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37083593d6
The embedding cache was keyed by skill_id alone, so any edit to a
SKILL.md body or description produced stale embeddings that
get_or_compute_embedding kept serving until a manual invalidate_cache
call or a file deletion. Previously this was mostly invisible because
select_skills_with_llm was the only caller exercising the cache; after
the preceding commit wires search_skills through the same path the
staleness becomes observable on every MCP query.
Use "{skill_id}:{sha256(embedding_text)[:16]}" as the cache key, so
any change to the text produced by _build_embedding_text (name +
description + body, truncated to SKILL_EMBEDDING_MAX_CHARS) causes
an automatic cache miss and a fresh embedding. Both
get_or_compute_embedding and _embedding_rank are updated.
Bounded growth: on each successful new compute, older entries with
the same "{skill_id}:" prefix are pruned in the same write. Net
result: at most one cached embedding per skill_id at any time, aside
from transient migration state.
Backward compatibility: existing pickle files keyed by skill_id alone
are migrated in place on first lookup (no API call needed); the old
key is dropped after migration.
invalidate_cache(skill_id) now removes every content-addressed entry
and any legacy entry for that skill_id, so historical versions do
not leak across evolutions.
Functional benchmark on a 28-skill local registry with
text-embedding-3-small via OpenRouter: top-1 match identity preserved
on all test queries, score drift below 0.001, warm latency
~260-400ms/query (unchanged from the previous commit).
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|---|---|---|
| .. | ||
| __init__.py | ||
| analyzer.py | ||
| conversation_formatter.py | ||
| evolver.py | ||
| fuzzy_match.py | ||
| patch.py | ||
| registry.py | ||
| retrieve_tool.py | ||
| skill_ranker.py | ||
| skill_utils.py | ||
| store.py | ||
| types.py | ||