mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
edd68e4ad4
## Description This PR introduces the new `langchain-qdrant` partner package, intending to deprecate the community package. ## Changes - Moved the Qdrant vector store implementation `/libs/partners/qdrant` with integration tests. - The conditional imports of the client library are now regular with minor implementation improvements. - Added a deprecation warning to `langchain_community.vectorstores.qdrant.Qdrant`. - Replaced references/imports from `langchain_community` with either `langchain_core` or by moving the definitions to the `langchain_qdrant` package itself. - Updated the Qdrant vector store documentation to reflect the changes. ## Testing - `QDRANT_URL` and [`QDRANT_API_KEY`](583e36bf6b) env values need to be set to [run integration tests](d608c93d1f) in the [cloud](https://cloud.qdrant.tech). - If a Qdrant instance is running at `http://localhost:6333`, the integration tests will use it too. - By default, tests use an [`in-memory`](https://github.com/qdrant/qdrant-client?tab=readme-ov-file#local-mode) instance(Not comprehensive). --------- Co-authored-by: Erick Friis <erick@langchain.dev> Co-authored-by: Erick Friis <erickfriis@gmail.com>
285 lines
9.5 KiB
Python
285 lines
9.5 KiB
Python
from typing import Optional
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import numpy as np
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import pytest
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from langchain_core.documents import Document
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from langchain_qdrant import Qdrant
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from tests.integration_tests.common import (
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ConsistentFakeEmbeddings,
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assert_documents_equals,
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)
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search(
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batch_size: int,
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content_payload_key: str,
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metadata_payload_key: str,
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vector_name: Optional[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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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batch_size=batch_size,
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vector_name=vector_name,
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)
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output = docsearch.similarity_search("foo", k=1)
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assert_documents_equals(actual=output, expected=[Document(page_content="foo")])
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_by_vector(
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batch_size: int,
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content_payload_key: str,
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metadata_payload_key: str,
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vector_name: Optional[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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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batch_size=batch_size,
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vector_name=vector_name,
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)
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embeddings = ConsistentFakeEmbeddings().embed_query("foo")
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output = docsearch.similarity_search_by_vector(embeddings, k=1)
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assert_documents_equals(output, [Document(page_content="foo")])
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_score_by_vector(
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batch_size: int,
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content_payload_key: str,
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metadata_payload_key: str,
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vector_name: Optional[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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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batch_size=batch_size,
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vector_name=vector_name,
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)
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embeddings = ConsistentFakeEmbeddings().embed_query("foo")
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output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
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assert len(output) == 1
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document, score = output[0]
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assert_documents_equals(actual=[document], expected=[Document(page_content="foo")])
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assert score >= 0
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_filters(
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batch_size: int, vector_name: Optional[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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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batch_size=batch_size,
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vector_name=vector_name,
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)
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output = docsearch.similarity_search(
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"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
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)
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assert_documents_equals(
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actual=output,
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expected=[
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Document(
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page_content="bar",
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metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
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)
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],
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)
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_relevance_score_no_threshold(
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vector_name: Optional[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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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vector_name=vector_name,
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)
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output = docsearch.similarity_search_with_relevance_scores(
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"foo", k=3, score_threshold=None
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)
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assert len(output) == 3
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for i in range(len(output)):
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assert round(output[i][1], 2) >= 0
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assert round(output[i][1], 2) <= 1
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_relevance_score_with_threshold(
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vector_name: Optional[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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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vector_name=vector_name,
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)
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score_threshold = 0.98
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kwargs = {"score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 1
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assert all([score >= score_threshold for _, score in output])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_relevance_score_with_threshold_and_filter(
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vector_name: Optional[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 = [
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{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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vector_name=vector_name,
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)
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score_threshold = 0.99 # for almost exact match
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# test negative filter condition
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negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
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kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 0
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# test positive filter condition
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positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
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kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
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assert len(output) == 1
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assert all([score >= score_threshold for _, score in output])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_filters_with_qdrant_filters(
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vector_name: Optional[str],
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) -> None:
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"""Test end to end construction and search."""
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from qdrant_client.http import models as rest
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texts = ["foo", "bar", "baz"]
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metadatas = [
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{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
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for i in range(len(texts))
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]
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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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metadatas=metadatas,
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location=":memory:",
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vector_name=vector_name,
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)
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qdrant_filter = rest.Filter(
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must=[
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rest.FieldCondition(
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key="metadata.page",
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match=rest.MatchValue(value=1),
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),
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rest.FieldCondition(
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key="metadata.details.page",
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match=rest.MatchValue(value=2),
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),
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rest.FieldCondition(
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key="metadata.details.pages",
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match=rest.MatchAny(any=[3]),
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),
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]
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)
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output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
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assert_documents_equals(
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actual=output,
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expected=[
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Document(
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page_content="bar",
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metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
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)
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],
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)
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
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@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_similarity_search_with_relevance_scores(
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batch_size: int,
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content_payload_key: str,
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metadata_payload_key: str,
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vector_name: Optional[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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docsearch = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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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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batch_size=batch_size,
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vector_name=vector_name,
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)
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output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
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assert all(
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(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
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)
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