ce7f5dbbdd
Deep research generated search queries from the LLM's training-cutoff knowledge, so it emitted stale-year queries like "best Python tutorials 2025" when the actual year is later (issue #1341). The chat/agent path already grounds the model with "Today is ..." (src/agent_loop.py); the deep research planning and query-generation prompts had no equivalent. Add a small current_date_context() helper and prepend it at the plan and query-generation prompt sites (and the research_handler plan preview path that reuses RESEARCH_PLAN_PROMPT). System-TZ local, portable strftime. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
"""Regression tests for issue #1341 — deep research used the model's
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training-cutoff year (e.g. "best Python tutorials 2025") because the
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query-generation and planning prompts never told the LLM the current date.
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The chat/agent path already injects "Today is ..." (src/agent_loop.py); deep
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research had no equivalent. These tests pin that the current year now reaches
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the LLM at both the planning and query-generation steps, without needing a live
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LLM or DB.
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"""
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import asyncio
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from datetime import datetime
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from src.deep_research import (
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DeepResearcher,
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current_date_context,
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RESEARCH_PLAN_PROMPT,
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)
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def _this_year() -> str:
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return datetime.now().astimezone().strftime("%Y")
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def test_current_date_context_names_the_real_year():
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ctx = current_date_context()
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assert _this_year() in ctx
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# It must actively steer the model away from training-data years.
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assert "training data" in ctx.lower()
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def test_generate_queries_prompt_carries_the_current_year():
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# Build without the heavy __init__; _generate_queries only needs these.
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r = DeepResearcher.__new__(DeepResearcher)
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r.research_plan = ""
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r.queries_used = set()
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seen = {}
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async def _fake_llm(messages, **kwargs):
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seen["prompt"] = messages[0]["content"]
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return '["python tutorials", "python guides"]'
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r._llm = _fake_llm
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queries = asyncio.run(r._generate_queries("best python tutorials", "", 1))
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assert queries # sanity: the JSON array parsed
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# The fix: the real current year is in the prompt the LLM actually sees.
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assert _this_year() in seen["prompt"]
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def test_plan_prompt_carries_the_current_year():
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r = DeepResearcher.__new__(DeepResearcher)
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seen = {}
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async def _fake_llm(messages, **kwargs):
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seen["prompt"] = messages[0]["content"]
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return "{}"
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r._llm = _fake_llm
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asyncio.run(r._create_plan("what changed this year"))
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assert _this_year() in seen["prompt"]
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# The base template itself stays year-agnostic; the year comes from the
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# prepended context, proving the wiring (not a hard-coded prompt edit).
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assert _this_year() not in RESEARCH_PLAN_PROMPT
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