mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-02 08:44:44 +00:00
Merge branch 'main' into add-watsonx-inference-adapter
This commit is contained in:
Sajikumar JS
commit
6fe8b292b1
74 changed files with 5033 additions and 1685 deletions
1
.github/workflows/integration-tests.yml
vendored
1
.github/workflows/integration-tests.yml
vendored
|
|
@ -6,7 +6,6 @@ on:
|
|||
pull_request:
|
||||
branches: [ main ]
|
||||
paths:
|
||||
- 'distributions/**'
|
||||
- 'llama_stack/**'
|
||||
- 'tests/integration/**'
|
||||
- 'uv.lock'
|
||||
|
|
|
|||
38
.github/workflows/providers-build.yml
vendored
38
.github/workflows/providers-build.yml
vendored
|
|
@ -107,3 +107,41 @@ jobs:
|
|||
- name: Build a single provider
|
||||
run: |
|
||||
USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --image-type venv --image-name test --providers inference=remote::ollama
|
||||
|
||||
build-custom-container-distribution:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55 # v5.5.0
|
||||
with:
|
||||
python-version: '3.10'
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182 # v5.4.1
|
||||
with:
|
||||
python-version: "3.10"
|
||||
|
||||
- name: Install LlamaStack
|
||||
run: |
|
||||
uv venv
|
||||
source .venv/bin/activate
|
||||
uv pip install -e .
|
||||
|
||||
- name: Build a single provider
|
||||
run: |
|
||||
yq -i '.image_type = "container"' llama_stack/templates/dev/build.yaml
|
||||
yq -i '.image_name = "test"' llama_stack/templates/dev/build.yaml
|
||||
USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config llama_stack/templates/dev/build.yaml
|
||||
|
||||
- name: Inspect the container image entrypoint
|
||||
run: |
|
||||
IMAGE_ID=$(docker images --format "{{.Repository}}:{{.Tag}}" | head -n 1)
|
||||
entrypoint=$(docker inspect --format '{{ .Config.Entrypoint }}' $IMAGE_ID)
|
||||
echo "Entrypoint: $entrypoint"
|
||||
if [ "$entrypoint" != "[python -m llama_stack.distribution.server.server --config /app/run.yaml]" ]; then
|
||||
echo "Entrypoint is not correct"
|
||||
exit 1
|
||||
fi
|
||||
|
|
|
|||
30
.github/workflows/test-external-providers.yml
vendored
30
.github/workflows/test-external-providers.yml
vendored
|
|
@ -5,10 +5,22 @@ on:
|
|||
branches: [ main ]
|
||||
pull_request:
|
||||
branches: [ main ]
|
||||
paths:
|
||||
- 'llama_stack/**'
|
||||
- 'tests/integration/**'
|
||||
- 'uv.lock'
|
||||
- 'pyproject.toml'
|
||||
- 'requirements.txt'
|
||||
- '.github/workflows/test-external-providers.yml' # This workflow
|
||||
|
||||
jobs:
|
||||
test-external-providers:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
image-type: [venv]
|
||||
# We don't do container yet, it's tricky to install a package from the host into the
|
||||
# container and point 'uv pip install' to the correct path...
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
|
@ -35,17 +47,25 @@ jobs:
|
|||
uv sync --extra dev --extra test
|
||||
uv pip install -e .
|
||||
|
||||
- name: Install Ollama custom provider
|
||||
- name: Apply image type to config file
|
||||
run: |
|
||||
yq -i '.image_type = "${{ matrix.image-type }}"' tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
|
||||
cat tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
|
||||
|
||||
- name: Setup directory for Ollama custom provider
|
||||
run: |
|
||||
mkdir -p tests/external-provider/llama-stack-provider-ollama/src/
|
||||
cp -a llama_stack/providers/remote/inference/ollama/ tests/external-provider/llama-stack-provider-ollama/src/llama_stack_provider_ollama
|
||||
uv pip install tests/external-provider/llama-stack-provider-ollama
|
||||
|
||||
- name: Create provider configuration
|
||||
run: |
|
||||
mkdir -p /tmp/providers.d/remote/inference
|
||||
cp tests/external-provider/llama-stack-provider-ollama/custom_ollama.yaml /tmp/providers.d/remote/inference/custom_ollama.yaml
|
||||
|
||||
- name: Build distro from config file
|
||||
run: |
|
||||
USE_COPY_NOT_MOUNT=true LLAMA_STACK_DIR=. uv run llama stack build --config tests/external-provider/llama-stack-provider-ollama/custom-distro.yaml
|
||||
|
||||
- name: Wait for Ollama to start
|
||||
run: |
|
||||
echo "Waiting for Ollama..."
|
||||
|
|
@ -62,11 +82,13 @@ jobs:
|
|||
exit 1
|
||||
|
||||
- name: Start Llama Stack server in background
|
||||
if: ${{ matrix.image-type }} == 'venv'
|
||||
env:
|
||||
INFERENCE_MODEL: "meta-llama/Llama-3.2-3B-Instruct"
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
nohup uv run llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type venv > server.log 2>&1 &
|
||||
source ci-test/bin/activate
|
||||
uv run pip list
|
||||
nohup uv run --active llama stack run tests/external-provider/llama-stack-provider-ollama/run.yaml --image-type ${{ matrix.image-type }} > server.log 2>&1 &
|
||||
|
||||
- name: Wait for Llama Stack server to be ready
|
||||
run: |
|
||||
|
|
|
|||
1
.github/workflows/unit-tests.yml
vendored
1
.github/workflows/unit-tests.yml
vendored
|
|
@ -6,7 +6,6 @@ on:
|
|||
pull_request:
|
||||
branches: [ main ]
|
||||
paths:
|
||||
- 'distributions/**'
|
||||
- 'llama_stack/**'
|
||||
- 'tests/unit/**'
|
||||
- 'uv.lock'
|
||||
|
|
|
|||
|
|
@ -68,7 +68,8 @@ chunks_response = client.vector_io.query(
|
|||
### Using the RAG Tool
|
||||
|
||||
A better way to ingest documents is to use the RAG Tool. This tool allows you to ingest documents from URLs, files, etc.
|
||||
and automatically chunks them into smaller pieces.
|
||||
and automatically chunks them into smaller pieces. More examples for how to format a RAGDocument can be found in the
|
||||
[appendix](#more-ragdocument-examples).
|
||||
|
||||
```python
|
||||
from llama_stack_client import RAGDocument
|
||||
|
|
@ -178,3 +179,38 @@ for vector_db_id in client.vector_dbs.list():
|
|||
print(f"Unregistering vector database: {vector_db_id.identifier}")
|
||||
client.vector_dbs.unregister(vector_db_id=vector_db_id.identifier)
|
||||
```
|
||||
|
||||
### Appendix
|
||||
|
||||
#### More RAGDocument Examples
|
||||
```python
|
||||
from llama_stack_client import RAGDocument
|
||||
import base64
|
||||
|
||||
RAGDocument(document_id="num-0", content={"uri": "file://path/to/file"})
|
||||
RAGDocument(document_id="num-1", content="plain text")
|
||||
RAGDocument(
|
||||
document_id="num-2",
|
||||
content={
|
||||
"type": "text",
|
||||
"text": "plain text input",
|
||||
}, # for inputs that should be treated as text explicitly
|
||||
)
|
||||
RAGDocument(
|
||||
document_id="num-3",
|
||||
content={
|
||||
"type": "image",
|
||||
"image": {"url": {"uri": "https://mywebsite.com/image.jpg"}},
|
||||
},
|
||||
)
|
||||
B64_ENCODED_IMAGE = base64.b64encode(
|
||||
requests.get(
|
||||
"https://raw.githubusercontent.com/meta-llama/llama-stack/refs/heads/main/docs/_static/llama-stack.png"
|
||||
).content
|
||||
)
|
||||
RAGDocuemnt(
|
||||
document_id="num-4",
|
||||
content={"type": "image", "image": {"data": B64_ENCODED_IMAGE}},
|
||||
)
|
||||
```
|
||||
for more strongly typed interaction use the typed dicts found [here](https://github.com/meta-llama/llama-stack-client-python/blob/38cd91c9e396f2be0bec1ee96a19771582ba6f17/src/llama_stack_client/types/shared_params/document.py).
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ client.toolgroups.register(
|
|||
|
||||
The tool requires an API key which can be provided either in the configuration or through the request header `X-LlamaStack-Provider-Data`. The format of the header is `{"<provider_name>_api_key": <your api key>}`.
|
||||
|
||||
|
||||
> **NOTE:** When using Tavily Search and Bing Search, the inference output will still display "Brave Search." This is because Llama models have been trained with Brave Search as a built-in tool. Tavily and bing is just being used in lieu of Brave search.
|
||||
|
||||
#### Code Interpreter
|
||||
|
||||
|
|
@ -214,3 +214,69 @@ response = agent.create_turn(
|
|||
session_id=session_id,
|
||||
)
|
||||
```
|
||||
## Simple Example 2: Using an Agent with the Web Search Tool
|
||||
1. Start by registering a Tavily API key at [Tavily](https://tavily.com/).
|
||||
2. [Optional] Provide the API key directly to the Llama Stack server
|
||||
```bash
|
||||
export TAVILY_SEARCH_API_KEY="your key"
|
||||
```
|
||||
```bash
|
||||
--env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY}
|
||||
```
|
||||
3. Run the following script.
|
||||
```python
|
||||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.types.agent_create_params import AgentConfig
|
||||
from llama_stack_client.lib.agents.event_logger import EventLogger
|
||||
from llama_stack_client import LlamaStackClient
|
||||
|
||||
client = LlamaStackClient(
|
||||
base_url=f"http://localhost:8321",
|
||||
provider_data={
|
||||
"tavily_search_api_key": "your_TAVILY_SEARCH_API_KEY"
|
||||
}, # Set this from the client side. No need to provide it if it has already been configured on the Llama Stack server.
|
||||
)
|
||||
|
||||
agent = Agent(
|
||||
client,
|
||||
model="meta-llama/Llama-3.2-3B-Instruct",
|
||||
instructions=(
|
||||
"You are a web search assistant, must use websearch tool to look up the most current and precise information available. "
|
||||
),
|
||||
tools=["builtin::websearch"],
|
||||
)
|
||||
|
||||
session_id = agent.create_session("websearch-session")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{"role": "user", "content": "How did the USA perform in the last Olympics?"}
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
for log in EventLogger().log(response):
|
||||
log.print()
|
||||
```
|
||||
|
||||
## Simple Example3: Using an Agent with the WolframAlpha Tool
|
||||
1. Start by registering for a WolframAlpha API key at [WolframAlpha Developer Portal](https://developer.wolframalpha.com/access).
|
||||
2. Provide the API key either when starting the Llama Stack server:
|
||||
```bash
|
||||
--env WOLFRAM_ALPHA_API_KEY=${WOLFRAM_ALPHA_API_KEY}
|
||||
```
|
||||
or from the client side:
|
||||
```python
|
||||
client = LlamaStackClient(
|
||||
base_url="http://localhost:8321",
|
||||
provider_data={"wolfram_alpha_api_key": wolfram_api_key},
|
||||
)
|
||||
```
|
||||
3. Configure the tools in the Agent by setting `tools=["builtin::wolfram_alpha"]`.
|
||||
4. Example user query:
|
||||
```python
|
||||
response = agent.create_turn(
|
||||
messages=[{"role": "user", "content": "Solve x^2 + 2x + 1 = 0 using WolframAlpha"}],
|
||||
session_id=session_id,
|
||||
)
|
||||
```
|
||||
```
|
||||
|
|
|
|||
|
|
@ -176,7 +176,11 @@ distribution_spec:
|
|||
safety: inline::llama-guard
|
||||
agents: inline::meta-reference
|
||||
telemetry: inline::meta-reference
|
||||
image_name: ollama
|
||||
image_type: conda
|
||||
|
||||
# If some providers are external, you can specify the path to the implementation
|
||||
external_providers_dir: /etc/llama-stack/providers.d
|
||||
```
|
||||
|
||||
```
|
||||
|
|
@ -184,6 +188,57 @@ llama stack build --config llama_stack/templates/ollama/build.yaml
|
|||
```
|
||||
:::
|
||||
|
||||
:::{tab-item} Building with External Providers
|
||||
|
||||
Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently or use community-provided providers.
|
||||
|
||||
To build a distribution with external providers, you need to:
|
||||
|
||||
1. Configure the `external_providers_dir` in your build configuration file:
|
||||
|
||||
```yaml
|
||||
# Example my-external-stack.yaml with external providers
|
||||
version: '2'
|
||||
distribution_spec:
|
||||
description: Custom distro for CI tests
|
||||
providers:
|
||||
inference:
|
||||
- remote::custom_ollama
|
||||
# Add more providers as needed
|
||||
image_type: container
|
||||
image_name: ci-test
|
||||
# Path to external provider implementations
|
||||
external_providers_dir: /etc/llama-stack/providers.d
|
||||
```
|
||||
|
||||
Here's an example for a custom Ollama provider:
|
||||
|
||||
```yaml
|
||||
adapter:
|
||||
adapter_type: custom_ollama
|
||||
pip_packages:
|
||||
- ollama
|
||||
- aiohttp
|
||||
- llama-stack-provider-ollama # This is the provider package
|
||||
config_class: llama_stack_ollama_provider.config.OllamaImplConfig
|
||||
module: llama_stack_ollama_provider
|
||||
api_dependencies: []
|
||||
optional_api_dependencies: []
|
||||
```
|
||||
|
||||
The `pip_packages` section lists the Python packages required by the provider, as well as the
|
||||
provider package itself. The package must be available on PyPI or can be provided from a local
|
||||
directory or a git repository (git must be installed on the build environment).
|
||||
|
||||
2. Build your distribution using the config file:
|
||||
|
||||
```
|
||||
llama stack build --config my-external-stack.yaml
|
||||
```
|
||||
|
||||
For more information on external providers, including directory structure, provider types, and implementation requirements, see the [External Providers documentation](../providers/external.md).
|
||||
:::
|
||||
|
||||
:::{tab-item} Building Container
|
||||
|
||||
```{admonition} Podman Alternative
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
|
|||
|-----|-------------|
|
||||
| agents | `inline::meta-reference` |
|
||||
| datasetio | `inline::localfs` |
|
||||
| eval | `inline::meta-reference` |
|
||||
| eval | `remote::nvidia` |
|
||||
| inference | `remote::nvidia` |
|
||||
| post_training | `remote::nvidia` |
|
||||
| safety | `remote::nvidia` |
|
||||
|
|
@ -22,13 +22,13 @@ The `llamastack/distribution-nvidia` distribution consists of the following prov
|
|||
The following environment variables can be configured:
|
||||
|
||||
- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
|
||||
- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
|
||||
- `NVIDIA_APPEND_API_VERSION`: Whether to append the API version to the base_url (default: `True`)
|
||||
- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
|
||||
- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
|
||||
- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
|
||||
- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
|
||||
- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
|
||||
- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
|
||||
- `NVIDIA_EVALUATOR_URL`: URL for the NeMo Evaluator Service (default: `http://0.0.0.0:7331`)
|
||||
- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
|
||||
- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
|
||||
|
||||
|
|
@ -58,7 +58,7 @@ The following models are available by default:
|
|||
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
|
||||
|
||||
### Deploy NeMo Microservices Platform
|
||||
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/set-up/deploy-as-platform/index.html) for platform prerequisites and instructions to install and deploy the platform.
|
||||
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
|
||||
|
||||
## Supported Services
|
||||
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
|
||||
|
|
@ -118,7 +118,7 @@ curl --location "$NEMO_URL/v1/deployment/model-deployments" \
|
|||
}
|
||||
}'
|
||||
```
|
||||
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/get-started/tutorials/deploy-nims.html#) for more information on how to deploy a NIM and verify it's available for inference.
|
||||
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
|
||||
|
||||
You can also remove a deployed NIM to free up GPU resources, if needed.
|
||||
```sh
|
||||
|
|
@ -171,7 +171,3 @@ llama stack run ./run.yaml \
|
|||
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
|
||||
--env INFERENCE_MODEL=$INFERENCE_MODEL
|
||||
```
|
||||
|
||||
### Example Notebooks
|
||||
You can reference the Jupyter notebooks in `docs/notebooks/nvidia/` for example usage of these APIs.
|
||||
- [Llama_Stack_NVIDIA_E2E_Flow.ipynb](/docs/notebooks/nvidia/Llama_Stack_NVIDIA_E2E_Flow.ipynb) contains an end-to-end workflow for running inference, customizing, and evaluating models using your deployed NeMo Microservices platform.
