* Add Hindsight as git subtree + BCGU noise filtering tests
Adds hindsight server source as a subtree under hindsight-api/ so we
can iterate on server-side fixes directly.
test_bcgu_noise_filtering.py proves that a well-crafted
retain_custom_instructions (BCGU_RETAIN_MISSION) can suppress
talking-head noise at fact extraction time — eliminating the need for
client-side --filter-vision-noise preprocessing.
Tests cover:
- Default mode extracts 3 noise facts from talking-head frame (problem documented)
- BCGU mission produces 0 noise facts from same talking-head frame
- BCGU mission still extracts 2 high-value ChatGPT screen facts correctly
- Mixed doc (2 talking-head + 2 screen): 0% noise ratio with BCGU mission
- Pure talking-head doc: 0 facts extracted
All 5 tests pass in ~32s using gpt-4o-mini.
* fix(consolidation): respect mission context over ephemeral-state heuristic
Two related fixes for the consolidation engine when a bank mission is
configured:
1. **Mission override for ephemeral-state filter** (`prompts.py`):
The system prompt previously instructed the LLM to discard any fact
that looked like "ephemeral state" (e.g. current position, transient
actions). When a mission is active the mission itself defines what is
valuable — timestamped screen actions, session events, tool interactions
may all be mission-critical even though they look ephemeral. Added a
MISSION OVERRIDE block that explicitly tells the LLM the mission takes
priority over the generic ephemeral-state guidance.
2. **Remove contradictory durable-knowledge nudge** (`consolidator.py`):
The user-prompt builder was injecting "Focus on DURABLE knowledge that
serves this mission, not ephemeral state" alongside the mission text.
This phrasing contradicted missions that intentionally capture
timestamped events. Replaced with a neutral directive that simply
signals the mission overrides general rules.
3. **JSON control-character sanitisation** (`consolidator.py`):
LLMs occasionally embed literal ASCII control characters (0x00–0x1f)
inside JSON string values, causing `json.loads` to raise a
JSONDecodeError. Added a try/except that strips control characters
and retries the parse before re-raising, preventing spurious failures.
* refactor(consolidation): move sanitize_llm_output to llm_wrapper, reuse in consolidator
- Add `sanitize_llm_output()` to `llm_wrapper.py` as the single canonical
function for stripping characters that break downstream systems
(ASCII control chars 0x00-0x08/0x0B-0x0C/0x0E-0x1F/0x7F and Unicode
surrogates). Tab, newline, and carriage-return are preserved.
- Reduce `_sanitize_text()` in `fact_extraction.py` to a thin wrapper
that delegates to `sanitize_llm_output()`.
- Update `consolidator.py` to import and call `sanitize_llm_output()`
directly instead of reimplementing the logic inline.
- Remove test_bcgu_noise_filtering.py (should not have been committed).
* fix(consolidation): apply sanitize_llm_output to observation text fields
sanitize_llm_output was imported but unused after the old _call_llm_once
path was removed. The batch flow uses structured Pydantic output so
there's no raw json.loads call — instead, apply sanitization via
field_validator on _CreateAction.text and _UpdateAction.text so control
characters are stripped before observation text reaches the database.
* fix(entity-resolver): correct mention_count for new entities in batch retain
When the same entity (e.g. "Bob") appears across N items in a single batch
retain, _resolve_entities_batch_impl deduplicates them into one name group
before inserting, then queued only ONE _EntityStat regardless of N. The
flush therefore always incremented mention_count by 1 beyond the INSERT
value — giving 2 for any number of mentions.
Two-part fix:
- INSERT with mention_count=0 so the post-transaction flush is the single
source of truth for the count (avoids an off-by-one for N=1 as well).
- Append one _EntityStat per original mention (len(g.indices)) instead of
one per unique name, so flush_pending_stats() adds the correct total N.
This makes the batch path consistent with the single-entity path, which
already accumulates one stat per mention via entities_to_update.
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