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refactor component path

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-07-29 21:20:21 -04:00
parent a29b62f820
commit 19d2555424
116 changed files with 7064 additions and 1265 deletions
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11
llama_stack/providers/remote/vector_io/milvus/milvus.py
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@ -13,6 +13,7 @@ from typing import Any
from numpy.typing import NDArray
from pymilvus import DataType, Function, FunctionType, MilvusClient
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files.files import Files
from llama_stack.apis.inference import Inference, InterleavedContent
from llama_stack.apis.vector_dbs import VectorDB
@ -329,11 +330,11 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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 = await 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,
@ -356,7 +357,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
) -> 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)
await index.insert_chunks(chunks)
@ -368,7 +369,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
) -> 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)
if params and params.get("mode") == "keyword":
# Check if this is inline Milvus (Milvus-Lite)
@ -384,7 +385,7 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
"""Delete a chunk from a milvus vector store."""
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