test_qdrant_from_texts_stores_ids() — langchain Function Reference
Architecture documentation for the test_qdrant_from_texts_stores_ids() function in test_from_texts.py from the langchain codebase.
Entity Profile
Dependency Diagram
graph TD a0c8b7fc_ab6c_4502_1405_fe50cf12e131["test_qdrant_from_texts_stores_ids()"] 911ffb0f_3570_ae90_9ff9_2c9c03151aea["test_from_texts.py"] a0c8b7fc_ab6c_4502_1405_fe50cf12e131 -->|defined in| 911ffb0f_3570_ae90_9ff9_2c9c03151aea style a0c8b7fc_ab6c_4502_1405_fe50cf12e131 fill:#6366f1,stroke:#818cf8,color:#fff
Relationship Graph
Source Code
libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py lines 44–72
def test_qdrant_from_texts_stores_ids(
batch_size: int,
vector_name: str,
sparse_vector_name: str,
location: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test end to end Qdrant.from_texts stores provided ids."""
collection_name = uuid.uuid4().hex
ids: list[str | int] = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
786,
]
vec_store = QdrantVectorStore.from_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
location=location,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
batch_size=batch_size,
vector_name=vector_name,
sparse_vector_name=sparse_vector_name,
)
assert vec_store.client.count(collection_name).count == 2
stored_ids = [point.id for point in vec_store.client.retrieve(collection_name, ids)]
assert set(ids) == set(stored_ids)
Domain
Subdomains
Source
Frequently Asked Questions
What does test_qdrant_from_texts_stores_ids() do?
test_qdrant_from_texts_stores_ids() is a function in the langchain codebase, defined in libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py.
Where is test_qdrant_from_texts_stores_ids() defined?
test_qdrant_from_texts_stores_ids() is defined in libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py at line 44.
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