aiming-lab/SimpleMem

Features

• Long-term Memory Stores - Provides a persistent memory store that allows AI assistants to maintain context across different user chat sessions.
• Multimodal Context Providers - Retrieves and assembles context from text, images, audio, and video to provide comprehensive situational awareness.
• Context Compression - Provides an engine for summarizing long interaction histories into compact units to minimize LLM token usage.
• Interaction Compression - Distills complex multimodal dialogue and media interactions into compact units to minimize LLM token consumption.
• Retrieval Optimization - Provides an iterative loop to diagnose retrieval failures and automatically tune configurations for better search precision.
• Multi-Modal Memory Stores - Operates as a persistent store managing text, images, and dialogue history across sessions using a standardized protocol.
• LLM Token Compression - Reduces token consumption by compressing interaction histories into compact, non-redundant units.
• Hybrid Vector-Keyword Indexing - Combines dense vector embeddings with inverted keyword indices to retrieve precise multimodal context.
• History Distillation - Compresses multimodal interaction histories into compact memory units to reduce token usage and eliminate redundancy.
• Multimodal Search - Indexes and retrieves information across text, image, audio, and video using a multimodal semantic search system.
• MCP Servers - Implements a Model Context Protocol server to expose persistent memory stores to AI assistants.
• Model Context Protocol Servers - Exposes a standardized Model Context Protocol interface for AI assistants to maintain persistent session context.
• Diagnostic Tuning - Diagnoses retrieval failures and automatically adjusts search configurations to improve memory recovery precision.
• RAG Optimizations - Includes a diagnostic framework to optimize the precision of memories recovered in RAG pipelines.
• Memory Processing Processors - Implements a memory processing pipeline that distributes retrieval and storage across multiple worker threads.
• Distributed Memory Orchestration - Coordinates multiple workers to reduce latency when querying large-scale dialogue datasets.
• High-Throughput Task Processing - Implements high-throughput task processing using distributed workers to accelerate memory building and retrieval.
• Parallel Retrieval Processing - Uses multiple background worker threads to parallelize memory building and query retrieval, reducing latency for large datasets.

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