Chroma Context-1: Training a Self-Editing Search Agent

1. 1Understand the limitations of traditional retrieval pipelines
2. 2Learn about agentic search and its application in multi-hop retrieval
3. 3Study the architecture and training of Chroma Context-1
4. 4Experiment with using Chroma Context-1 as a subagent in conjunction with a frontier reasoning model

This research benefits AI engineers and ML researchers working on large language models and information retrieval systems, as it provides a more efficient and cost-effective solution for multi-hop retrieval

💡 Chroma Context-1 is designed to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration

🚀 Chroma Context-1: a 20B parameter agentic search model that achieves comparable performance to frontier-scale LLMs at a fraction of the cost! 🤖