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ The following environment variables can be configured:
|
|||
In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
|
||||
server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
|
||||
[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
|
||||
that we only use GPUs here for demonstration purposes.
|
||||
that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
|
||||
|
||||
### Setting up vLLM server on AMD GPU
|
||||
|
||||
|
|
|
|||
|
|
@ -50,9 +50,10 @@ Llama Stack supports two types of external providers:
|
|||
|
||||
Here's a list of known external providers that you can use with Llama Stack:
|
||||
|
||||
| Type | Name | Description | Repository |
|
||||
|------|------|-------------|------------|
|
||||
| Remote | KubeFlow Training | Train models with KubeFlow | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
|
||||
| Name | Description | API | Type | Repository |
|
||||
|------|-------------|-----|------|------------|
|
||||
| KubeFlow Training | Train models with KubeFlow | Post Training | Remote | [llama-stack-provider-kft](https://github.com/opendatahub-io/llama-stack-provider-kft) |
|
||||
| RamaLama | Inference models with RamaLama | Inference | Remote | [ramalama-stack](https://github.com/containers/ramalama-stack) |
|
||||
|
||||
### Remote Provider Specification
|
||||
|
||||
|
|
|
|||
|
|
@ -210,16 +210,9 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
|
|||
)
|
||||
sys.exit(1)
|
||||
|
||||
if build_config.image_type == LlamaStackImageType.CONTAINER.value and not args.image_name:
|
||||
cprint(
|
||||
"Please specify --image-name when building a container from a config file",
|
||||
color="red",
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
if args.print_deps_only:
|
||||
print(f"# Dependencies for {args.template or args.config or image_name}")
|
||||
normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
|
||||
normal_deps, special_deps = get_provider_dependencies(build_config)
|
||||
normal_deps += SERVER_DEPENDENCIES
|
||||
print(f"uv pip install {' '.join(normal_deps)}")
|
||||
for special_dep in special_deps:
|
||||
|
|
@ -274,9 +267,10 @@ def _generate_run_config(
|
|||
image_name=image_name,
|
||||
apis=apis,
|
||||
providers={},
|
||||
external_providers_dir=build_config.external_providers_dir if build_config.external_providers_dir else None,
|
||||
)
|
||||
# build providers dict
|
||||
provider_registry = get_provider_registry()
|
||||
provider_registry = get_provider_registry(build_config)
|
||||
for api in apis:
|
||||
run_config.providers[api] = []
|
||||
provider_types = build_config.distribution_spec.providers[api]
|
||||
|
|
@ -290,8 +284,22 @@ def _generate_run_config(
|
|||
if p.deprecation_error:
|
||||
raise InvalidProviderError(p.deprecation_error)
|
||||
|
||||
config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class)
|
||||
if hasattr(config_type, "sample_run_config"):
|
||||
try:
|
||||
config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class)
|
||||
except ModuleNotFoundError:
|
||||
# HACK ALERT:
|
||||
# This code executes after building is done, the import cannot work since the
|
||||
# package is either available in the venv or container - not available on the host.
|
||||
# TODO: use a "is_external" flag in ProviderSpec to check if the provider is
|
||||
# external
|
||||
cprint(
|
||||
f"Failed to import provider {provider_type} for API {api} - assuming it's external, skipping",
|
||||
color="yellow",
|
||||
)
|
||||
# Set config_type to None to avoid UnboundLocalError
|
||||
config_type = None
|
||||
|
||||
if config_type is not None and hasattr(config_type, "sample_run_config"):
|
||||
config = config_type.sample_run_config(__distro_dir__=f"~/.llama/distributions/{image_name}")
|
||||
else:
|
||||
config = {}
|
||||
|
|
@ -309,11 +317,15 @@ def _generate_run_config(
|
|||
to_write = json.loads(run_config.model_dump_json())
|
||||
f.write(yaml.dump(to_write, sort_keys=False))
|
||||
|
||||
# this path is only invoked when no template is provided
|
||||
cprint(
|
||||
f"You can now run your stack with `llama stack run {run_config_file}`",
|
||||
color="green",
|
||||
)
|
||||
# Only print this message for non-container builds since it will be displayed before the
|
||||
# container is built
|
||||
# For non-container builds, the run.yaml is generated at the very end of the build process so it
|
||||
# makes sense to display this message
|
||||
if build_config.image_type != LlamaStackImageType.CONTAINER.value:
|
||||
cprint(
|
||||
f"You can now run your stack with `llama stack run {run_config_file}`",
|
||||
color="green",
|
||||
)
|
||||
return run_config_file
|
||||
|
||||
|
||||
|
|
@ -323,6 +335,7 @@ def _run_stack_build_command_from_build_config(
|
|||
template_name: Optional[str] = None,
|
||||
config_path: Optional[str] = None,
|
||||
) -> str:
|
||||
image_name = image_name or build_config.image_name
|
||||
if build_config.image_type == LlamaStackImageType.CONTAINER.value:
|
||||
if template_name:
|
||||
image_name = f"distribution-{template_name}"
|
||||
|
|
@ -346,6 +359,13 @@ def _run_stack_build_command_from_build_config(
|
|||
build_file_path = build_dir / f"{image_name}-build.yaml"
|
||||
|
||||
os.makedirs(build_dir, exist_ok=True)
|
||||
run_config_file = None
|
||||
# Generate the run.yaml so it can be included in the container image with the proper entrypoint
|
||||
# Only do this if we're building a container image and we're not using a template
|
||||
if build_config.image_type == LlamaStackImageType.CONTAINER.value and not template_name and config_path:
|
||||
cprint("Generating run.yaml file", color="green")
|
||||
run_config_file = _generate_run_config(build_config, build_dir, image_name)
|
||||
|
||||
with open(build_file_path, "w") as f:
|
||||
to_write = json.loads(build_config.model_dump_json())
|
||||
f.write(yaml.dump(to_write, sort_keys=False))
|
||||
|
|
@ -355,6 +375,7 @@ def _run_stack_build_command_from_build_config(
|
|||
build_file_path,
|
||||
image_name,
|
||||
template_or_config=template_name or config_path or str(build_file_path),
|
||||
run_config=run_config_file,
|
||||
)
|
||||
if return_code != 0:
|
||||
raise RuntimeError(f"Failed to build image {image_name}")
|
||||
|
|
|
|||
|
|
@ -7,16 +7,16 @@
|
|||
import importlib.resources
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
from pydantic import BaseModel
|
||||
from termcolor import cprint
|
||||
|
||||
from llama_stack.distribution.datatypes import BuildConfig, Provider
|
||||
from llama_stack.distribution.datatypes import BuildConfig
|
||||
from llama_stack.distribution.distribution import get_provider_registry
|
||||
from llama_stack.distribution.utils.exec import run_command
|
||||
from llama_stack.distribution.utils.image_types import LlamaStackImageType
|
||||
from llama_stack.providers.datatypes import Api
|
||||
from llama_stack.templates.template import DistributionTemplate
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -37,19 +37,24 @@ class ApiInput(BaseModel):
|
|||
|
||||
|
||||
def get_provider_dependencies(
|
||||
config_providers: Dict[str, List[Provider]],
|
||||
config: BuildConfig | DistributionTemplate,
|
||||
) -> tuple[list[str], list[str]]:
|
||||
"""Get normal and special dependencies from provider configuration."""
|
||||
all_providers = get_provider_registry()
|
||||
# Extract providers based on config type
|
||||
if isinstance(config, DistributionTemplate):
|
||||
providers = config.providers
|
||||
elif isinstance(config, BuildConfig):
|
||||
providers = config.distribution_spec.providers
|
||||
deps = []
|
||||
registry = get_provider_registry(config)
|
||||
|
||||
for api_str, provider_or_providers in config_providers.items():
|
||||
providers_for_api = all_providers[Api(api_str)]
|
||||
for api_str, provider_or_providers in providers.items():
|
||||
providers_for_api = registry[Api(api_str)]
|
||||
|
||||
providers = provider_or_providers if isinstance(provider_or_providers, list) else [provider_or_providers]
|
||||
|
||||
for provider in providers:
|
||||
# Providers from BuildConfig and RunConfig are subtly different – not great
|
||||
# Providers from BuildConfig and RunConfig are subtly different – not great
|
||||
provider_type = provider if isinstance(provider, str) else provider.provider_type
|
||||
|
||||
if provider_type not in providers_for_api:
|
||||
|
|
@ -71,8 +76,8 @@ def get_provider_dependencies(
|
|||
return list(set(normal_deps)), list(set(special_deps))
|
||||
|
||||
|
||||
def print_pip_install_help(providers: Dict[str, List[Provider]]):
|
||||
normal_deps, special_deps = get_provider_dependencies(providers)
|
||||
def print_pip_install_help(config: BuildConfig):
|
||||
normal_deps, special_deps = get_provider_dependencies(config)
|
||||
|
||||
cprint(
|
||||
f"Please install needed dependencies using the following commands:\n\nuv pip install {' '.join(normal_deps)}",
|
||||
|
|
@ -88,10 +93,11 @@ def build_image(
|
|||
build_file_path: Path,
|
||||
image_name: str,
|
||||
template_or_config: str,
|
||||
run_config: str | None = None,
|
||||
):
|
||||
container_base = build_config.distribution_spec.container_image or "python:3.10-slim"
|
||||
|
||||
normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
|
||||
normal_deps, special_deps = get_provider_dependencies(build_config)
|
||||
normal_deps += SERVER_DEPENDENCIES
|
||||
|
||||
if build_config.image_type == LlamaStackImageType.CONTAINER.value:
|
||||
|
|
@ -103,6 +109,11 @@ def build_image(
|
|||
container_base,
|
||||
" ".join(normal_deps),
|
||||
]
|
||||
|
||||
# When building from a config file (not a template), include the run config path in the
|
||||
# build arguments
|
||||
if run_config is not None:
|
||||
args.append(run_config)
|
||||
elif build_config.image_type == LlamaStackImageType.CONDA.value:
|
||||
script = str(importlib.resources.files("llama_stack") / "distribution/build_conda_env.sh")
|
||||
args = [
|
||||
|
|
|
|||
|
|
@ -19,12 +19,16 @@ UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
|
|||
# mounting is not supported by docker buildx, so we use COPY instead
|
||||
USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
|
||||
|
||||
# Path to the run.yaml file in the container
|
||||
RUN_CONFIG_PATH=/app/run.yaml
|
||||
|
||||
BUILD_CONTEXT_DIR=$(pwd)
|
||||
|
||||
if [ "$#" -lt 4 ]; then
|
||||
# This only works for templates
|
||||
echo "Usage: $0 <template_or_config> <image_name> <container_base> <pip_dependencies> [<special_pip_deps>]" >&2
|
||||
echo "Usage: $0 <template_or_config> <image_name> <container_base> <pip_dependencies> [<run_config>] [<special_pip_deps>]" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
template_or_config="$1"
|
||||
|
|
@ -35,8 +39,27 @@ container_base="$1"
|
|||
shift
|
||||
pip_dependencies="$1"
|
||||
shift
|
||||
special_pip_deps="${1:-}"
|
||||
|
||||
# Handle optional arguments
|
||||
run_config=""
|
||||
special_pip_deps=""
|
||||
|
||||
# Check if there are more arguments
|
||||
# The logics is becoming cumbersom, we should refactor it if we can do better
|
||||
if [ $# -gt 0 ]; then
|
||||
# Check if the argument ends with .yaml
|
||||
if [[ "$1" == *.yaml ]]; then
|
||||
run_config="$1"
|
||||
shift
|
||||
# If there's another argument after .yaml, it must be special_pip_deps
|
||||
if [ $# -gt 0 ]; then
|
||||
special_pip_deps="$1"
|
||||
fi
|
||||
else
|
||||
# If it's not .yaml, it must be special_pip_deps
|
||||
special_pip_deps="$1"
|
||||
fi
|
||||
fi
|
||||
|
||||
# Define color codes
|
||||
RED='\033[0;31m'
|
||||
|
|
@ -72,9 +95,13 @@ if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then
|
|||
FROM $container_base
|
||||
WORKDIR /app
|
||||
|
||||
RUN dnf -y update && dnf install -y iputils net-tools wget \
|
||||
# We install the Python 3.11 dev headers and build tools so that any
|
||||
# C‑extension wheels (e.g. polyleven, faiss‑cpu) can compile successfully.
|
||||
|
||||
RUN dnf -y update && dnf install -y iputils git net-tools wget \
|
||||
vim-minimal python3.11 python3.11-pip python3.11-wheel \
|
||||
python3.11-setuptools && ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all
|
||||
python3.11-setuptools python3.11-devel gcc make && \
|
||||
ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all
|
||||
|
||||
ENV UV_SYSTEM_PYTHON=1
|
||||
RUN pip install uv
|
||||
|
|
@ -86,7 +113,7 @@ WORKDIR /app
|
|||
|
||||
RUN apt-get update && apt-get install -y \
|
||||
iputils-ping net-tools iproute2 dnsutils telnet \
|
||||
curl wget telnet \
|
||||
curl wget telnet git\
|
||||
procps psmisc lsof \
|
||||
traceroute \
|
||||
bubblewrap \
|
||||
|
|
@ -115,6 +142,45 @@ EOF
|
|||
done
|
||||
fi
|
||||
|
||||
# Function to get Python command
|
||||
get_python_cmd() {
|
||||
if is_command_available python; then
|
||||
echo "python"
|
||||
elif is_command_available python3; then
|
||||
echo "python3"
|
||||
else
|
||||
echo "Error: Neither python nor python3 is installed. Please install Python to continue." >&2
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
if [ -n "$run_config" ]; then
|
||||
# Copy the run config to the build context since it's an absolute path
|
||||
cp "$run_config" "$BUILD_CONTEXT_DIR/run.yaml"
|
||||
add_to_container << EOF
|
||||
COPY run.yaml $RUN_CONFIG_PATH
|
||||
EOF
|
||||
|
||||
# Parse the run.yaml configuration to identify external provider directories
|
||||
# If external providers are specified, copy their directory to the container
|
||||
# and update the configuration to reference the new container path
|
||||
python_cmd=$(get_python_cmd)
|
||||
external_providers_dir=$($python_cmd -c "import yaml; config = yaml.safe_load(open('$run_config')); print(config.get('external_providers_dir') or '')")
|
||||
if [ -n "$external_providers_dir" ]; then
|
||||
echo "Copying external providers directory: $external_providers_dir"
|
||||
add_to_container << EOF
|
||||
COPY $external_providers_dir /app/providers.d
|
||||
EOF
|
||||
# Edit the run.yaml file to change the external_providers_dir to /app/providers.d
|
||||
if [ "$(uname)" = "Darwin" ]; then
|
||||
sed -i.bak -e 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
|
||||
rm -f "$BUILD_CONTEXT_DIR/run.yaml.bak"
|
||||
else
|
||||
sed -i 's|external_providers_dir:.*|external_providers_dir: /app/providers.d|' "$BUILD_CONTEXT_DIR/run.yaml"
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
stack_mount="/app/llama-stack-source"
|
||||
client_mount="/app/llama-stack-client-source"
|
||||
|
||||
|
|
@ -174,15 +240,16 @@ fi
|
|||
RUN pip uninstall -y uv
|
||||
EOF
|
||||
|
||||
# if template_or_config ends with .yaml, it is not a template and we should not use the --template flag
|
||||
if [[ "$template_or_config" != *.yaml ]]; then
|
||||
# If a run config is provided, we use the --config flag
|
||||
if [[ -n "$run_config" ]]; then
|
||||
add_to_container << EOF
|
||||
ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--config", "$RUN_CONFIG_PATH"]
|
||||
EOF
|
||||
# If a template is provided (not a yaml file), we use the --template flag
|
||||
elif [[ "$template_or_config" != *.yaml ]]; then
|
||||
add_to_container << EOF
|
||||
ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--template", "$template_or_config"]
|
||||
EOF
|
||||
else
|
||||
add_to_container << EOF
|
||||
ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server"]
|
||||
EOF
|
||||
fi
|
||||
|
||||
# Add other require item commands genearic to all containers
|
||||
|
|
@ -254,9 +321,10 @@ $CONTAINER_BINARY build \
|
|||
"${CLI_ARGS[@]}" \
|
||||
-t "$image_tag" \
|
||||
-f "$TEMP_DIR/Containerfile" \
|
||||
"."
|
||||
"$BUILD_CONTEXT_DIR"
|
||||
|
||||
# clean up tmp/configs
|
||||
rm -f "$BUILD_CONTEXT_DIR/run.yaml"
|
||||
set +x
|
||||
|
||||
echo "Success!"
|
||||
|
|
|
|||
|
|
@ -326,3 +326,12 @@ class BuildConfig(BaseModel):
|
|||
default="conda",
|
||||
description="Type of package to build (conda | container | venv)",
|
||||
)
|
||||
image_name: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Name of the distribution to build",
|
||||
)
|
||||
external_providers_dir: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Path to directory containing external provider implementations. The providers packages will be resolved from this directory. "
|
||||
"pip_packages MUST contain the provider package name.",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ from typing import Any, Dict, List
|
|||
import yaml
|
||||
from pydantic import BaseModel
|
||||
|
||||
from llama_stack.distribution.datatypes import StackRunConfig
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack.providers.datatypes import (
|
||||
AdapterSpec,
|
||||
|
|
@ -97,7 +96,9 @@ def _load_inline_provider_spec(spec_data: Dict[str, Any], api: Api, provider_nam
|
|||
return spec
|
||||
|
||||
|
||||
def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dict[str, ProviderSpec]]:
|
||||
def get_provider_registry(
|
||||
config=None,
|
||||
) -> Dict[Api, Dict[str, ProviderSpec]]:
|
||||
"""Get the provider registry, optionally including external providers.
|
||||
|
||||
This function loads both built-in providers and external providers from YAML files.
