llama-stack-mirror/tests/unit
mergify[bot] 05b4394cf9
fix: enforce allowed_models during inference requests (backport #4197) (#4228)
The `allowed_models` configuration was only being applied when listing
models via the `/v1/models` endpoint, but the actual inference requests
weren't checking this restriction. This meant users could directly
request any model the provider supports by specifying it in their
inference call, completely bypassing the intended cost controls.

The fix adds validation to all three inference methods (chat
completions, completions, and embeddings) that checks the requested
model against the allowed_models list before making the provider API
call.

### Test plan

Added unit tests <hr>This is an automatic backport of pull request #4197
done by [Mergify](https://mergify.com).

Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
2025-11-24 11:31:36 -08:00
..
cli chore(release-0.3.x): handle missing external_providers_dir (#4011) 2025-10-31 12:55:34 -07:00
conversations feat(stores)!: use backend storage references instead of configs (#3697) 2025-10-20 13:20:09 -07:00
core feat(cherry-pick): fixes for 0.3.1 release (#3998) 2025-10-30 21:51:42 -07:00
distribution chore: remove build.py (#3869) 2025-10-20 16:28:15 -07:00
files feat(stores)!: use backend storage references instead of configs (#3697) 2025-10-20 13:20:09 -07:00
models feat(tools)!: substantial clean up of "Tool" related datatypes (#3627) 2025-10-02 15:12:03 -07:00
prompts/prompts feat(stores)!: use backend storage references instead of configs (#3697) 2025-10-20 13:20:09 -07:00
providers fix: enforce allowed_models during inference requests (backport #4197) (#4228) 2025-11-24 11:31:36 -08:00
rag revert: "chore(cleanup)!: remove tool_runtime.rag_tool" (#3877) 2025-10-21 11:22:06 -07:00
registry chore(cleanup)!: kill vector_db references as far as possible (#3864) 2025-10-20 20:06:16 -07:00
server chore(cleanup)!: kill vector_db references as far as possible (#3864) 2025-10-20 20:06:16 -07:00
tools feat(tools)!: substantial clean up of "Tool" related datatypes (#3627) 2025-10-02 15:12:03 -07:00
utils feat(stores)!: use backend storage references instead of configs (#3697) 2025-10-20 13:20:09 -07:00
__init__.py chore: Add fixtures to conftest.py (#2067) 2025-05-06 13:57:48 +02:00
conftest.py fix(tests): reduce some test noise (#3825) 2025-10-16 09:52:16 -07:00
fixtures.py chore(rename): move llama_stack.distribution to llama_stack.core (#2975) 2025-07-30 23:30:53 -07:00
README.md test: Measure and track code coverage (#2636) 2025-07-18 18:08:36 +02:00

Llama Stack Unit Tests

Unit Tests

Unit tests verify individual components and functions in isolation. They are fast, reliable, and don't require external services.

Prerequisites

  1. Python Environment: Ensure you have Python 3.12+ installed
  2. uv Package Manager: Install uv if not already installed

You can run the unit tests by running:

./scripts/unit-tests.sh [PYTEST_ARGS]

Any additional arguments are passed to pytest. For example, you can specify a test directory, a specific test file, or any pytest flags (e.g., -vvv for verbosity). If no test directory is specified, it defaults to "tests/unit", e.g:

./scripts/unit-tests.sh tests/unit/registry/test_registry.py -vvv

If you'd like to run for a non-default version of Python (currently 3.12), pass PYTHON_VERSION variable as follows:

source .venv/bin/activate
PYTHON_VERSION=3.13 ./scripts/unit-tests.sh

Test Configuration

  • Test Discovery: Tests are automatically discovered in the tests/unit/ directory
  • Async Support: Tests use --asyncio-mode=auto for automatic async test handling
  • Coverage: Tests generate coverage reports in htmlcov/ directory
  • Python Version: Defaults to Python 3.12, but can be overridden with PYTHON_VERSION environment variable

Coverage Reports

After running tests, you can view coverage reports:

# Open HTML coverage report in browser
open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux
start htmlcov/index.html  # Windows