Can I generate embeddings and rerank results with the Mixedbread API?

Yes. One operation returns embedding vectors for text inputs, and another reranks a list of candidate documents against a query so the most relevant rise to the top. An agent connected through Jentic can call both to build and refine a retrieval pipeline.

How do vector stores work in the Mixedbread API?

You create a vector store, add documents to it, and then run search or question answering over the store. The store indexes your content so an agent can retrieve the most relevant passages at query time without managing its own vector database.

What are the rate limits for the Mixedbread API?

The OpenAPI spec does not declare rate limits for the Mixedbread API. Apply conservative client-side throttling, back off on HTTP 429 responses, and check the Mixedbread developer documentation for the current published limits.

Can I limit what my agent is allowed to do with the Mixedbread API?

Yes. You choose which operations the agent may call, so it can be allowed to generate embeddings and search a store while store deletion stays out of the allowed set unless you add it. Because each store is addressed by an identifier in the URL path, you can also keep the agent to the stores it needs, and every call it makes is logged.

Is there a Mixedbread API MCP server?

You don't need an MCP server to give your agent Mixedbread. Jentic connects it directly from the API Directory: import it, store the key once, and your agent calls the embedding, reranking, and search operations on demand without a separate server to run.

How do I build a retrieval pipeline with Mixedbread through Jentic?

Search Jentic for an intent like 'generate embeddings' or 'search my documents', which returns the matching Mixedbread operation with its input schema so the agent supplies the text or query and reads the response. To run it on your own infrastructure, install Jentic One from its GitHub repo.