Today, Contextual AI announces the world’s first instruction-following reranker, available in both agents and as a separate /rerank component API. 

The instruction-following reranker enables users to specify natural language instructions about how the reranker should rank retrievals, which improves accuracy in reranking and response generation. The reranker ranks documents according to their relevance to the query first and your custom instructions secondarily. We evaluated the model on instructions for recency, document type, source, and metadata, and it can generalize to other instructions as well. For instructions related to recency and timeframe, specify the timeframe (e.g., instead of saying “this year”) because the reranker doesn’t know the current date. The reranker is state-of-the-art on the industry-standard BEIR benchmark, as well as our internal benchmarks.

To get started for free with the /rerank component API, create a Contextual AI account, visit the Getting Started tab, and either get an API key for the /rerank API or use the /rerank UI playground. We provide credits for the first 50M tokens, and you can buy additional credits as your needs grow. To request custom rate limits and pricing, please contact us. If you have any feedback or need support, please email reranker-feedback@contextual.ai. 

This reranker is the default for new agents created with the Contextual AI platform. To  specify instructions, use the reranker_instruction parameter in the Create/Edit Agent APIs and in the UI. See blog post for more details.