llama-stack-mirror/tests/unit
Cesare Pompeiano fe517f1ac7
feat: Add vector_db_id to chunk metadata (#3304)
# What does this PR do?

When running RAG in a multi vector DB setting, it can be difficult to
trace where retrieved chunks originate from. This PR adds the
`vector_db_id` into each chunk’s metadata, making it easier to
understand which database a given chunk came from. This is helpful for
debugging and for analyzing retrieval behavior of multiple DBs.

Relevant code:

```python
for vector_db_id, result in zip(vector_db_ids, results):
    for chunk, score in zip(result.chunks, result.scores):
        if not hasattr(chunk, "metadata") or chunk.metadata is None:
            chunk.metadata = {}
        chunk.metadata["vector_db_id"] = vector_db_id

        chunks.append(chunk)
        scores.append(score)
```

## Test Plan

* Ran Llama Stack in debug mode.
* Verified that `vector_db_id` was added to each chunk’s metadata.
* Confirmed that the metadata was printed in the console when using the
RAG tool.

---------

Co-authored-by: are-ces <cpompeia@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
2025-09-10 13:40:27 +02:00
..
cli chore(rename): move llama_stack.distribution to llama_stack.core (#2975) 2025-07-30 23:30:53 -07:00
distribution feat!: Migrate Vector DB IDs to Vector Store IDs (breaking change) (#3253) 2025-09-05 15:40:34 +02:00
files chore(files tests): update files integration tests and fix inline::localfs (#3195) 2025-08-20 14:22:40 -04:00
models chore(test): migrate unit tests from unittest to pytest for system prompt (#2789) 2025-07-18 11:54:02 +02:00
prompts/prompts feat: Adding OpenAI Prompts API (#3319) 2025-09-08 11:05:13 -04:00
providers chore: update the groq inference impl to use openai-python for openai-compat functions (#3348) 2025-09-06 15:36:27 -07:00
rag feat: Add vector_db_id to chunk metadata (#3304) 2025-09-10 13:40:27 +02:00
registry chore(rename): move llama_stack.distribution to llama_stack.core (#2975) 2025-07-30 23:30:53 -07:00
server feat: Add Kubernetes auth provider to use SelfSubjectReview and kubernetes api server (#2559) 2025-09-08 11:25:10 +02:00
utils chore(dev): add inequality support to sqlstore where clause (#3272) 2025-08-28 14:49:36 -07:00
__init__.py chore: Add fixtures to conftest.py (#2067) 2025-05-06 13:57:48 +02:00
conftest.py chore: block network access from unit tests (#2732) 2025-07-12 16:53:54 -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