Embedding-Based Tool Selection for AI Agents (zarar.dev)

A new embedding-based tool selection system has been introduced for AI agents, addressing the significant issue of "tool explosion." Originally, the AI assistant faced inefficiencies by sending excessive information about nearly 40 tools with each user query, leading to high token usage and increased latency. The new approach utilizes semantic embeddings to represent tool descriptions as vectors stored in Postgres, allowing the AI to dynamically identify the most relevant tools based on incoming queries. This method reduces the amount of data sent to the language model (LLM) by 75-90%, enhancing both performance and accuracy. The implementation involved comparing user queries to tool embeddings using cosine distance, with a specific focus on category expansion to ensure multi-step operations remain seamless. This strategy not only cut token costs by 60-80% but also improved response times by around 200 milliseconds. Key technical choices included using OpenAI's embedding API for its reliability and quality, while maintaining flexibility by designing an abstraction layer for potentially switching providers in the future. Overall, this solution offers a practical way for developers working with AI agents to manage tool complexity efficiently, yielding substantial benefits as tool inventories grow.