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chore(cleanup)!: kill vector_db references as far as possible (#3864)
There should not be "vector db" anywhere.
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46 changed files with 701 additions and 822 deletions
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llama_stack/apis/vector_stores/__init__.py
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llama_stack/apis/vector_stores/__init__.py
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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 .vector_stores import *
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llama_stack/apis/vector_stores/vector_stores.py
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llama_stack/apis/vector_stores/vector_stores.py
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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 Literal
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from pydantic import BaseModel
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from llama_stack.apis.resource import Resource, ResourceType
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# Internal resource type for storing the vector store routing and other information
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class VectorStore(Resource):
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"""Vector database resource for storing and querying vector embeddings.
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:param type: Type of resource, always 'vector_store' for vector stores
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:param embedding_model: Name of the embedding model to use for vector generation
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:param embedding_dimension: Dimension of the embedding vectors
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"""
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type: Literal[ResourceType.vector_store] = ResourceType.vector_store
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embedding_model: str
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embedding_dimension: int
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vector_store_name: str | None = None
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@property
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def vector_store_id(self) -> str:
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return self.identifier
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@property
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def provider_vector_store_id(self) -> str | None:
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return self.provider_resource_id
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class VectorStoreInput(BaseModel):
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"""Input parameters for creating or configuring a vector database.
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:param vector_store_id: Unique identifier for the vector store
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:param embedding_model: Name of the embedding model to use for vector generation
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:param embedding_dimension: Dimension of the embedding vectors
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:param provider_vector_store_id: (Optional) Provider-specific identifier for the vector store
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"""
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vector_store_id: str
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embedding_model: str
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embedding_dimension: int
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provider_id: str | None = None
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provider_vector_store_id: str | None = None
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