Initial commit: workspace setup with skills, memory, config
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159
skills/qdrant-memory/scripts/store_memory.py
Executable file
159
skills/qdrant-memory/scripts/store_memory.py
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#!/usr/bin/env python3
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"""
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Enhanced memory storage with metadata support
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Usage: store_memory.py "Memory text" [--tags tag1,tag2] [--importance medium]
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[--confidence high] [--source user|inferred|external]
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[--verified] [--expires 2026-03-01] [--related id1,id2]
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"""
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import argparse
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import json
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import sys
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import urllib.request
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import uuid
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from datetime import datetime, timedelta
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QDRANT_URL = "http://10.0.0.40:6333"
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COLLECTION_NAME = "kimi_memories"
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OLLAMA_URL = "http://10.0.0.10:11434/v1"
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def get_embedding(text):
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"""Generate embedding using snowflake-arctic-embed2 via Ollama"""
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data = json.dumps({
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"model": "snowflake-arctic-embed2",
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"input": text[:8192]
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}).encode()
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req = urllib.request.Request(
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f"{OLLAMA_URL}/embeddings",
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data=data,
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headers={"Content-Type": "application/json"}
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)
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try:
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with urllib.request.urlopen(req, timeout=30) as response:
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result = json.loads(response.read().decode())
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return result["data"][0]["embedding"]
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except Exception as e:
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print(f"Error generating embedding: {e}", file=sys.stderr)
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return None
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def store_memory(text, embedding, tags=None, importance="medium", date=None,
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source="conversation", confidence="high", source_type="user",
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verified=True, expires_at=None, related_memories=None):
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"""Store memory in Qdrant with enhanced metadata"""
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if date is None:
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date = datetime.now().strftime("%Y-%m-%d")
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# Generate a UUID for the point ID
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point_id = str(uuid.uuid4())
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# Build payload with all metadata
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payload = {
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"text": text,
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"date": date,
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"tags": tags or [],
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"importance": importance,
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"source": source,
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"confidence": confidence, # high/medium/low
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"source_type": source_type, # user/inferred/external
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"verified": verified, # bool
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"created_at": datetime.now().isoformat(),
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"access_count": 0,
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"last_accessed": datetime.now().isoformat()
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}
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# Optional metadata
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if expires_at:
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payload["expires_at"] = expires_at
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if related_memories:
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payload["related_memories"] = related_memories
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# Qdrant upsert format
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upsert_data = {
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"points": [
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{
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"id": point_id,
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"vector": embedding,
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"payload": payload
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}
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]
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}
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req = urllib.request.Request(
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f"{QDRANT_URL}/collections/{COLLECTION_NAME}/points?wait=true",
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data=json.dumps(upsert_data).encode(),
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headers={"Content-Type": "application/json"},
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method="PUT"
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)
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try:
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with urllib.request.urlopen(req, timeout=10) as response:
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result = json.loads(response.read().decode())
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if result.get("status") == "ok":
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return point_id
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else:
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print(f"Qdrant response: {result}", file=sys.stderr)
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return None
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except urllib.error.HTTPError as e:
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error_body = e.read().decode()
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print(f"HTTP Error {e.code}: {error_body}", file=sys.stderr)
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return None
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except Exception as e:
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print(f"Error storing memory: {e}", file=sys.stderr)
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return None
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def link_memories(point_id, related_ids):
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"""Link this memory to related memories (bidirectional)"""
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# Update this memory to include related
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# Then update each related memory to include this one
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pass # Implementation would update existing points
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Store a memory in Qdrant with metadata")
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parser.add_argument("text", help="Memory text to store")
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parser.add_argument("--tags", help="Comma-separated tags")
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parser.add_argument("--importance", default="medium", choices=["low", "medium", "high"])
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parser.add_argument("--date", help="Date in YYYY-MM-DD format")
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parser.add_argument("--source", default="conversation", help="Source of the memory")
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parser.add_argument("--confidence", default="high", choices=["high", "medium", "low"],
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help="Confidence in this memory's accuracy")
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parser.add_argument("--source-type", default="user", choices=["user", "inferred", "external"],
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help="How this memory was obtained")
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parser.add_argument("--verified", action="store_true", default=True,
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help="Whether this memory has been verified")
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parser.add_argument("--expires", help="Expiration date YYYY-MM-DD (for temporary memories)")
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parser.add_argument("--related", help="Comma-separated related memory IDs")
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args = parser.parse_args()
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# Parse tags and related memories
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tags = [t.strip() for t in args.tags.split(",")] if args.tags else []
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related = [r.strip() for r in args.related.split(",")] if args.related else None
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print(f"Generating embedding...")
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embedding = get_embedding(args.text)
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if embedding is None:
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print("❌ Failed to generate embedding", file=sys.stderr)
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sys.exit(1)
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print(f"Storing memory (vector dim: {len(embedding)})...")
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point_id = store_memory(
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args.text, embedding, tags, args.importance, args.date, args.source,
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args.confidence, args.source_type, args.verified, args.expires, related
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)
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if point_id:
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print(f"✅ Memory stored successfully")
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print(f" ID: {point_id}")
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print(f" Tags: {tags}")
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print(f" Importance: {args.importance}")
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print(f" Confidence: {args.confidence}")
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print(f" Source: {args.source_type}")
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if args.expires:
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print(f" Expires: {args.expires}")
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else:
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print(f"❌ Failed to store memory", file=sys.stderr)
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sys.exit(1)
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