forked from Archives/langchain
3033c6b964
### Background Continuing to implement all the interface methods defined by the `VectorStore` class. This PR pertains to implementation of the `max_marginal_relevance_search_by_vector` method. ### Changes - a `max_marginal_relevance_search_by_vector` method implementation has been added in `weaviate.py` - tests have been added to the the new method - vcr cassettes have been added for the weaviate tests ### Test Plan Added tests for the `max_marginal_relevance_search_by_vector` implementation ### Change Safety - [x] I have added tests to cover my changes
120 lines
4.4 KiB
Python
120 lines
4.4 KiB
Python
"""Test Weaviate functionality."""
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import logging
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import os
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from typing import Generator, Union
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import pytest
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from weaviate import Client
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from langchain.docstore.document import Document
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores.weaviate import Weaviate
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logging.basicConfig(level=logging.DEBUG)
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"""
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cd tests/integration_tests/vectorstores/docker-compose
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docker compose -f weaviate.yml up
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"""
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class TestWeaviate:
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@classmethod
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def setup_class(cls) -> None:
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if not os.getenv("OPENAI_API_KEY"):
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raise ValueError("OPENAI_API_KEY environment variable is not set")
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@pytest.fixture(scope="class", autouse=True)
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def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
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"""Return the weaviate url."""
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url = "http://localhost:8080"
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yield url
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# Clear the test index
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client = Client(url)
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client.schema.delete_all()
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@pytest.mark.vcr(ignore_localhost=True)
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def test_similarity_search_without_metadata(
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self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
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) -> None:
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"""Test end to end construction and search without metadata."""
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texts = ["foo", "bar", "baz"]
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docsearch = Weaviate.from_texts(
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texts,
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embedding_openai,
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weaviate_url=weaviate_url,
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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.vcr(ignore_localhost=True)
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def test_similarity_search_with_metadata(
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self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
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) -> None:
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"""Test end to end construction and search with metadata."""
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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 = Weaviate.from_texts(
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texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
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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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@pytest.mark.vcr(ignore_localhost=True)
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def test_max_marginal_relevance_search(
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self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
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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 = Weaviate.from_texts(
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texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
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)
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# if lambda=1 the algorithm should be equivalent to standard ranking
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standard_ranking = docsearch.similarity_search("foo", k=2)
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output = docsearch.max_marginal_relevance_search(
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"foo", k=2, fetch_k=3, lambda_mult=1.0
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)
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assert output == standard_ranking
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# if lambda=0 the algorithm should favour maximal diversity
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output = docsearch.max_marginal_relevance_search(
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"foo", k=2, fetch_k=3, lambda_mult=0.0
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)
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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.vcr(ignore_localhost=True)
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def test_max_marginal_relevance_search_by_vector(
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self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
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) -> None:
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"""Test end to end construction and MRR search by vector."""
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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 = Weaviate.from_texts(
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texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
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)
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foo_embedding = embedding_openai.embed_query("foo")
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# if lambda=1 the algorithm should be equivalent to standard ranking
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standard_ranking = docsearch.similarity_search("foo", k=2)
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output = docsearch.max_marginal_relevance_search_by_vector(
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foo_embedding, k=2, fetch_k=3, lambda_mult=1.0
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)
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assert output == standard_ranking
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# if lambda=0 the algorithm should favour maximal diversity
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output = docsearch.max_marginal_relevance_search_by_vector(
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foo_embedding, k=2, fetch_k=3, lambda_mult=0.0
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)
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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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