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chore: Enabling VectorIO Integration tests for Milvus (#2546)
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5785ccda35
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0066135944
3 changed files with 17 additions and 5 deletions
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@ -22,7 +22,7 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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vector-io-provider: ["inline::faiss", "inline::sqlite-vec", "remote::chromadb", "remote::pgvector"]
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vector-io-provider: ["inline::faiss", "inline::sqlite-vec", "inline::milvus", "remote::chromadb", "remote::pgvector"]
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python-version: ["3.12", "3.13"]
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fail-fast: false # we want to run all tests regardless of failure
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@ -520,7 +520,7 @@ Please refer to the inline provider documentation.
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Api.vector_io,
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AdapterSpec(
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adapter_type="milvus",
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pip_packages=["pymilvus"],
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pip_packages=["pymilvus[marshmallow<3.13.0]"],
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module="llama_stack.providers.remote.vector_io.milvus",
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config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
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description="""
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@ -123,6 +123,9 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
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def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, embedding_model_id, embedding_dimension):
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vector_io_provider_params_dict = {
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"inline::milvus": {"score_threshold": -1.0},
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}
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vector_db_id = "test_precomputed_embeddings_db"
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client_with_empty_registry.vector_dbs.register(
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vector_db_id=vector_db_id,
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@ -133,7 +136,7 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
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chunks_with_embeddings = [
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Chunk(
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content="This is a test chunk with precomputed embedding.",
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metadata={"document_id": "doc1", "source": "precomputed"},
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metadata={"document_id": "doc1", "source": "precomputed", "chunk_id": "chunk1"},
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embedding=[0.1] * int(embedding_dimension),
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),
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]
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@ -143,22 +146,29 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
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chunks=chunks_with_embeddings,
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)
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# Query for the first document
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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=vector_db_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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# Verify the top result is the expected document
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assert response is not None
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assert len(response.chunks) > 0
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assert len(response.chunks) > 0, (
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f"provider params for {provider} = {vector_io_provider_params_dict.get(provider, None)}"
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)
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assert response.chunks[0].metadata["document_id"] == "doc1"
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assert response.chunks[0].metadata["source"] == "precomputed"
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# expect this test to fail
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def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
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client_with_empty_registry, embedding_model_id, embedding_dimension
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):
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vector_io_provider_params_dict = {
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"inline::milvus": {"score_threshold": 0.0},
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}
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vector_db_id = "test_precomputed_embeddings_db"
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client_with_empty_registry.vector_dbs.register(
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vector_db_id=vector_db_id,
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@ -179,9 +189,11 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
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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=vector_db_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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# Verify the top result is the expected document
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