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chore: Updating how default embedding model is set in stack (#3818)
# What does this PR do?
Refactor setting default vector store provider and embedding model to
use an optional `vector_stores` config in the `StackRunConfig` and clean
up code to do so (had to add back in some pieces of VectorDB). Also
added remote Qdrant and Weaviate to starter distro (based on other PR
where inference providers were added for UX).
New config is simply (default for Starter distro):
```yaml
vector_stores:
default_provider_id: faiss
default_embedding_model:
provider_id: sentence-transformers
model_id: nomic-ai/nomic-embed-text-v1.5
```
## Test Plan
CI and Unit tests.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This commit is contained in:
Francisco Arceo
GitHub
parent
2c43285e22
commit
48581bf651
48 changed files with 973 additions and 818 deletions
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@ -134,12 +134,15 @@ class CommonRoutingTableImpl(RoutingTable):
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from .scoring_functions import ScoringFunctionsRoutingTable
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from .shields import ShieldsRoutingTable
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from .toolgroups import ToolGroupsRoutingTable
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from .vector_dbs import VectorDBsRoutingTable
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def apiname_object():
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if isinstance(self, ModelsRoutingTable):
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return ("Inference", "model")
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elif isinstance(self, ShieldsRoutingTable):
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return ("Safety", "shield")
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elif isinstance(self, VectorDBsRoutingTable):
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return ("VectorIO", "vector_db")
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elif isinstance(self, DatasetsRoutingTable):
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return ("DatasetIO", "dataset")
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elif isinstance(self, ScoringFunctionsRoutingTable):
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323
llama_stack/core/routing_tables/vector_dbs.py
Normal file
323
llama_stack/core/routing_tables/vector_dbs.py
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@ -0,0 +1,323 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any
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from pydantic import TypeAdapter
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from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
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from llama_stack.apis.models import ModelType
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from llama_stack.apis.resource import ResourceType
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# Removed VectorDBs import to avoid exposing public API
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from llama_stack.apis.vector_io.vector_io import (
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OpenAICreateVectorStoreRequestWithExtraBody,
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SearchRankingOptions,
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VectorStoreChunkingStrategy,
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VectorStoreDeleteResponse,
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VectorStoreFileContentsResponse,
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VectorStoreFileDeleteResponse,
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VectorStoreFileObject,
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VectorStoreFileStatus,
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VectorStoreObject,
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VectorStoreSearchResponsePage,
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)
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from llama_stack.core.datatypes import (
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VectorDBWithOwner,
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)
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from llama_stack.log import get_logger
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from .common import CommonRoutingTableImpl, lookup_model
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logger = get_logger(name=__name__, category="core::routing_tables")
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class VectorDBsRoutingTable(CommonRoutingTableImpl):
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"""Internal routing table for vector_db operations.
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Does not inherit from VectorDBs to avoid exposing public API endpoints.
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Only provides internal routing functionality for VectorIORouter.
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"""
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# Internal methods only - no public API exposure
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async def register_vector_db(
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self,
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vector_db_id: str,
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embedding_model: str,
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embedding_dimension: int | None = 384,
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provider_id: str | None = None,
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provider_vector_db_id: str | None = None,
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vector_db_name: str | None = None,
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) -> Any:
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if provider_id is None:
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if len(self.impls_by_provider_id) > 0:
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provider_id = list(self.impls_by_provider_id.keys())[0]
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if len(self.impls_by_provider_id) > 1:
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logger.warning(
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f"No provider specified and multiple providers available. Arbitrarily selected the first provider {provider_id}."
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)
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else:
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raise ValueError("No provider available. Please configure a vector_io provider.")
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model = await lookup_model(self, embedding_model)
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if model is None:
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raise ModelNotFoundError(embedding_model)
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if model.model_type != ModelType.embedding:
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raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
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if "embedding_dimension" not in model.metadata:
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raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
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try:
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provider = self.impls_by_provider_id[provider_id]
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except KeyError:
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available_providers = list(self.impls_by_provider_id.keys())
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raise ValueError(
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f"Provider '{provider_id}' not found in routing table. Available providers: {available_providers}"
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) from None
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logger.warning(
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"VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
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)
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request = OpenAICreateVectorStoreRequestWithExtraBody(
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name=vector_db_name or vector_db_id,
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embedding_model=embedding_model,
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embedding_dimension=model.metadata["embedding_dimension"],
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provider_id=provider_id,
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provider_vector_db_id=provider_vector_db_id,
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)
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vector_store = await provider.openai_create_vector_store(request)
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vector_store_id = vector_store.id
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actual_provider_vector_db_id = provider_vector_db_id or vector_store_id
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logger.warning(
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f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name"
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)
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vector_db_data = {
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"identifier": vector_store_id,
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"type": ResourceType.vector_db.value,
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"provider_id": provider_id,
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"provider_resource_id": actual_provider_vector_db_id,
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"embedding_model": embedding_model,
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"embedding_dimension": model.metadata["embedding_dimension"],
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"vector_db_name": vector_store.name,
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}
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vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
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await self.register_object(vector_db)
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return vector_db
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async def openai_retrieve_vector_store(
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self,
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vector_store_id: str,
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) -> VectorStoreObject:
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_retrieve_vector_store(vector_store_id)
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async def openai_update_vector_store(
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self,
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vector_store_id: str,
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name: str | None = None,
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expires_after: dict[str, Any] | None = None,
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metadata: dict[str, Any] | None = None,
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) -> VectorStoreObject:
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await self.assert_action_allowed("update", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_update_vector_store(
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vector_store_id=vector_store_id,
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name=name,
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expires_after=expires_after,
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metadata=metadata,
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)
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async def openai_delete_vector_store(
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self,
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vector_store_id: str,
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) -> VectorStoreDeleteResponse:
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await self.assert_action_allowed("delete", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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result = await provider.openai_delete_vector_store(vector_store_id)
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await self.unregister_vector_db(vector_store_id)
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return result
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async def unregister_vector_db(self, vector_store_id: str) -> None:
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"""Remove the vector store from the routing table registry."""
