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.
135 lines
3.7 KiB
Python
135 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""User-profile ingest hook.
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Reads the persisted user profile for the given agent and, when enough
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interaction data has been collected (>= 5 interactions), returns a
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compact profile summary as injected memory so the agent can personalise
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its responses.
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This hook is READ-ONLY -- it never modifies the profile 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 sys
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# ---------------------------------------------------------------------------
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# Constants
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# ---------------------------------------------------------------------------
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STORE_DIR = os.path.join(
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os.path.expanduser("~"), ".librefang", "plugins", "user-profile"
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)
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MIN_INTERACTIONS = 5
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MAX_SUMMARY_LEN = 200
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TOP_EXPERTISE = 5
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def empty_result():
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"""Return an empty ingest result."""
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return json.dumps({"type": "ingest_result", "memories": []})
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def load_profile(agent_id):
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"""Load the profile JSON for an agent. Returns None 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("interaction_count"), int):
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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 None
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def message_length_bucket(avg_len):
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"""Classify average message length into a human-readable bucket."""
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if avg_len < 50:
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return "brief"
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elif avg_len <= 200:
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return "moderate"
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else:
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return "detailed"
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def build_summary(profile):
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"""Build a compact profile summary string (max MAX_SUMMARY_LEN chars)."""
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parts = []
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# Top expertise areas
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expertise = profile.get("expertise_areas", {})
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if expertise:
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sorted_areas = sorted(expertise.items(), key=lambda x: x[1], reverse=True)
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top = [area for area, _count in sorted_areas[:TOP_EXPERTISE]]
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parts.append("expertise=" + ",".join(top))
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# Communication style
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avg_len = profile.get("avg_message_length", 0)
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parts.append("style=" + message_length_bucket(avg_len))
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# Technical level
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tech_level = profile.get("technical_level", "")
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if tech_level:
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parts.append("level=" + tech_level)
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# Question ratio
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q_ratio = profile.get("question_ratio", 0.0)
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if q_ratio > 0.5:
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parts.append("asks-many-questions")
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summary = "; ".join(parts)
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if len(summary) > MAX_SUMMARY_LEN:
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summary = summary[:MAX_SUMMARY_LEN - 3] + "..."
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return summary
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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(empty_result())
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return
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agent_id = request.get("agent_id", "")
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if not agent_id:
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print(empty_result())
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return
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profile = load_profile(agent_id)
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if profile is None:
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print(empty_result())
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return
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interaction_count = profile.get("interaction_count", 0)
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if interaction_count < MIN_INTERACTIONS:
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print(empty_result())
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return
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summary = build_summary(profile)
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if not summary:
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print(empty_result())
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return
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memory = {"content": f"[user-profile] User context: {summary}"}
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print(json.dumps({"type": "ingest_result", "memories": [memory]}))
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if __name__ == "__main__":
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main()
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