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adding back relevant vector_db files
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> fix tests Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updating tests and fixing routing logic for single provider Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> setting default provider to update tests Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updated provider_id Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> updated VectorStoreConfig to use (provider_id, embedding_model_id) and add defautl vector store provider Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> special handling for replay mode for available providers Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo
parent
accc4c437e
commit
b3addc94d1
23 changed files with 637 additions and 261 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
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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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