Jina The Jina Embedding Serving API

Name: The Jina Embedding Serving API
Brand: The Jina Embedding Serving API
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★ Only Publicly Available OpenAPI DocumentAI/MLEmbeddings Searchbearer8 EndpointsREST

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.

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.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic 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

GET STARTED

Start building with The Jina Embedding Serving API

Explore with Jentic

View OpenAPI Document

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.

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 the Jentic vault.

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

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

The Jina Bearer key is stored encrypted in the Jentic vault and injected into the Authorization header at execution time. Raw keys never enter the agent's prompt context.

Intent-based discovery

Agents search Jentic with intents like 'generate text embeddings' or 'rerank documents against a query' and Jentic returns the matching Jina operation with its body schema.

Time to first call

Direct Jina integration: a few hours for auth, batching, and bulk-job polling. Through Jentic: under 30 minutes — search, load schema, execute.

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.

Jina The Jina Embedding Serving API

Connect the The Jina Embedding Serving API to your agent

Capabilities

Start building with The Jina Embedding Serving API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs