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	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
		
			
				
	
	
		
			42 lines
		
	
	
		
			1.1 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			42 lines
		
	
	
		
			1.1 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| import pytest
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| 
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| from langchain_community.embeddings.ernie import ErnieEmbeddings
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| 
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| 
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| def test_embedding_documents_1() -> None:
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|     documents = ["foo bar"]
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|     embedding = ErnieEmbeddings()
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|     output = embedding.embed_documents(documents)
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|     assert len(output) == 1
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|     assert len(output[0]) == 384
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| 
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| 
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| def test_embedding_documents_2() -> None:
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|     documents = ["foo", "bar"]
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|     embedding = ErnieEmbeddings()
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|     output = embedding.embed_documents(documents)
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|     assert len(output) == 2
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|     assert len(output[0]) == 384
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|     assert len(output[1]) == 384
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| 
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| 
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| def test_embedding_query() -> None:
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|     query = "foo"
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|     embedding = ErnieEmbeddings()
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|     output = embedding.embed_query(query)
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|     assert len(output) == 384
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| 
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| 
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| def test_max_chunks() -> None:
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|     documents = [f"text-{i}" for i in range(20)]
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|     embedding = ErnieEmbeddings()
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|     output = embedding.embed_documents(documents)
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|     assert len(output) == 20
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| 
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| 
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| def test_too_many_chunks() -> None:
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|     documents = [f"text-{i}" for i in range(20)]
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|     embedding = ErnieEmbeddings(chunk_size=20)
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|     with pytest.raises(ValueError):
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|         embedding.embed_documents(documents)
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