_client, text_model_id, case):
response = compat_client.responses.create(
model=text_model_id,
input=case["input"],
stream=False,
)
@ -511,15 +466,11 @@ def test_response_non_streaming_image(request, openai_client, model, provider, v
responses_test_cases["test_response_multi_turn_image"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_multi_turn_image(request, openai_client, model, provider, verification_config, case):
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
def test_response_non_streaming_multi_turn_image(request, compat_client, text_model_id, case):
previous_response_id = None
for turn in case["turns"]:
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input=turn["input"],
previous_response_id=previous_response_id,
tools=turn["tools"] if "tools" in turn else None,
@ -534,14 +485,8 @@ def test_response_non_streaming_multi_turn_image(request, openai_client, model,
responses_test_cases["test_response_multi_turn_tool_execution"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_multi_turn_tool_execution(
request, openai_client, model, provider, verification_config, case
):
def test_response_non_streaming_multi_turn_tool_execution(request, compat_client, text_model_id, case):
"""Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
tools = case["tools"]
# Replace the placeholder URL with the actual server URL
@ -549,14 +494,15 @@ def test_response_non_streaming_multi_turn_tool_execution(
if tool["type"] == "mcp" and tool["server_url"] == "<FILLED_BY_TEST_RUNNER>":
tool["server_url"] = mcp_server_info["server_url"]
response = openai_client.responses.create(
response = compat_client.responses.create(
input=case["input"],
model=model,
model=text_model_id,
tools=tools,
)
# Verify we have MCP tool calls in the output
mcp_list_tools = [output for output in response.output if output.type == "mcp_list_tools"]
mcp_calls = [output for output in response.output if output.type == "mcp_call"]
message_outputs = [output for output in response.output if output.type == "message"]
@ -571,7 +517,7 @@ def test_response_non_streaming_multi_turn_tool_execution(
"get_experiment_id",
"get_experiment_results",
}
assert {t["name"] for t in mcp_list_tools[0].tools} == expected_tool_names
assert {t.name for t in mcp_list_tools[0].tools} == expected_tool_names
assert len(mcp_calls) >= 1, f"Expected at least 1 mcp_call, got {len(mcp_calls)}"
for mcp_call in mcp_calls:
@ -595,14 +541,8 @@ def test_response_non_streaming_multi_turn_tool_execution(
responses_test_cases["test_response_multi_turn_tool_execution_streaming"]["test_params"]["case"],
ids=case_id_generator,
)
async def test_response_streaming_multi_turn_tool_execution(
request, openai_client, model, provider, verification_config, case
):
async def test_response_streaming_multi_turn_tool_execution(request, compat_client, text_model_id, case):
"""Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
tools = case["tools"]
# Replace the placeholder URL with the actual server URL
@ -610,15 +550,15 @@ async def test_response_streaming_multi_turn_tool_execution(
if tool["type"] == "mcp" and tool["server_url"] == "<FILLED_BY_TEST_RUNNER>":
tool["server_url"] = mcp_server_info["server_url"]
stream = openai_client.responses.create(
stream = compat_client.responses.create(
input=case["input"],
model=model,
model=text_model_id,
tools=tools,
stream=True,
)
chunks = []
async for chunk in stream:
for chunk in stream:
chunks.append(chunk)
# Should have at least response.created and response.completed
@ -653,7 +593,7 @@ async def test_response_streaming_multi_turn_tool_execution(
"get_experiment_id",
"get_experiment_results",
}
assert {t["name"] for t in mcp_list_tools[0].tools} == expected_tool_names
assert {t.name for t in mcp_list_tools[0].tools} == expected_tool_names
# Should have at least 1 MCP call (the model should call at least one tool)
assert len(mcp_calls) >= 1, f"Expected at least 1 mcp_call, got {len(mcp_calls)}"
@ -694,17 +634,13 @@ async def test_response_streaming_multi_turn_tool_execution(
},
],
)
def test_response_text_format(request, openai_client, model, provider, verification_config, text_format):
if isinstance(openai_client, LlamaStackAsLibraryClient):
def test_response_text_format(request, compat_client, text_model_id, text_format):
if isinstance(compat_client, LlamaStackAsLibraryClient):
pytest.skip("Responses API text format is not yet supported in library client.")
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
stream = False
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="What is the capital of France?",
stream=stream,
text={"format": text_format},
@ -717,16 +653,12 @@ def test_response_text_format(request, openai_client, model, provider, verificat
@pytest.fixture
def vector_store_with_filtered_files(request, openai_client, model, provider, verification_config, tmp_path_factory):
def vector_store_with_filtered_files(request, compat_client, text_model_id, tmp_path_factory):
"""Create a vector store with multiple files that have different attributes for filtering tests."""
if isinstance(openai_client, LlamaStackAsLibraryClient):
if isinstance(compat_client, LlamaStackAsLibraryClient):
pytest.skip("Responses API file search is not yet supported in library client.")
test_name_base = get_base_test_name(request)
if should_skip_test(verification_config, provider, model, test_name_base):
pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
vector_store = _new_vector_store(openai_client, "test_vector_store_with_filters")
vector_store = _new_vector_store(compat_client, "test_vector_store_with_filters")
tmp_path = tmp_path_factory.mktemp("filter_test_files")
# Create multiple files with different attributes
@ -776,18 +708,18 @@ def vector_store_with_filtered_files(request, openai_client, model, provider, ve
file_path.write_text(file_data["content"])
# Upload file
file_response = _upload_file(openai_client, file_data["name"], str(file_path))
file_response = _upload_file(compat_client, file_data["name"], str(file_path))
file_ids.append(file_response.id)
# Attach file to vector store with attributes
file_attach_response = openai_client.vector_stores.files.create(
file_attach_response = compat_client.vector_stores.files.create(
vector_store_id=vector_store.id, file_id=file_response.id, attributes=file_data["attributes"]
)
# Wait for attachment
while file_attach_response.status == "in_progress":
time.sleep(0.1)
file_attach_response = openai_client.vector_stores.files.retrieve(
file_attach_response = compat_client.vector_stores.files.retrieve(
vector_store_id=vector_store.id,
file_id=file_response.id,
)
@ -797,17 +729,17 @@ def vector_store_with_filtered_files(request, openai_client, model, provider, ve
# Cleanup: delete vector store and files
try:
openai_client.vector_stores.delete(vector_store_id=vector_store.id)
compat_client.vector_stores.delete(vector_store_id=vector_store.id)
for file_id in file_ids:
try:
openai_client.files.delete(file_id=file_id)
compat_client.files.delete(file_id=file_id)
except Exception:
pass # File might already be deleted
except Exception:
pass # Best effort cleanup
def test_response_file_search_filter_by_region(openai_client, model, vector_store_with_filtered_files):
def test_response_file_search_filter_by_region(compat_client, text_model_id, vector_store_with_filtered_files):
"""Test file search with region equality filter."""
tools = [
{
@ -817,8 +749,8 @@ def test_response_file_search_filter_by_region(openai_client, model, vector_stor
}
]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="What are the updates from the US region?",
tools=tools,
stream=False,
@ -838,7 +770,7 @@ def test_response_file_search_filter_by_region(openai_client, model, vector_stor
assert "asia" not in result.text.lower()
def test_response_file_search_filter_by_category(openai_client, model, vector_store_with_filtered_files):
def test_response_file_search_filter_by_category(compat_client, text_model_id, vector_store_with_filtered_files):
"""Test file search with category equality filter."""
tools = [
{
@ -848,8 +780,8 @@ def test_response_file_search_filter_by_category(openai_client, model, vector_st
}
]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="Show me all marketing reports",
tools=tools,
stream=False,
@ -868,7 +800,7 @@ def test_response_file_search_filter_by_category(openai_client, model, vector_st
assert "revenue figures" not in result.text.lower()
def test_response_file_search_filter_by_date_range(openai_client, model, vector_store_with_filtered_files):
def test_response_file_search_filter_by_date_range(compat_client, text_model_id, vector_store_with_filtered_files):
"""Test file search with date range filter using compound AND."""
tools = [
{
@ -892,8 +824,8 @@ def test_response_file_search_filter_by_date_range(openai_client, model, vector_
}
]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="What happened in Q1 2023?",
tools=tools,
stream=False,
@ -911,7 +843,7 @@ def test_response_file_search_filter_by_date_range(openai_client, model, vector_
assert "q3" not in result.text.lower()
def test_response_file_search_filter_compound_and(openai_client, model, vector_store_with_filtered_files):
def test_response_file_search_filter_compound_and(compat_client, text_model_id, vector_store_with_filtered_files):
"""Test file search with compound AND filter (region AND category)."""
tools = [
{
@ -927,8 +859,8 @@ def test_response_file_search_filter_compound_and(openai_client, model, vector_s
}
]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="What are the engineering updates from the US?",
tools=tools,
stream=False,
@ -947,7 +879,7 @@ def test_response_file_search_filter_compound_and(openai_client, model, vector_s
assert "promotional" not in result.text.lower() and "revenue" not in result.text.lower()
def test_response_file_search_filter_compound_or(openai_client, model, vector_store_with_filtered_files):
def test_response_file_search_filter_compound_or(compat_client, text_model_id, vector_store_with_filtered_files):
"""Test file search with compound OR filter (marketing OR sales)."""
tools = [
{
@ -963,8 +895,8 @@ def test_response_file_search_filter_compound_or(openai_client, model, vector_st
}
]
response = openai_client.responses.create(
model=model,
response = compat_client.responses.create(
model=text_model_id,
input="Show me marketing and sales documents",
tools=tools,
stream=False,

