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.
140 lines
4.3 KiB
Python
140 lines
4.3 KiB
Python
#!/usr/bin/env python3
|
|
"""Topic-memory ingest hook.
|
|
|
|
Reads the topic store for the given agent and returns the top matching
|
|
topic summaries based on Jaccard similarity between the incoming message
|
|
keywords and each stored topic cluster.
|
|
|
|
This hook is READ-ONLY -- it never modifies the topic store.
|
|
|
|
Receives via stdin:
|
|
{"type": "ingest", "agent_id": "...", "message": "user message text"}
|
|
|
|
Prints to stdout:
|
|
{"type": "ingest_result", "memories": [{"content": "..."}]}
|
|
"""
|
|
import json
|
|
import os
|
|
import re
|
|
import sys
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Stopwords & constants
|
|
# ---------------------------------------------------------------------------
|
|
|
|
STOPWORDS = frozenset({
|
|
"a", "an", "the", "and", "or", "but", "in", "on", "at", "to", "for",
|
|
"of", "with", "by", "from", "is", "are", "was", "were", "be", "been",
|
|
"being", "have", "has", "had", "do", "does", "did", "will", "would",
|
|
"could", "should", "may", "might", "shall", "can", "need", "must",
|
|
"it", "its", "i", "me", "my", "you", "your", "he", "she", "we",
|
|
"they", "them", "their", "this", "that", "these", "those", "what",
|
|
"which", "who", "how", "when", "where", "why", "if", "then", "so",
|
|
"not", "no", "just", "also", "very", "too", "about", "up", "out",
|
|
"all", "some", "any", "each", "every", "into", "over", "after",
|
|
})
|
|
|
|
MIN_WORD_LEN = 3
|
|
MAX_RESULTS = 3
|
|
MIN_SIMILARITY = 0.15
|
|
|
|
STORE_DIR = os.path.join(os.path.expanduser("~"), ".librefang", "plugins", "topic-memory")
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helpers
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def extract_keywords(text):
|
|
"""Extract lowercase keywords from text, filtering stopwords and short tokens."""
|
|
words = re.findall(r"[a-zA-Z][a-zA-Z0-9\-]*[a-zA-Z0-9]|[a-zA-Z]", text)
|
|
seen = set()
|
|
keywords = set()
|
|
for w in words:
|
|
lower = w.lower()
|
|
if lower not in STOPWORDS and len(lower) >= MIN_WORD_LEN and lower not in seen:
|
|
seen.add(lower)
|
|
keywords.add(lower)
|
|
return keywords
|
|
|
|
|
|
def jaccard_similarity(set_a, set_b):
|
|
"""Compute Jaccard similarity between two sets."""
|
|
if not set_a or not set_b:
|
|
return 0.0
|
|
intersection = len(set_a & set_b)
|
|
union = len(set_a | set_b)
|
|
if union == 0:
|
|
return 0.0
|
|
return intersection / union
|
|
|
|
|
|
def load_store(agent_id):
|
|
"""Load the topic store JSON for an agent. Returns empty structure on any error."""
|
|
path = os.path.join(STORE_DIR, f"{agent_id}.json")
|
|
try:
|
|
with open(path, "r", encoding="utf-8") as f:
|
|
data = json.load(f)
|
|
if isinstance(data, dict) and isinstance(data.get("topics"), list):
|
|
return data
|
|
except (OSError, json.JSONDecodeError, ValueError):
|
|
pass
|
|
return {"topics": []}
|
|
|
|
|
|
def empty_result():
|
|
"""Return an empty ingest result."""
|
|
return json.dumps({"type": "ingest_result", "memories": []})
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Main
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def main():
|
|
try:
|
|
request = json.loads(sys.stdin.read())
|
|
except (json.JSONDecodeError, ValueError):
|
|
print(empty_result())
|
|
return
|
|
|
|
message = request.get("message", "")
|
|
agent_id = request.get("agent_id", "")
|
|
|
|
if not message.strip() or not agent_id:
|
|
print(empty_result())
|
|
return
|
|
|
|
msg_keywords = extract_keywords(message)
|
|
if not msg_keywords:
|
|
print(empty_result())
|
|
return
|
|
|
|
store = load_store(agent_id)
|
|
topics = store.get("topics", [])
|
|
|
|
# Score each topic cluster against the current message keywords
|
|
scored = []
|
|
for topic in topics:
|
|
topic_kw = set(topic.get("keywords", []))
|
|
sim = jaccard_similarity(msg_keywords, topic_kw)
|
|
if sim >= MIN_SIMILARITY:
|
|
scored.append((sim, topic))
|
|
|
|
# Sort descending by similarity, take top N
|
|
scored.sort(key=lambda x: x[0], reverse=True)
|
|
top = scored[:MAX_RESULTS]
|
|
|
|
memories = []
|
|
for _sim, topic in top:
|
|
summary = topic.get("summary", "")
|
|
if summary:
|
|
memories.append({"content": f"[topic-memory] Related context: {summary}"})
|
|
|
|
print(json.dumps({"type": "ingest_result", "memories": memories}))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|