REMO (Rolling Episodic Memory Organizer) is an AI microservice designed to manage and organize large volumes of conversational data for autonomous AI systems. It creates a hierarchical, tree-like taxonomy of memories, enabling efficient context-aware recall and improved conversational performance. The target audience includes developers of AI agents, chatbots, and other systems requiring robust memory management.

How It Works

REMO organizes text data into a hierarchical taxonomy by clustering semantically similar message pairs. It uses the Universal Sentence Encoder (v5) for generating embeddings and cosine similarity for clustering. Higher ranks in the taxonomy consist of summaries of lower ranks, creating a structure that ranges from specific message pairs to broad thematic summaries. This approach allows for efficient navigation and retrieval of relevant information from extensive conversation histories.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.7+, FastAPI, TensorFlow, TensorFlow Hub, scikit-learn, OpenAI, PyYAML. Requires an OpenAI API key in key_openai.txt.
• Usage: Start server with uvicorn remo:app --reload. Interact via REST API at http://localhost:8000.
• Documentation: https://github.com/daveshap/REMO_Framework
• LangFlow Integration: https://github.com/hunter-meloche/REMO-langflow

Highlighted Details

• Organizes data into a hierarchical taxonomy using semantic embeddings and clustering.
• Utilizes Universal Sentence Encoder v5 for embeddings and cosine similarity for clustering.
• Employs FastAPI for a RESTful API interface.
• Stores data in human-readable YAML files within a structured directory.
• Supports add_message, search, rebuild_tree, and maintain_tree operations.

Maintenance & Community

The project is noted as being in "early alpha" with expected testing and bugs. No specific community channels or active contributors are highlighted in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

This code is in early alpha, and users should expect bugs and incomplete functionality. The README does not specify licensing terms, which may impact commercial adoption.