| import os | |
| from lightmem.memory.lightmem import LightMemory | |
| def load_lightmem(collection_name): | |
| config = { | |
| "memory_manager": { | |
| "model_name": "openai", | |
| "configs": { | |
| "model": "gpt-4o-mini", | |
| "api_key": "", | |
| "max_tokens": 16000, | |
| "openai_base_url": "" | |
| } | |
| }, | |
| "retrieve_strategy": "embedding", | |
| "embedding_retriever": { | |
| "model_name": "qdrant", | |
| "configs": { | |
| "collection_name": collection_name, | |
| "embedding_model_dims": 384, | |
| "path": f"/{collection_name}", | |
| } | |
| }, | |
| "update": "offline", | |
| } | |
| lightmem = LightMemory.from_config(config) | |
| return lightmem | |
| base_dir = "" | |
| for collection_name in os.listdir(base_dir): | |
| collection_path = os.path.join(base_dir, collection_name) | |
| if not os.path.isdir(collection_path): | |
| continue | |
| print(f"Processing collection: {collection_name}") | |
| try: | |
| lightmem = load_lightmem(collection_name) | |
| lightmem.construct_update_queue_all_entries() | |
| lightmem.offline_update_all_entries(score_threshold=0.8) | |
| print(f"Finished updating {collection_name}") | |
| except Exception as e: | |
| print(f"Error processing {collection_name}: {e}") | |