|
||||
|
|
@ -122,7 +123,7 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic
|
|||
llama-guard.yaml
|
||||
|
||||
Args:
|
||||
config: Optional StackRunConfig containing the external providers directory path
|
||||
config: Optional object containing the external providers directory path
|
||||
|
||||
Returns:
|
||||
A dictionary mapping APIs to their available providers
|
||||
|
|
@ -142,7 +143,8 @@ def get_provider_registry(config: StackRunConfig | None = None) -> Dict[Api, Dic
|
|||
except ImportError as e:
|
||||
logger.warning(f"Failed to import module {name}: {e}")
|
||||
|
||||
if config and config.external_providers_dir:
|
||||
# Check if config has the external_providers_dir attribute
|
||||
if config and hasattr(config, "external_providers_dir") and config.external_providers_dir:
|
||||
external_providers_dir = os.path.abspath(config.external_providers_dir)
|
||||
if not os.path.exists(external_providers_dir):
|
||||
raise FileNotFoundError(f"External providers directory not found: {external_providers_dir}")
|
||||
|
|
|
|||
|
|
@ -8,6 +8,11 @@ import asyncio
|
|||
import time
|
||||
from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
|
||||
|
||||
from openai.types.chat import ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam
|
||||
from openai.types.chat import ChatCompletionToolParam as OpenAIChatCompletionToolParam
|
||||
from pydantic import Field, TypeAdapter
|
||||
from typing_extensions import Annotated
|
||||
|
||||
from llama_stack.apis.common.content_types import (
|
||||
URL,
|
||||
InterleavedContent,
|
||||
|
|
@ -526,7 +531,7 @@ class InferenceRouter(Inference):
|
|||
async def openai_chat_completion(
|
||||
self,
|
||||
model: str,
|
||||
messages: List[OpenAIMessageParam],
|
||||
messages: Annotated[List[OpenAIMessageParam], Field(..., min_length=1)],
|
||||
frequency_penalty: Optional[float] = None,
|
||||
function_call: Optional[Union[str, Dict[str, Any]]] = None,
|
||||
functions: Optional[List[Dict[str, Any]]] = None,
|
||||
|
|
@ -558,6 +563,16 @@ class InferenceRouter(Inference):
|
|||
if model_obj.model_type == ModelType.embedding:
|
||||
raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions")
|
||||
|
||||
# Use the OpenAI client for a bit of extra input validation without
|
||||
# exposing the OpenAI client itself as part of our API surface
|
||||
if tool_choice:
|
||||
TypeAdapter(OpenAIChatCompletionToolChoiceOptionParam).validate_python(tool_choice)
|
||||
if tools is None:
|
||||
raise ValueError("'tool_choice' is only allowed when 'tools' is also provided")
|
||||
if tools:
|
||||
for tool in tools:
|
||||
TypeAdapter(OpenAIChatCompletionToolParam).validate_python(tool)
|
||||
|
||||
params = dict(
|
||||
model=model_obj.identifier,
|
||||
messages=messages,
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ from fastapi import Body, FastAPI, HTTPException, Request
|
|||
from fastapi import Path as FastapiPath
|
||||
from fastapi.exceptions import RequestValidationError
|
||||
from fastapi.responses import JSONResponse, StreamingResponse
|
||||
from openai import BadRequestError
|
||||
from pydantic import BaseModel, ValidationError
|
||||
from typing_extensions import Annotated
|
||||
|
||||
|
|
@ -92,7 +93,7 @@ async def global_exception_handler(request: Request, exc: Exception):
|
|||
|
||||
def translate_exception(exc: Exception) -> Union[HTTPException, RequestValidationError]:
|
||||
if isinstance(exc, ValidationError):
|
||||
exc = RequestValidationError(exc.raw_errors)
|
||||
exc = RequestValidationError(exc.errors())
|
||||
|
||||
if isinstance(exc, RequestValidationError):
|
||||
return HTTPException(
|
||||
|
|
@ -110,6 +111,8 @@ def translate_exception(exc: Exception) -> Union[HTTPException, RequestValidatio
|
|||
)
|
||||
elif isinstance(exc, ValueError):
|
||||
return HTTPException(status_code=400, detail=f"Invalid value: {str(exc)}")
|
||||
elif isinstance(exc, BadRequestError):
|
||||
return HTTPException(status_code=400, detail=str(exc))
|
||||
elif isinstance(exc, PermissionError):
|
||||
return HTTPException(status_code=403, detail=f"Permission denied: {str(exc)}")
|
||||
elif isinstance(exc, TimeoutError):
|
||||
|
|
@ -162,14 +165,17 @@ async def maybe_await(value):
|
|||
return value
|
||||
|
||||
|
||||
async def sse_generator(event_gen):
|
||||
async def sse_generator(event_gen_coroutine):
|
||||
event_gen = None
|
||||
try:
|
||||
async for item in await event_gen:
|
||||
event_gen = await event_gen_coroutine
|
||||
async for item in event_gen:
|
||||
yield create_sse_event(item)
|
||||
await asyncio.sleep(0.01)
|
||||
except asyncio.CancelledError:
|
||||
logger.info("Generator cancelled")
|
||||
await event_gen.aclose()
|
||||
if event_gen:
|
||||
await event_gen.aclose()
|
||||
except Exception as e:
|
||||
logger.exception("Error in sse_generator")
|
||||
yield create_sse_event(
|
||||
|
|
|
|||
|
|
@ -24,6 +24,13 @@ def rag_chat_page():
|
|||
def should_disable_input():
|
||||
return "displayed_messages" in st.session_state and len(st.session_state.displayed_messages) > 0
|
||||
|
||||
def log_message(message):
|
||||
with st.chat_message(message["role"]):
|
||||
if "tool_output" in message and message["tool_output"]:
|
||||
with st.expander(label="Tool Output", expanded=False, icon="🛠"):
|
||||
st.write(message["tool_output"])
|
||||
st.markdown(message["content"])
|
||||
|
||||
with st.sidebar:
|
||||
# File/Directory Upload Section
|
||||
st.subheader("Upload Documents", divider=True)
|
||||
|
|
@ -146,8 +153,7 @@ def rag_chat_page():
|
|||
|
||||
# Display chat history
|
||||
for message in st.session_state.displayed_messages:
|
||||
with st.chat_message(message["role"]):
|
||||
st.markdown(message["content"])
|
||||
log_message(message)
|
||||
|
||||
if temperature > 0.0:
|
||||
strategy = {
|
||||
|
|
@ -201,7 +207,7 @@ def rag_chat_page():
|
|||
|
||||
# Display assistant response
|
||||
with st.chat_message("assistant"):
|
||||
retrieval_message_placeholder = st.empty()
|
||||
retrieval_message_placeholder = st.expander(label="Tool Output", expanded=False, icon="🛠")
|
||||
message_placeholder = st.empty()
|
||||
full_response = ""
|
||||
retrieval_response = ""
|
||||
|
|
@ -209,14 +215,16 @@ def rag_chat_page():
|
|||
log.print()
|
||||
if log.role == "tool_execution":
|
||||
retrieval_response += log.content.replace("====", "").strip()
|
||||
retrieval_message_placeholder.info(retrieval_response)
|
||||
retrieval_message_placeholder.write(retrieval_response)
|
||||
else:
|
||||
full_response += log.content
|
||||
message_placeholder.markdown(full_response + "▌")
|
||||
message_placeholder.markdown(full_response)
|
||||
|
||||
st.session_state.messages.append({"role": "assistant", "content": full_response})
|
||||
st.session_state.displayed_messages.append({"role": "assistant", "content": full_response})
|
||||
st.session_state.displayed_messages.append(
|
||||
{"role": "assistant", "content": full_response, "tool_output": retrieval_response}
|
||||
)
|
||||
|
||||
def direct_process_prompt(prompt):
|
||||
# Add the system prompt in the beginning of the conversation
|
||||
|
|
@ -230,15 +238,14 @@ def rag_chat_page():
|
|||
prompt_context = rag_response.content
|
||||
|
||||
with st.chat_message("assistant"):
|
||||
with st.expander(label="Retrieval Output", expanded=False):
|
||||
st.write(prompt_context)
|
||||
|
||||
retrieval_message_placeholder = st.empty()
|
||||
message_placeholder = st.empty()
|
||||
full_response = ""
|
||||
retrieval_response = ""
|
||||
|
||||
# Display the retrieved content
|
||||
retrieval_response += str(prompt_context)
|
||||
retrieval_message_placeholder.info(retrieval_response)
|
||||
|
||||
# Construct the extended prompt
|
||||
extended_prompt = f"Please answer the following query using the context below.\n\nCONTEXT:\n{prompt_context}\n\nQUERY:\n{prompt}"
|
||||
|
||||
|
|
|
|||
|
|
@ -29,12 +29,19 @@ def tool_chat_page():
|
|||
st.cache_resource.clear()
|
||||
|
||||
with st.sidebar:
|
||||
st.title("Configuration")
|
||||
st.subheader("Model")
|
||||
model = st.selectbox(label="models", options=model_list, on_change=reset_agent)
|
||||
model = st.selectbox(label="Model", options=model_list, on_change=reset_agent, label_visibility="collapsed")
|
||||
|
||||
st.subheader("Available ToolGroups")
|
||||
|
||||
st.subheader("Builtin Tools")
|
||||
toolgroup_selection = st.pills(
|
||||
label="Available ToolGroups", options=builtin_tools_list, selection_mode="multi", on_change=reset_agent
|
||||
label="Built-in tools",
|
||||
options=builtin_tools_list,
|
||||
selection_mode="multi",
|
||||
on_change=reset_agent,
|
||||
format_func=lambda tool: "".join(tool.split("::")[1:]),
|
||||
help="List of built-in tools from your llama stack server.",
|
||||
)
|
||||
|
||||
if "builtin::rag" in toolgroup_selection:
|
||||
|
|
@ -48,9 +55,13 @@ def tool_chat_page():
|
|||
on_change=reset_agent,
|
||||
)
|
||||
|
||||
st.subheader("MCP Servers")
|
||||
mcp_selection = st.pills(
|
||||
label="Available MCP Servers", options=mcp_tools_list, selection_mode="multi", on_change=reset_agent
|
||||
label="MCP Servers",
|
||||
options=mcp_tools_list,
|
||||
selection_mode="multi",
|
||||
on_change=reset_agent,
|
||||
format_func=lambda tool: "".join(tool.split("::")[1:]),
|
||||
help="List of MCP servers registered to your llama stack server.",
|
||||
)
|
||||
|
||||
toolgroup_selection.extend(mcp_selection)
|
||||
|
|
@ -64,10 +75,10 @@ def tool_chat_page():
|
|||
]
|
||||
)
|
||||
|
||||
st.subheader(f"Active Tools: 🛠 {len(active_tool_list)}")
|
||||
st.markdown(f"Active Tools: 🛠 {len(active_tool_list)}", help="List of currently active tools.")
|
||||
st.json(active_tool_list)
|
||||
|
||||
st.subheader("Chat Configurations")
|
||||
st.subheader("Agent Configurations")
|
||||
max_tokens = st.slider(
|
||||
"Max Tokens",
|
||||
min_value=0,
|
||||
|
|
@ -133,7 +144,11 @@ def tool_chat_page():
|
|||
yield response.event.payload.delta.text
|
||||
if response.event.payload.event_type == "step_complete":
|
||||
if response.event.payload.step_details.step_type == "tool_execution":
|
||||
yield " 🛠 "
|
||||
if response.event.payload.step_details.tool_calls:
|
||||
tool_name = str(response.event.payload.step_details.tool_calls[0].tool_name)
|
||||
yield f'\n\n🛠 :grey[_Using "{tool_name}" tool:_]\n\n'
|
||||
else:
|
||||
yield "No tool_calls present in step_details"
|
||||
else:
|
||||
yield f"Error occurred in the Llama Stack Cluster: {response}"
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
import io
|
||||
import json
|
||||
import uuid
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
|
@ -299,6 +300,7 @@ class ChatFormat:
|
|||
call_id=call_id,
|
||||
tool_name=tool_name,
|
||||
arguments=tool_arguments,
|
||||
arguments_json=json.dumps(tool_arguments),
|
||||
)
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -253,7 +253,8 @@ class MetaReferenceInferenceImpl(
|
|||
def impl():
|
||||
stop_reason = None
|
||||
|
||||
for token_result in self.generator.completion(request):
|
||||
for token_results in self.generator.completion([request]):
|
||||
token_result = token_results[0]
|
||||
if token_result.token == tokenizer.eot_id:
|
||||
stop_reason = StopReason.end_of_turn
|
||||
text = ""
|
||||
|
|
@ -515,7 +516,8 @@ class MetaReferenceInferenceImpl(
|
|||
stop_reason = None
|
||||
ipython = False
|
||||
|
||||
for token_result in self.generator.chat_completion(request):
|
||||
for token_results in self.generator.chat_completion([request]):
|
||||
token_result = token_results[0]
|
||||
if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1":
|
||||
cprint(token_result.text, "cyan", end="")
|
||||
if os.environ.get("LLAMA_MODELS_DEBUG", "0") == "2":
|
||||
|
|
|
|||
|
|
@ -69,7 +69,10 @@ class CancelSentinel(BaseModel):
|
|||
|
||||
class TaskRequest(BaseModel):
|
||||
type: Literal[ProcessingMessageName.task_request] = ProcessingMessageName.task_request
|
||||
task: Tuple[str, List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent]]
|
||||
task: Tuple[
|
||||
str,
|
||||
List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent],
|
||||
]
|
||||
|
||||
|
||||
class TaskResponse(BaseModel):
|
||||
|
|
@ -231,10 +234,10 @@ def worker_process_entrypoint(
|
|||
while True:
|
||||
try:
|
||||
task = req_gen.send(result)
|
||||
if isinstance(task, str) and task == EndSentinel():
|
||||
if isinstance(task, EndSentinel):
|
||||
break
|
||||
|
||||
assert isinstance(task, TaskRequest)
|
||||
assert isinstance(task, TaskRequest), task
|
||||
result = model(task.task)
|
||||
except StopIteration:
|
||||
break
|
||||
|
|
@ -331,7 +334,10 @@ class ModelParallelProcessGroup:
|
|||
|
||||
def run_inference(
|
||||
self,
|
||||
req: Tuple[str, List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent]],
|
||||
req: Tuple[
|
||||
str,
|
||||
List[CompletionRequestWithRawContent] | List[ChatCompletionRequestWithRawContent],
|
||||
],
|
||||
) -> Generator:
|
||||
assert not self.running, "inference already running"
|
||||
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ from llama_stack.apis.tools import (
|
|||
)
|
||||
from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
|
||||
from llama_stack.providers.datatypes import ToolsProtocolPrivate
|
||||
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
content_from_doc,
|
||||
make_overlapped_chunks,
|
||||
|
|
@ -153,6 +154,11 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
|
|||
)
|
||||
)
|
||||
picked.append(TextContentItem(text="END of knowledge_search tool results.\n"))
|
||||
picked.append(
|
||||
TextContentItem(
|
||||
text=f'The above results were retrieved to help answer the user\'s query: "{interleaved_content_as_str(content)}". Use them as supporting information only in answering this query.\n',
|
||||
)
|
||||
)
|
||||
|
||||
return RAGQueryResult(
|
||||
content=picked,
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
from typing import List
|
||||
|
||||
from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec
|
||||
from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
|
||||
|
||||
|
||||
def available_providers() -> List[ProviderSpec]:
|
||||
|
|
@ -25,4 +25,22 @@ def available_providers() -> List[ProviderSpec]:
|
|||
Api.agents,
|
||||
],
|
||||
),
|
||||
remote_provider_spec(
|
||||
api=Api.eval,
|
||||
adapter=AdapterSpec(
|
||||
adapter_type="nvidia",
|
||||
pip_packages=[
|
||||
"requests",
|
||||
],
|
||||
module="llama_stack.providers.remote.eval.nvidia",
|
||||
config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig",
|
||||
),
|
||||
api_dependencies=[
|
||||
Api.datasetio,
|
||||
Api.datasets,
|
||||
Api.scoring,
|
||||
Api.inference,
|
||||
Api.agents,
|
||||
],
|
||||
),
|
||||
]
|
||||
|
|
|
|||
5
llama_stack/providers/remote/eval/__init__.py
Normal file
5
llama_stack/providers/remote/eval/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
134
llama_stack/providers/remote/eval/nvidia/README.md
Normal file
134
llama_stack/providers/remote/eval/nvidia/README.md
Normal file
|
|
@ -0,0 +1,134 @@
|
|||
# NVIDIA NeMo Evaluator Eval Provider
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
For the first integration, Benchmarks are mapped to Evaluation Configs on in the NeMo Evaluator. The full evaluation config object is provided as part of the meta-data. The `dataset_id` and `scoring_functions` are not used.
|
||||
|
||||
Below are a few examples of how to register a benchmark, which in turn will create an evaluation config in NeMo Evaluator and how to trigger an evaluation.
