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# What does this PR do?
This change migrates the VectorDB id generation to Vector Stores.
This is a breaking change for **_some users_** that may have application
code using the `vector_db_id` parameter in the request of the VectorDB
protocol instead of the `VectorDB.identifier` in the response.
By default we will now create a Vector Store every time we register a
VectorDB. The caveat with this approach is that this maps the
`vector_db_id` → `vector_store.name`. This is a reasonable tradeoff to
transition users towards OpenAI Vector Stores.
As an added benefit, registering VectorDBs will result in them appearing
in the VectorStores admin UI.
### Why?
This PR makes the `POST` API call to `/v1/vector-dbs` swap the
`vector_db_id` parameter in the **request body** into the VectorStore's
name field and sets the `vector_db_id` to the generated vector store id
(e.g., `vs_038247dd-4bbb-4dbb-a6be-d5ecfd46cfdb`).
That means that users would have to do something like follows in their
application code:
```python
res = client.vector_dbs.register(
vector_db_id='my-vector-db-id',
embedding_model='ollama/all-minilm:l6-v2',
embedding_dimension=384,
)
vector_db_id = res.identifier
```
And then the rest of their code would behave, including `VectorIO`'s
insert protocol using `vector_db_id` in the request.
An alternative implementation would be to just delete the `vector_db_id`
parameter in `VectorDB` but the end result would still require users
having to write `vector_db_id = res.identifier` since
`VectorStores.create()` generates the ID for you.
So this approach felt the easiest way to migrate users towards
VectorStores (subsequent PRs will be added to trigger `files.create()`
and `vector_stores.files.create()`).
## Test Plan
Unit tests and integration tests have been added.
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
|
||
|---|---|---|
| .. | ||
| cli | ||
| distribution | ||
| files | ||
| models | ||
| providers | ||
| rag | ||
| registry | ||
| server | ||
| utils | ||
| __init__.py | ||
| conftest.py | ||
| fixtures.py | ||
| README.md | ||
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
- Python Environment: Ensure you have Python 3.12+ installed
- uv Package Manager: Install
uvif 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=autofor 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_VERSIONenvironment 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