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feat: configure vector-io provider with an embedding model

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Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
This commit is contained in:
Mustafa Elbehery 2025-07-31 13:07:03 +02:00
parent 1f0766308d
commit d8f013b35a
29 changed files with 228 additions and 24 deletions
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10
llama_stack/providers/inline/vector_io/chroma/config.py
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@ -9,6 +9,7 @@ from typing import Any
from pydantic import BaseModel, Field
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
from llama_stack.providers.utils.vector_io.embedding_config import EmbeddingConfig
from llama_stack.schema_utils import json_schema_type
@ -16,6 +17,10 @@ from llama_stack.schema_utils import json_schema_type
class ChromaVectorIOConfig(BaseModel):
db_path: str
kvstore: KVStoreConfig = Field(description="Config for KV store backend")
embedding: EmbeddingConfig | None = Field(
default=None,
description="Default embedding configuration for this provider. When specified, vector databases created with this provider will use these embedding settings as defaults.",
)
@classmethod
def sample_run_config(
@ -27,4 +32,9 @@ class ChromaVectorIOConfig(BaseModel):
__distro_dir__=__distro_dir__,
db_name="chroma_inline_registry.db",
),
# Optional: Configure default embedding model for this provider
# "embedding": {
# "model": "${env.CHROMA_EMBEDDING_MODEL:=all-MiniLM-L6-v2}",
# "dimensions": 384
# },
}