Chroma Context-1: A 20B Parameter Agentic Search Model for Multi-Hop Retrieval
By
philip1209
Front-window bakery material. Catches the eye, delivers the goods.
Summary
Chroma Context-1 is a 20B parameter agentic search model designed to improve retrieval-augmented generation (RAG) systems. Unlike traditional single-pass retrieval pipelines, it performs multi-hop search by decomposing queries into subqueries, iteratively searching a corpus, and selectively editing its own context. The model achieves retrieval performance comparable to frontier-scale LLMs but at a fraction of the cost and up to 10x faster inference speed, making it suitable as a subagent alongside frontier reasoning models.
Key quotes
· 4 pulledThis approach, broadly known as retrieval-augmented-generation (RAG), has traditionally relied on single-stage retrieval pipelines composed of vector search, lexical search, or regular expression matching, optionally followed by a learned reranker.
In practice, many real-world queries require multi-hop retrieval, in which the output of one search informs the next.
We introduce Chroma Context-1, a 20B parameter agentic search model derived from gpt-oss-20B that achieves retrieval performance comparable to frontier-scale LLMs at a fraction of the cost and up to 10x faster inference speed.
The model is trained to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration.
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