--- name: global-instance-9c102bea990f description: "global-instance-9c102bea990f" --- #!/usr/bin/env python3 """ LanceDB integration for long-term memory management. Provides vector search and semantic memory capabilities. """ import os import json import lancedb from datetime import datetime from typing import List, Dict, Any, Optional from pathlib import Path class LanceMemoryDB: """LanceDB wrapper for long-term memory storage and retrieval.""" def __init__(self, db_path: str = "/Users/prerak/clawd/memory/lancedb"): self.db_path = Path(db_path) self.db_path.mkdir(parents=True, exist_ok=True) self.db = lancedb.connect(self.db_path) # Ensure memory table exists if "memory" not in self.db.table_names(): self._create_memory_table() def _create_memory_table(self): """Create the memory table with appropriate schema.""" schema = [ {"name": "id", "type": "int", "nullable": False}, {"name": "timestamp", "type": "timestamp", "nullable": False}, {"name": "content", "type": "str", "nullable": False}, {"name": "category", "type": "str", "nullable": True}, {"name": "tags", "type": "str[]", "nullable": True}, {"name": "importance", "type": "int", "nullable": True}, {"name": "metadata", "type": "json", "nullable": True}, ] self.db.create_table("memory", schema=schema) def add_memory(self, content: str, category: str = "general", tags: List[str] = None, importance: int = 5, metadata: Dict[str, Any] = None) -> int: """Add a new memory entry.""" table = self.db.open_table("memory") # Get next ID max_id = table.to_pandas()["id"].max() if len(table) > 0 else 0 new_id = max_id + 1 # Insert new memory memory_data = { "id": new_id, "timestamp": datetime.now(), "content": content, "category": category, "tags": tags or [], "importance": importance, "metadata": metadata or {} } table.add([memory_data]) return new_id def search_memories(self, query: str, category: str = None, limit: int = 10) -> List[Dict]: """Search memories using vector similarity.""" table = self.db.open_table("memory") # Build filter where_clause = [] if category: where_clause.append(f"category = '{category}'") filter_expr = " AND ".join(where_clause) if where_clause else None # Vector search results = table.vector_search(query).limit(limit).where(filter_expr).to_list() return results def get_memories_by_category(self, category: str, limit: int = 50) -> List[Dict]: """Get memories by category.""" table = self.db.open_table("memory") df = table.to_pandas() filtered = df[df["category"] == category].head(limit) return filtered.to_dict("records") def get_memory_by_id(self, memory_id: int) -> Optional[Dict]: """Get a specific memory by ID.""" table = self.db.open_table("memory") df = table.to_pandas() result = df[df["id"] == memory_id] return result.to_dict("records")[0] if len(result) > 0 else None def update_memory(self, memory_id: int, **kwargs) -> bool: """Update a memory entry.""" table = self.db.open_table("memory") valid_fields = ["content", "category", "tags", "importance", "metadata"] updates = {k: v for k, v in kwargs.items() if k in valid_fields} if not updates: return False # Convert to proper types for LanceDB if "tags" in updates and isinstance(updates["tags"], list): updates["tags"] = str(updates["tags"]).replace("'", '"') table.update(updates, where=f"id = {memory_id}") return True def delete_memory(self, memory_id: int) -> bool: """Delete a memory entry.""" table = self.db.open_table("memory") current_count = len(table) table.delete(f"id = {memory_id}") return len(table) < current_count def get_all_categories(self) -> List[str]: """Get all unique categories.""" table = self.db.open_table("memory") df = table.to_pandas() return df["category"].dropna().unique().tolist() def get_memory_stats(self) -> Dict[str, Any]: """Get statistics about memory storage.""" table = self.db.open_table("memory") df = table.to_pandas() return { "total_memories": len(df), "categories": len(self.get_all_categories()), "by_category": df["category"].value_counts().to_dict(), "date_range": { "earliest": df["timestamp"].min().isoformat() if len(df) > 0 else None, "latest": df["timestamp"].max().isoformat() if len(df) > 0 else None } } # Global instance lancedb_memory = LanceMemoryDB() def add_memory(content: str, category: str = "general", tags: List[str] = None, importance: int = 5, metadata: Dict[str, Any] = None) -> int: """Add a memory to the LanceDB store.""" return lancedb_memory.add_memory(content, category, tags, importance, metadata) def search_memories(query: str, category: str = None, limit: int = 10) -> List[Dict]: """Search memories using semantic similarity.""" return lancedb_memory.search_memories(query, category, limit) def get_memories_by_category(category: str, limit: int = 50) -> List[Dict]: """Get memories by category.""" return lancedb_memory.get_memories_by_category(category, limit) def get_memory_stats() -> Dict[str, Any]: """Get memory storage statistics.""" return lancedb_memory.get_memory_stats() # Example usage if __name__ == "__main__": # Test the database print("Testing LanceDB memory integration...") # Add a test memory test_id = add_memory( content="This is a test memory for LanceDB integration", category="test", tags=["lancedb", "integration", "test"], importance=8 ) print(f"Added memory with ID: {test_id}") # Search for memories results = search_memories("test memory") print(f"Search results: {len(results)} memories found") # Get stats stats = get_memory_stats() print(f"Memory stats: {stats}") --- Source attribution: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/pntrivedy/lancedb-memory/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 clawdbot Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: kbarbel640-del Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/pntrivedy/lancedb-memory/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Data Engineer, ML / AI Engineer, Product Manager Specialties: API, Database Tools: Lancedb Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b5226b1afe1ce0494cdcf21408a5e1b683774d15a46f844f6bb31e8359d633dc