Full sync - all projects, memory, configs

tools/auto-memory-recall.py

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+#!/usr/bin/env python3
+"""
+Auto-Memory Recall — query the auto-memory ChromaDB collection.
+Usage:
+  python3 auto-memory-recall.py "how did we fix nginx"
+  python3 auto-memory-recall.py --query "chromadb setup" --limit 3 --topic infrastructure
+"""
+
+import sys, json, os, argparse
+import requests
+import chromadb
+
+CHROMADB_HOST = os.environ.get("CHROMADB_HOST", "192.168.86.25")
+CHROMADB_PORT = int(os.environ.get("CHROMADB_PORT", "8000"))
+OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://192.168.86.40:11434")
+EMBED_MODEL = "nomic-embed-text"
+COLLECTION = "auto-memory"
+
+
+def ollama_embed(text: str) -> list[float]:
+    r = requests.post(
+        f"{OLLAMA_URL}/api/embed",
+        json={"model": EMBED_MODEL, "input": text},
+        timeout=10,
+    )
+    r.raise_for_status()
+    return r.json()["embeddings"][0]
+
+
+def recall(query: str, limit: int = 5, topic: str = None, agent_id: str = None) -> list[dict]:
+    client = chromadb.HttpClient(host=CHROMADB_HOST, port=CHROMADB_PORT)
+    collection = client.get_or_create_collection(name=COLLECTION, metadata={"hnsw:space": "cosine"})
+
+    where = {}
+    if topic:
+        where["topic"] = topic
+    if agent_id:
+        where["agent_id"] = agent_id
+
+    try:
+        embedding = ollama_embed(query)
+        results = collection.query(
+            query_embeddings=[embedding],
+            n_results=limit,
+            where=where if where else None,
+        )
+    except Exception:
+        # Fallback to text query if embedding fails
+        results = collection.query(
+            query_texts=[query],
+            n_results=limit,
+            where=where if where else None,
+        )
+
+    memories = []
+    if results and results["documents"]:
+        for i, doc in enumerate(results["documents"][0]):
+            meta = results["metadatas"][0][i] if results["metadatas"] else {}
+            dist = results["distances"][0][i] if results["distances"] else None
+            memories.append({
+                "memory": doc,
+                "similarity": round(1 - dist, 3) if dist is not None else None,
+                "id": results["ids"][0][i] if results["ids"] else None,
+                **meta,
+            })
+    return memories
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Recall memories from auto-memory collection")
+    parser.add_argument("query", nargs="?", help="Search query")
+    parser.add_argument("--query", "-q", dest="query_flag")
+    parser.add_argument("--limit", "-n", type=int, default=5)
+    parser.add_argument("--topic", "-t")
+    parser.add_argument("--agent-id", "-a")
+    args = parser.parse_args()
+
+    query = args.query or args.query_flag
+    if not query:
+        parser.print_help()
+        sys.exit(1)
+
+    results = recall(query, args.limit, args.topic, args.agent_id)
+    print(json.dumps(results, indent=2))
+
+
+if __name__ == "__main__":
+    main()