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.
227 lines
7.0 KiB
Python
227 lines
7.0 KiB
Python
#!/usr/bin/env python3
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"""Topic-memory after_turn hook.
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After each conversation turn, extracts keywords from the latest exchange,
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then either merges into an existing topic cluster or creates a new one.
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This hook is WRITE-ONLY -- it never returns memories.
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Receives via stdin:
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{"type": "after_turn", "agent_id": "...", "messages": [
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{"role": "user"|"assistant", "content": "..."}
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]}
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Prints to stdout:
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{"type": "ok"}
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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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from datetime import datetime, timezone
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# ---------------------------------------------------------------------------
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# Stopwords & constants
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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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MAX_TOPICS = 50
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MERGE_THRESHOLD = 0.3
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SUMMARY_MAX_LEN = 200
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STORE_DIR = os.path.join(os.path.expanduser("~"), ".librefang", "plugins", "topic-memory")
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def extract_keywords(text):
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"""Extract lowercase keywords from text, filtering stopwords and short tokens."""
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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 = set()
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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.add(lower)
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return keywords
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def jaccard_similarity(set_a, set_b):
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"""Compute Jaccard similarity between two sets."""
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if not set_a or not set_b:
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return 0.0
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intersection = len(set_a & set_b)
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union = len(set_a | set_b)
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if union == 0:
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return 0.0
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return intersection / union
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def load_store(agent_id):
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"""Load the topic store JSON for an agent. Returns empty structure on any error."""
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path = os.path.join(STORE_DIR, f"{agent_id}.json")
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try:
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with open(path, "r", encoding="utf-8") as f:
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data = json.load(f)
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if isinstance(data, dict) and isinstance(data.get("topics"), list):
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return data
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except (OSError, json.JSONDecodeError, ValueError):
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pass
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return {"topics": []}
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def save_store(agent_id, store):
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"""Persist the topic store to disk."""
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os.makedirs(STORE_DIR, exist_ok=True)
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path = os.path.join(STORE_DIR, f"{agent_id}.json")
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tmp_path = path + ".tmp"
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with open(tmp_path, "w", encoding="utf-8") as f:
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json.dump(store, f, ensure_ascii=False, indent=2)
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os.replace(tmp_path, path)
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def next_topic_id(topics):
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"""Generate the next t_XXX topic ID."""
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max_num = 0
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for t in topics:
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tid = t.get("id", "")
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if tid.startswith("t_"):
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try:
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num = int(tid[2:])
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if num > max_num:
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max_num = num
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except ValueError:
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pass
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return f"t_{max_num + 1:03d}"
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def build_summary(user_content, assistant_content):
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"""Build a truncated summary from the latest user + assistant exchange."""
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parts = []
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if user_content:
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parts.append(f"User: {user_content.strip()}")
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if assistant_content:
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parts.append(f"Assistant: {assistant_content.strip()}")
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raw = " | ".join(parts)
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if len(raw) > SUMMARY_MAX_LEN:
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return raw[: SUMMARY_MAX_LEN - 3] + "..."
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return raw
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def now_iso():
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"""Return current UTC time as ISO 8601 string."""
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return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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def ok_result():
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"""Return the standard ok response."""
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return json.dumps({"type": "ok"})
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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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(ok_result())
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return
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agent_id = request.get("agent_id", "")
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messages = request.get("messages", [])
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if not agent_id or not isinstance(messages, list) or not messages:
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print(ok_result())
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return
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# Extract the latest user and assistant messages
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latest_user = ""
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latest_assistant = ""
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for msg in reversed(messages):
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role = msg.get("role", "")
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content = msg.get("content", "")
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if role == "assistant" and not latest_assistant:
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latest_assistant = content
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elif role == "user" and not latest_user:
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latest_user = content
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if latest_user and latest_assistant:
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break
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if not latest_user and not latest_assistant:
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print(ok_result())
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return
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# Extract keywords from the combined exchange
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combined_text = f"{latest_user} {latest_assistant}"
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current_keywords = extract_keywords(combined_text)
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if not current_keywords:
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print(ok_result())
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return
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store = load_store(agent_id)
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topics = store.get("topics", [])
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timestamp = now_iso()
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# Find the best matching existing topic cluster
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best_sim = 0.0
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best_idx = -1
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for idx, topic in enumerate(topics):
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topic_kw = set(topic.get("keywords", []))
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sim = jaccard_similarity(current_keywords, topic_kw)
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if sim > best_sim:
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best_sim = sim
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best_idx = idx
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if best_sim >= MERGE_THRESHOLD and best_idx >= 0:
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# Merge into existing topic cluster
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topic = topics[best_idx]
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existing_kw = set(topic.get("keywords", []))
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merged_kw = existing_kw | current_keywords
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topic["keywords"] = sorted(merged_kw)
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topic["summary"] = build_summary(latest_user, latest_assistant)
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topic["last_seen"] = timestamp
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topic["hit_count"] = topic.get("hit_count", 0) + 1
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else:
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# Create a new topic cluster
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new_topic = {
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"id": next_topic_id(topics),
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"keywords": sorted(current_keywords),
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"summary": build_summary(latest_user, latest_assistant),
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"last_seen": timestamp,
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"hit_count": 1,
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}
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topics.append(new_topic)
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# Evict lowest hit_count topics if over capacity
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if len(topics) > MAX_TOPICS:
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topics.sort(key=lambda t: (t.get("hit_count", 0), t.get("last_seen", "")))
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topics = topics[len(topics) - MAX_TOPICS:]
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store["topics"] = topics
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save_store(agent_id, store)
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print(ok_result())
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if __name__ == "__main__":
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main()
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