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44b48d9518
# Fix for `update_document` Function in Chroma ## Summary This pull request addresses an issue with the `update_document` function in the Chroma class, as described in [#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947). The issue was identified as an `AttributeError` raised when calling `update_document` due to a missing corresponding method in the `Collection` object. This fix refactors the `update_document` method in `Chroma` to correctly interact with the `Collection` object. ## Changes 1. Fixed the `update_document` method in the `Chroma` class to correctly call methods on the `Collection` object. 2. Added the corresponding test `test_chroma_update_document` in `tests/integration_tests/vectorstores/test_chroma.py` to reflect the updated method call. 3. Added an example and explanation of how to use the `update_document` function in the Jupyter notebook tutorial for Chroma. ## Test Plan All existing tests pass after this change. In addition, the `test_chroma_update_document` test case now correctly checks the functionality of `update_document`, ensuring that the function works as expected and updates the content of documents correctly. ## Reviewers @dev2049 This fix will ensure that users are able to use the `update_document` function as expected, without encountering the previous `AttributeError`. This will enhance the usability and reliability of the Chroma class for all users. Thank you for considering this pull request. I look forward to your feedback and suggestions.
197 lines
6.9 KiB
Python
197 lines
6.9 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 FakeEmbeddings
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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_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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# 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=FakeEmbeddings(),
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ids=[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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