Jina The Jina Embedding Serving API
Community OpenAPI document · agent-readyAI/MLEmbeddings Searchbearer8 EndpointsREST
Know of an official OpenAPI document? Contribute it →
For Agents
Generate text embeddings (single or bulk), produce multi-vector representations, and rerank candidate documents against a query through Jina AI's hosted model API.
Use for: Generate embeddings for a batch of product descriptions, Rerank search results against a user query for higher relevance, Submit a bulk embedding job for a million documents, Check the status of a Jina bulk embedding job
Not supported: Does not host vector indices, run LLM completions, or store documents - use for text embedding generation and reranking only.
The Jina Embedding Serving API provides hosted access to Jina AI's family of embedding and reranking models through eight endpoints covering single-call /v1/embeddings, asynchronous /v1/bulk-embeddings jobs with downloadable result files, late-interaction /v1/multi-vector and /v1/multi-embeddings endpoints, and a /v1/rerank endpoint that scores candidate documents against a query. Authentication uses a Bearer API key sent in the Authorization header. The API is purpose-built for retrieval-augmented generation pipelines and supports multilingual and long-context embedding models.
Install Jentic One Beta
Connect the The Jina Embedding Serving API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the The Jina Embedding Serving API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fjina.ai%2Fjina-ai" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fjina.ai%2Fjina-ai" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with The Jina Embedding Serving API.
Generate dense embeddings for one or more text inputs in a single /v1/embeddings call
Submit large embedding jobs asynchronously via /v1/bulk-embeddings and download the results as a file
Produce late-interaction multi-vector representations for fine-grained retrieval via /v1/multi-vector
Generate multi-embeddings for documents that need several vectors per record via /v1/multi-embeddings
Rerank a list of candidate passages against a query using /v1/rerank for higher retrieval quality
Track the status of asynchronous bulk jobs via /v1/bulk-embeddings/{job_id}
Probe service health via the root / endpoint
Use Cases
Patterns agents use The Jina Embedding Serving API for, with concrete tasks.
★ Retrieval-Augmented Generation Pipeline
An RAG system embeds the user's query with /v1/embeddings, retrieves the top-k candidates from a vector database, then sends query + candidates to /v1/rerank for a quality pass before passing the best passages to the LLM. Jina's reranker is purpose-trained for this last-mile step and typically lifts answer quality more than tweaking the retriever alone.
POST a query to /v1/embeddings, retrieve top-50 from the vector store, then POST {query, documents:[...top-50]} to /v1/rerank and use the top-5 by relevance score as LLM context.
Bulk Corpus Indexing
Indexing millions of documents in real time is impractical with a single synchronous endpoint. The /v1/bulk-embeddings job accepts large input batches, runs them asynchronously, and exposes /v1/bulk-embeddings/{job_id} for status and /v1/bulk-embeddings/{job_id}/download-result for the resulting vectors. This is the right path for one-off corpus migrations or nightly index refreshes.
POST a CSV of 1M document texts to /v1/bulk-embeddings, poll /v1/bulk-embeddings/{job_id} every 60s until completed, then GET /v1/bulk-embeddings/{job_id}/download-result and ingest the vectors into the chosen store.
Multilingual Semantic Search
Jina's multilingual embedding models map queries and documents in 30+ languages into a shared vector space, so a Spanish query can retrieve relevant English documents (and vice versa). This unlocks one global search index instead of per-language silos and is especially useful for support knowledge bases serving multinational customers.
POST a Spanish query to /v1/embeddings using a multilingual Jina model, then ANN-search a vector store containing English-embedded support articles and return the top match.
AI Agent Integration via Jentic
An AI agent that builds search experiences uses Jentic to discover Jina operations by intent. The agent searches for 'rerank documents against a query' and Jentic returns the /v1/rerank operation with its expected query/documents body, so the agent calls it directly without reading API docs. The Jina Bearer key stays in your Jentic One instance.
Search Jentic for 'rerank candidate passages against a query', load the /v1/rerank schema, and execute it with a query and 20 retrieved candidates.
