ixoblakp
8dec8f6038
11 plugins from github.com/librefang/librefang-registry plugins/. index.json / index.json.sig are signed with Arka's Ed25519 key (not upstream stats.librefang.ai). Private key is not in this repo.
145 lines
4.5 KiB
Python
145 lines
4.5 KiB
Python
#!/usr/bin/env python3
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"""Episodic memory ingest hook.
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Loads the episode store for the current agent, scores completed episodes
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against the incoming message's keywords using keyword overlap ratio, and
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returns the top 2 matching episodes as contextual memories.
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This hook is read-only -- it never modifies the episode store.
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Receives via stdin:
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{"type": "ingest", "agent_id": "...", "message": "user message text"}
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Prints to stdout:
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{"type": "ingest_result", "memories": [{"content": "..."}]}
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"""
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import json
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import os
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import re
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import sys
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# ---------------------------------------------------------------------------
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# Stopwords & keyword extraction
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# ---------------------------------------------------------------------------
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STOPWORDS = frozenset({
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"a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
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"of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
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"being", "have", "has", "had", "do", "does", "did", "will", "would",
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"could", "should", "may", "might", "shall", "can", "need", "must",
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"it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
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"they", "them", "their", "this", "that", "these", "those", "what",
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"which", "who", "how", "when", "where", "why", "if", "then", "so",
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"not", "no", "just", "also", "very", "too", "about", "up", "out",
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"all", "some", "any", "each", "every", "into", "over", "after",
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})
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MIN_WORD_LEN = 3
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# Minimum overlap ratio to consider an episode relevant
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MIN_OVERLAP = 0.2
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# Maximum episodes to return
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MAX_RESULTS = 2
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def extract_keywords(text):
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"""Extract deduplicated keywords from text.
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Lowercases, removes stopwords, filters words shorter than MIN_WORD_LEN.
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"""
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words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
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seen = set()
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keywords = []
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for w in words:
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lower = w.lower()
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if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
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seen.add(lower)
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keywords.append(lower)
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return keywords
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def load_episode_store(agent_id):
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"""Load the episode store JSON for the given agent. Returns None on failure."""
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store_dir = os.path.join(
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os.path.expanduser("~"), ".librefang", "plugins", "episodic-memory"
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)
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store_path = os.path.join(store_dir, f"{agent_id}.json")
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if not os.path.isfile(store_path):
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return None
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try:
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with open(store_path, "r", encoding="utf-8") as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return None
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def score_episode(episode_keywords, query_keywords):
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"""Compute keyword overlap ratio between an episode and the query.
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overlap_ratio = |intersection| / |union| (Jaccard similarity)
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"""
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if not episode_keywords or not query_keywords:
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return 0.0
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ep_set = set(episode_keywords)
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q_set = set(query_keywords)
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intersection = ep_set & q_set
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union = ep_set | q_set
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if not union:
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return 0.0
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return len(intersection) / len(union)
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def main():
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try:
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request = json.loads(sys.stdin.read())
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except (json.JSONDecodeError, ValueError):
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print(json.dumps({"type": "ingest_result", "memories": []}))
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return
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message = request.get("message", "")
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agent_id = request.get("agent_id", "")
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if not message.strip() or not agent_id:
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print(json.dumps({"type": "ingest_result", "memories": []}))
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return
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query_keywords = extract_keywords(message)
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if not query_keywords:
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print(json.dumps({"type": "ingest_result", "memories": []}))
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return
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store = load_episode_store(agent_id)
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if not store:
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print(json.dumps({"type": "ingest_result", "memories": []}))
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return
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episodes = store.get("episodes", [])
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# Score only completed episodes
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scored = []
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for ep in episodes:
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if ep.get("status") != "completed":
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continue
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overlap = score_episode(ep.get("keywords", []), query_keywords)
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if overlap > MIN_OVERLAP:
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scored.append((overlap, ep))
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# Sort by overlap descending, take top MAX_RESULTS
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scored.sort(key=lambda x: x[0], reverse=True)
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top = scored[:MAX_RESULTS]
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memories = []
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for _score, ep in top:
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timestamp = ep.get("ended", ep.get("started", "unknown"))
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summary = ep.get("summary", "no summary")
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memories.append({
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"content": f"[episodic-memory] Past episode ({timestamp}): {summary}"
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})
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print(json.dumps({"type": "ingest_result", "memories": memories}))
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if __name__ == "__main__":
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main()
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