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try:
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vector_db_obj = await self.get_object_by_identifier("vector_db", vector_store_id)
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if vector_db_obj:
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await self.unregister_object(vector_db_obj)
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except Exception as e:
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# Log the error but don't fail the operation
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logger.warning(f"Failed to unregister vector store {vector_store_id} from routing table: {e}")
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async def openai_search_vector_store(
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self,
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vector_store_id: str,
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query: str | list[str],
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filters: dict[str, Any] | None = None,
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max_num_results: int | None = 10,
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ranking_options: SearchRankingOptions | None = None,
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rewrite_query: bool | None = False,
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search_mode: str | None = "vector",
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) -> VectorStoreSearchResponsePage:
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_search_vector_store(
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vector_store_id=vector_store_id,
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query=query,
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filters=filters,
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max_num_results=max_num_results,
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ranking_options=ranking_options,
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rewrite_query=rewrite_query,
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search_mode=search_mode,
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)
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async def openai_attach_file_to_vector_store(
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self,
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vector_store_id: str,
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file_id: str,
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attributes: dict[str, Any] | None = None,
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chunking_strategy: VectorStoreChunkingStrategy | None = None,
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) -> VectorStoreFileObject:
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await self.assert_action_allowed("update", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_attach_file_to_vector_store(
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vector_store_id=vector_store_id,
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file_id=file_id,
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attributes=attributes,
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chunking_strategy=chunking_strategy,
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)
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async def openai_list_files_in_vector_store(
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self,
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vector_store_id: str,
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limit: int | None = 20,
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order: str | None = "desc",
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after: str | None = None,
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before: str | None = None,
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filter: VectorStoreFileStatus | None = None,
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) -> list[VectorStoreFileObject]:
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_list_files_in_vector_store(
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vector_store_id=vector_store_id,
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limit=limit,
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order=order,
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after=after,
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before=before,
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filter=filter,
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)
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async def openai_retrieve_vector_store_file(
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self,
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vector_store_id: str,
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file_id: str,
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) -> VectorStoreFileObject:
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_retrieve_vector_store_file(
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vector_store_id=vector_store_id,
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file_id=file_id,
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)
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async def openai_retrieve_vector_store_file_contents(
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self,
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vector_store_id: str,
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file_id: str,
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) -> VectorStoreFileContentsResponse:
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_retrieve_vector_store_file_contents(
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vector_store_id=vector_store_id,
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file_id=file_id,
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)
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async def openai_update_vector_store_file(
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self,
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vector_store_id: str,
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file_id: str,
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attributes: dict[str, Any],
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) -> VectorStoreFileObject:
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await self.assert_action_allowed("update", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_update_vector_store_file(
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vector_store_id=vector_store_id,
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file_id=file_id,
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attributes=attributes,
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)
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async def openai_delete_vector_store_file(
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self,
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vector_store_id: str,
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file_id: str,
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) -> VectorStoreFileDeleteResponse:
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await self.assert_action_allowed("delete", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_delete_vector_store_file(
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vector_store_id=vector_store_id,
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file_id=file_id,
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)
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async def openai_create_vector_store_file_batch(
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self,
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vector_store_id: str,
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file_ids: list[str],
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attributes: dict[str, Any] | None = None,
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chunking_strategy: Any | None = None,
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):
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await self.assert_action_allowed("update", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_create_vector_store_file_batch(
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vector_store_id=vector_store_id,
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file_ids=file_ids,
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attributes=attributes,
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chunking_strategy=chunking_strategy,
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)
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async def openai_retrieve_vector_store_file_batch(
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self,
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batch_id: str,
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vector_store_id: str,
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):
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_retrieve_vector_store_file_batch(
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batch_id=batch_id,
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vector_store_id=vector_store_id,
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)
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async def openai_list_files_in_vector_store_file_batch(
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self,
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batch_id: str,
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vector_store_id: str,
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after: str | None = None,
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before: str | None = None,
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filter: str | None = None,
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limit: int | None = 20,
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order: str | None = "desc",
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):
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await self.assert_action_allowed("read", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_list_files_in_vector_store_file_batch(
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batch_id=batch_id,
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vector_store_id=vector_store_id,
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after=after,
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before=before,
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filter=filter,
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limit=limit,
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order=order,
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)
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async def openai_cancel_vector_store_file_batch(
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self,
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batch_id: str,
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vector_store_id: str,
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):
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await self.assert_action_allowed("update", "vector_db", vector_store_id)
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provider = await self.get_provider_impl(vector_store_id)
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return await provider.openai_cancel_vector_store_file_batch(
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batch_id=batch_id,
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vector_store_id=vector_store_id,
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)
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