ContextCache - LLM Memory Engine
Open-source hybrid retrieval system that achieves 40% faster recall than vector-only approaches through intelligent caching and ranking algorithms.
Technologies Used
About This Project
ContextCache is an innovative open-source LLM memory engine that revolutionizes how language models recall and utilize information. By combining FAISS vector search with PageRank algorithms and time-decay mechanisms, the system achieves 40% faster recall compared to traditional vector-only approaches. The hybrid retrieval system intelligently caches frequently accessed information while maintaining relevance through sophisticated ranking algorithms. Built with performance and scalability in mind, ContextCache includes a comprehensive CLI tool for easy integration and management.
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Challenges & Solutions
The main challenge was optimizing retrieval speed while maintaining accuracy across different types of queries. Traditional vector-only approaches were fast but lacked contextual understanding, while graph-based methods were more accurate but slower.
Implemented a hybrid approach that combines the speed of FAISS vector search with the contextual understanding of PageRank algorithms. Added time-decay mechanisms to ensure recent information is prioritized while maintaining historical context relevance.
Key Learnings
Learned the importance of balancing multiple algorithmic approaches to achieve optimal performance. Understanding the trade-offs between speed and accuracy in information retrieval systems was crucial for the project's success.