Multimodal search foundation

How to use
Jina AI.

Building multilingual or multimodal semantic search, retrieval-augmented generation, relevance reranking, classification, and web-to-LLM ingestion workflows.

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What it isSearch foundation platform for multilingual and multimodal embeddings, reranking, classification, web reading, and deep search APIs Workflows2 UpdatedJuly 2026

Jina AI is a Search foundation platform for multilingual and multimodal embeddings, reranking, classification, web reading, and deep search APIs. Building multilingual or multimodal semantic search, retrieval-augmented generation, relevance reranking, classification, and web-to-LLM ingestion workflows. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

01

Access & setup

Find, install, and sign in to Jina AI

Get into the official Jina AI experience with the right account and a setup you understand.

  1. 01

    Start at https://jina.ai/ and confirm the domain before entering account or payment information.

  2. 02

    Availability: Jina AI is a browser-managed API platform accessed through HTTPS endpoints and compatible SDKs; selected models can also be evaluated or deployed under their individual licenses.

  3. 03

    A Jina API key for authenticated use, a selected model and license, a vector store or search stack where needed, authorized source data, relevance evaluations, and server-side secret handling.

  4. 04

    Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.

  5. 05

    Run one low-risk test task before connecting sensitive files, repositories, or workspace data.

  • Use official download pages.
  • Review permissions during setup.
  • Keep installers and applications updated.
02

First session

Your first useful Jina AI session

Learn the interaction loop using a small task with a clear outcome.

  1. 01

    Define the users, corpus, languages, modalities, query types, relevance labels, freshness, privacy class, latency, recall, precision, and cost targets.

  2. 02

    Choose an embedding model for candidate retrieval and a reranker for final ordering, recording dimensions, context, license, task mode, and model version.

  3. 03

    Create a versioned evaluation set with difficult positives and negatives, index authorized content, and run retrieval and reranking as separate measurable stages.

  4. 04

    Review relevance, missing sources, stale pages, language balance, prompt injection, citations, latency, and cost before integrating results into any answer or action.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.
03

Quality control

Check the quality of Jina AI output

Establish that a retrieved answer is actually supported by the sources it cites.

  1. 01

    Restate the question precisely; retrieval quality degrades fastest on vague or compound queries.

  2. 02

    Open the cited sources and confirm each one genuinely contains the claim attributed to it.

  3. 03

    Check the publication and capture dates — stale sources are the most common cause of confidently wrong answers.

  4. 04

    Look for what is missing: retrieval returns what matches the query, not what would contradict it.

  5. 05

    Confirm quotations are verbatim and that summaries have not reversed a qualification or a negation.

  • Open the citation; do not trust that a link supports the claim beside it.
  • Check dates on every retrieved source.
  • Treat an absent contradiction as unverified, not as agreement.
04

Privacy & permissions

Use Jina AI safely

Control what you index and what leaves your systems when you query it.

  1. 01

    Classify the corpus before indexing: confidential, personal, and regulated material needs an explicit decision.

  2. 02

    Confirm where the index is stored, who can query it, and how long content is retained.

  3. 03

    Scope crawling and scraping to sources you are permitted to collect, and respect robots and terms of use.

  4. 04

    Strip secrets and personal data before indexing rather than filtering them at query time.

  5. 05

    Review query logs — the questions asked can be as sensitive as the documents indexed.

  • Embeddings and rerankers produce similarity and relevance estimates, not verified truth. Model versions, dimensions, licenses, context limits, rate limits, and task-specific prefixes differ, and changing a model normally requires re-embedding and reevaluation. Reader and search output can contain hostile page instructions, copyrighted text, personal data, and stale facts. Keep keys server-side, respect source access rules, minimize stored content, and preserve citations and human review.
  • Follow your organisation's approved-use policy.
  • Never treat fluent output as authorization to act.
05

Core workflows

Step-by-step ways to use Jina AI for the work it does best.

Each workflow is a separate guide with its own steps and review checkpoints.

01 WorkflowBuild a multilingual retrieval pipeline with Jina AI

Combine embeddings and reranking with an explicit relevance benchmark, source controls, and citation-preserving answer generation.

 02 WorkflowCreate a controlled web-research collector with Jina Reader

Convert permitted web pages into reviewable evidence without turning page text, stale results, or hostile instructions into trusted commands.

06

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.

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