cat.split()[0] was called in the condition and again in the body,
wasting a second split. More importantly, if cat were ever
whitespace-only, split() returns [] and [0] raises IndexError.
Assign to a local variable and guard with a truthiness check.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The goal-based extractor passed raw fetched webpage content straight
into the LLM prompt via string substitution, bypassing the
prompt-injection hardening layer in src/prompt_security.py.
Split EXTRACTOR_PROMPT into EXTRACTOR_SYSTEM (task instructions +
goal, trusted) and a second message built with untrusted_context_message()
(raw page content, sandboxed with <<<UNTRUSTED_SOURCE_DATA>>> guards).
This aligns the extractor with every other external-content injection
site in the codebase (agent_loop, chat_processor, chat_routes).
Fixes#3044
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
The STOP_PROMPT did not include the target round count, so the LLM
could decide to stop after 2-3 rounds even when the user requested 8.
Additionally, min_rounds was capped at 3 regardless of max_rounds.
- Add max_rounds to STOP_PROMPT so the LLM knows the target
- Change min_rounds from min(3, max_rounds) to max(2, max_rounds - 2)
Fixes#2863
Co-authored-by: michaelxer <michaelxer@users.noreply.github.com>
Two problems made deep research report "No information could be gathered" even
after it had extracted findings, on slow local models (reporter served a 20B
via LM Studio):
- _synthesize hard-capped its LLM call at timeout=60, while extraction uses the
user's extraction_timeout (300s here) and the final report uses 180s. The slow
model needed >60s to synthesize the round's findings, so synthesis timed out
after 3 attempts. Raised it to 180s to match the final-report call.
- When synthesis produced no report (it returns the unchanged, still-empty
report on failure during round 1), the run hit
`if not report: return "No information could be gathered…"` and discarded the
findings it had already gathered. Now it falls back to a compiled report built
from those findings (_fallback_report) so the user keeps the gathered material.
Tests stub the LLM (no live model/DB), pin the synthesis timeout >= 180, that the
fallback surfaces the findings rather than the give-up message, and that a failed
synthesis preserves the previous report.
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
Deep Research surfaced 'Error: unknown error' whenever every search
provider returned an empty result set without raising (e.g. SearXNG is
reachable but all its engines fail internally). _last_search_error was
only set on exceptions, so the empty-but-no-exception path left it unset
and the caller fell back to 'unknown error'.
Record an actionable reason on that path naming the providers that were
tried, so users can tell it's a search-backend problem rather than a
model problem. The provider-raised path is unchanged.
Re: #344.
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
ooovenenoso