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chore: standardize vector store not found error (#2968)

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# 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>
This commit is contained in:
Nathan Weinberg 2025-07-30 18:19:16 -04:00 • committed by GitHub
parent 272a3e9937
commit cd5c6a2fcd
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9 changed files with 46 additions and 31 deletions
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3
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -15,6 +15,7 @@ import faiss
import numpy as np
from numpy.typing import NDArray
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference, InterleavedContent
from llama_stack.apis.vector_dbs import VectorDB
@ -285,7 +286,7 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
) -> QueryChunksResponse:
index = self.cache.get(vector_db_id)
if index is None:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)

11
llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -15,6 +15,7 @@ import numpy as np
import sqlite_vec
from numpy.typing import NDArray
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference
from llama_stack.apis.vector_dbs import VectorDB
@ -508,11 +509,11 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
return self.cache[vector_db_id]
if self.vector_db_store is None:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise VectorStoreNotFoundError(vector_db_id)
vector_db = self.vector_db_store.get_vector_db(vector_db_id)
if not vector_db:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise VectorStoreNotFoundError(vector_db_id)
index = VectorDBWithIndex(
vector_db=vector_db,
@ -537,7 +538,7 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
async def insert_chunks(self, vector_db_id: str, chunks: list[Chunk], ttl_seconds: int | None = None) -> None:
index = await self._get_and_cache_vector_db_index(vector_db_id)
if not index:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise VectorStoreNotFoundError(vector_db_id)
# The VectorDBWithIndex helper is expected to compute embeddings via the inference_api
# and then call our index's add_chunks.
await index.insert_chunks(chunks)
@ -547,14 +548,14 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
) -> QueryChunksResponse:
index = await self._get_and_cache_vector_db_index(vector_db_id)
if not index:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise VectorStoreNotFoundError(vector_db_id)
return await index.query_chunks(query, params)
async def delete_chunks(self, store_id: str, chunk_ids: list[str]) -> None:
"""Delete a chunk from a sqlite_vec index."""
index = await self._get_and_cache_vector_db_index(store_id)
if not index:
raise ValueError(f"Vector DB {store_id} not found")
raise VectorStoreNotFoundError(store_id)
for chunk_id in chunk_ids:
# Use the index's delete_chunk method