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README.md

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python -m pytest
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```
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## Known Issues
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- All background tasks run as async coroutines in the same process as the REST API server, using Starlette's `BackgroundTask`
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- ~~The MCP server from the Python MCP SDK often refuses to shut down with Control-C if it's connected to a client~~
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### Contributing
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1. Fork the repository
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2. Create a feature branch
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3. Commit your changes
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4. Push to the branch
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5. Create a Pull Request
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## Running the Background Task Worker
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The Redis Memory Server uses Docket for background task management. There are two ways to run the worker:
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The memory compaction functionality optimizes storage by merging duplicate and semantically similar memories. This improves retrieval quality and reduces storage costs.
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### Running Compaction
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Currently, memory compaction is only available as a function in `agent_memory_server.long_term_memory.compact_long_term_memories`. You can run it manually or trigger it (manually, via code) to run as a background task.
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### Key Features
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- **Hash-based Deduplication**: Identifies and merges exact duplicate memories using content hashing
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- **Semantic Deduplication**: Finds and merges memories with similar meaning using vector search
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- **LLM-powered Merging**: Uses language models to intelligently combine memories
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### Testing Approach
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Testing the memory compaction functionality involves:
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1. **Unit Tests**: Testing individual helper functions like `generate_memory_hash` and `merge_memories_with_llm`
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2. **Integration Tests**: Testing the complete workflow with minimal mocking
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3. **Mocked Tests**: Using helper functions to test specific parts of the workflow
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The main integration test (`test_compact_memories_integration`) demonstrates the memory merging functionality without relying on Redis search, which makes it more robust and less prone to environment-specific failures.
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### Contributing
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1. Fork the repository
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2. Create a feature branch
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3. Commit your changes
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4. Push to the branch
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5. Create a Pull Request
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### Running Tests
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