Jina

The framework provides a set of components for building neural‑based search and retrieval pipelines. It includes embeddings that support multilingual and multimodal data, a reranker to improve result relevance, and a reader that converts web pages to Markdown for better grounding of language models. Developers can also run the models directly inside Elasticsearch through an inference service, and use command‑line tools to fetch URLs, search the web, or access a managed API.

Typical users are engineers who need to create fast, scalable search applications that leverage deep‑learning models rather than traditional keyword indexing. The library offers APIs for embedding generation, reranking, and content extraction, allowing integration with custom data sources and downstream LLM workflows.

What distinguishes the project is its focus on a unified “search foundation” that combines embeddings, reranking, and web‑content retrieval in a single open‑source package, supporting both multilingual text and other modalities while providing optional hosted services for higher rate limits and additional features.