forked from Archives/langchain
2324f19c85
Hello 1) Passing `embedding_function` as a callable seems to be outdated and the common interface is to pass `Embeddings` instance 2) At the moment `Qdrant.add_texts` is designed to be used with `embeddings.embed_query`, which is 1) slow 2) causes ambiguity due to 1. It should be used with `embeddings.embed_documents` This PR solves both problems and also provides some new tests
168 lines
5.2 KiB
Python
168 lines
5.2 KiB
Python
"""Test Qdrant functionality."""
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from typing import Callable, Optional
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import pytest
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.vectorstores import Qdrant
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from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("foo", "bar"),
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(Qdrant.CONTENT_KEY, "bar"),
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("foo", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant(content_payload_key: str, metadata_payload_key: str) -> 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 = Qdrant.from_texts(
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texts,
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FakeEmbeddings(),
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location=":memory:",
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content_payload_key=content_payload_key,
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metadata_payload_key=metadata_payload_key,
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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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def test_qdrant_add_documents() -> 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: Qdrant = Qdrant.from_texts(texts, FakeEmbeddings(), location=":memory:")
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new_texts = ["foobar", "foobaz"]
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docsearch.add_documents([Document(page_content=content) for content in new_texts])
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output = docsearch.similarity_search("foobar", k=1)
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# FakeEmbeddings return the same query embedding as the first document embedding
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# computed in `embedding.embed_documents`. Since embed_documents is called twice,
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# "foo" embedding is the same as "foobar" embedding
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assert output == [Document(page_content="foobar")] or output == [
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Document(page_content="foo")
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]
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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("test_content", "test_payload"),
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(Qdrant.CONTENT_KEY, "payload_test"),
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("content_test", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_with_metadatas(
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content_payload_key: str, metadata_payload_key: str
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) -> 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 = Qdrant.from_texts(
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texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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content_payload_key=content_payload_key,
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metadata_payload_key=metadata_payload_key,
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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_qdrant_similarity_search_filters() -> 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 = Qdrant.from_texts(
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texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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)
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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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@pytest.mark.parametrize(
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["content_payload_key", "metadata_payload_key"],
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[
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(Qdrant.CONTENT_KEY, Qdrant.METADATA_KEY),
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("test_content", "test_payload"),
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(Qdrant.CONTENT_KEY, "payload_test"),
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("content_test", Qdrant.METADATA_KEY),
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],
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)
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def test_qdrant_max_marginal_relevance_search(
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content_payload_key: str, metadata_payload_key: str
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) -> None:
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"""Test end to end construction and MRR 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 = Qdrant.from_texts(
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texts,
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FakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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content_payload_key=content_payload_key,
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metadata_payload_key=metadata_payload_key,
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)
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output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3)
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assert output == [
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Document(page_content="foo", metadata={"page": 0}),
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Document(page_content="bar", metadata={"page": 1}),
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]
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@pytest.mark.parametrize(
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["embeddings", "embedding_function"],
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[
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(FakeEmbeddings(), None),
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(FakeEmbeddings().embed_query, None),
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(None, FakeEmbeddings().embed_query),
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],
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)
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def test_qdrant_embedding_interface(
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embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
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) -> None:
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from qdrant_client import QdrantClient
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client = QdrantClient(":memory:")
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collection_name = "test"
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Qdrant(
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client,
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collection_name,
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embeddings=embeddings,
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embedding_function=embedding_function,
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)
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@pytest.mark.parametrize(
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["embeddings", "embedding_function"],
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[
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(FakeEmbeddings(), FakeEmbeddings().embed_query),
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(None, None),
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],
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)
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def test_qdrant_embedding_interface_raises(
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embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
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) -> None:
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from qdrant_client import QdrantClient
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client = QdrantClient(":memory:")
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collection_name = "test"
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with pytest.raises(ValueError):
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Qdrant(
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client,
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collection_name,
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embeddings=embeddings,
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embedding_function=embedding_function,
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)
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