|
||||
|
||||
### Example for register an academic benchmark
|
||||
|
||||
```
|
||||
POST /eval/benchmarks
|
||||
```
|
||||
```json
|
||||
{
|
||||
"benchmark_id": "mmlu",
|
||||
"dataset_id": "",
|
||||
"scoring_functions": [],
|
||||
"metadata": {
|
||||
"type": "mmlu"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Example for register a custom evaluation
|
||||
|
||||
```
|
||||
POST /eval/benchmarks
|
||||
```
|
||||
```json
|
||||
{
|
||||
"benchmark_id": "my-custom-benchmark",
|
||||
"dataset_id": "",
|
||||
"scoring_functions": [],
|
||||
"metadata": {
|
||||
"type": "custom",
|
||||
"params": {
|
||||
"parallelism": 8
|
||||
},
|
||||
"tasks": {
|
||||
"qa": {
|
||||
"type": "completion",
|
||||
"params": {
|
||||
"template": {
|
||||
"prompt": "{{prompt}}",
|
||||
"max_tokens": 200
|
||||
}
|
||||
},
|
||||
"dataset": {
|
||||
"files_url": "hf://datasets/default/sample-basic-test/testing/testing.jsonl"
|
||||
},
|
||||
"metrics": {
|
||||
"bleu": {
|
||||
"type": "bleu",
|
||||
"params": {
|
||||
"references": [
|
||||
"{{ideal_response}}"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Example for triggering a benchmark/custom evaluation
|
||||
|
||||
```
|
||||
POST /eval/benchmarks/{benchmark_id}/jobs
|
||||
```
|
||||
```json
|
||||
{
|
||||
"benchmark_id": "my-custom-benchmark",
|
||||
"benchmark_config": {
|
||||
"eval_candidate": {
|
||||
"type": "model",
|
||||
"model": "meta-llama/Llama3.1-8B-Instruct",
|
||||
"sampling_params": {
|
||||
"max_tokens": 100,
|
||||
"temperature": 0.7
|
||||
}
|
||||
},
|
||||
"scoring_params": {}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Response example:
|
||||
```json
|
||||
{
|
||||
"job_id": "eval-1234",
|
||||
"status": "in_progress"
|
||||
}
|
||||
```
|
||||
|
||||
### Example for getting the status of a job
|
||||
```
|
||||
GET /eval/benchmarks/{benchmark_id}/jobs/{job_id}
|
||||
```
|
||||
|
||||
Response example:
|
||||
```json
|
||||
{
|
||||
"job_id": "eval-1234",
|
||||
"status": "in_progress"
|
||||
}
|
||||
```
|
||||
|
||||
### Example for cancelling a job
|
||||
```
|
||||
POST /eval/benchmarks/{benchmark_id}/jobs/{job_id}/cancel
|
||||
```
|
||||
|
||||
### Example for getting the results
|
||||
```
|
||||
GET /eval/benchmarks/{benchmark_id}/results
|
||||
```
|
||||
```json
|
||||
{
|
||||
"generations": [],
|
||||
"scores": {
|
||||
"{benchmark_id}": {
|
||||
"score_rows": [],
|
||||
"aggregated_results": {
|
||||
"tasks": {},
|
||||
"groups": {}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
31
llama_stack/providers/remote/eval/nvidia/__init__.py
Normal file
31
llama_stack/providers/remote/eval/nvidia/__init__.py
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
from typing import Any, Dict
|
||||
|
||||
from llama_stack.distribution.datatypes import Api
|
||||
|
||||
from .config import NVIDIAEvalConfig
|
||||
|
||||
|
||||
async def get_adapter_impl(
|
||||
config: NVIDIAEvalConfig,
|
||||
deps: Dict[Api, Any],
|
||||
):
|
||||
from .eval import NVIDIAEvalImpl
|
||||
|
||||
impl = NVIDIAEvalImpl(
|
||||
config,
|
||||
deps[Api.datasetio],
|
||||
deps[Api.datasets],
|
||||
deps[Api.scoring],
|
||||
deps[Api.inference],
|
||||
deps[Api.agents],
|
||||
)
|
||||
await impl.initialize()
|
||||
return impl
|
||||
|
||||
|
||||
__all__ = ["get_adapter_impl", "NVIDIAEvalImpl"]
|
||||
29
llama_stack/providers/remote/eval/nvidia/config.py
Normal file
29
llama_stack/providers/remote/eval/nvidia/config.py
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
import os
|
||||
from typing import Any, Dict
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class NVIDIAEvalConfig(BaseModel):
|
||||
"""
|
||||
Configuration for the NVIDIA NeMo Evaluator microservice endpoint.
|
||||
|
||||
Attributes:
|
||||
evaluator_url (str): A base url for accessing the NVIDIA evaluation endpoint, e.g. http://localhost:8000.
|
||||
"""
|
||||
|
||||
evaluator_url: str = Field(
|
||||
default_factory=lambda: os.getenv("NVIDIA_EVALUATOR_URL", "http://0.0.0.0:7331"),
|
||||
description="The url for accessing the evaluator service",
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
|
||||
return {
|
||||
"evaluator_url": "${env.NVIDIA_EVALUATOR_URL:http://localhost:7331}",
|
||||
}
|
||||
154
llama_stack/providers/remote/eval/nvidia/eval.py
Normal file
154
llama_stack/providers/remote/eval/nvidia/eval.py
Normal file
|
|
@ -0,0 +1,154 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import requests
|
||||
|
||||
from llama_stack.apis.agents import Agents
|
||||
from llama_stack.apis.benchmarks import Benchmark
|
||||
from llama_stack.apis.datasetio import DatasetIO
|
||||
from llama_stack.apis.datasets import Datasets
|
||||
from llama_stack.apis.inference import Inference
|
||||
from llama_stack.apis.scoring import Scoring, ScoringResult
|
||||
from llama_stack.providers.datatypes import BenchmarksProtocolPrivate
|
||||
from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES
|
||||
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
|
||||
|
||||
from .....apis.common.job_types import Job, JobStatus
|
||||
from .....apis.eval.eval import BenchmarkConfig, Eval, EvaluateResponse
|
||||
from .config import NVIDIAEvalConfig
|
||||
|
||||
DEFAULT_NAMESPACE = "nvidia"
|
||||
|
||||
|
||||
class NVIDIAEvalImpl(
|
||||
Eval,
|
||||
BenchmarksProtocolPrivate,
|
||||
ModelRegistryHelper,
|
||||
):
|
||||
def __init__(
|
||||
self,
|
||||
config: NVIDIAEvalConfig,
|
||||
datasetio_api: DatasetIO,
|
||||
datasets_api: Datasets,
|
||||
scoring_api: Scoring,
|
||||
inference_api: Inference,
|
||||
agents_api: Agents,
|
||||
) -> None:
|
||||
self.config = config
|
||||
self.datasetio_api = datasetio_api
|
||||
self.datasets_api = datasets_api
|
||||
self.scoring_api = scoring_api
|
||||
self.inference_api = inference_api
|
||||
self.agents_api = agents_api
|
||||
|
||||
ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
|
||||
|
||||
async def initialize(self) -> None: ...
|
||||
|
||||
async def shutdown(self) -> None: ...
|
||||
|
||||
async def _evaluator_get(self, path):
|
||||
"""Helper for making GET requests to the evaluator service."""
|
||||
response = requests.get(url=f"{self.config.evaluator_url}{path}")
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
async def _evaluator_post(self, path, data):
|
||||
"""Helper for making POST requests to the evaluator service."""
|
||||
response = requests.post(url=f"{self.config.evaluator_url}{path}", json=data)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
async def register_benchmark(self, task_def: Benchmark) -> None:
|
||||
"""Register a benchmark as an evaluation configuration."""
|
||||
await self._evaluator_post(
|
||||
"/v1/evaluation/configs",
|
||||
{
|
||||
"namespace": DEFAULT_NAMESPACE,
|
||||
"name": task_def.benchmark_id,
|
||||
# metadata is copied to request body as-is
|
||||
**task_def.metadata,
|
||||
},
|
||||
)
|
||||
|
||||
async def run_eval(
|
||||
self,
|
||||
benchmark_id: str,
|
||||
benchmark_config: BenchmarkConfig,
|
||||
) -> Job:
|
||||
"""Run an evaluation job for a benchmark."""
|
||||
model = (
|
||||
benchmark_config.eval_candidate.model
|
||||
if benchmark_config.eval_candidate.type == "model"
|
||||
else benchmark_config.eval_candidate.config.model
|
||||
)
|
||||
nvidia_model = self.get_provider_model_id(model) or model
|
||||
|
||||
result = await self._evaluator_post(
|
||||
"/v1/evaluation/jobs",
|
||||
{
|
||||
"config": f"{DEFAULT_NAMESPACE}/{benchmark_id}",
|
||||
"target": {"type": "model", "model": nvidia_model},
|
||||
},
|
||||
)
|
||||
|
||||
return Job(job_id=result["id"], status=JobStatus.in_progress)
|
||||
|
||||
async def evaluate_rows(
|
||||
self,
|
||||
benchmark_id: str,
|
||||
input_rows: List[Dict[str, Any]],
|
||||
scoring_functions: List[str],
|
||||
benchmark_config: BenchmarkConfig,
|
||||
) -> EvaluateResponse:
|
||||
raise NotImplementedError()
|
||||
|
||||
async def job_status(self, benchmark_id: str, job_id: str) -> Job:
|
||||
"""Get the status of an evaluation job.
|
||||
|
||||
EvaluatorStatus: "created", "pending", "running", "cancelled", "cancelling", "failed", "completed".
|
||||
JobStatus: "scheduled", "in_progress", "completed", "cancelled", "failed"
|
||||
"""
|
||||
result = await self._evaluator_get(f"/v1/evaluation/jobs/{job_id}")
|
||||
result_status = result["status"]
|
||||
|
||||
job_status = JobStatus.failed
|
||||
if result_status in ["created", "pending"]:
|
||||
job_status = JobStatus.scheduled
|
||||
elif result_status in ["running"]:
|
||||
job_status = JobStatus.in_progress
|
||||
elif result_status in ["completed"]:
|
||||
job_status = JobStatus.completed
|
||||
elif result_status in ["cancelled"]:
|
||||
job_status = JobStatus.cancelled
|
||||
|
||||
return Job(job_id=job_id, status=job_status)
|
||||
|
||||
async def job_cancel(self, benchmark_id: str, job_id: str) -> None:
|
||||
"""Cancel the evaluation job."""
|
||||
await self._evaluator_post(f"/v1/evaluation/jobs/{job_id}/cancel", {})
|
||||
|
||||
async def job_result(self, benchmark_id: str, job_id: str) -> EvaluateResponse:
|
||||
"""Returns the results of the evaluation job."""
|
||||
|
||||
job = await self.job_status(benchmark_id, job_id)
|
||||
status = job.status
|
||||
if not status or status != JobStatus.completed:
|
||||
raise ValueError(f"Job {job_id} not completed. Status: {status.value}")
|
||||
|
||||
result = await self._evaluator_get(f"/v1/evaluation/jobs/{job_id}/results")
|
||||
|
||||
return EvaluateResponse(
|
||||
# TODO: these are stored in detailed results on NeMo Evaluator side; can be added
|
||||
generations=[],
|
||||
scores={
|
||||
benchmark_id: ScoringResult(
|
||||
score_rows=[],
|
||||
aggregated_results=result,
|
||||
)
|
||||
},
|
||||
)
|
||||
|
|
@ -362,6 +362,39 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
|
|||
user: Optional[str] = None,
|
||||
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
|
||||
model_obj = await self.model_store.get_model(model)
|
||||
|
||||
# Divert Llama Models through Llama Stack inference APIs because
|
||||
# Fireworks chat completions OpenAI-compatible API does not support
|
||||
# tool calls properly.
|
||||
llama_model = self.get_llama_model(model_obj.provider_resource_id)
|
||||
if llama_model:
|
||||
return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion(
|
||||
self,
|
||||
model=model,
|
||||
messages=messages,
|
||||
frequency_penalty=frequency_penalty,
|
||||
function_call=function_call,
|
||||
functions=functions,
|
||||
logit_bias=logit_bias,
|
||||
logprobs=logprobs,
|
||||
max_completion_tokens=max_completion_tokens,
|
||||
max_tokens=max_tokens,
|
||||
n=n,
|
||||
parallel_tool_calls=parallel_tool_calls,
|
||||
presence_penalty=presence_penalty,
|
||||
response_format=response_format,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
stream_options=stream_options,
|
||||
temperature=temperature,
|
||||
tool_choice=tool_choice,
|
||||
tools=tools,
|
||||
top_logprobs=top_logprobs,
|
||||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
|
||||
params = await prepare_openai_completion_params(
|
||||
messages=messages,
|
||||
frequency_penalty=frequency_penalty,
|
||||
|
|
@ -387,11 +420,4 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
|
|||
user=user,
|
||||
)
|
||||
|
||||
# Divert Llama Models through Llama Stack inference APIs because
|
||||
# Fireworks chat completions OpenAI-compatible API does not support
|
||||
# tool calls properly.
|
||||
llama_model = self.get_llama_model(model_obj.provider_resource_id)
|
||||
if llama_model:
|
||||
return await OpenAIChatCompletionToLlamaStackMixin.openai_chat_completion(self, model=model, **params)
|
||||
|
||||
return await self._get_openai_client().chat.completions.create(model=model_obj.provider_resource_id, **params)
|
||||
|
|
|
|||
|
|
@ -47,10 +47,15 @@ class NVIDIAConfig(BaseModel):
|
|||
default=60,
|
||||
description="Timeout for the HTTP requests",
|
||||
)
|
||||
append_api_version: bool = Field(
|
||||
default_factory=lambda: os.getenv("NVIDIA_APPEND_API_VERSION", "True").lower() != "false",
|
||||
description="When set to false, the API version will not be appended to the base_url. By default, it is true.",
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
|
||||
return {
|
||||
"url": "${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}",
|
||||
"api_key": "${env.NVIDIA_API_KEY:}",
|
||||
"append_api_version": "${env.NVIDIA_APPEND_API_VERSION:True}",
|
||||
}
|
||||
|
|
|
|||
|
|
@ -33,7 +33,6 @@ from llama_stack.apis.inference import (
|
|||
TextTruncation,
|
||||
ToolChoice,
|
||||
ToolConfig,
|
||||
ToolDefinition,
|
||||
)
|
||||
from llama_stack.apis.inference.inference import (
|
||||
OpenAIChatCompletion,
|
||||
|
|
@ -42,7 +41,11 @@ from llama_stack.apis.inference.inference import (
|
|||
OpenAIMessageParam,
|
||||
OpenAIResponseFormatParam,
|
||||
)
|
||||
from llama_stack.models.llama.datatypes import ToolPromptFormat
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
|
||||
from llama_stack.providers.utils.inference import (
|
||||
ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR,
|
||||
)
|
||||
from llama_stack.providers.utils.inference.model_registry import (
|
||||
ModelRegistryHelper,
|
||||
)
|
||||
|
|
@ -120,12 +123,20 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
"meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
|
||||
}
|
||||
|
||||
base_url = f"{self._config.url}/v1"
|
||||
base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
|
||||
|
||||
if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
|
||||
base_url = special_model_urls[provider_model_id]
|
||||
|
||||
return _get_client_for_base_url(base_url)
|
||||
|
||||
async def _get_provider_model_id(self, model_id: str) -> str:
|
||||
if not self.model_store:
|
||||
raise RuntimeError("Model store is not set")
|
||||
model = await self.model_store.get_model(model_id)
|
||||
if model is None:
|
||||
raise ValueError(f"Model {model_id} is unknown")
|
||||
return model.provider_model_id
|
||||
|
||||
async def completion(
|
||||
self,
|
||||
model_id: str,
|
||||
|
|
@ -144,7 +155,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
# removing this health check as NeMo customizer endpoint health check is returning 404
|
||||
# await check_health(self._config) # this raises errors
|
||||
|
||||
provider_model_id = self.get_provider_model_id(model_id)
|
||||
provider_model_id = await self._get_provider_model_id(model_id)
|
||||
request = convert_completion_request(
|
||||
request=CompletionRequest(
|
||||
model=provider_model_id,
|
||||
|
|
@ -188,7 +199,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
#
|
||||
flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents]
|
||||
input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents]
|
||||
model = self.get_provider_model_id(model_id)
|
||||
provider_model_id = await self._get_provider_model_id(model_id)
|
||||
|
||||
extra_body = {}
|
||||
|
||||
|
|
@ -211,8 +222,8 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
extra_body["input_type"] = task_type_options[task_type]
|
||||
|
||||
try:
|
||||
response = await self._get_client(model).embeddings.create(
|
||||
model=model,
|
||||
response = await self._get_client(provider_model_id).embeddings.create(
|
||||
model=provider_model_id,
|
||||
input=input,
|
||||
extra_body=extra_body,
|
||||
)
|
||||
|
|
@ -246,10 +257,10 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
|
||||
# await check_health(self._config) # this raises errors
|
||||
|
||||
provider_model_id = self.get_provider_model_id(model_id)
|
||||
provider_model_id = await self._get_provider_model_id(model_id)
|
||||
request = await convert_chat_completion_request(
|
||||
request=ChatCompletionRequest(
|
||||
model=self.get_provider_model_id(model_id),
|
||||
model=provider_model_id,
|
||||
messages=messages,
|
||||
sampling_params=sampling_params,
|
||||
response_format=response_format,
|
||||
|
|
@ -294,7 +305,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
guided_choice: Optional[List[str]] = None,
|
||||
prompt_logprobs: Optional[int] = None,
|
||||
) -> OpenAICompletion:
|
||||
provider_model_id = self.get_provider_model_id(model)
|
||||
provider_model_id = await self._get_provider_model_id(model)
|
||||
|
||||
params = await prepare_openai_completion_params(
|
||||
model=provider_model_id,
|
||||
|
|
@ -347,7 +358,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
top_p: Optional[float] = None,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
|
||||
provider_model_id = self.get_provider_model_id(model)
|
||||
provider_model_id = await self._get_provider_model_id(model)
|
||||
|
||||
params = await prepare_openai_completion_params(
|
||||
model=provider_model_id,
|
||||
|
|
@ -379,3 +390,44 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
|
|||
return await self._get_client(provider_model_id).chat.completions.create(**params)
|
||||
except APIConnectionError as e:
|
||||
raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
|
||||
|
||||
async def register_model(self, model: Model) -> Model:
|
||||
"""
|
||||
Allow non-llama model registration.
|
||||
|
||||
Non-llama model registration: API Catalogue models, post-training models, etc.
