SEARCH & RESEARCH · FOR AI AGENTS
Jina AI
Embed content and rerank retrieved candidates through hosted models.
Use Jina’s hosted embeddings and reranking APIs to turn content into vectors or reorder candidate documents for a question. Keep your source corpus and vector storage separate.
Your integration checklist
- Capabilities
- What you need from your owner
- Obtain a Jina API key, choose a model and configure token funding for sustained usage. Decide which content may be sent to the hosted API.
- Authentication
- Send the API key as an Authorization bearer token. Match each request to the selected model’s input schema.
- Your first task
- Rerank a small set of already retrieved documents for a question with a known relevant source, preserving the original document identifiers.
- Billing & payment
- Token packages fund usage across Jina APIs on the same key. New keys include trial tokens; optional auto top-up charges the saved payment method when the configured threshold is reached.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Context limits, supported modalities and rate limits vary by model and key tier. Reranking cannot recover a relevant document missing from the candidate set. Self-hosted weights have separate licensing requirements.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Keep retrieval coverage separate from ranking quality
Editorial recommendation · Reviewed 2026-10-18
When to choose this service
Choose hosted Jina when you need specialized embedding or relevance models without operating their inference infrastructure. Use a vector database separately when the workflow needs persistent retrieval.
The service supplies trained retrieval models and hosted inference capacity. A sorting script cannot recreate those learned representations; self-hosting instead requires suitable weights, licensing and compute.
Suggested workflow
- Build a small evaluation set containing a known answer, distracting documents and an unrelated question. Preserve stable source IDs.
- Retrieve candidates, then rerank the same bounded set. Compare the answer-bearing source’s position and inspect whether truncation removed important content.
- Set a token budget and retry ceiling before expanding. Store model and input configuration with the results so a later model change can be evaluated.
Acceptance checks before real work
- Keep embedding dimensions and model configuration compatible with the target vector index.
- Treat relevance scores as ranking signals, not proof that a claim is true.
- Check token usage and candidate coverage before increasing request size.
If the workflow fails
If the expected source disappears, inspect initial retrieval and truncation before retrying the reranker. Back off on rate limits and stop when the approved token budget is exhausted.