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https://github.com/meta-llama/llama-stack.git
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chore(cleanup)!: kill vector_db references as far as possible (#3864)
There should not be "vector db" anywhere.
This commit is contained in:
parent
444f6c88f3
commit
122de785c4
46 changed files with 701 additions and 822 deletions
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@ -49,46 +49,50 @@ def client_with_empty_registry(client_with_models):
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@vector_provider_wrapper
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def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
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vector_db_name = "test_vector_db"
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def test_vector_store_retrieve(
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client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
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):
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vector_store_name = "test_vector_store"
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create_response = client_with_empty_registry.vector_stores.create(
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name=vector_db_name,
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name=vector_store_name,
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extra_body={
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"provider_id": vector_io_provider_id,
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},
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)
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actual_vector_db_id = create_response.id
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actual_vector_store_id = create_response.id
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# Retrieve the vector store and validate its properties
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response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_db_id)
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response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_store_id)
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assert response is not None
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assert response.id == actual_vector_db_id
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assert response.name == vector_db_name
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assert response.id == actual_vector_store_id
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assert response.name == vector_store_name
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assert response.id.startswith("vs_")
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@vector_provider_wrapper
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def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id):
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vector_db_name = "test_vector_db"
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def test_vector_store_register(
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client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
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):
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vector_store_name = "test_vector_store"
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response = client_with_empty_registry.vector_stores.create(
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name=vector_db_name,
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name=vector_store_name,
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extra_body={
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"provider_id": vector_io_provider_id,
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},
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)
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actual_vector_db_id = response.id
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assert actual_vector_db_id.startswith("vs_")
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assert actual_vector_db_id != vector_db_name
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actual_vector_store_id = response.id
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assert actual_vector_store_id.startswith("vs_")
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assert actual_vector_store_id != vector_store_name
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vector_stores = client_with_empty_registry.vector_stores.list()
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assert len(vector_stores.data) == 1
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vector_store = vector_stores.data[0]
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assert vector_store.id == actual_vector_db_id
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assert vector_store.name == vector_db_name
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assert vector_store.id == actual_vector_store_id
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assert vector_store.name == vector_store_name
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client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_db_id)
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client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_store_id)
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vector_stores = client_with_empty_registry.vector_stores.list()
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assert len(vector_stores.data) == 0
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@ -108,23 +112,23 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
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def test_insert_chunks(
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client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case, vector_io_provider_id
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):
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vector_db_name = "test_vector_db"
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vector_store_name = "test_vector_store"
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create_response = client_with_empty_registry.vector_stores.create(
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name=vector_db_name,
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name=vector_store_name,
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extra_body={
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"provider_id": vector_io_provider_id,
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},
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)
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actual_vector_db_id = create_response.id
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actual_vector_store_id = create_response.id
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client_with_empty_registry.vector_io.insert(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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chunks=sample_chunks,
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)
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response = client_with_empty_registry.vector_io.query(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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query="What is the capital of France?",
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)
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assert response is not None
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@ -133,7 +137,7 @@ def test_insert_chunks(
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query, expected_doc_id = test_case
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response = client_with_empty_registry.vector_io.query(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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query=query,
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)
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assert response is not None
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@ -151,15 +155,15 @@ def test_insert_chunks_with_precomputed_embeddings(
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"inline::qdrant": {"score_threshold": -1.0},
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"remote::qdrant": {"score_threshold": -1.0},
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}
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vector_db_name = "test_precomputed_embeddings_db"
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vector_store_name = "test_precomputed_embeddings_db"
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register_response = client_with_empty_registry.vector_stores.create(
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name=vector_db_name,
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name=vector_store_name,
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extra_body={
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"provider_id": vector_io_provider_id,
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},
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)
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actual_vector_db_id = register_response.id
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actual_vector_store_id = register_response.id
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chunks_with_embeddings = [
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Chunk(
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@ -170,13 +174,13 @@ def test_insert_chunks_with_precomputed_embeddings(
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]
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client_with_empty_registry.vector_io.insert(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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chunks=chunks_with_embeddings,
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)
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provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
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response = client_with_empty_registry.vector_io.query(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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query="precomputed embedding test",
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params=vector_io_provider_params_dict.get(provider, None),
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)
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@ -200,16 +204,16 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
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"remote::qdrant": {"score_threshold": 0.0},
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"inline::qdrant": {"score_threshold": 0.0},
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}
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vector_db_name = "test_precomputed_embeddings_db"
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vector_store_name = "test_precomputed_embeddings_db"
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register_response = client_with_empty_registry.vector_stores.create(
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name=vector_db_name,
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name=vector_store_name,
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extra_body={
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"embedding_model": embedding_model_id,
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"provider_id": vector_io_provider_id,
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},
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)
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actual_vector_db_id = register_response.id
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actual_vector_store_id = register_response.id
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chunks_with_embeddings = [
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Chunk(
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@ -220,13 +224,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
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]
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client_with_empty_registry.vector_io.insert(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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chunks=chunks_with_embeddings,
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)
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provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
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response = client_with_empty_registry.vector_io.query(
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vector_db_id=actual_vector_db_id,
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vector_db_id=actual_vector_store_id,
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query="duplicate",
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params=vector_io_provider_params_dict.get(provider, None),
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)
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