|
||||
client = LlamaStackAsLibraryClient("nvidia")
|
||||
client.models.register(
|
||||
model_id="mistralai/mixtral-8x7b-instruct-v0.1",
|
||||
model_type=ModelType.llm,
|
||||
provider_id="nvidia",
|
||||
provider_model_id="mistralai/mixtral-8x7b-instruct-v0.1"
|
||||
)
|
||||
|
||||
NOTE: Only supports models endpoints compatible with AsyncOpenAI base_url format.
|
||||
"""
|
||||
if model.model_type == ModelType.embedding:
|
||||
# embedding models are always registered by their provider model id and does not need to be mapped to a llama model
|
||||
provider_resource_id = model.provider_resource_id
|
||||
else:
|
||||
provider_resource_id = self.get_provider_model_id(model.provider_resource_id)
|
||||
|
||||
if provider_resource_id:
|
||||
model.provider_resource_id = provider_resource_id
|
||||
else:
|
||||
llama_model = model.metadata.get("llama_model")
|
||||
existing_llama_model = self.get_llama_model(model.provider_resource_id)
|
||||
if existing_llama_model:
|
||||
if existing_llama_model != llama_model:
|
||||
raise ValueError(
|
||||
f"Provider model id '{model.provider_resource_id}' is already registered to a different llama model: '{existing_llama_model}'"
|
||||
)
|
||||
else:
|
||||
# not llama model
|
||||
if llama_model in ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR:
|
||||
self.provider_id_to_llama_model_map[model.provider_resource_id] = (
|
||||
ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model]
|
||||
)
|
||||
else:
|
||||
self.alias_to_provider_id_map[model.provider_model_id] = model.provider_model_id
|
||||
return model
|
||||
|
|
|
|||
|
|
@ -76,8 +76,11 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
|
|||
|
||||
async def shutdown(self) -> None:
|
||||
if self._client:
|
||||
await self._client.close()
|
||||
# Together client has no close method, so just set to None
|
||||
self._client = None
|
||||
if self._openai_client:
|
||||
await self._openai_client.close()
|
||||
self._openai_client = None
|
||||
|
||||
async def completion(
|
||||
self,
|
||||
|
|
@ -359,7 +362,7 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
|
|||
top_p=top_p,
|
||||
user=user,
|
||||
)
|
||||
if params.get("stream", True):
|
||||
if params.get("stream", False):
|
||||
return self._stream_openai_chat_completion(params)
|
||||
return await self._get_openai_client().chat.completions.create(**params) # type: ignore
|
||||
|
||||
|
|
|
|||
|
|
@ -231,12 +231,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
self.client = None
|
||||
|
||||
async def initialize(self) -> None:
|
||||
log.info(f"Initializing VLLM client with base_url={self.config.url}")
|
||||
self.client = AsyncOpenAI(
|
||||
base_url=self.config.url,
|
||||
api_key=self.config.api_token,
|
||||
http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
|
||||
)
|
||||
pass
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
pass
|
||||
|
|
@ -249,6 +244,20 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
raise ValueError("Model store not set")
|
||||
return await self.model_store.get_model(model_id)
|
||||
|
||||
def _lazy_initialize_client(self):
|
||||
if self.client is not None:
|
||||
return
|
||||
|
||||
log.info(f"Initializing vLLM client with base_url={self.config.url}")
|
||||
self.client = self._create_client()
|
||||
|
||||
def _create_client(self):
|
||||
return AsyncOpenAI(
|
||||
base_url=self.config.url,
|
||||
api_key=self.config.api_token,
|
||||
http_client=None if self.config.tls_verify else httpx.AsyncClient(verify=False),
|
||||
)
|
||||
|
||||
async def completion(
|
||||
self,
|
||||
model_id: str,
|
||||
|
|
@ -258,6 +267,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
stream: Optional[bool] = False,
|
||||
logprobs: Optional[LogProbConfig] = None,
|
||||
) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]:
|
||||
self._lazy_initialize_client()
|
||||
if sampling_params is None:
|
||||
sampling_params = SamplingParams()
|
||||
model = await self._get_model(model_id)
|
||||
|
|
@ -287,6 +297,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
logprobs: Optional[LogProbConfig] = None,
|
||||
tool_config: Optional[ToolConfig] = None,
|
||||
) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
|
||||
self._lazy_initialize_client()
|
||||
if sampling_params is None:
|
||||
sampling_params = SamplingParams()
|
||||
model = await self._get_model(model_id)
|
||||
|
|
@ -357,9 +368,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
yield chunk
|
||||
|
||||
async def register_model(self, model: Model) -> Model:
|
||||
assert self.client is not None
|
||||
# register_model is called during Llama Stack initialization, hence we cannot init self.client if not initialized yet.
|
||||
# self.client should only be created after the initialization is complete to avoid asyncio cross-context errors.
|
||||
# Changing this may lead to unpredictable behavior.
|
||||
client = self._create_client() if self.client is None else self.client
|
||||
model = await self.register_helper.register_model(model)
|
||||
res = await self.client.models.list()
|
||||
res = await client.models.list()
|
||||
available_models = [m.id async for m in res]
|
||||
if model.provider_resource_id not in available_models:
|
||||
raise ValueError(
|
||||
|
|
@ -410,6 +424,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
output_dimension: Optional[int] = None,
|
||||
task_type: Optional[EmbeddingTaskType] = None,
|
||||
) -> EmbeddingsResponse:
|
||||
self._lazy_initialize_client()
|
||||
assert self.client is not None
|
||||
model = await self._get_model(model_id)
|
||||
|
||||
|
|
@ -449,6 +464,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
guided_choice: Optional[List[str]] = None,
|
||||
prompt_logprobs: Optional[int] = None,
|
||||
) -> OpenAICompletion:
|
||||
self._lazy_initialize_client()
|
||||
model_obj = await self._get_model(model)
|
||||
|
||||
extra_body: Dict[str, Any] = {}
|
||||
|
|
@ -505,6 +521,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
|
|||
top_p: Optional[float] = None,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
|
||||
self._lazy_initialize_client()
|
||||
model_obj = await self._get_model(model)
|
||||
params = await prepare_openai_completion_params(
|
||||
model=model_obj.provider_resource_id,
|
||||
|
|
|
|||
|
|
@ -36,7 +36,6 @@ import os
|
|||
|
||||
os.environ["NVIDIA_API_KEY"] = "your-api-key"
|
||||
os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
|
||||
os.environ["NVIDIA_USER_ID"] = "llama-stack-user"
|
||||
os.environ["NVIDIA_DATASET_NAMESPACE"] = "default"
|
||||
os.environ["NVIDIA_PROJECT_ID"] = "test-project"
|
||||
os.environ["NVIDIA_OUTPUT_MODEL_DIR"] = "test-example-model@v1"
|
||||
|
|
@ -125,6 +124,21 @@ client.post_training.job.cancel(job_uuid="your-job-id")
|
|||
|
||||
### Inference with the fine-tuned model
|
||||
|
||||
#### 1. Register the model
|
||||
|
||||
```python
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
|
||||
client.models.register(
|
||||
model_id="test-example-model@v1",
|
||||
provider_id="nvidia",
|
||||
provider_model_id="test-example-model@v1",
|
||||
model_type=ModelType.llm,
|
||||
)
|
||||
```
|
||||
|
||||
#### 2. Inference with the fine-tuned model
|
||||
|
||||
```python
|
||||
response = client.inference.completion(
|
||||
content="Complete the sentence using one word: Roses are red, violets are ",
|
||||
|
|
|
|||
|
|
@ -8,7 +8,17 @@ import logging
|
|||
import time
|
||||
import uuid
|
||||
import warnings
|
||||
from typing import Any, AsyncGenerator, AsyncIterator, Awaitable, Dict, Iterable, List, Optional, Union
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncGenerator,
|
||||
AsyncIterator,
|
||||
Awaitable,
|
||||
Dict,
|
||||
Iterable,
|
||||
List,
|
||||
Optional,
|
||||
Union,
|
||||
)
|
||||
|
||||
from openai import AsyncStream
|
||||
from openai.types.chat import (
|
||||
|
|
@ -78,6 +88,7 @@ from llama_stack.apis.common.content_types import (
|
|||
TextDelta,
|
||||
ToolCallDelta,
|
||||
ToolCallParseStatus,
|
||||
_URLOrData,
|
||||
)
|
||||
from llama_stack.apis.inference import (
|
||||
ChatCompletionRequest,
|
||||
|
|
@ -93,6 +104,7 @@ from llama_stack.apis.inference import (
|
|||
SamplingParams,
|
||||
SystemMessage,
|
||||
TokenLogProbs,
|
||||
ToolChoice,
|
||||
ToolResponseMessage,
|
||||
TopKSamplingStrategy,
|
||||
TopPSamplingStrategy,
|
||||
|
|
@ -103,7 +115,6 @@ from llama_stack.apis.inference.inference import (
|
|||
OpenAIChatCompletion,
|
||||
OpenAICompletion,
|
||||
OpenAICompletionChoice,
|
||||
OpenAIMessageParam,
|
||||
OpenAIResponseFormatParam,
|
||||
ToolConfig,
|
||||
)
|
||||
|
|
@ -513,11 +524,26 @@ async def convert_message_to_openai_dict(message: Message, download: bool = Fals
|
|||
else:
|
||||
content = [await _convert_content(message.content)]
|
||||
|
||||
return {
|
||||
result = {
|
||||
"role": message.role,
|
||||
"content": content,
|
||||
}
|
||||
|
||||
if hasattr(message, "tool_calls") and message.tool_calls:
|
||||
result["tool_calls"] = []
|
||||
for tc in message.tool_calls:
|
||||
result["tool_calls"].append(
|
||||
{
|
||||
"id": tc.call_id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tc.tool_name,
|
||||
"arguments": tc.arguments_json if hasattr(tc, "arguments_json") else json.dumps(tc.arguments),
|
||||
},
|
||||
}
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
class UnparseableToolCall(BaseModel):
|
||||
"""
|
||||
|
|
@ -612,13 +638,10 @@ async def convert_message_to_openai_dict_new(
|
|||
)
|
||||
for tool in message.tool_calls
|
||||
]
|
||||
params = {}
|
||||
if tool_calls:
|
||||
params = {"tool_calls": tool_calls}
|
||||
out = OpenAIChatCompletionAssistantMessage(
|
||||
role="assistant",
|
||||
content=await _convert_message_content(message.content),
|
||||
**params,
|
||||
tool_calls=tool_calls or None,
|
||||
)
|
||||
elif isinstance(message, ToolResponseMessage):
|
||||
out = OpenAIChatCompletionToolMessage(
|
||||
|
|
@ -695,7 +718,10 @@ def to_openai_param_type(param_type: str) -> dict:
|
|||
if param_type.startswith("list[") and param_type.endswith("]"):
|
||||
inner_type = param_type[5:-1]
|
||||
if inner_type in basic_types:
|
||||
return {"type": "array", "items": {"type": basic_types.get(inner_type, inner_type)}}
|
||||
return {
|
||||
"type": "array",
|
||||
"items": {"type": basic_types.get(inner_type, inner_type)},
|
||||
}
|
||||
|
||||
return {"type": param_type}
|
||||
|
||||
|
|
@ -815,6 +841,10 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason:
|
|||
def _convert_openai_request_tool_config(tool_choice: Optional[Union[str, Dict[str, Any]]] = None) -> ToolConfig:
|
||||
tool_config = ToolConfig()
|
||||
if tool_choice:
|
||||
try:
|
||||
tool_choice = ToolChoice(tool_choice)
|
||||
except ValueError:
|
||||
pass
|
||||
tool_config.tool_choice = tool_choice
|
||||
return tool_config
|
||||
|
||||
|
|
@ -849,7 +879,9 @@ def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None)
|
|||
return lls_tools
|
||||
|
||||
|
||||
def _convert_openai_request_response_format(response_format: OpenAIResponseFormatParam = None):
|
||||
def _convert_openai_request_response_format(
|
||||
response_format: OpenAIResponseFormatParam = None,
|
||||
):
|
||||
if not response_format:
|
||||
return None
|
||||
# response_format can be a dict or a pydantic model
|
||||
|
|
@ -957,38 +989,50 @@ def _convert_openai_sampling_params(
|
|||
return sampling_params
|
||||
|
||||
|
||||
def _convert_openai_request_messages(messages: List[OpenAIMessageParam]):
|
||||
# Llama Stack messages and OpenAI messages are similar, but not identical.
|
||||
lls_messages = []
|
||||
def openai_messages_to_messages(
|
||||
messages: List[OpenAIChatCompletionMessage],
|
||||
) -> List[Message]:
|
||||
"""
|
||||
Convert a list of OpenAIChatCompletionMessage into a list of Message.
|
||||
"""
|
||||
converted_messages = []
|
||||
for message in messages:
|
||||
lls_message = dict(message)
|
||||
if message.role == "system":
|
||||
converted_message = SystemMessage(content=message.content)
|
||||
elif message.role == "user":
|
||||
converted_message = UserMessage(content=openai_content_to_content(message.content))
|
||||
elif message.role == "assistant":
|
||||
converted_message = CompletionMessage(
|
||||
content=message.content,
|
||||
tool_calls=_convert_openai_tool_calls(message.tool_calls),
|
||||
stop_reason=StopReason.end_of_turn,
|
||||
)
|
||||
elif message.role == "tool":
|
||||
converted_message = ToolResponseMessage(
|
||||
role="tool",
|
||||
call_id=message.tool_call_id,
|
||||
content=openai_content_to_content(message.content),
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unknown role {message.role}")
|
||||
converted_messages.append(converted_message)
|
||||
return converted_messages
|
||||
|
||||
# Llama Stack expects `call_id` but OpenAI uses `tool_call_id`
|
||||
tool_call_id = lls_message.pop("tool_call_id", None)
|
||||
if tool_call_id:
|
||||
lls_message["call_id"] = tool_call_id
|
||||
|
||||
content = lls_message.get("content", None)
|
||||
if isinstance(content, list):
|
||||
lls_content = []
|
||||
for item in content:
|
||||
# items can either by pydantic models or dicts here...
|
||||
item = dict(item)
|
||||
if item.get("type", "") == "image_url":
|
||||
lls_item = ImageContentItem(
|
||||
type="image",
|
||||
image=URL(uri=item.get("image_url", {}).get("url", "")),
|
||||
)
|
||||
elif item.get("type", "") == "text":
|
||||
lls_item = TextContentItem(
|
||||
type="text",
|
||||
text=item.get("text", ""),
|
||||
)
|
||||
lls_content.append(lls_item)
|
||||
lls_message["content"] = lls_content
|
||||
lls_messages.append(lls_message)
|
||||
|
||||
return lls_messages
|
||||
def openai_content_to_content(content: Union[str, Iterable[OpenAIChatCompletionContentPartParam]]):
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
elif isinstance(content, list):
|
||||
return [openai_content_to_content(c) for c in content]
|
||||
elif hasattr(content, "type"):
|
||||
if content.type == "text":
|
||||
return TextContentItem(type="text", text=content.text)
|
||||
elif content.type == "image_url":
|
||||
return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url)))
|
||||
else:
|
||||
raise ValueError(f"Unknown content type: {content.type}")
|
||||
else:
|
||||
raise ValueError(f"Unknown content type: {content}")
|
||||
|
||||
|
||||
def convert_openai_chat_completion_choice(
|
||||
|
|
@ -1313,7 +1357,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
top_p: Optional[float] = None,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
|
||||
messages = _convert_openai_request_messages(messages)
|
||||
messages = openai_messages_to_messages(messages)
|
||||
response_format = _convert_openai_request_response_format(response_format)
|
||||
sampling_params = _convert_openai_sampling_params(
|
||||
max_tokens=max_tokens,
|
||||
|
|
@ -1321,7 +1365,10 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
top_p=top_p,
|
||||
)
|
||||
tool_config = _convert_openai_request_tool_config(tool_choice)
|
||||
|
||||
tools = _convert_openai_request_tools(tools)
|
||||
if tool_config.tool_choice == ToolChoice.none:
|
||||
tools = []
|
||||
|
||||
outstanding_responses = []
|
||||
# "n" is the number of completions to generate per prompt
|
||||
|
|
@ -1346,7 +1393,9 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
)
|
||||
|
||||
async def _process_stream_response(
|
||||
self, model: str, outstanding_responses: List[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]]
|
||||
self,
|
||||
model: str,
|
||||
outstanding_responses: List[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]],
|
||||
):
|
||||
id = f"chatcmpl-{uuid.uuid4()}"
|
||||
for outstanding_response in outstanding_responses:
|
||||
|
|
@ -1369,11 +1418,31 @@ class OpenAIChatCompletionToLlamaStackMixin:
|
|||
elif isinstance(event.delta, ToolCallDelta):
|
||||
if event.delta.parse_status == ToolCallParseStatus.succeeded:
|
||||
tool_call = event.delta.tool_call
|
||||
|
||||
# First chunk includes full structure
|
||||
openai_tool_call = OpenAIChoiceDeltaToolCall(
|
||||
index=0,
|
||||
id=tool_call.call_id,
|
||||
function=OpenAIChoiceDeltaToolCallFunction(
|
||||
name=tool_call.tool_name, arguments=tool_call.arguments_json
|
||||
name=tool_call.tool_name,
|
||||
arguments="",
|
||||
),
|
||||
)
|
||||
delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call])
|
||||
yield OpenAIChatCompletionChunk(
|
||||
id=id,
|
||||
choices=[
|
||||
OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
|
||||
],
|
||||
created=int(time.time()),
|
||||
model=model,
|
||||
object="chat.completion.chunk",
|
||||
)
|
||||
# arguments
|
||||
openai_tool_call = OpenAIChoiceDeltaToolCall(
|
||||
index=0,
|
||||
function=OpenAIChoiceDeltaToolCallFunction(
|
||||
arguments=tool_call.arguments_json,
|
||||
),
|
||||
)
|
||||
delta = OpenAIChoiceDelta(tool_calls=[openai_tool_call])
|
||||
|
|
|
|||
|
|
@ -394,12 +394,10 @@
|
|||
"aiosqlite",
|
||||
"blobfile",
|
||||
"chardet",
|
||||
"emoji",
|
||||
"faiss-cpu",
|
||||
"fastapi",
|
||||
"fire",
|
||||
"httpx",
|
||||
"langdetect",
|
||||
"matplotlib",
|
||||
"nltk",
|
||||
"numpy",
|
||||
|
|
@ -411,7 +409,6 @@
|
|||
"psycopg2-binary",
|
||||
"pymongo",
|
||||
"pypdf",
|
||||
"pythainlp",
|
||||
"redis",
|
||||
"requests",
|
||||
"scikit-learn",
|
||||
|
|
@ -419,7 +416,6 @@
|
|||
"sentencepiece",
|
||||
"tqdm",
|
||||
"transformers",
|
||||
"tree_sitter",
|
||||
"uvicorn"
|
||||
],
|
||||
"ollama": [
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
version: '2'
|
||||
distribution_spec:
|
||||
description: Use NVIDIA NIM for running LLM inference and safety
|
||||
description: Use NVIDIA NIM for running LLM inference, evaluation and safety
|
||||
providers:
|
||||
inference:
|
||||
- remote::nvidia
|
||||
|
|
@ -13,7 +13,7 @@ distribution_spec:
|
|||
telemetry:
|
||||
- inline::meta-reference
|
||||
eval:
|
||||
- inline::meta-reference
|
||||
- remote::nvidia
|
||||
post_training:
|
||||
- remote::nvidia
|
||||
datasetio:
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ The following models are available by default:
|
|||
Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/). Use this key for the `NVIDIA_API_KEY` environment variable.
