ReaderLM-v2 is a 1.5B parameter language model developed by Jina AI, designed for converting raw HTML into clean markdown and JSON with high accuracy and improved handling of longer contexts. It supports multilingual text in 29 languages and offers advanced features such as direct HTML-to-JSON extraction. The model improves upon its predecessor by addressing issues like repetition in long sequences and enhancing markdown syntax generation.
The article discusses four open-source AI research agents that serve as cost-effective alternatives to OpenAI’s Deep Research AI Agent. These alternatives offer robust search capabilities, AI-powered extraction, and reasoning features, allowing researchers to automate and optimize their workflows without incurring high costs.
This pull request adds initial support for reranking to libllama, llama-embeddings, and llama-server using two models: BAAI/bge-reranker-v2-m3 and jinaai/jina-reranker-v1-tiny-en. The reranking is implemented as a classification head added to the model graph. Testing and benchmarking were performed with server integration.
This page provides documentation for the rerank API, including endpoints, request parameters, and response formats.
Maximize search relevancy and RAG accuracy with Jina Reranker. Features include multilingual retrieval, code search, and a 6x speedup over the previous version.