Key Endpoints
8 endpoints — the jina embedding serving api provides hosted access to jina ai's family of embedding and reranking models through eight endpoints covering single-call /v1/embeddings, asynchronous /v1/bulk-embeddings jobs with downloadable result files, late-interaction /v1/multi-vector and /v1/multi-embeddings endpoints, and a /v1/rerank endpoint that scores candidate documents against a query.
METHOD
PATH
DESCRIPTION
POST
/v1/embeddings
Generate dense embeddings for input texts
POST
/v1/rerank
Rerank candidate documents against a query
POST
/v1/bulk-embeddings
Submit an asynchronous bulk embedding job
GET
/v1/bulk-embeddings/{job_id}
Check status of a bulk embedding job
GET
/v1/bulk-embeddings/{job_id}/download-result
Download results of a completed bulk job
POST
/v1/multi-vector
Generate late-interaction multi-vector embeddings
POST
/v1/embeddings
Generate dense embeddings for input texts
POST
/v1/rerank
Rerank candidate documents against a query
POST
/v1/bulk-embeddings
Submit an asynchronous bulk embedding job
GET
/v1/bulk-embeddings/{job_id}
Check status of a bulk embedding job
GET
/v1/bulk-embeddings/{job_id}/download-result
Download results of a completed bulk job
POST
/v1/multi-vector
Generate late-interaction multi-vector embeddings
Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Jina Embedding Serving API by hand means handling its bearer key and managing synchronous and bulk embedding jobs yourself. Through Jentic you install once, import Jina from the API Directory, store the key once, and your agent calls it.
Permission scoping
Jina takes its text and query inputs in the request body, so scope by operation: limit the agent to the operations it needs, such as generating embeddings or reranking documents. You pick the allowed set, so it stays within embedding generation and reranking.
Credential isolation
Your Jina bearer key is stored once, encrypted, by your own Jentic One instance and injected into the Authorization header at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'generate text embeddings' or 'rerank documents against a query', and Jentic returns the matching Jina operation with its body schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using The Jina Embedding Serving API through Jentic.
What authentication does the Jina Embedding Serving API use?
A Bearer token issued from jina.ai/api-dashboard, sent as Authorization: Bearer <key>. Through Jentic the key is stored encrypted in the vault and injected at execution time so the agent never sees the raw value.
Can I rerank a list of documents against a query with the Jina API?
Yes. POST {query, documents:[...]} to /v1/rerank and the response includes a relevance score per document. This is purpose-built for the final-stage reranking pass after a vector-store retrieval in an RAG pipeline.
What are the rate limits for the Jina API?
Limits depend on the plan attached to the API key. Free-tier keys are throttled per minute and per day; paid plans offer higher concurrency. For very large batches, prefer /v1/bulk-embeddings over many synchronous /v1/embeddings calls to avoid 429s.
How do I generate embeddings for a query through Jentic?
Run pip install jentic, search for 'generate text embeddings', then load and execute the POST /v1/embeddings operation with the input text array and the desired Jina model. Jentic returns the vectors directly to your agent.
When should I use multi-vector instead of single-vector embeddings?
Use /v1/multi-vector or /v1/multi-embeddings for late-interaction retrieval (ColBERT-style) when document granularity matters more than a single dense vector can capture. Single /v1/embeddings is the right default for typical semantic search and RAG.
Is the Jina API free?
Jina offers a free tier with a monthly token allotment that is enough to evaluate embeddings and rerank for prototypes. Production volume requires a paid plan; pricing is published at jina.ai.
Can I limit what my agent is allowed to do with the Jina Embedding Serving API?
Yes. Jentic One is self-hosted, so you run it and your own rules decide which Jina operations the agent may call and which credentials it may use. Because Jina takes its text and query inputs in the request body, you scope by operation: allow only the endpoints the agent needs, such as POST /v1/embeddings for embedding generation or POST /v1/rerank for reranking documents, while excluding bulk jobs or multi-vector calls it should not touch. The allowed set you define keeps the agent within embedding generation and reranking, and your stored bearer key is injected only for those operations.
GET STARTED