| <h1 align="center"> StructMem: Structured Memory for Long-Horizon Behavior in LLMs </h1> |
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| **StructMem**, a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. By temporally anchoring dual perspectives and performing periodic semantic consolidation, StructMem improves temporal reasoning and multi-hop performance. |
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| <div align=center><img src="./figs/StructMem.png" width="100%" height="60%" /></div> |
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| ## Key Contributions |
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| ### 1. Event-Level Extraction Mode |
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| StructMem introduces an **event extraction mode** that goes beyond flat factual entries by capturing: |
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| - **Factual components**: Core information units (who, what, when, where) |
| - **Relational components**: Interpersonal dynamics, causal influences, and temporal dependencies. |
| - **Temporal binding**: Event sequences and causal relationships |
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| This mode is particularly effective for narrative-heavy conversations and time-sensitive scenarios. |
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| **Usage:** |
| ```python |
| config = { |
| # ... other configs ... |
| "extraction_mode": "event", # 'flat' or 'event' |
| # ... other configs ... |
| } |
| ``` |
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| ### 2. Cross-Event Summarization |
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| StructMem implements a **hierarchical summarization mechanism** that: |
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| - **Semantic Event Connections**: Buffers related events within time windows |
| - **Memory Consolidation through Synthesis**: Generates cross-event summaries stored separately from detailed memories |
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| **Usage:** |
| ```python |
| # Generate summaries after building memories |
| summary_result = lightmem.summarize( |
| retrieval_scope="global", |
| time_window=3600, # seconds |
| top_k=15, # number of seed memories |
| process_all=True |
| ) |
| ``` |
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| **Configuration:** |
| ```python |
| config = { |
| # ... other configs ... |
| "summary_retriever": { |
| "model_name": "qdrant", |
| "configs": { |
| "collection_name": "my_summaries", |
| "embedding_model_dims": 384, |
| "path": "./my_summaries", |
| } |
| }, |
| # ... other configs ... |
| } |
| ``` |
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| ## Usage Example |
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| See the complete example in [`experiments/locomo/`](./experiments/locomo/) for building and evaluating StructMem on the LoCoMo dataset. |
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| ### Quick Start |
| ```python |
| from lightmem.memory.lightmem import LightMemory |
| |
| # Configure StructMem |
| config = { |
| # ... base LightMem configs ... |
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| # StructMem-specific settings |
| "extraction_mode": "event", |
| "summary_retriever": { |
| "model_name": "qdrant", |
| "configs": { |
| "collection_name": "my_summaries", |
| "embedding_model_dims": 384, |
| "path": "./my_summaries", |
| } |
| }, |
| # ... other configs ... |
| } |
| |
| # Initialize |
| lightmem = LightMemory.from_config(config) |
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| # Add memories with event extraction, providing both factual and relational prompts. |
| lightmem.add_memory( |
| messages=turn_messages, |
| METADATA_GENERATE_PROMPT={ |
| "factual": LoCoMo_Event_Binding_factual, |
| "relational": LoCoMo_Event_Binding_relational |
| }, |
| force_segment=True, |
| force_extract=True |
| ) |
| |
| # Generate cross-event summaries |
| lightmem.summarize( |
| retrieval_scope="global", |
| time_window=3600, |
| top_k=15, |
| process_all=True |
| ) |
| ``` |
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| ## Experimental Results |
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| StructMem demonstrates improved performance on long-context conversation benchmarks. For complete results and configurations, see [LoCoMo Results](./experiments/locomo/readme.md#structmem-results) |
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