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Merge 765cf1c9f5 into 4237eb4aaa

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Enrico Zimuel 2025-12-03 01:04:14 +00:00 • committed by GitHub
commit ae3e825640
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GPG key ID: B5690EEEBB952194
25 changed files with 654 additions and 5 deletions
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1
tests/integration/conftest.py
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@ -379,6 +379,7 @@ def vector_provider_wrapper(func):
"pgvector",
"weaviate",
"qdrant",
"elasticsearch",
]
else:
# For CI tests (replay/record), only use providers that are available in ci-tests environment

4
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -34,6 +34,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
"remote::pgvector",
"remote::qdrant",
"remote::weaviate",
"remote::elasticsearch",
]:
return
@ -54,6 +55,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
"remote::pgvector",
"remote::qdrant",
"remote::weaviate",
"remote::elasticsearch",
],
"keyword": [
"inline::milvus",
@ -61,6 +63,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
"remote::milvus",
"remote::pgvector",
"remote::weaviate",
"remote::elasticsearch",
],
"hybrid": [
"inline::milvus",
@ -68,6 +71,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
"remote::milvus",
"remote::pgvector",
"remote::weaviate",
"remote::elasticsearch",
],
}
supported_providers = search_mode_support.get(search_mode, [])

2
tests/integration/vector_io/test_vector_io.py
View file

@ -164,6 +164,7 @@ def test_insert_chunks_with_precomputed_embeddings(
"inline::milvus": {"score_threshold": -1.0},
"inline::qdrant": {"score_threshold": -1.0},
"remote::qdrant": {"score_threshold": -1.0},
"remote::elasticsearch": {"score_threshold": -1.0},
}
vector_store_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_stores.create(
@ -214,6 +215,7 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
"inline::milvus": {"score_threshold": 0.0},
"remote::qdrant": {"score_threshold": 0.0},
"inline::qdrant": {"score_threshold": 0.0},
"remote::elasticsearch": {"score_threshold": 0.0},
}
vector_store_name = "test_precomputed_embeddings_db"
register_response = client_with_empty_registry.vector_stores.create(