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langchain/libs/community/tests/integration_tests/utilities/test_arxiv.py

174 lines
5.7 KiB
Python

"""Integration test for Arxiv API Wrapper."""
from typing import Any, List
import pytest
from langchain_core.documents import Document
from langchain_core.tools import BaseTool
from langchain_community.tools import ArxivQueryRun
from langchain_community.utilities import ArxivAPIWrapper
@pytest.fixture
def api_client() -> ArxivAPIWrapper:
return ArxivAPIWrapper()
def test_run_success_paper_name(api_client: ArxivAPIWrapper) -> None:
"""Test a query of paper name that returns the correct answer"""
output = api_client.run("Heat-bath random walks with Markov bases")
assert "Probability distributions for Markov chains based quantum walks" in output
assert (
"Transformations of random walks on groups via Markov stopping times" in output
)
assert (
"Recurrence of Multidimensional Persistent Random Walks. Fourier and Series "
"Criteria" in output
)
def test_run_success_arxiv_identifier(api_client: ArxivAPIWrapper) -> None:
"""Test a query of an arxiv identifier returns the correct answer"""
output = api_client.run("1605.08386v1")
assert "Heat-bath random walks with Markov bases" in output
def test_run_success_multiple_arxiv_identifiers(api_client: ArxivAPIWrapper) -> None:
"""Test a query of multiple arxiv identifiers that returns the correct answer"""
output = api_client.run("1605.08386v1 2212.00794v2 2308.07912")
assert "Heat-bath random walks with Markov bases" in output
assert "Scaling Language-Image Pre-training via Masking" in output
assert (
"Ultra-low mass PBHs in the early universe can explain the PTA signal" in output
)
def test_run_returns_several_docs(api_client: ArxivAPIWrapper) -> None:
"""Test that returns several docs"""
output = api_client.run("Caprice Stanley")
assert "On Mixing Behavior of a Family of Random Walks" in output
def test_run_returns_no_result(api_client: ArxivAPIWrapper) -> None:
"""Test that gives no result."""
output = api_client.run("1605.08386WWW")
assert "No good Arxiv Result was found" == output
def assert_docs(docs: List[Document]) -> None:
for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata) == {"Published", "Title", "Authors", "Summary"}
def test_load_success_paper_name(api_client: ArxivAPIWrapper) -> None:
"""Test a query of paper name that returns one document"""
docs = api_client.load("Heat-bath random walks with Markov bases")
assert len(docs) == 3
assert_docs(docs)
def test_load_success_arxiv_identifier(api_client: ArxivAPIWrapper) -> None:
"""Test a query of an arxiv identifier that returns one document"""
docs = api_client.load("1605.08386v1")
assert len(docs) == 1
assert_docs(docs)
def test_load_success_multiple_arxiv_identifiers(api_client: ArxivAPIWrapper) -> None:
"""Test a query of arxiv identifiers that returns the correct answer"""
docs = api_client.load("1605.08386v1 2212.00794v2 2308.07912")
assert len(docs) == 3
assert_docs(docs)
def test_load_returns_no_result(api_client: ArxivAPIWrapper) -> None:
"""Test that returns no docs"""
docs = api_client.load("1605.08386WWW")
assert len(docs) == 0
def test_load_returns_limited_docs() -> None:
"""Test that returns several docs"""
expected_docs = 2
api_client = ArxivAPIWrapper(load_max_docs=expected_docs)
docs = api_client.load("ChatGPT")
assert len(docs) == expected_docs
assert_docs(docs)
def test_load_returns_limited_doc_content_chars() -> None:
"""Test that returns limited doc_content_chars_max"""
doc_content_chars_max = 100
api_client = ArxivAPIWrapper(doc_content_chars_max=doc_content_chars_max)
docs = api_client.load("1605.08386")
assert len(docs[0].page_content) == doc_content_chars_max
def test_load_returns_unlimited_doc_content_chars() -> None:
"""Test that returns unlimited doc_content_chars_max"""
doc_content_chars_max = None
api_client = ArxivAPIWrapper(doc_content_chars_max=doc_content_chars_max)
docs = api_client.load("1605.08386")
assert len(docs[0].page_content) == pytest.approx(54338, rel=1e-2)
def test_load_returns_full_set_of_metadata() -> None:
"""Test that returns several docs"""
api_client = ArxivAPIWrapper(load_max_docs=1, load_all_available_meta=True)
docs = api_client.load("ChatGPT")
assert len(docs) == 1
for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata).issuperset(
{"Published", "Title", "Authors", "Summary"}
)
print(doc.metadata) # noqa: T201
assert len(set(doc.metadata)) > 4
def _load_arxiv_from_universal_entry(**kwargs: Any) -> BaseTool:
from langchain.agents.load_tools import load_tools
tools = load_tools(["arxiv"], **kwargs)
assert len(tools) == 1, "loaded more than 1 tool"
return tools[0]
def test_load_arxiv_from_universal_entry() -> None:
arxiv_tool = _load_arxiv_from_universal_entry()
output = arxiv_tool("Caprice Stanley")
assert (
"On Mixing Behavior of a Family of Random Walks" in output
), "failed to fetch a valid result"
def test_load_arxiv_from_universal_entry_with_params() -> None:
params = {
"top_k_results": 1,
"load_max_docs": 10,
"load_all_available_meta": True,
}
arxiv_tool = _load_arxiv_from_universal_entry(**params)
assert isinstance(arxiv_tool, ArxivQueryRun)
wp = arxiv_tool.api_wrapper
assert wp.top_k_results == 1, "failed to assert top_k_results"
assert wp.load_max_docs == 10, "failed to assert load_max_docs"
assert (
wp.load_all_available_meta is True
), "failed to assert load_all_available_meta"