langchain/libs/community/tests/integration_tests/embeddings/test_qianfan_endpoint.py

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"""Test Baidu Qianfan Embedding Endpoint."""
community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463) Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
2023-12-11 21:53:30 +00:00
from langchain_community.embeddings.baidu_qianfan_endpoint import (
QianfanEmbeddingsEndpoint,
)
def test_embedding_multiple_documents() -> None:
documents = ["foo", "bar"]
embedding = QianfanEmbeddingsEndpoint() # type: ignore[call-arg]
output = embedding.embed_documents(documents)
assert len(output) == 2
assert len(output[0]) == 384
assert len(output[1]) == 384
def test_embedding_query() -> None:
query = "foo"
embedding = QianfanEmbeddingsEndpoint() # type: ignore[call-arg]
output = embedding.embed_query(query)
assert len(output) == 384
def test_model() -> None:
documents = ["hi", "qianfan"]
embedding = QianfanEmbeddingsEndpoint(model="Embedding-V1") # type: ignore[call-arg]
output = embedding.embed_documents(documents)
assert len(output) == 2
def test_rate_limit() -> None:
llm = QianfanEmbeddingsEndpoint( # type: ignore[call-arg]
model="Embedding-V1", init_kwargs={"query_per_second": 2}
)
assert llm.client._client._rate_limiter._sync_limiter._query_per_second == 2
documents = ["foo", "bar"]
output = llm.embed_documents(documents)
assert len(output) == 2
assert len(output[0]) == 384
assert len(output[1]) == 384