|
||||
|
||||
### Deploy NeMo Microservices Platform
|
||||
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/set-up/deploy-as-platform/index.html) for platform prerequisites and instructions to install and deploy the platform.
|
||||
The NVIDIA NeMo microservices platform supports end-to-end microservice deployment of a complete AI flywheel on your Kubernetes cluster through the NeMo Microservices Helm Chart. Please reference the [NVIDIA NeMo Microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for platform prerequisites and instructions to install and deploy the platform.
|
||||
|
||||
## Supported Services
|
||||
Each Llama Stack API corresponds to a specific NeMo microservice. The core microservices (Customizer, Evaluator, Guardrails) are exposed by the same endpoint. The platform components (Data Store) are each exposed by separate endpoints.
|
||||
|
|
@ -91,7 +91,7 @@ curl --location "$NEMO_URL/v1/deployment/model-deployments" \
|
|||
}
|
||||
}'
|
||||
```
|
||||
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/documentation/latest/nemo-microservices/latest-early_access/get-started/tutorials/deploy-nims.html#) for more information on how to deploy a NIM and verify it's available for inference.
|
||||
This NIM deployment should take approximately 10 minutes to go live. [See the docs](https://docs.nvidia.com/nemo/microservices/latest/get-started/tutorials/deploy-nims.html) for more information on how to deploy a NIM and verify it's available for inference.
|
||||
|
||||
You can also remove a deployed NIM to free up GPU resources, if needed.
|
||||
```sh
|
||||
|
|
@ -144,7 +144,3 @@ llama stack run ./run.yaml \
|
|||
--env NVIDIA_API_KEY=$NVIDIA_API_KEY \
|
||||
--env INFERENCE_MODEL=$INFERENCE_MODEL
|
||||
```
|
||||
|
||||
### Example Notebooks
|
||||
You can reference the Jupyter notebooks in `docs/notebooks/nvidia/` for example usage of these APIs.
|
||||
- [Llama_Stack_NVIDIA_E2E_Flow.ipynb](/docs/notebooks/nvidia/Llama_Stack_NVIDIA_E2E_Flow.ipynb) contains an end-to-end workflow for running inference, customizing, and evaluating models using your deployed NeMo Microservices platform.
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@
|
|||
from pathlib import Path
|
||||
|
||||
from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput, ToolGroupInput
|
||||
from llama_stack.providers.remote.eval.nvidia import NVIDIAEvalConfig
|
||||
from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig
|
||||
from llama_stack.providers.remote.inference.nvidia.models import MODEL_ENTRIES
|
||||
from llama_stack.providers.remote.safety.nvidia import NVIDIASafetyConfig
|
||||
|
|
@ -20,7 +21,7 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
"safety": ["remote::nvidia"],
|
||||
"agents": ["inline::meta-reference"],
|
||||
"telemetry": ["inline::meta-reference"],
|
||||
"eval": ["inline::meta-reference"],
|
||||
"eval": ["remote::nvidia"],
|
||||
"post_training": ["remote::nvidia"],
|
||||
"datasetio": ["inline::localfs"],
|
||||
"scoring": ["inline::basic"],
|
||||
|
|
@ -37,6 +38,11 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
provider_type="remote::nvidia",
|
||||
config=NVIDIASafetyConfig.sample_run_config(),
|
||||
)
|
||||
eval_provider = Provider(
|
||||
provider_id="nvidia",
|
||||
provider_type="remote::nvidia",
|
||||
config=NVIDIAEvalConfig.sample_run_config(),
|
||||
)
|
||||
inference_model = ModelInput(
|
||||
model_id="${env.INFERENCE_MODEL}",
|
||||
provider_id="nvidia",
|
||||
|
|
@ -60,7 +66,7 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
return DistributionTemplate(
|
||||
name="nvidia",
|
||||
distro_type="self_hosted",
|
||||
description="Use NVIDIA NIM for running LLM inference and safety",
|
||||
description="Use NVIDIA NIM for running LLM inference, evaluation and safety",
|
||||
container_image=None,
|
||||
template_path=Path(__file__).parent / "doc_template.md",
|
||||
providers=providers,
|
||||
|
|
@ -69,6 +75,7 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
"run.yaml": RunConfigSettings(
|
||||
provider_overrides={
|
||||
"inference": [inference_provider],
|
||||
"eval": [eval_provider],
|
||||
},
|
||||
default_models=default_models,
|
||||
default_tool_groups=default_tool_groups,
|
||||
|
|
@ -78,7 +85,8 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
"inference": [
|
||||
inference_provider,
|
||||
safety_provider,
|
||||
]
|
||||
],
|
||||
"eval": [eval_provider],
|
||||
},
|
||||
default_models=[inference_model, safety_model],
|
||||
default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}", provider_id="nvidia")],
|
||||
|
|
@ -90,19 +98,15 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
"",
|
||||
"NVIDIA API Key",
|
||||
),
|
||||
## Nemo Customizer related variables
|
||||
"NVIDIA_USER_ID": (
|
||||
"llama-stack-user",
|
||||
"NVIDIA User ID",
|
||||
"NVIDIA_APPEND_API_VERSION": (
|
||||
"True",
|
||||
"Whether to append the API version to the base_url",
|
||||
),
|
||||
## Nemo Customizer related variables
|
||||
"NVIDIA_DATASET_NAMESPACE": (
|
||||
"default",
|
||||
"NVIDIA Dataset Namespace",
|
||||
),
|
||||
"NVIDIA_ACCESS_POLICIES": (
|
||||
"{}",
|
||||
"NVIDIA Access Policies",
|
||||
),
|
||||
"NVIDIA_PROJECT_ID": (
|
||||
"test-project",
|
||||
"NVIDIA Project ID",
|
||||
|
|
@ -119,6 +123,10 @@ def get_distribution_template() -> DistributionTemplate:
|
|||
"http://0.0.0.0:7331",
|
||||
"URL for the NeMo Guardrails Service",
|
||||
),
|
||||
"NVIDIA_EVALUATOR_URL": (
|
||||
"http://0.0.0.0:7331",
|
||||
"URL for the NeMo Evaluator Service",
|
||||
),
|
||||
"INFERENCE_MODEL": (
|
||||
"Llama3.1-8B-Instruct",
|
||||
"Inference model",
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ providers:
|
|||
config:
|
||||
url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
|
||||
api_key: ${env.NVIDIA_API_KEY:}
|
||||
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True}
|
||||
- provider_id: nvidia
|
||||
provider_type: remote::nvidia
|
||||
config:
|
||||
|
|
@ -53,13 +54,10 @@ providers:
|
|||
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
|
||||
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db}
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
- provider_id: nvidia
|
||||
provider_type: remote::nvidia
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/meta_reference_eval.db
|
||||
evaluator_url: ${env.NVIDIA_EVALUATOR_URL:http://localhost:7331}
|
||||
post_training:
|
||||
- provider_id: nvidia
|
||||
provider_type: remote::nvidia
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ providers:
|
|||
config:
|
||||
url: ${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}
|
||||
api_key: ${env.NVIDIA_API_KEY:}
|
||||
append_api_version: ${env.NVIDIA_APPEND_API_VERSION:True}
|
||||
vector_io:
|
||||
- provider_id: faiss
|
||||
provider_type: inline::faiss
|
||||
|
|
@ -48,13 +49,10 @@ providers:
|
|||
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
|
||||
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/nvidia/trace_store.db}
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
- provider_id: nvidia
|
||||
provider_type: remote::nvidia
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/nvidia}/meta_reference_eval.db
|
||||
evaluator_url: ${env.NVIDIA_EVALUATOR_URL:http://localhost:7331}
|
||||
post_training:
|
||||
- provider_id: nvidia
|
||||
provider_type: remote::nvidia
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ The following environment variables can be configured:
|
|||
In the following sections, we'll use AMD, NVIDIA or Intel GPUs to serve as hardware accelerators for the vLLM
|
||||
server, which acts as both the LLM inference provider and the safety provider. Note that vLLM also
|
||||
[supports many other hardware accelerators](https://docs.vllm.ai/en/latest/getting_started/installation.html) and
|
||||
that we only use GPUs here for demonstration purposes.
|
||||
that we only use GPUs here for demonstration purposes. Note that if you run into issues, you can include the environment variable `--env VLLM_DEBUG_LOG_API_SERVER_RESPONSE=true` (available in vLLM v0.8.3 and above) in the `docker run` command to enable log response from API server for debugging.
|
||||
|
||||
### Setting up vLLM server on AMD GPU
|
||||
|
||||
|
|
|
|||
|
|
@ -58,7 +58,16 @@ dev = [
|
|||
"ruamel.yaml", # needed for openapi generator
|
||||
]
|
||||
# These are the dependencies required for running unit tests.
|
||||
unit = ["sqlite-vec", "openai", "aiosqlite", "aiohttp", "pypdf", "chardet", "qdrant-client"]
|
||||
unit = [
|
||||
"sqlite-vec",
|
||||
"openai",
|
||||
"aiosqlite",
|
||||
"aiohttp",
|
||||
"pypdf",
|
||||
"chardet",
|
||||
"qdrant-client",
|
||||
"opentelemetry-exporter-otlp-proto-http"
|
||||
]
|
||||
# These are the core dependencies required for running integration tests. They are shared across all
|
||||
# providers. If a provider requires additional dependencies, please add them to your environment
|
||||
# separately. If you are using "uv" to execute your tests, you can use the "--with" flag to specify extra
|
||||
|
|
|
|||
|
|
@ -98,7 +98,7 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
|
|||
|
||||
if template_func := getattr(module, "get_distribution_template", None):
|
||||
template = template_func()
|
||||
normal_deps, special_deps = get_provider_dependencies(template.providers)
|
||||
normal_deps, special_deps = get_provider_dependencies(template)
|
||||
# Combine all dependencies in order: normal deps, special deps, server deps
|
||||
all_deps = sorted(set(normal_deps + SERVER_DEPENDENCIES)) + sorted(set(special_deps))
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,9 @@
|
|||
version: '2'
|
||||
distribution_spec:
|
||||
description: Custom distro for CI tests
|
||||
providers:
|
||||
inference:
|
||||
- remote::custom_ollama
|
||||
image_type: container
|
||||
image_name: ci-test
|
||||
external_providers_dir: /tmp/providers.d
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
adapter:
|
||||
adapter_type: custom_ollama
|
||||
pip_packages: ["ollama", "aiohttp"]
|
||||
pip_packages: ["ollama", "aiohttp", "tests/external-provider/llama-stack-provider-ollama"]
|
||||
config_class: llama_stack_provider_ollama.config.OllamaImplConfig
|
||||
module: llama_stack_provider_ollama
|
||||
api_dependencies: []
|
||||
|
|
|
|||
|
|
@ -1,14 +1,10 @@
|
|||
version: '2'
|
||||
image_name: ollama
|
||||
apis:
|
||||
- agents
|
||||
- datasetio
|
||||
- eval
|
||||
- inference
|
||||
- safety
|
||||
- scoring
|
||||
- telemetry
|
||||
- tool_runtime
|
||||
- datasetio
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
|
|
@ -24,19 +20,6 @@ providers:
|
|||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/faiss_store.db
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config:
|
||||
excluded_categories: []
|
||||
agents:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db
|
||||
telemetry:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
|
|
@ -44,14 +27,6 @@ providers:
|
|||
service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
|
||||
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
|
||||
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db}
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
kvstore:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/meta_reference_eval.db
|
||||
datasetio:
|
||||
- provider_id: huggingface
|
||||
provider_type: remote::huggingface
|
||||
|
|
@ -67,17 +42,6 @@ providers:
|
|||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/localfs_datasetio.db
|
||||
scoring:
|
||||
- provider_id: basic
|
||||
provider_type: inline::basic
|
||||
config: {}
|
||||
- provider_id: llm-as-judge
|
||||
provider_type: inline::llm-as-judge
|
||||
config: {}
|
||||
- provider_id: braintrust
|
||||
provider_type: inline::braintrust
|
||||
config:
|
||||
openai_api_key: ${env.OPENAI_API_KEY:}
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
|
|
|
|||
|
|
@ -16,8 +16,9 @@ from llama_stack.distribution.utils.image_types import LlamaStackImageType
|
|||
def test_container_build_passes_path(monkeypatch, tmp_path):
|
||||
called_with = {}
|
||||
|
||||
def spy_build_image(cfg, build_file_path, image_name, template_or_config):
|
||||
def spy_build_image(cfg, build_file_path, image_name, template_or_config, run_config=None):
|
||||
called_with["path"] = template_or_config
|
||||
called_with["run_config"] = run_config
|
||||
return 0
|
||||
|
||||
monkeypatch.setattr(
|
||||
|
|
@ -36,3 +37,4 @@ def test_container_build_passes_path(monkeypatch, tmp_path):
|
|||
assert "path" in called_with
|
||||
assert isinstance(called_with["path"], str)
|
||||
assert Path(called_with["path"]).exists()
|
||||
assert called_with["run_config"] is None
|
||||
|
|
|
|||
|
|
@ -28,12 +28,15 @@ from openai.types.model import Model as OpenAIModel
|
|||
|
||||
from llama_stack.apis.inference import (
|
||||
ChatCompletionRequest,
|
||||
CompletionMessage,
|
||||
SystemMessage,
|
||||
ToolChoice,
|
||||
ToolConfig,
|
||||
ToolResponseMessage,
|
||||
UserMessage,
|
||||
)
|
||||
from llama_stack.apis.models import Model
|
||||
from llama_stack.models.llama.datatypes import StopReason
|
||||
from llama_stack.models.llama.datatypes import StopReason, ToolCall
|
||||
from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig
|
||||
from llama_stack.providers.remote.inference.vllm.vllm import (
|
||||
VLLMInferenceAdapter,
|
||||
|
|
@ -135,6 +138,49 @@ async def test_old_vllm_tool_choice(vllm_inference_adapter):
|
|||
assert request.tool_config.tool_choice == ToolChoice.none
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_tool_call_response(vllm_inference_adapter):
|
||||
"""Verify that tool call arguments from a CompletionMessage are correctly converted
|
||||
into the expected JSON format."""
