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chore(cleanup)!: kill vector_db references as far as possible (#3864)

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There should not be "vector db" anywhere.
This commit is contained in:
Ashwin Bharambe 2025-10-20 20:06:16 -07:00 • committed by GitHub
parent 444f6c88f3
commit 122de785c4
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46 changed files with 701 additions and 822 deletions
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34
tests/unit/providers/vector_io/conftest.py
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@ -10,8 +10,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
import numpy as np
import pytest
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
@ -31,7 +31,7 @@ def vector_provider(request):
@pytest.fixture
def vector_db_id() -> str:
def vector_store_id() -> str:
return f"test-vector-db-{random.randint(1, 100)}"
@ -149,8 +149,8 @@ async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inf
)
collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}"
await adapter.initialize()
await adapter.register_vector_db(
VectorDB(
await adapter.register_vector_store(
VectorStore(
identifier=collection_id,
provider_id="test_provider",
embedding_model="test_model",
@ -186,8 +186,8 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
files_api=None,
)
await adapter.initialize()
await adapter.register_vector_db(
VectorDB(
await adapter.register_vector_store(
VectorStore(
identifier=f"faiss_test_collection_{np.random.randint(1e6)}",
provider_id="test_provider",
embedding_model="test_model",
@ -215,7 +215,7 @@ def mock_psycopg2_connection():
async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
connection, cursor = mock_psycopg2_connection
vector_db = VectorDB(
vector_store = VectorStore(
identifier="test-vector-db",
embedding_model="test-model",
embedding_dimension=embedding_dimension,
@ -225,7 +225,7 @@ async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"):
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"):
index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE")
index = PGVectorIndex(vector_store, embedding_dimension, connection, distance_metric="COSINE")
index._test_chunks = []
original_add_chunks = index.add_chunks
@ -281,30 +281,30 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
await adapter.initialize()
adapter.conn = mock_conn
async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None):
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
async def mock_insert_chunks(vector_store_id, chunks, ttl_seconds=None):
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise ValueError(f"Vector DB {vector_store_id} not found")
await index.insert_chunks(chunks)
adapter.insert_chunks = mock_insert_chunks
async def mock_query_chunks(vector_db_id, query, params=None):
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
async def mock_query_chunks(vector_store_id, query, params=None):
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
if not index:
raise ValueError(f"Vector DB {vector_db_id} not found")
raise ValueError(f"Vector DB {vector_store_id} not found")
return await index.query_chunks(query, params)
adapter.query_chunks = mock_query_chunks
test_vector_db = VectorDB(
test_vector_store = VectorStore(
identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}",
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=embedding_dimension,
)
await adapter.register_vector_db(test_vector_db)
adapter.test_collection_id = test_vector_db.identifier
await adapter.register_vector_store(test_vector_store)
adapter.test_collection_id = test_vector_store.identifier
yield adapter
await adapter.shutdown()