mirror of
https://github.com/hwchase17/langchain
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88 lines
2.8 KiB
Python
88 lines
2.8 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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from uuid import uuid4
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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.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
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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 TestWeaviateHybridSearchRetriever:
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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_get_relevant_documents(self, weaviate_url: str) -> 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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client = Client(weaviate_url)
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retriever = WeaviateHybridSearchRetriever(
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client=client,
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index_name=f"LangChain_{uuid4().hex}",
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text_key="text",
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attributes=["page"],
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)
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for i, text in enumerate(texts):
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retriever.add_documents(
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[Document(page_content=text, metadata=metadatas[i])]
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)
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output = retriever.get_relevant_documents("foo")
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assert output == [
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Document(page_content="foo", metadata={"page": 0}),
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Document(page_content="baz", metadata={"page": 2}),
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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_get_relevant_documents_with_filter(self, weaviate_url: str) -> 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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client = Client(weaviate_url)
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retriever = WeaviateHybridSearchRetriever(
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client=client,
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index_name=f"LangChain_{uuid4().hex}",
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text_key="text",
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attributes=["page"],
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)
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for i, text in enumerate(texts):
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retriever.add_documents(
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[Document(page_content=text, metadata=metadatas[i])]
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)
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where_filter = {"path": ["page"], "operator": "Equal", "valueNumber": 0}
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output = retriever.get_relevant_documents("foo", where_filter=where_filter)
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assert output == [
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Document(page_content="foo", metadata={"page": 0}),
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]
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