mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
e0605b464b
### Feature
Using FAISS on a retrievalQA task, I found myself wanting to allow in
multiple sources. From what I understood, the filter feature takes in a
dict of form {key: value} which then will check in the metadata for the
exact value linked to that key.
I added some logic to be able to pass a list which will be checked
against instead of an exact value. Passing an exact value will also
work.
Here's an example of how I could then use it in my own project:
```
pdfs_to_filter_in = ["file_A", "file_B"]
filter_dict = {
"source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in]
}
retriever = db.as_retriever()
retriever.search_kwargs = {"filter": filter_dict}
```
I added an integration test based on the other ones I found in
`tests/integration_tests/vectorstores/test_faiss.py` under
`test_faiss_with_metadatas_and_list_filter()`.
It doesn't feel like this is worthy of its own notebook or doc, but I'm
open to suggestions if needed.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
197 lines
7.6 KiB
Python
197 lines
7.6 KiB
Python
"""Test FAISS functionality."""
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import datetime
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import math
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import tempfile
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import pytest
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from langchain.docstore.document import Document
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from langchain.docstore.in_memory import InMemoryDocstore
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from langchain.docstore.wikipedia import Wikipedia
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from langchain.vectorstores.faiss import FAISS
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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def test_faiss() -> 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 = FAISS.from_texts(texts, FakeEmbeddings())
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index_to_id = docsearch.index_to_docstore_id
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expected_docstore = InMemoryDocstore(
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{
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index_to_id[0]: Document(page_content="foo"),
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index_to_id[1]: Document(page_content="bar"),
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index_to_id[2]: Document(page_content="baz"),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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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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def test_faiss_vector_sim() -> None:
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"""Test vector similarity."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings())
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index_to_id = docsearch.index_to_docstore_id
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expected_docstore = InMemoryDocstore(
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{
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index_to_id[0]: Document(page_content="foo"),
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index_to_id[1]: Document(page_content="bar"),
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index_to_id[2]: Document(page_content="baz"),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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query_vec = FakeEmbeddings().embed_query(text="foo")
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output = docsearch.similarity_search_by_vector(query_vec, k=1)
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assert output == [Document(page_content="foo")]
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# make sure we can have k > docstore size
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output = docsearch.max_marginal_relevance_search_by_vector(query_vec, k=10)
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assert len(output) == len(texts)
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def test_faiss_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": i} for i in range(len(texts))]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
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expected_docstore = InMemoryDocstore(
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{
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docsearch.index_to_docstore_id[0]: Document(
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page_content="foo", metadata={"page": 0}
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),
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docsearch.index_to_docstore_id[1]: Document(
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page_content="bar", metadata={"page": 1}
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),
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docsearch.index_to_docstore_id[2]: Document(
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page_content="baz", metadata={"page": 2}
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),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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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_faiss_with_metadatas_and_filter() -> None:
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texts = ["foo", "bar", "baz"]
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metadatas = [{"page": i} for i in range(len(texts))]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
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expected_docstore = InMemoryDocstore(
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{
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docsearch.index_to_docstore_id[0]: Document(
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page_content="foo", metadata={"page": 0}
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),
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docsearch.index_to_docstore_id[1]: Document(
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page_content="bar", metadata={"page": 1}
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),
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docsearch.index_to_docstore_id[2]: Document(
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page_content="baz", metadata={"page": 2}
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),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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output = docsearch.similarity_search("foo", k=1, filter={"page": 1})
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assert output == [Document(page_content="bar", metadata={"page": 1})]
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def test_faiss_with_metadatas_and_list_filter() -> None:
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texts = ["foo", "bar", "baz", "foo", "qux"]
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metadatas = [{"page": i} if i <= 3 else {"page": 3} for i in range(len(texts))]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)
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expected_docstore = InMemoryDocstore(
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{
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docsearch.index_to_docstore_id[0]: Document(
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page_content="foo", metadata={"page": 0}
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),
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docsearch.index_to_docstore_id[1]: Document(
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page_content="bar", metadata={"page": 1}
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),
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docsearch.index_to_docstore_id[2]: Document(
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page_content="baz", metadata={"page": 2}
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),
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docsearch.index_to_docstore_id[3]: Document(
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page_content="foo", metadata={"page": 3}
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),
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docsearch.index_to_docstore_id[4]: Document(
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page_content="qux", metadata={"page": 3}
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),
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}
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)
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assert docsearch.docstore.__dict__ == expected_docstore.__dict__
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output = docsearch.similarity_search("foor", k=1, filter={"page": [0, 1, 2]})
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assert output == [Document(page_content="foo", metadata={"page": 0})]
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def test_faiss_search_not_found() -> None:
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"""Test what happens when document is not found."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings())
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# Get rid of the docstore to purposefully induce errors.
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docsearch.docstore = InMemoryDocstore({})
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with pytest.raises(ValueError):
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docsearch.similarity_search("foo")
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def test_faiss_add_texts() -> None:
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"""Test end to end adding of texts."""
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# Create initial doc store.
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings())
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# Test adding a similar document as before.
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docsearch.add_texts(["foo"])
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output = docsearch.similarity_search("foo", k=2)
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assert output == [Document(page_content="foo"), Document(page_content="foo")]
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def test_faiss_add_texts_not_supported() -> None:
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"""Test adding of texts to a docstore that doesn't support it."""
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docsearch = FAISS(FakeEmbeddings().embed_query, None, Wikipedia(), {})
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with pytest.raises(ValueError):
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docsearch.add_texts(["foo"])
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def test_faiss_local_save_load() -> None:
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"""Test end to end serialization."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(texts, FakeEmbeddings())
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temp_timestamp = datetime.datetime.utcnow().strftime("%Y%m%d-%H%M%S")
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with tempfile.TemporaryDirectory(suffix="_" + temp_timestamp + "/") as temp_folder:
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docsearch.save_local(temp_folder)
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new_docsearch = FAISS.load_local(temp_folder, FakeEmbeddings())
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assert new_docsearch.index is not None
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def test_faiss_similarity_search_with_relevance_scores() -> None:
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"""Test the similarity search with normalized similarities."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(
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texts,
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FakeEmbeddings(),
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relevance_score_fn=lambda score: 1.0 - score / math.sqrt(2),
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)
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outputs = docsearch.similarity_search_with_relevance_scores("foo", k=1)
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output, score = outputs[0]
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assert output == Document(page_content="foo")
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assert score == 1.0
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def test_faiss_invalid_normalize_fn() -> None:
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"""Test the similarity search with normalized similarities."""
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texts = ["foo", "bar", "baz"]
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docsearch = FAISS.from_texts(
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texts, FakeEmbeddings(), relevance_score_fn=lambda _: 2.0
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)
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with pytest.warns(Warning, match="scores must be between"):
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docsearch.similarity_search_with_relevance_scores("foo", k=1)
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def test_missing_normalize_score_fn() -> None:
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"""Test doesn't perform similarity search without a normalize score function."""
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with pytest.raises(ValueError):
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texts = ["foo", "bar", "baz"]
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faiss_instance = FAISS.from_texts(texts, FakeEmbeddings())
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faiss_instance.relevance_score_fn = None
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faiss_instance.similarity_search_with_relevance_scores("foo", k=2)
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