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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fjina.ai%2Fjina-ai" | sh

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 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

Generate multi-embeddings for documents that need several vectors per record via /v1/multi-embeddings

GET STARTED

Start building with The Jina Embedding Serving API

Explore with Jentic One

View OpenAPI Document

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.

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.

Jina The Jina Embedding Serving API

AI ReadinessFoundational (C-)51/100

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Community OpenAPI document · agent-readyAI/MLEmbeddings Searchbearer8 EndpointsREST

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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.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

100

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

98%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

100%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: C-Signals: 4

10%

Example Density

How richly the API is illustrated with examples.

46%

Example Validity

Percentage of examples that conform to their schemas.

47%

Response Coverage

Percentage of operations with complete response definitions (success, client error, server error).

100%

Tooling Readiness

Health of API ingestion, bundling, and resolution within Jentic pipelines.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: FSignals: 4

51%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

50%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: A+Signals: 1

94%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

100

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: A+Signals: 1

100%

Descriptive Richness

Clarity and depth of descriptions across API elements.

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

Jina The Jina Embedding Serving API

Jentic AI Readiness Score

The Jina Embedding Serving API

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness