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4: finished rename I think

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This commit is contained in:
Ashwin Bharambe 2025-10-20 19:27:24 -07:00
parent 3d7b463a80
commit 44f104baae
15 changed files with 273 additions and 272 deletions
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12
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -367,7 +367,7 @@ def test_openai_vector_store_with_chunks(
# Insert chunks using the native LlamaStack API (since OpenAI API doesn't have direct chunk insertion)
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -434,7 +434,7 @@ def test_openai_vector_store_search_relevance(
# Insert chunks using native API
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -484,7 +484,7 @@ def test_openai_vector_store_search_with_ranking_options(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -544,7 +544,7 @@ def test_openai_vector_store_search_with_high_score_filter(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -610,7 +610,7 @@ def test_openai_vector_store_search_with_max_num_results(
# Insert chunks
llama_client.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
@ -1175,7 +1175,7 @@ def test_openai_vector_store_search_modes(
)
client_with_models.vector_io.insert(
vector_db_id=vector_store.id,
vector_store_id=vector_store.id,
chunks=sample_chunks,
)
query = "Python programming language"

64
tests/integration/vector_io/test_vector_io.py
View file

@ -49,46 +49,46 @@ def client_with_empty_registry(client_with_models):
@vector_provider_wrapper
def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
vector_db_name = "test_vector_db"
def test_vector_store_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
vector_store_name = "test_vector_store"
create_response = client_with_empty_registry.vector_stores.create(
name=vector_db_name,
name=vector_store_name,
extra_body={
"provider_id": vector_io_provider_id,
},
)
actual_vector_db_id = create_response.id
actual_vector_store_id = create_response.id
# Retrieve the vector store and validate its properties
response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_db_id)
response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_store_id)
assert response is not None
assert response.id == actual_vector_db_id
assert response.name == vector_db_name
assert response.id == actual_vector_store_id
assert response.name == vector_store_name
assert response.id.startswith("vs_")
@vector_provider_wrapper
def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
vector_db_name = "test_vector_db"
def test_vector_store_register(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
vector_store_name = "test_vector_store"
response = client_with_empty_registry.vector_stores.create(
name=vector_db_name,
name=vector_store_name,
extra_body={
"provider_id": vector_io_provider_id,
},
)
actual_vector_db_id = response.id
assert actual_vector_db_id.startswith("vs_")
assert actual_vector_db_id != vector_db_name
actual_vector_store_id = response.id
assert actual_vector_store_id.startswith("vs_")
assert actual_vector_store_id != vector_store_name
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
assert vector_store.id == actual_vector_store_id
assert vector_store.name == vector_store_name
client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_db_id)
client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_store_id)
vector_stores = client_with_empty_registry.vector_stores.list()
assert len(vector_stores.data) == 0
@ -108,23 +108,23 @@ 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_io_provider_id
):
vector_db_name = "test_vector_db"
vector_store_name = "test_vector_store"
create_response = client_with_empty_registry.vector_stores.create(
name=vector_db_name,
name=vector_store_name,
extra_body={
"provider_id": vector_io_provider_id,
},
)
actual_vector_db_id = create_response.id
actual_vector_store_id = create_response.id
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_db_id,
vector_store_id=actual_vector_store_id,
chunks=sample_chunks,
)
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_db_id,
vector_store_id=actual_vector_store_id,
query="What is the capital of France?",
)
assert response is not None
@ -133,7 +133,7 @@ def test_insert_chunks(
query, expected_doc_id = test_case
response = client_with_empty_registry.vector_io.query(
vector_db_id=actual_vector_db_id,
vector_store_id=actual_vector_store_id,
query=query,
)
assert response is not None
@ -151,15 +151,15 @@ def test_insert_chunks_with_precomputed_embeddings(
"inline::qdrant": {"score_threshold": -1.0},
"remote::qdrant": {"score_threshold": -1.0},
}
vector_db_name = "test_precomputed_embeddings_db"
vector_store_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_stores.create(
name=vector_db_name,
name=vector_store_name,
extra_body={
"provider_id": vector_io_provider_id,
},
)
actual_vector_db_id = register_response.id
actual_vector_store_id = register_response.id
chunks_with_embeddings = [
Chunk(
@ -170,13 +170,13 @@ def test_insert_chunks_with_precomputed_embeddings(
]
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_db_id,
vector_store_id=actual_vector_store_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=actual_vector_db_id,
vector_store_id=actual_vector_store_id,
query="precomputed embedding test",
params=vector_io_provider_params_dict.get(provider, None),
)
@ -200,16 +200,16 @@ 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_name = "test_precomputed_embeddings_db"
vector_store_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_stores.create(
name=vector_db_name,
name=vector_store_name,
extra_body={
"embedding_model": embedding_model_id,
"provider_id": vector_io_provider_id,
},
)
actual_vector_db_id = register_response.id
actual_vector_store_id = register_response.id
chunks_with_embeddings = [
Chunk(
@ -220,13 +220,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
]
client_with_empty_registry.vector_io.insert(
vector_db_id=actual_vector_db_id,
vector_store_id=actual_vector_store_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=actual_vector_db_id,
vector_store_id=actual_vector_store_id,
query="duplicate",
params=vector_io_provider_params_dict.get(provider, None),
)