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fix(research): keep Discuss chats grounded on their report (#4006)

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* fix(research): preserve Discuss spin-off primer during context trimming

trim_for_context() kept only system_msgs[:1] as essential and dropped the
rest under budget pressure. A research "Discuss" spin-off seeds the report
as a system message that sits after the preface system messages, so it
landed in extra_system and was the first thing evicted once the chat grew
— the conversation then lost its grounding and drifted off task.

Treat any system message carrying research_spinoff_from metadata as
essential, alongside the leading system prompt, so the seeded report
survives trimming. maybe_compact already retains all system messages.

Tests: tests/test_context_compactor.py::TestResearchPrimerPreserved

* fix(research): ground Discuss spin-off chats on the seeded report

build_chat_context injected global memory (pinned + hybrid-retrieved) and
personal-doc RAG every turn, keyed off the user-level memory_enabled pref
and a request-scoped use_rag flag — never the session. A research spin-off,
whose primer declares the report the sole knowledge base, thus had
unrelated keyword-matched facts pulled in ("wrong data") competing with the
report; its rag=False flag was also ignored (use_rag defaulted on).

Add _session_is_research_spinoff(sess) (detects the primer research_spinoff_from
metadata; handles ChatMessage and dict forms) and, for such sessions,
disable memory injection and force RAG off.

Tests: tests/test_chat_helpers.py spin-off detection cases

---------

Co-authored-by: Dan (cirim) <claude@cirim.org>
This commit is contained in:
cirim
2026-06-15 11:31:57 +00:00
committed by GitHub
parent 172a8ea7b0
commit e7abb7559d
4 changed files with 127 additions and 5 deletions
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src/context_compactor.py
+11 -3
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@@ -244,9 +244,17 @@ def trim_for_context(messages: List[Dict], context_length: int, reserve_tokens:
protected_tokens = estimate_tokens(protected_msgs)
budget -= protected_tokens
# Priority: keep first system msg (preset prompt), drop others (memory, RAG, memo)
essential_system = system_msgs[:1] if system_msgs else []
extra_system = system_msgs[1:]
# Priority: keep first system msg (preset prompt), drop others (memory, RAG, memo).
# Exception: a research-spinoff primer (the seeded report that grounds a
# "Discuss" chat) must never be dropped — it is the conversation's whole
# knowledge base. Treat any system message carrying research_spinoff_from
# metadata as essential alongside the leading system prompt.
def _is_research_primer(m):
return bool((m.get("metadata") or {}).get("research_spinoff_from"))
_primers = [m for m in system_msgs if _is_research_primer(m)]
_non_primer = [m for m in system_msgs if not _is_research_primer(m)]
essential_system = (_non_primer[:1] if _non_primer else []) + _primers
extra_system = _non_primer[1:]
# Try dropping extra system messages one by one (from the end)
trimmed = essential_system + convo_msgs