mirror of
https://github.com/hwchase17/langchain
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302 lines
10 KiB
Python
302 lines
10 KiB
Python
"""Test Chroma functionality."""
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import pytest
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from langchain.docstore.document import Document
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from langchain.vectorstores import Chroma
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from tests.integration_tests.vectorstores.fake_embeddings import (
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ConsistentFakeEmbeddings,
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FakeEmbeddings,
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)
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def test_chroma() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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@pytest.mark.asyncio
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async def test_chroma_async() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
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)
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output = await docsearch.asimilarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_chroma_with_metadatas() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo", metadata={"page": "0"})]
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def test_chroma_with_metadatas_with_scores() -> None:
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"""Test end to end construction and scored search."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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)
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output = docsearch.similarity_search_with_score("foo", k=1)
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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def test_chroma_with_metadatas_with_scores_using_vector() -> None:
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"""Test end to end construction and scored search, using embedding vector."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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embeddings = FakeEmbeddings()
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=embeddings,
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metadatas=metadatas,
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)
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embedded_query = embeddings.embed_query("foo")
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output = docsearch.similarity_search_by_vector_with_relevance_scores(
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embedding=embedded_query, k=1
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)
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assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
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def test_chroma_search_filter() -> None:
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"""Test end to end construction and search with metadata filtering."""
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texts = ["far", "bar", "baz"]
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metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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)
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output = docsearch.similarity_search("far", k=1, filter={"first_letter": "f"})
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assert output == [Document(page_content="far", metadata={"first_letter": "f"})]
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output = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
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assert output == [Document(page_content="bar", metadata={"first_letter": "b"})]
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def test_chroma_search_filter_with_scores() -> None:
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"""Test end to end construction and scored search with metadata filtering."""
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texts = ["far", "bar", "baz"]
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metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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)
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output = docsearch.similarity_search_with_score(
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"far", k=1, filter={"first_letter": "f"}
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)
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assert output == [
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(Document(page_content="far", metadata={"first_letter": "f"}), 0.0)
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]
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output = docsearch.similarity_search_with_score(
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"far", k=1, filter={"first_letter": "b"}
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)
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assert output == [
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(Document(page_content="bar", metadata={"first_letter": "b"}), 1.0)
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]
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def test_chroma_with_persistence() -> None:
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"""Test end to end construction and search, with persistence."""
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chroma_persist_dir = "./tests/persist_dir"
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collection_name = "test_collection"
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name=collection_name,
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texts=texts,
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embedding=FakeEmbeddings(),
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persist_directory=chroma_persist_dir,
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)
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output = docsearch.similarity_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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docsearch.persist()
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# Get a new VectorStore from the persisted directory
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docsearch = Chroma(
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collection_name=collection_name,
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embedding_function=FakeEmbeddings(),
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persist_directory=chroma_persist_dir,
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)
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output = docsearch.similarity_search("foo", k=1)
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# Clean up
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docsearch.delete_collection()
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# Persist doesn't need to be called again
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# Data will be automatically persisted on object deletion
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# Or on program exit
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def test_chroma_mmr() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
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)
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output = docsearch.max_marginal_relevance_search("foo", k=1)
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assert output == [Document(page_content="foo")]
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def test_chroma_mmr_by_vector() -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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embeddings = FakeEmbeddings()
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docsearch = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=embeddings
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)
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embedded_query = embeddings.embed_query("foo")
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output = docsearch.max_marginal_relevance_search_by_vector(embedded_query, k=1)
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assert output == [Document(page_content="foo")]
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def test_chroma_with_include_parameter() -> None:
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"""Test end to end construction and include parameter."""
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texts = ["foo", "bar", "baz"]
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docsearch = Chroma.from_texts(
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collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
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)
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output = docsearch.get(include=["embeddings"])
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assert output["embeddings"] is not None
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output = docsearch.get()
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assert output["embeddings"] is None
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def test_chroma_update_document() -> None:
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"""Test the update_document function in the Chroma class."""
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# Make a consistent embedding
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embedding = ConsistentFakeEmbeddings()
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# Initial document content and id
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initial_content = "foo"
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document_id = "doc1"
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# Create an instance of Document with initial content and metadata
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original_doc = Document(page_content=initial_content, metadata={"page": "0"})
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# Initialize a Chroma instance with the original document
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docsearch = Chroma.from_documents(
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collection_name="test_collection",
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documents=[original_doc],
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embedding=embedding,
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ids=[document_id],
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)
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old_embedding = docsearch._collection.peek()["embeddings"][
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docsearch._collection.peek()["ids"].index(document_id)
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]
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# Define updated content for the document
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updated_content = "updated foo"
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# Create a new Document instance with the updated content and the same id
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updated_doc = Document(page_content=updated_content, metadata={"page": "0"})
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# Update the document in the Chroma instance
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docsearch.update_document(document_id=document_id, document=updated_doc)
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# Perform a similarity search with the updated content
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output = docsearch.similarity_search(updated_content, k=1)
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# Assert that the updated document is returned by the search
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assert output == [Document(page_content=updated_content, metadata={"page": "0"})]
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# Assert that the new embedding is correct
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new_embedding = docsearch._collection.peek()["embeddings"][
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docsearch._collection.peek()["ids"].index(document_id)
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]
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assert new_embedding == embedding.embed_documents([updated_content])[0]
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assert new_embedding != old_embedding
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def test_chroma_with_relevance_score() -> None:
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"""Test to make sure the relevance score is scaled to 0-1."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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collection_metadata={"hnsw:space": "l2"},
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)
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
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assert output == [
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(Document(page_content="foo", metadata={"page": "0"}), 1.0),
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(Document(page_content="bar", metadata={"page": "1"}), 0.8),
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(Document(page_content="baz", metadata={"page": "2"}), 0.5),
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]
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def test_chroma_with_relevance_score_custom_normalization_fn() -> None:
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"""Test searching with relevance score and custom normalization function."""
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": str(i)} for i in range(len(texts))]
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docsearch = Chroma.from_texts(
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collection_name="test_collection",
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texts=texts,
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embedding=FakeEmbeddings(),
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metadatas=metadatas,
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relevance_score_fn=lambda d: d * 0,
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collection_metadata={"hnsw:space": "l2"},
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)
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
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assert output == [
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(Document(page_content="foo", metadata={"page": "0"}), -0.0),
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(Document(page_content="bar", metadata={"page": "1"}), -0.0),
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(Document(page_content="baz", metadata={"page": "2"}), -0.0),
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]
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def test_init_from_client() -> None:
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import chromadb
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client = chromadb.Client(chromadb.config.Settings())
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Chroma(client=client)
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def test_init_from_client_settings() -> None:
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import chromadb
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client_settings = chromadb.config.Settings()
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Chroma(client_settings=client_settings)
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def test_chroma_add_documents_no_metadata() -> None:
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db = Chroma(embedding_function=FakeEmbeddings())
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db.add_documents([Document(page_content="foo")])
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def test_chroma_add_documents_mixed_metadata() -> None:
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db = Chroma(embedding_function=FakeEmbeddings())
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docs = [
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Document(page_content="foo"),
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Document(page_content="bar", metadata={"baz": 1}),
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]
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db.add_documents(docs)
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search = db.similarity_search("foo bar")
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assert sorted(search, key=lambda d: d.page_content) == sorted(
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docs, key=lambda d: d.page_content
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)
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