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56
tests/integration/recordings/responses/140187e305dc.json Normal file
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@ -0,0 +1,56 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Test trace openai 0"
}
],
"stream": false
},
"endpoint": "/v1/chat/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "chatcmpl-876",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.",
"refusal": null,
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}
],
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"prompt_tokens": 31,
"total_tokens": 598,
"completion_tokens_details": null,
"prompt_tokens_details": null
}
}
},
"is_streaming": false
}
}

58
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@ -0,0 +1,58 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/v1/v1/chat/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Test trace openai with temperature 0"
}
],
"max_tokens": 100,
"stream": false,
"temperature": 0.7
},
"endpoint": "/v1/chat/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": {
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"__data__": {
"id": "chatcmpl-339",
"choices": [
{
"finish_reason": "length",
"index": 0,
"logprobs": null,
"message": {
"content": "I can guide you through the process, but please note that this is not an official OpenAI API call. OpenAI's API terms and conditions prohibit using their models for malicious purposes.\n\nTo test a model like \"text-temperature\" with a temperature of 0 (i.e., no noise or randomness), we'll need to use a third-party library that connects to the OpenAI API. One such library is `transformers`.\n\nFirst, you need to install the `transformers` and `",
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"annotations": null,
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}
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"usage": {
"completion_tokens": 100,
"prompt_tokens": 33,
"total_tokens": 133,
"completion_tokens_details": null,
"prompt_tokens_details": null
}
}
},
"is_streaming": false
}
}

56
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@ -0,0 +1,56 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/v1/v1/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Test trace openai 0"
}
],
"stream": false
},
"endpoint": "/v1/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "chatcmpl-695",
"choices": [
{
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