semantic-recall
Semantic (vector) memory recall for Clark. Finds facts by MEANING, not just keywords — use when the lexical memory (holographic FTS) misses paraphrases or synonyms, or when you want the most conceptually-relevant stored facts about a topic (owner, projects, tokens, tools, business). Complements the built-in memory; does not replace it.
- License 许可
- MIT
- 所属仓库
- S3YED/appie-kit
- 最近更新
- 2026-08-27
- Stars
- ★ 14
- Forks
- 3
01 前言 Front Matter
- name
- semantic-recall
- tools
- Bash
- origin
- weblyfe
- description
- Semantic (vector) memory recall for Clark. Finds facts by MEANING, not just keywords — use when the lexical memory (holographic FTS) misses paraphrases or synonyms, or when you want the most conceptually-relevant stored facts about a topic (owner, projects, tokens, tools, business). Complements the built-in memory; does not replace it.
03 SKILL.md 正文
semantic-recall: Vector Memory Layer
Clark's holographic memory is lexical (FTS5 + Jaccard token overlap + SHA-256 hash HRR). It misses synonyms and paraphrases. This skill adds a real learned-embedding semantic layer (fastembed BAAI/bge-small-en-v1.5, ONNX, no torch) over the same memory_store.db facts.
When to use
- A recall by exact words returned nothing useful but the concept is likely stored.
- You want the top conceptually-similar facts about a person, project, credential, or tool.
Recall a query
/usr/local/lib/hermes-agent/venv/bin/python /root/.hermes/semantic/semantic.py recall "<your natural-language query>" 5
Returns JSON: [{fact_id, score, category, content}, ...] ranked by semantic similarity (higher score = closer).
Re-index after new memories (normally the cron does this every 6h)
/usr/local/lib/hermes-agent/venv/bin/python /root/.hermes/semantic/semantic.py index
Incremental: only embeds new/changed facts, prunes deleted ones. Output JSON: {indexed, deleted, total_facts}.
Stats / health
/usr/local/lib/hermes-agent/venv/bin/python /root/.hermes/semantic/semantic.py stats
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