|
||||
|
||||
# Patch the call to vllm so we can inspect the arguments sent were correct
|
||||
with patch.object(
|
||||
vllm_inference_adapter.client.chat.completions, "create", new_callable=AsyncMock
|
||||
) as mock_nonstream_completion:
|
||||
messages = [
|
||||
SystemMessage(content="You are a helpful assistant"),
|
||||
UserMessage(content="How many?"),
|
||||
CompletionMessage(
|
||||
content="",
|
||||
stop_reason=StopReason.end_of_turn,
|
||||
tool_calls=[
|
||||
ToolCall(
|
||||
call_id="foo",
|
||||
tool_name="knowledge_search",
|
||||
arguments={"query": "How many?"},
|
||||
arguments_json='{"query": "How many?"}',
|
||||
)
|
||||
],
|
||||
),
|
||||
ToolResponseMessage(call_id="foo", content="knowledge_search found 5...."),
|
||||
]
|
||||
await vllm_inference_adapter.chat_completion(
|
||||
"mock-model",
|
||||
messages,
|
||||
stream=False,
|
||||
tools=[],
|
||||
tool_config=ToolConfig(tool_choice=ToolChoice.auto),
|
||||
)
|
||||
|
||||
assert mock_nonstream_completion.call_args.kwargs["messages"][2]["tool_calls"] == [
|
||||
{
|
||||
"id": "foo",
|
||||
"type": "function",
|
||||
"function": {"name": "knowledge_search", "arguments": '{"query": "How many?"}'},
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_tool_call_delta_empty_tool_call_buf():
|
||||
"""
|
||||
|
|
|
|||
201
tests/unit/providers/nvidia/test_eval.py
Normal file
201
tests/unit/providers/nvidia/test_eval.py
Normal file
|
|
@ -0,0 +1,201 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import os
|
||||
import unittest
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.benchmarks import Benchmark
|
||||
from llama_stack.apis.common.job_types import Job, JobStatus
|
||||
from llama_stack.apis.eval.eval import BenchmarkConfig, EvaluateResponse, ModelCandidate, SamplingParams
|
||||
from llama_stack.models.llama.sku_types import CoreModelId
|
||||
from llama_stack.providers.remote.eval.nvidia.config import NVIDIAEvalConfig
|
||||
from llama_stack.providers.remote.eval.nvidia.eval import NVIDIAEvalImpl
|
||||
|
||||
MOCK_DATASET_ID = "default/test-dataset"
|
||||
MOCK_BENCHMARK_ID = "test-benchmark"
|
||||
|
||||
|
||||
class TestNVIDIAEvalImpl(unittest.TestCase):
|
||||
def setUp(self):
|
||||
os.environ["NVIDIA_EVALUATOR_URL"] = "http://nemo.test"
|
||||
|
||||
# Create mock APIs
|
||||
self.datasetio_api = MagicMock()
|
||||
self.datasets_api = MagicMock()
|
||||
self.scoring_api = MagicMock()
|
||||
self.inference_api = MagicMock()
|
||||
self.agents_api = MagicMock()
|
||||
|
||||
self.config = NVIDIAEvalConfig(
|
||||
evaluator_url=os.environ["NVIDIA_EVALUATOR_URL"],
|
||||
)
|
||||
|
||||
self.eval_impl = NVIDIAEvalImpl(
|
||||
config=self.config,
|
||||
datasetio_api=self.datasetio_api,
|
||||
datasets_api=self.datasets_api,
|
||||
scoring_api=self.scoring_api,
|
||||
inference_api=self.inference_api,
|
||||
agents_api=self.agents_api,
|
||||
)
|
||||
|
||||
# Mock the HTTP request methods
|
||||
self.evaluator_get_patcher = patch(
|
||||
"llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_get"
|
||||
)
|
||||
self.evaluator_post_patcher = patch(
|
||||
"llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post"
|
||||
)
|
||||
|
||||
self.mock_evaluator_get = self.evaluator_get_patcher.start()
|
||||
self.mock_evaluator_post = self.evaluator_post_patcher.start()
|
||||
|
||||
def tearDown(self):
|
||||
"""Clean up after each test."""
|
||||
self.evaluator_get_patcher.stop()
|
||||
self.evaluator_post_patcher.stop()
|
||||
|
||||
def _assert_request_body(self, expected_json):
|
||||
"""Helper method to verify request body in Evaluator POST request is correct"""
|
||||
call_args = self.mock_evaluator_post.call_args
|
||||
actual_json = call_args[0][1]
|
||||
|
||||
# Check that all expected keys contain the expected values in the actual JSON
|
||||
for key, value in expected_json.items():
|
||||
assert key in actual_json, f"Key '{key}' missing in actual JSON"
|
||||
|
||||
if isinstance(value, dict):
|
||||
for nested_key, nested_value in value.items():
|
||||
assert nested_key in actual_json[key], f"Nested key '{nested_key}' missing in actual JSON['{key}']"
|
||||
assert actual_json[key][nested_key] == nested_value, f"Value mismatch for '{key}.{nested_key}'"
|
||||
else:
|
||||
assert actual_json[key] == value, f"Value mismatch for '{key}'"
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def inject_fixtures(self, run_async):
|
||||
self.run_async = run_async
|
||||
|
||||
def test_register_benchmark(self):
|
||||
eval_config = {
|
||||
"type": "custom",
|
||||
"params": {"parallelism": 8},
|
||||
"tasks": {
|
||||
"qa": {
|
||||
"type": "completion",
|
||||
"params": {"template": {"prompt": "{{prompt}}", "max_tokens": 200}},
|
||||
"dataset": {"files_url": f"hf://datasets/{MOCK_DATASET_ID}/testing/testing.jsonl"},
|
||||
"metrics": {"bleu": {"type": "bleu", "params": {"references": ["{{ideal_response}}"]}}},
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
benchmark = Benchmark(
|
||||
provider_id="nvidia",
|
||||
type="benchmark",
|
||||
identifier=MOCK_BENCHMARK_ID,
|
||||
dataset_id=MOCK_DATASET_ID,
|
||||
scoring_functions=["basic::equality"],
|
||||
metadata=eval_config,
|
||||
)
|
||||
|
||||
# Mock Evaluator API response
|
||||
mock_evaluator_response = {"id": MOCK_BENCHMARK_ID, "status": "created"}
|
||||
self.mock_evaluator_post.return_value = mock_evaluator_response
|
||||
|
||||
# Register the benchmark
|
||||
self.run_async(self.eval_impl.register_benchmark(benchmark))
|
||||
|
||||
# Verify the Evaluator API was called correctly
|
||||
self.mock_evaluator_post.assert_called_once()
|
||||
self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config})
|
||||
|
||||
def test_run_eval(self):
|
||||
benchmark_config = BenchmarkConfig(
|
||||
eval_candidate=ModelCandidate(
|
||||
type="model",
|
||||
model=CoreModelId.llama3_1_8b_instruct.value,
|
||||
sampling_params=SamplingParams(max_tokens=100, temperature=0.7),
|
||||
)
|
||||
)
|
||||
|
||||
# Mock Evaluator API response
|
||||
mock_evaluator_response = {"id": "job-123", "status": "created"}
|
||||
self.mock_evaluator_post.return_value = mock_evaluator_response
|
||||
|
||||
# Run the Evaluation job
|
||||
result = self.run_async(
|
||||
self.eval_impl.run_eval(benchmark_id=MOCK_BENCHMARK_ID, benchmark_config=benchmark_config)
|
||||
)
|
||||
|
||||
# Verify the Evaluator API was called correctly
|
||||
self.mock_evaluator_post.assert_called_once()
|
||||
self._assert_request_body(
|
||||
{
|
||||
"config": f"nvidia/{MOCK_BENCHMARK_ID}",
|
||||
"target": {"type": "model", "model": "meta/llama-3.1-8b-instruct"},
|
||||
}
|
||||
)
|
||||
|
||||
# Verify the result
|
||||
assert isinstance(result, Job)
|
||||
assert result.job_id == "job-123"
|
||||
assert result.status == JobStatus.in_progress
|
||||
|
||||
def test_job_status(self):
|
||||
# Mock Evaluator API response
|
||||
mock_evaluator_response = {"id": "job-123", "status": "completed"}
|
||||
self.mock_evaluator_get.return_value = mock_evaluator_response
|
||||
|
||||
# Get the Evaluation job
|
||||
result = self.run_async(self.eval_impl.job_status(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
|
||||
|
||||
# Verify the result
|
||||
assert isinstance(result, Job)
|
||||
assert result.job_id == "job-123"
|
||||
assert result.status == JobStatus.completed
|
||||
|
||||
# Verify the API was called correctly
|
||||
self.mock_evaluator_get.assert_called_once_with(f"/v1/evaluation/jobs/{result.job_id}")
|
||||
|
||||
def test_job_cancel(self):
|
||||
# Mock Evaluator API response
|
||||
mock_evaluator_response = {"id": "job-123", "status": "cancelled"}
|
||||
self.mock_evaluator_post.return_value = mock_evaluator_response
|
||||
|
||||
# Cancel the Evaluation job
|
||||
self.run_async(self.eval_impl.job_cancel(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
|
||||
|
||||
# Verify the API was called correctly
|
||||
self.mock_evaluator_post.assert_called_once_with("/v1/evaluation/jobs/job-123/cancel", {})
|
||||
|
||||
def test_job_result(self):
|
||||
# Mock Evaluator API responses
|
||||
mock_job_status_response = {"id": "job-123", "status": "completed"}
|
||||
mock_job_results_response = {
|
||||
"id": "job-123",
|
||||
"status": "completed",
|
||||
"results": {MOCK_BENCHMARK_ID: {"score": 0.85, "details": {"accuracy": 0.85, "f1": 0.84}}},
|
||||
}
|
||||
self.mock_evaluator_get.side_effect = [
|
||||
mock_job_status_response, # First call to retrieve job
|
||||
mock_job_results_response, # Second call to retrieve job results
|
||||
]
|
||||
|
||||
# Get the Evaluation job results
|
||||
result = self.run_async(self.eval_impl.job_result(benchmark_id=MOCK_BENCHMARK_ID, job_id="job-123"))
|
||||
|
||||
# Verify the result
|
||||
assert isinstance(result, EvaluateResponse)
|
||||
assert MOCK_BENCHMARK_ID in result.scores
|
||||
assert result.scores[MOCK_BENCHMARK_ID].aggregated_results["results"][MOCK_BENCHMARK_ID]["score"] == 0.85
|
||||
|
||||
# Verify the API was called correctly
|
||||
assert self.mock_evaluator_get.call_count == 2
|
||||
self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123")
|
||||
self.mock_evaluator_get.assert_any_call("/v1/evaluation/jobs/job-123/results")
|
||||
|
|
@ -17,6 +17,8 @@ from llama_stack_client.types.post_training_supervised_fine_tune_params import (
|
|||
TrainingConfigOptimizerConfig,
|
||||
)
|
||||
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAConfig, NVIDIAInferenceAdapter
|
||||
from llama_stack.providers.remote.post_training.nvidia.post_training import (
|
||||
ListNvidiaPostTrainingJobs,
|
||||
NvidiaPostTrainingAdapter,
|
||||
|
|
@ -40,8 +42,22 @@ class TestNvidiaPostTraining(unittest.TestCase):
|
|||
)
|
||||
self.mock_make_request = self.make_request_patcher.start()
|
||||
|
||||
# Mock the inference client
|
||||
inference_config = NVIDIAConfig(base_url=os.environ["NVIDIA_BASE_URL"], api_key=None)
|
||||
self.inference_adapter = NVIDIAInferenceAdapter(inference_config)
|
||||
|
||||
self.mock_client = unittest.mock.MagicMock()
|
||||
self.mock_client.chat.completions.create = unittest.mock.AsyncMock()
|
||||
self.inference_mock_make_request = self.mock_client.chat.completions.create
|
||||
self.inference_make_request_patcher = patch(
|
||||
"llama_stack.providers.remote.inference.nvidia.nvidia.NVIDIAInferenceAdapter._get_client",
|
||||
return_value=self.mock_client,
|
||||
)
|
||||
self.inference_make_request_patcher.start()
|
||||
|
||||
def tearDown(self):
|
||||
self.make_request_patcher.stop()
|
||||
self.inference_make_request_patcher.stop()
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def inject_fixtures(self, run_async):
|
||||
|
|
@ -303,6 +319,31 @@ class TestNvidiaPostTraining(unittest.TestCase):
|
|||
expected_params={"job_id": job_id},
|
||||
)
|
||||
|
||||
def test_inference_register_model(self):
|
||||
model_id = "default/job-1234"
|
||||
model_type = ModelType.llm
|
||||
model = Model(
|
||||
identifier=model_id,
|
||||
provider_id="nvidia",
|
||||
provider_model_id=model_id,
|
||||
provider_resource_id=model_id,
|
||||
model_type=model_type,
|
||||
)
|
||||
result = self.run_async(self.inference_adapter.register_model(model))
|
||||
assert result == model
|
||||
assert len(self.inference_adapter.alias_to_provider_id_map) > 1
|
||||
assert self.inference_adapter.get_provider_model_id(model.provider_model_id) == model_id
|
||||
|
||||
with patch.object(self.inference_adapter, "chat_completion") as mock_chat_completion:
|
||||
self.run_async(
|
||||
self.inference_adapter.chat_completion(
|
||||
model_id=model_id,
|
||||
messages=[{"role": "user", "content": "Hello, model"}],
|
||||
)
|
||||
)
|
||||
|
||||
mock_chat_completion.assert_called()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
|
|
|||
43
tests/unit/providers/utils/inference/test_openai_compat.py
Normal file
43
tests/unit/providers/utils/inference/test_openai_compat.py
Normal file
|
|
@ -0,0 +1,43 @@
|
|||
# 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
|
||||
|
||||
from llama_stack.apis.common.content_types import TextContentItem
|
||||
from llama_stack.apis.inference.inference import CompletionMessage, UserMessage
|
||||
from llama_stack.models.llama.datatypes import StopReason, ToolCall
|
||||
from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_convert_message_to_openai_dict():
|
||||
message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user")
|
||||
assert await convert_message_to_openai_dict(message) == {
|
||||
"role": "user",
|
||||
"content": [{"type": "text", "text": "Hello, world!"}],
|
||||
}
|
||||
|
||||
|
||||
# Test convert_message_to_openai_dict with a tool call
|
||||
@pytest.mark.asyncio
|
||||
async def test_convert_message_to_openai_dict_with_tool_call():
|
||||
message = CompletionMessage(
|
||||
content="",
|
||||
tool_calls=[
|
||||
ToolCall(call_id="123", tool_name="test_tool", arguments_json='{"foo": "bar"}', arguments={"foo": "bar"})
|
||||
],
|
||||
stop_reason=StopReason.end_of_turn,
|
||||
)
|
||||
|
||||
openai_dict = await convert_message_to_openai_dict(message)
|
||||
|
||||
assert openai_dict == {
|
||||
"role": "assistant",
|
||||
"content": [{"type": "text", "text": ""}],
|
||||
"tool_calls": [
|
||||
{"id": "123", "type": "function", "function": {"name": "test_tool", "arguments": '{"foo": "bar"}'}}
|
||||
],
|
||||
}
|
||||
91
tests/unit/server/test_sse.py
Normal file
91
tests/unit/server/test_sse.py
Normal file
|
|
@ -0,0 +1,91 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import asyncio
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.distribution.server.server import create_sse_event, sse_generator
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sse_generator_basic():
|
||||
# An AsyncIterator wrapped in an Awaitable, just like our web methods
|
||||
async def async_event_gen():
|
||||
async def event_gen():
|
||||
yield "Test event 1"
|
||||
yield "Test event 2"
|
||||
|
||||
return event_gen()
|
||||
|
||||
sse_gen = sse_generator(async_event_gen())
|
||||
assert sse_gen is not None
|
||||
|
||||
# Test that the events are streamed correctly
|
||||
seen_events = []
|
||||
async for event in sse_gen:
|
||||
seen_events.append(event)
|
||||
assert len(seen_events) == 2
|
||||
assert seen_events[0] == create_sse_event("Test event 1")
|
||||
assert seen_events[1] == create_sse_event("Test event 2")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sse_generator_client_disconnected():
|
||||
# An AsyncIterator wrapped in an Awaitable, just like our web methods
|
||||
async def async_event_gen():
|
||||
async def event_gen():
|
||||
yield "Test event 1"
|
||||
# Simulate a client disconnect before emitting event 2
|
||||
raise asyncio.CancelledError()
|
||||
|
||||
return event_gen()
|
||||
|
||||
sse_gen = sse_generator(async_event_gen())
|
||||
assert sse_gen is not None
|
||||
|
||||
seen_events = []
|
||||
async for event in sse_gen:
|
||||
seen_events.append(event)
|
||||
|
||||
# We should see 1 event before the client disconnected
|
||||
assert len(seen_events) == 1
|
||||
assert seen_events[0] == create_sse_event("Test event 1")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sse_generator_client_disconnected_before_response_starts():
|
||||
# Disconnect before the response starts
|
||||
async def async_event_gen():
|
||||
raise asyncio.CancelledError()
|
||||
|
||||
sse_gen = sse_generator(async_event_gen())
|
||||
assert sse_gen is not None
|
||||
|
||||
seen_events = []
|
||||
async for event in sse_gen:
|
||||
seen_events.append(event)
|
||||
|
||||
# No events should be seen since the client disconnected immediately
|
||||
assert len(seen_events) == 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_sse_generator_error_before_response_starts():
|
||||
# Raise an error before the response starts
|
||||
async def async_event_gen():
|
||||
raise Exception("Test error")
|
||||
|
||||
sse_gen = sse_generator(async_event_gen())
|
||||
assert sse_gen is not None
|
||||
|
||||
seen_events = []
|
||||
async for event in sse_gen:
|
||||
seen_events.append(event)
|
||||
|
||||
# We should have 1 error event
|
||||
assert len(seen_events) == 1
|
||||
assert 'data: {"error":' in seen_events[0]
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
# Test Results Report
|
||||
|
||||
*Generated on: 2025-04-16 15:10:57*
|
||||
*Generated on: 2025-04-17 12:42:33*
|
||||
|
||||
*This report was generated by running `python tests/verifications/generate_report.py`*
|
||||
|
||||
|
|
@ -15,22 +15,74 @@
|
|||
|
||||
| Provider | Pass Rate | Tests Passed | Total Tests |
|
||||
| --- | --- | --- | --- |
|
||||
| Together | 51.3% | 39 | 76 |
|
||||
| Fireworks | 47.4% | 36 | 76 |
|
||||
| Openai | 100.0% | 52 | 52 |
|
||||
| Meta_reference | 100.0% | 28 | 28 |
|
||||
| Together | 50.0% | 40 | 80 |
|
||||
| Fireworks | 50.0% | 40 | 80 |
|
||||
| Openai | 100.0% | 56 | 56 |
|
||||
|
||||
|
||||
|
||||
## Meta_reference
|
||||
|
||||
*Tests run on: 2025-04-17 12:37:11*
