c75d3e1975
_fallback_memory_candidates matched both positive (prefer/like/love) and
negative (hate / do not like / don't like) sentiment verbs in one regex
alternation, then formatted every hit as "User prefers {X}.". So
"I hate cilantro" was stored as "User prefers cilantro." -- the inverse of
what the user said. These fallback facts are persisted to memory and later
re-injected into the model's context, so the inverted preference actively
misleads the assistant.
Capture the matched verb and branch on it: negatives become
"User dislikes {X}.", positives stay "User prefers {X}." (still filed under
the existing "preference" category).
Supported by Claude Opus 4.8
Co-authored-by: SurprisedDuck <288741682+SurprisedDuck@users.noreply.github.com>
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|---|---|---|
| .. | ||
| __init__.py | ||
| memory.py | ||
| memory_extractor.py | ||
| memory_vector.py | ||
| service.py | ||
| skill_extractor.py | ||
| skill_format.py | ||
| skill_importer.py | ||
| skills.py | ||