Cohere (Embed & Rerank) Connector for AI agents.

It improves the retrieval part of your RAG pipeline. It helps your agent find the most relevant documents by turning text into vectors and then re-sorting those results so the best matches are always at the top.

It uses semantic reranking to understand the intent behind a search. Instead of just looking for keywords, it identifies which documents actually answer the user's question and prioritizes them.

Yes, you can use it to audit token counts before sending data. By knowing exactly how many tokens a block of text contains, you can trim your inputs to stay within your budget and model limits.

Yes, it allows your AI client to categorize inputs into specific labels. You can use it to sort data into categories like sentiment, topic, or intent with confidence scores.

You just need to subscribe to the Connector and provide your Cohere API key. Once connected, your agent can call all the embedding and ranking capabilities directly through your existing workspace.

It is designed for enterprise-grade workflows. It handles the complex math of vectorization and reranking, making it suitable for large-scale retrieval tasks where accuracy is critical.

Yes. The 'rerank_documents' capability is specifically designed for this. Provide a query and a list of documents, and Cohere will reorder them based on semantic relevance, ensuring the most accurate context is fed to your LLM.

Use the 'classify_texts' capability. Provide your input strings and a few-shot JSON array of examples (text and label). The agent will return the predicted categories along with confidence scores from the Cohere engine.

Trial keys are free for development but have strict rate limits (approx. 1,000 calls per month). Production keys remove these limits but require a paid plan. Both types work seamlessly with this server.