Provide a query and candidate documents to find the most relevant passages, or convert supplied inputs into vectors.
Copy a prompt into your agent with the oo Skill installed. Add your page links and source notes before running it.
Rerank supplied documents against a question and inspect the highest-ranked matches.
/oo Jina AI: Use Jina model [reranker model ID] to rank these documents [documents] for query [question]. Include the top five documents and their relevance scores.
Set up oo CLI and the oo Skill in your agent.
Install OOMOL →Connect a Jina AI API key from the key manager.
Connect Jina AI →Use /oo with the pages to read, the changes to make, and where to save the result.
Connection Guide →Choose Your Agent
This connection exposes Jina embedding and reranking operations. You supply the model and source inputs; it does not retrieve documents from a knowledge base.
Generate vectors for supplied inputs or rank documents by their relevance to a query.
Create vector embeddings for text or multimodal inputs with Jina AI.
Leina 
Wanta 
jina_ai.create_embeddings Rank documents by relevance to a query with Jina AI.
jina_ai.rerank_documents No matching tools.
Yes. Ask to include documents in the result and specify how many top matches you want.
The embedding request accepts dimensions and normalization options where the chosen model supports them.
Yes. Reranking compares the documents included in your request against the query.
Manage which optional storage OOMOL can use. You can change these choices from the footer at any time.