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https://github.com/meta-llama/llama-stack.git
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96cd51a0c8
* change from access_attributes to owner on dynamically created resources * define simpler string based conditions for more intuitive restriction
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
# 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 pydantic import TypeAdapter
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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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from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
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from llama_stack.distribution.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
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logger = get_logger(name=__name__, category="core")
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class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
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async def list_vector_dbs(self) -> ListVectorDBsResponse:
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return ListVectorDBsResponse(data=await self.get_all_with_type("vector_db"))
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async def get_vector_db(self, vector_db_id: str) -> VectorDB:
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vector_db = await self.get_object_by_identifier("vector_db", vector_db_id)
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if vector_db is None:
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raise ValueError(f"Vector DB '{vector_db_id}' not found")
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return vector_db
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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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) -> VectorDB:
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if provider_vector_db_id is None:
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provider_vector_db_id = vector_db_id
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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 self.get_object_by_identifier("model", embedding_model)
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if model is None:
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raise ValueError(f"Model {embedding_model} not found")
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if model.model_type != ModelType.embedding:
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raise ValueError(f"Model {embedding_model} is not an embedding model")
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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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vector_db_data = {
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"identifier": vector_db_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": 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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}
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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 unregister_vector_db(self, vector_db_id: str) -> None:
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existing_vector_db = await self.get_vector_db(vector_db_id)
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if existing_vector_db is None:
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raise ValueError(f"Vector DB {vector_db_id} not found")
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await self.unregister_object(existing_vector_db)
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