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chore: standardize vector store not found error (#2968)
# What does this PR do? 1. Creates a new `VectorStoreNotFoundError` class 2. Implements the new class where appropriate Relates to #2379 Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
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9 changed files with 46 additions and 31 deletions
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@ -12,6 +12,7 @@ from numpy.typing import NDArray
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from qdrant_client import AsyncQdrantClient, models
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from qdrant_client.models import PointStruct
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from llama_stack.apis.common.errors import VectorStoreNotFoundError
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from llama_stack.apis.inference import InterleavedContent
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from llama_stack.apis.vector_dbs import VectorDB
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from llama_stack.apis.vector_io import (
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@ -173,7 +174,7 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
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if not vector_db:
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raise ValueError(f"Vector DB {vector_db_id} not found")
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raise VectorStoreNotFoundError(vector_db_id)
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index = VectorDBWithIndex(
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vector_db=vector_db,
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@ -191,7 +192,7 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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) -> None:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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if not index:
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raise ValueError(f"Vector DB {vector_db_id} not found")
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raise VectorStoreNotFoundError(vector_db_id)
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await index.insert_chunks(chunks)
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@ -203,7 +204,7 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
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) -> QueryChunksResponse:
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index = await self._get_and_cache_vector_db_index(vector_db_id)
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if not index:
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raise ValueError(f"Vector DB {vector_db_id} not found")
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raise VectorStoreNotFoundError(vector_db_id)
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return await index.query_chunks(query, params)
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