mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
ed58eeb9c5
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
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
"""Test FastEmbed embeddings."""
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import pytest
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from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
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@pytest.mark.parametrize(
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"model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
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)
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@pytest.mark.parametrize("max_length", [50, 512])
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@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
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@pytest.mark.parametrize("threads", [0, 10])
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def test_fastembed_embedding_documents(
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model_name: str, max_length: int, doc_embed_type: str, threads: int
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) -> None:
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"""Test fastembed embeddings for documents."""
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documents = ["foo bar", "bar foo"]
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embedding = FastEmbedEmbeddings(
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model_name=model_name,
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max_length=max_length,
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doc_embed_type=doc_embed_type,
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threads=threads,
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)
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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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@pytest.mark.parametrize(
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"model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
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)
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@pytest.mark.parametrize("max_length", [50, 512])
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def test_fastembed_embedding_query(model_name: str, max_length: int) -> None:
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"""Test fastembed embeddings for query."""
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document = "foo bar"
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embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
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output = embedding.embed_query(document)
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assert len(output) == 384
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@pytest.mark.parametrize(
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"model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
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)
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@pytest.mark.parametrize("max_length", [50, 512])
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@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
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@pytest.mark.parametrize("threads", [0, 10])
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async def test_fastembed_async_embedding_documents(
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model_name: str, max_length: int, doc_embed_type: str, threads: int
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) -> None:
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"""Test fastembed embeddings for documents."""
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documents = ["foo bar", "bar foo"]
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embedding = FastEmbedEmbeddings(
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model_name=model_name,
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max_length=max_length,
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doc_embed_type=doc_embed_type,
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threads=threads,
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)
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output = await embedding.aembed_documents(documents)
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assert len(output) == 2
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assert len(output[0]) == 384
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@pytest.mark.parametrize(
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"model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
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)
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@pytest.mark.parametrize("max_length", [50, 512])
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async def test_fastembed_async_embedding_query(
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model_name: str, max_length: int
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) -> None:
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"""Test fastembed embeddings for query."""
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document = "foo bar"
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embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
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output = await embedding.aembed_query(document)
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assert len(output) == 384
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