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Merge branch 'main' into content-extension

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Francisco Arceo 2025-09-07 12:38:35 -06:00 • committed by GitHub
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2
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -58,11 +58,13 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
"inline::sqlite-vec",
"remote::milvus",
"inline::milvus",
"remote::pgvector",
],
"hybrid": [
"inline::sqlite-vec",
"inline::milvus",
"remote::milvus",
"remote::pgvector",
],
}
supported_providers = search_mode_support.get(search_mode, [])

75
tests/integration/vector_io/test_vector_io.py
View file

@ -47,34 +47,45 @@ def client_with_empty_registry(client_with_models):
def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension):
# Register a memory bank first
vector_db_id = "test_vector_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_vector_db"
register_response = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
actual_vector_db_id = register_response.identifier
# Retrieve the memory bank and validate its properties
response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=vector_db_id)
response = client_with_empty_registry.vector_dbs.retrieve(vector_db_id=actual_vector_db_id)
assert response is not None
assert response.identifier == vector_db_id
assert response.identifier == actual_vector_db_id
assert response.embedding_model == embedding_model_id
assert response.provider_resource_id == vector_db_id
assert response.identifier.startswith("vs_")
def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension):
vector_db_id = "test_vector_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_vector_db"
response = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
assert vector_dbs_after_register == [vector_db_id]
actual_vector_db_id = response.identifier
assert actual_vector_db_id.startswith("vs_")
assert actual_vector_db_id != vector_db_name
client_with_empty_registry.vector_dbs.unregister(vector_db_id=vector_db_id)
vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
assert vector_dbs_after_register == [actual_vector_db_id]
vector_stores = client_with_empty_registry.vector_stores.list()
assert len(vector_stores.data) == 1
vector_store = vector_stores.data[0]
assert vector_store.id == actual_vector_db_id
assert vector_store.name == vector_db_name
client_with_empty_registry.vector_dbs.unregister(vector_db_id=actual_vector_db_id)
vector_dbs = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
assert len(vector_dbs) == 0
@ -91,20 +102,22 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
],
)
def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case):
vector_db_id = "test_vector_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_vector_db"
register_response = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
actual_vector_db_id = register_response.identifier
client_with_empty_registry.vector_io.insert(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
chunks=sample_chunks,
)
response = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query="What is the capital of France?",
)
assert response is not None
@ -113,7 +126,7 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
query, expected_doc_id = test_case
response = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query=query,
)
assert response is not None
@ -128,13 +141,15 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
"remote::qdrant": {"score_threshold": -1.0},
"inline::qdrant": {"score_threshold": -1.0},
}
vector_db_id = "test_precomputed_embeddings_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
actual_vector_db_id = register_response.identifier
chunks_with_embeddings = [
Chunk(
content="This is a test chunk with precomputed embedding.",
@ -144,13 +159,13 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
]
client_with_empty_registry.vector_io.insert(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
chunks=chunks_with_embeddings,
)
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
response = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query="precomputed embedding test",
params=vector_io_provider_params_dict.get(provider, None),
)
@ -173,13 +188,15 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
"remote::qdrant": {"score_threshold": 0.0},
"inline::qdrant": {"score_threshold": 0.0},
}
vector_db_id = "test_precomputed_embeddings_db"
client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_id,
vector_db_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_dbs.register(
vector_db_id=vector_db_name,
embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension,
)
actual_vector_db_id = register_response.identifier
chunks_with_embeddings = [
Chunk(
content="duplicate",
@ -189,13 +206,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
]
client_with_empty_registry.vector_io.insert(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
chunks=chunks_with_embeddings,
)
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
response = client_with_empty_registry.vector_io.query(
vector_db_id=vector_db_id,
vector_db_id=actual_vector_db_id,
query="duplicate",
params=vector_io_provider_params_dict.get(provider, None),
)