| """ | |
| ARMS-HAT: Hierarchical Attention Tree for AI memory retrieval. | |
| A semantic memory index optimized for LLM conversation history. | |
| Example: | |
| >>> from arms_hat import HatIndex | |
| >>> | |
| >>> # Create index for OpenAI embeddings (1536 dims) | |
| >>> index = HatIndex.cosine(1536) | |
| >>> | |
| >>> # Add embeddings | |
| >>> id1 = index.add([0.1] * 1536) | |
| >>> | |
| >>> # Query | |
| >>> results = index.near([0.1] * 1536, k=10) | |
| >>> for r in results: | |
| ... print(f"{r.id}: {r.score}") | |
| >>> | |
| >>> # Session management | |
| >>> index.new_session() | |
| >>> | |
| >>> # Persistence | |
| >>> index.save("memory.hat") | |
| >>> loaded = HatIndex.load("memory.hat") | |
| """ | |
| from .arms_hat import ( | |
| HatIndex, | |
| HatConfig, | |
| SearchResult, | |
| SessionSummary, | |
| DocumentSummary, | |
| HatStats, | |
| ) | |
| __all__ = [ | |
| "HatIndex", | |
| "HatConfig", | |
| "SearchResult", | |
| "SessionSummary", | |
| "DocumentSummary", | |
| "HatStats", | |
| ] | |
| __version__ = "0.1.0" | |