Chroma Context-1
by Chroma
20B agentic search model that rewrites RAG pipelines — retrieves, validates, and reformulates queries for multi-hop reasoning
See https://huggingface.co/chroma/Context-1
Features
- 20B parameter agentic search model — purpose-built for retrieval, not general chat
- RAG pipeline rewriting — reformulates retrieval prompts for higher-quality context injection
- Multi-hop query support — handles complex queries requiring chaining across multiple documents
- ChromaDB integration — designed to work natively with the ChromaDB vector store ecosystem
- Locally deployable — run via Hugging Face model weights on sufficient hardware
Superpowers
Context-1 attacks a specific weak point in typical RAG pipelines: the query reformulation step. Standard RAG sends the user’s raw question to the retriever, which often fails on complex or multi-part questions. Context-1 acts as a dedicated retrieval model that reformulates the query into a retrieval-optimized form, extracts supporting documents, and validates their relevance before passing context to the generation model. This separates retrieval intelligence from generation intelligence — rather than asking one model to do both, you get specialized performance at each stage. Useful for production RAG systems where retrieval quality is the bottleneck.
Pricing
- Open weights — free to self-host