This change lets users configure default embedding models at the provider level instead of always relying on system defaults. Each vector store provider can now specify an embedding_model and optional embedding_dimension in their config.
Key features:
- Auto-dimension lookup for standard models from the registry
- Support for Matryoshka embeddings with custom dimensions
- Three-tier priority: explicit params > provider config > system fallback
- Full backward compatibility - existing setups work unchanged
- Comprehensive test coverage with 20 test cases
Updated all vector IO providers (FAISS, Chroma, Milvus, Qdrant, etc.) with the new config fields and added detailed documentation with examples.
Fixes#2729
# What does this PR do?
https://github.com/meta-llama/llama-stack/pull/2490 broke postgres_demo,
as the config expected a str but the value was converted to int.
This PR:
1. Updates the type of port in sqlstore to be int
2. template generation uses `dict` instead of `StackRunConfig` so as to
avoid failing pydantic typechecks.
3. Adds `replace_env_vars` to StackRunConfig instantiation in
`configure.py` (not sure why this wasn't needed before).
## Test Plan
`llama stack build --template postgres_demo --image-type conda --run`
# What does this PR do?
Simple approach to get some provider pages in the docs.
Add or update description fields in the provider configuration class
using Pydantic’s Field, ensuring these descriptions are clear and
complete, as they will be used to auto-generate provider documentation
via ./scripts/distro_codegen.py instead of editing the docs manually.
Signed-off-by: Sébastien Han <seb@redhat.com>
