99 lines
2.8 KiB
Python
99 lines
2.8 KiB
Python
import asyncio
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import json
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import sys
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import time
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import types
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import pytest
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from src.deep_research import DeepResearcher
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class _ControlledResearcher(DeepResearcher):
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def __init__(self, *args, **kwargs):
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super().__init__(
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llm_endpoint="http://local.test/v1/chat/completions",
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llm_model="local-model",
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*args,
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**kwargs,
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)
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self.active = 0
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self.max_active = 0
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async def _search(self, query):
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return [
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{"url": f"https://example.test/{query}/{i}", "title": f"{query}-{i}"}
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for i in range(4)
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]
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async def _fetch_and_extract(self, url, question, title):
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self.active += 1
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self.max_active = max(self.max_active, self.active)
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await asyncio.sleep(0.01)
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self.active -= 1
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return {"url": url, "title": title, "summary": "ok"}
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@pytest.mark.asyncio
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async def test_search_and_extract_respects_extraction_concurrency():
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researcher = _ControlledResearcher(extraction_concurrency=2, max_urls_per_round=4)
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researcher._start_time = time.time()
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findings = await researcher._search_and_extract(["a", "b"], "question")
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assert len(findings) == 8
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assert researcher.max_active == 2
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@pytest.mark.asyncio
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async def test_fetch_and_extract_uses_configured_timeout(monkeypatch):
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captured = {}
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search_mod = types.ModuleType("src.search")
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def fake_fetch_webpage_content(url, timeout):
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return {
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"success": True,
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"content": "useful page content",
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"title": "Page",
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"og_image": "",
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}
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search_mod.fetch_webpage_content = fake_fetch_webpage_content
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monkeypatch.setitem(sys.modules, "src.search", search_mod)
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async def immediate_to_thread(fn, *args, **kwargs):
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return fn(*args, **kwargs)
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monkeypatch.setattr(asyncio, "to_thread", immediate_to_thread)
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researcher = DeepResearcher(
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llm_endpoint="http://local.test/v1/chat/completions",
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llm_model="local-model",
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extraction_timeout=123,
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)
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async def fake_llm(messages, temperature=0.3, max_tokens=4096, timeout=60):
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captured["timeout"] = timeout
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return json.dumps({
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"rational": "relevant",
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"evidence": "evidence",
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"summary": "useful page content",
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})
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researcher._llm = fake_llm
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result = await researcher._fetch_and_extract("https://example.test", "question", "Title")
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assert result["summary"] == "useful page content"
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assert captured["timeout"] == 123
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def test_extraction_timeout_allows_long_local_model_runs():
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researcher = DeepResearcher(
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llm_endpoint="http://local.test/v1/chat/completions",
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llm_model="local-model",
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extraction_timeout=1800,
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)
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assert researcher.extraction_timeout == 1800
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