|
||||
|
||||
```bash
|
||||
# Run all tests for this provider:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=meta_reference -v
|
||||
|
||||
# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=meta_reference -k "test_chat_multi_turn_multiple_images and stream=False"
|
||||
```
|
||||
|
||||
|
||||
**Model Key (Meta_reference)**
|
||||
|
||||
| Display Name | Full Model ID |
|
||||
| --- | --- |
|
||||
| Llama-4-Scout-Instruct | `meta-llama/Llama-4-Scout-17B-16E-Instruct` |
|
||||
|
||||
|
||||
| Test | Llama-4-Scout-Instruct |
|
||||
| --- | --- |
|
||||
| test_chat_multi_turn_multiple_images (stream=False) | ✅ |
|
||||
| test_chat_multi_turn_multiple_images (stream=True) | ✅ |
|
||||
| test_chat_non_streaming_basic (earth) | ✅ |
|
||||
| test_chat_non_streaming_basic (saturn) | ✅ |
|
||||
| test_chat_non_streaming_image | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ |
|
||||
| test_chat_non_streaming_structured_output (calendar) | ✅ |
|
||||
| test_chat_non_streaming_structured_output (math) | ✅ |
|
||||
| test_chat_non_streaming_tool_calling | ✅ |
|
||||
| test_chat_non_streaming_tool_choice_none | ✅ |
|
||||
| test_chat_non_streaming_tool_choice_required | ✅ |
|
||||
| test_chat_streaming_basic (earth) | ✅ |
|
||||
| test_chat_streaming_basic (saturn) | ✅ |
|
||||
| test_chat_streaming_image | ✅ |
|
||||
| test_chat_streaming_multi_turn_tool_calling (add_product_tool) | ✅ |
|
||||
| test_chat_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ |
|
||||
| test_chat_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ |
|
||||
| test_chat_streaming_multi_turn_tool_calling (text_then_weather_tool) | ✅ |
|
||||
| test_chat_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ |
|
||||
| test_chat_streaming_structured_output (calendar) | ✅ |
|
||||
| test_chat_streaming_structured_output (math) | ✅ |
|
||||
| test_chat_streaming_tool_calling | ✅ |
|
||||
| test_chat_streaming_tool_choice_none | ✅ |
|
||||
| test_chat_streaming_tool_choice_required | ✅ |
|
||||
|
||||
## Together
|
||||
|
||||
*Tests run on: 2025-04-16 15:03:51*
|
||||
*Tests run on: 2025-04-17 12:27:45*
|
||||
|
||||
```bash
|
||||
# Run all tests for this provider:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -v
|
||||
|
||||
# Example: Run only the 'earth' case of test_chat_non_streaming_basic:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -k "test_chat_non_streaming_basic and earth"
|
||||
# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=together -k "test_chat_multi_turn_multiple_images and stream=False"
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -45,12 +97,14 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=togethe
|
|||
|
||||
| Test | Llama-3.3-70B-Instruct | Llama-4-Maverick-Instruct | Llama-4-Scout-Instruct |
|
||||
| --- | --- | --- | --- |
|
||||
| test_chat_multi_turn_multiple_images (stream=False) | ⚪ | ✅ | ✅ |
|
||||
| test_chat_multi_turn_multiple_images (stream=True) | ⚪ | ❌ | ❌ |
|
||||
| test_chat_non_streaming_basic (earth) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_basic (saturn) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_image | ⚪ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (add_product_tool) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (compare_monthly_expense_tool) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (get_then_create_event_tool) | ✅ | ❌ | ✅ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (text_then_weather_tool) | ❌ | ❌ | ❌ |
|
||||
| test_chat_non_streaming_multi_turn_tool_calling (weather_tool_then_text) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_structured_output (calendar) | ✅ | ✅ | ✅ |
|
||||
|
|
@ -74,14 +128,14 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=togethe
|
|||
|
||||
## Fireworks
|
||||
|
||||
*Tests run on: 2025-04-16 15:05:54*
|
||||
*Tests run on: 2025-04-17 12:29:53*
|
||||
|
||||
```bash
|
||||
# Run all tests for this provider:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -v
|
||||
|
||||
# Example: Run only the 'earth' case of test_chat_non_streaming_basic:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -k "test_chat_non_streaming_basic and earth"
|
||||
# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=fireworks -k "test_chat_multi_turn_multiple_images and stream=False"
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -96,6 +150,8 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=firewor
|
|||
|
||||
| Test | Llama-3.3-70B-Instruct | Llama-4-Maverick-Instruct | Llama-4-Scout-Instruct |
|
||||
| --- | --- | --- | --- |
|
||||
| test_chat_multi_turn_multiple_images (stream=False) | ⚪ | ✅ | ✅ |
|
||||
| test_chat_multi_turn_multiple_images (stream=True) | ⚪ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_basic (earth) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_basic (saturn) | ✅ | ✅ | ✅ |
|
||||
| test_chat_non_streaming_image | ⚪ | ✅ | ✅ |
|
||||
|
|
@ -125,14 +181,14 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=firewor
|
|||
|
||||
## Openai
|
||||
|
||||
*Tests run on: 2025-04-16 15:09:18*
|
||||
*Tests run on: 2025-04-17 12:34:08*
|
||||
|
||||
```bash
|
||||
# Run all tests for this provider:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -v
|
||||
|
||||
# Example: Run only the 'earth' case of test_chat_non_streaming_basic:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -k "test_chat_non_streaming_basic and earth"
|
||||
# Example: Run only the 'stream=False' case of test_chat_multi_turn_multiple_images:
|
||||
pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai -k "test_chat_multi_turn_multiple_images and stream=False"
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -146,6 +202,8 @@ pytest tests/verifications/openai_api/test_chat_completion.py --provider=openai
|
|||
|
||||
| Test | gpt-4o | gpt-4o-mini |
|
||||
| --- | --- | --- |
|
||||
| test_chat_multi_turn_multiple_images (stream=False) | ✅ | ✅ |
|
||||
| test_chat_multi_turn_multiple_images (stream=True) | ✅ | ✅ |
|
||||
| test_chat_non_streaming_basic (earth) | ✅ | ✅ |
|
||||
| test_chat_non_streaming_basic (saturn) | ✅ | ✅ |
|
||||
| test_chat_non_streaming_image | ✅ | ✅ |
|
||||
|
|
|
|||
|
|
@ -8,3 +8,4 @@ test_exclusions:
|
|||
llama-3.3-70b:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
fireworks/llama-v3p3-70b-instruct:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
accounts/fireworks/models/llama-v3p3-70b-instruct:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
groq/llama-3.3-70b-versatile:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
llama-3.3-70b-versatile:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
8
tests/verifications/conf/meta_reference.yaml
Normal file
8
tests/verifications/conf/meta_reference.yaml
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
# LLAMA_STACK_PORT=5002 llama stack run meta-reference-gpu --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct --env INFERENCE_CHECKPOINT_DIR=<path_to_ckpt>
|
||||
base_url: http://localhost:5002/v1/openai/v1
|
||||
api_key_var: foo
|
||||
models:
|
||||
- meta-llama/Llama-4-Scout-17B-16E-Instruct
|
||||
model_display_names:
|
||||
meta-llama/Llama-4-Scout-17B-16E-Instruct: Llama-4-Scout-Instruct
|
||||
test_exclusions: {}
|
||||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
together/meta-llama/Llama-3.3-70B-Instruct-Turbo:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -12,3 +12,4 @@ test_exclusions:
|
|||
meta-llama/Llama-3.3-70B-Instruct-Turbo:
|
||||
- test_chat_non_streaming_image
|
||||
- test_chat_streaming_image
|
||||
- test_chat_multi_turn_multiple_images
|
||||
|
|
|
|||
|
|
@ -60,6 +60,7 @@ RESULTS_DIR.mkdir(exist_ok=True)
|
|||
MAX_RESULTS_PER_PROVIDER = 1
|
||||
|
||||
DEFAULT_PROVIDERS = [
|
||||
"meta_reference",
|
||||
"together",
|
||||
"fireworks",
|
||||
"openai",
|
||||
|
|
|
|||
BIN
tests/verifications/openai_api/fixtures/images/vision_test_1.jpg
Normal file
BIN
tests/verifications/openai_api/fixtures/images/vision_test_1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 108 KiB |
BIN
tests/verifications/openai_api/fixtures/images/vision_test_2.jpg
Normal file
BIN
tests/verifications/openai_api/fixtures/images/vision_test_2.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 148 KiB |
BIN
tests/verifications/openai_api/fixtures/images/vision_test_3.jpg
Normal file
BIN
tests/verifications/openai_api/fixtures/images/vision_test_3.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 139 KiB |
|
|
@ -15,6 +15,52 @@ test_chat_basic:
|
|||
S?
|
||||
role: user
|
||||
output: Saturn
|
||||
test_chat_input_validation:
|
||||
test_name: test_chat_input_validation
|
||||
test_params:
|
||||
case:
|
||||
- case_id: "messages_missing"
|
||||
input:
|
||||
messages: []
|
||||
output:
|
||||
error:
|
||||
status_code: 400
|
||||
- case_id: "messages_role_invalid"
|
||||
input:
|
||||
messages:
|
||||
- content: Which planet do humans live on?
|
||||
role: fake_role
|
||||
output:
|
||||
error:
|
||||
status_code: 400
|
||||
- case_id: "tool_choice_invalid"
|
||||
input:
|
||||
messages:
|
||||
- content: Which planet do humans live on?
|
||||
role: user
|
||||
tool_choice: invalid
|
||||
output:
|
||||
error:
|
||||
status_code: 400
|
||||
- case_id: "tool_choice_no_tools"
|
||||
input:
|
||||
messages:
|
||||
- content: Which planet do humans live on?
|
||||
role: user
|
||||
tool_choice: required
|
||||
output:
|
||||
error:
|
||||
status_code: 400
|
||||
- case_id: "tools_type_invalid"
|
||||
input:
|
||||
messages:
|
||||
- content: Which planet do humans live on?
|
||||
role: user
|
||||
tools:
|
||||
- type: invalid
|
||||
output:
|
||||
error:
|
||||
status_code: 400
|
||||
test_chat_image:
|
||||
test_name: test_chat_image
|
||||
test_params:
|
||||
|
|
|
|||
|
|
@ -4,19 +4,26 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import base64
|
||||
import copy
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from openai import APIError
|
||||
from pydantic import BaseModel
|
||||
|
||||
from tests.verifications.openai_api.fixtures.fixtures import _load_all_verification_configs
|
||||
from tests.verifications.openai_api.fixtures.fixtures import (
|
||||
_load_all_verification_configs,
|
||||
)
|
||||
from tests.verifications.openai_api.fixtures.load import load_test_cases
|
||||
|
||||
chat_completion_test_cases = load_test_cases("chat_completion")
|
||||
|
||||
THIS_DIR = Path(__file__).parent
|
||||
|
||||
|
||||
def case_id_generator(case):
|
||||
"""Generate a test ID from the case's 'case_id' field, or use a default."""
|
||||
|
|
@ -69,6 +76,21 @@ def get_base_test_name(request):
|
|||
return request.node.originalname
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def multi_image_data():
|
||||
files = [
|
||||
THIS_DIR / "fixtures/images/vision_test_1.jpg",
|
||||
THIS_DIR / "fixtures/images/vision_test_2.jpg",
|
||||
THIS_DIR / "fixtures/images/vision_test_3.jpg",
|
||||
]
|
||||
encoded_files = []
|
||||
for file in files:
|
||||
with open(file, "rb") as image_file:
|
||||
base64_data = base64.b64encode(image_file.read()).decode("utf-8")
|
||||
encoded_files.append(f"data:image/jpeg;base64,{base64_data}")
|
||||
return encoded_files
|
||||
|
||||
|
||||
# --- Test Functions ---
|
||||
|
||||
|
||||
|
|
@ -115,6 +137,50 @@ def test_chat_streaming_basic(request, openai_client, model, provider, verificat
|
|||
assert case["output"].lower() in content.lower()
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"case",
|
||||
chat_completion_test_cases["test_chat_input_validation"]["test_params"]["case"],
|
||||
ids=case_id_generator,
|
||||
)
|
||||
def test_chat_non_streaming_error_handling(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.")
|
||||
|
||||
with pytest.raises(APIError) as e:
|
||||
openai_client.chat.completions.create(
|
||||
model=model,
|
||||
messages=case["input"]["messages"],
|
||||
stream=False,
|
||||
tool_choice=case["input"]["tool_choice"] if "tool_choice" in case["input"] else None,
|
||||
tools=case["input"]["tools"] if "tools" in case["input"] else None,
|
||||
)
|
||||
assert case["output"]["error"]["status_code"] == e.value.status_code
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"case",
|
||||
chat_completion_test_cases["test_chat_input_validation"]["test_params"]["case"],
|
||||
ids=case_id_generator,
|
||||
)
|
||||
def test_chat_streaming_error_handling(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.")
|
||||
|
||||
with pytest.raises(APIError) as e:
|
||||
response = openai_client.chat.completions.create(
|
||||
model=model,
|
||||
messages=case["input"]["messages"],
|
||||
stream=True,
|
||||
tool_choice=case["input"]["tool_choice"] if "tool_choice" in case["input"] else None,
|
||||
tools=case["input"]["tools"] if "tools" in case["input"] else None,
|
||||
)
|
||||
for _chunk in response:
|
||||
pass
|
||||
assert str(case["output"]["error"]["status_code"]) in e.value.message
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"case",
|
||||
chat_completion_test_cases["test_chat_image"]["test_params"]["case"],
|
||||
|
|
@ -272,7 +338,6 @@ def test_chat_non_streaming_tool_choice_required(request, openai_client, model,
|
|||
tool_choice="required", # Force tool call
|
||||
stream=False,
|
||||
)
|
||||
print(response)
|
||||
|
||||
assert response.choices[0].message.role == "assistant"
|
||||
assert len(response.choices[0].message.tool_calls) > 0, "Expected tool call when tool_choice='required'"
|
||||
|
|
@ -532,6 +597,86 @@ def test_chat_streaming_multi_turn_tool_calling(request, openai_client, model, p
|
|||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("stream", [False, True], ids=["stream=False", "stream=True"])
|
||||
def test_chat_multi_turn_multiple_images(
|
||||
request, openai_client, model, provider, verification_config, multi_image_data, stream
|
||||
):
|
||||
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.")
|
||||
|
||||
messages_turn1 = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": multi_image_data[0],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": multi_image_data[1],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "text",
|
||||
"text": "What furniture is in the first image that is not in the second image?",
|
||||
},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
# First API call
|
||||
response1 = openai_client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages_turn1,
|
||||
stream=stream,
|
||||
)
|
||||
if stream:
|
||||
message_content1 = ""
|
||||
for chunk in response1:
|
||||
message_content1 += chunk.choices[0].delta.content or ""
|
||||
else:
|
||||
message_content1 = response1.choices[0].message.content
|
||||
assert len(message_content1) > 0
|
||||
assert any(expected in message_content1.lower().strip() for expected in {"chair", "table"}), message_content1
|
||||
|
||||
# Prepare messages for the second turn
|
||||
messages_turn2 = messages_turn1 + [
|
||||
{"role": "assistant", "content": message_content1},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": multi_image_data[2],
|
||||
},
|
||||
},
|
||||
{"type": "text", "text": "What is in this image that is also in the first image?"},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
# Second API call
|
||||
response2 = openai_client.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages_turn2,
|
||||
stream=stream,
|
||||
)
|
||||
if stream:
|
||||
message_content2 = ""
|
||||
for chunk in response2:
|
||||
message_content2 += chunk.choices[0].delta.content or ""
|
||||
else:
|
||||
message_content2 = response2.choices[0].message.content
|
||||
assert len(message_content2) > 0
|
||||
assert any(expected in message_content2.lower().strip() for expected in {"bed"}), message_content2
|
||||
|
||||
|
||||
# --- Helper functions (structured output validation) ---
|
||||
|
||||
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
1097
tests/verifications/test_results/meta_reference.json
Normal file
1097
tests/verifications/test_results/meta_reference.json
Normal file
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
File diff suppressed because one or more lines are too long
2
uv.lock
generated
2
uv.lock
generated
|
|
@ -1458,6 +1458,7 @@ unit = [
|
|||
{ name = "aiosqlite" },
|
||||
{ name = "chardet" },
|
||||
{ name = "openai" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http" },
|
||||
{ name = "pypdf" },
|
||||
{ name = "qdrant-client" },
|
||||
{ name = "sqlite-vec" },
|
||||
|
|
@ -1491,6 +1492,7 @@ requires-dist = [
|
|||
{ name = "openai", marker = "extra == 'test'" },
|
||||
{ name = "openai", marker = "extra == 'unit'" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", marker = "extra == 'test'" },
|
||||
{ name = "opentelemetry-exporter-otlp-proto-http", marker = "extra == 'unit'" },
|
||||
{ name = "opentelemetry-sdk", marker = "extra == 'test'" },
|
||||
{ name = "pandas", marker = "extra == 'ui'" },
|
||||
{ name = "pillow" },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue