Master Jina AI with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign Up and Get API Key: Visit jina.ai and create a free account to receive an API key with 10 million free tokens. No credit card required — the free allocation is sufficient for building and testing complete RAG pipelines. Test Reader API with Simple URL: Try the Reader API immediately by prepending r.jina.ai/ to any web URL in your browser (e.g., r.jina.ai/https://example.com). This gives you clean markdown output and demonstrates the API's core functionality without any code. Integrate Embedding API for Vector Search: Use the Embedding API to convert text/images into vectors for semantic search. Start with jina
v4 for multimodal and multilingual capabilities, testing with both single
vector and multi
vector modes to understand the precision differences. Add Reranking for Precision Improvement: Implement the two
stage retrieval pattern: initial vector search followed by reranking. Use jina
score your top candidates against the original query for significant precision improvements in search results.
💡 Quick Start: Follow these 6 steps in order to get up and running with Jina AI quickly.
Explore the key features that make Jina AI powerful for ai search & embeddings workflows.
State-of-the-art 3.8B parameter multimodal embedding model built on Qwen2.5-VL architecture. Supports text and images in unified embedding space with 89+ languages, single-vector and multi-vector (late interaction) output modes for different retrieval strategies.
An e-commerce platform indexes both product descriptions and images into the same embedding space, enabling customers to search by text in any language and get visually similar product matches. Multi-vector mode provides higher precision for complex queries like 'sustainable outdoor gear for winter hiking.'
Cross-encoder reranking model achieving 61.94 nDCG-10 on BEIR benchmark — the highest among evaluated rerankers. Re-scores initial search results for maximum relevance with support for structured prompts and contextual understanding.
After vector search returns 100 candidate documents for 'machine learning model deployment best practices,' the reranker re-scores them against the actual query, pushing the most relevant 10 results to the top — improving precision from 60% to 90%+ for complex technical queries.
Converts any URL into clean, LLM-ready markdown by simply prepending r.jina.ai/ to the URL. Handles JavaScript rendering, paywalls, cookie banners, and complex layouts to extract main content without HTML parsing or scraping infrastructure.
An AI research assistant needs current data from a news article. It calls r.jina.ai/https://techcrunch.com/article and gets clean markdown that fits directly into an LLM context window, eliminating the need for custom web scraping or HTML parsing libraries.
Web search endpoint that returns results formatted for LLM consumption rather than human browsing. Queries the web and returns structured text snippets ready for AI processing, eliminating the need for additional result parsing or cleaning.
A RAG pipeline uses s.jina.ai to search for current information about 'quantum computing breakthroughs 2026,' receiving clean text snippets that feed directly into the retrieval-augmented generation context without web scraping or result formatting.
Autonomous research system that iteratively searches, reads, and reasons until finding comprehensive answers to complex questions. Compatible with OpenAI's Chat API schema — swap endpoints for deep research capabilities without code changes.
A business analyst asks 'Compare AI regulation compliance requirements across EU, US, and China for fintech applications.' DeepSearch autonomously searches regulatory documents, reads relevant pages, cross-references findings, and synthesizes a comprehensive comparative analysis with citations.
Single API key works across all Jina services — embeddings, reranking, reading, search, classification, and DeepSearch. Shared token pool with 10M free tokens for new accounts eliminates credential management complexity for multi-service pipelines.
A startup signs up once, gets an API key with 10M free tokens, and uses it across their embedding pipeline, reranker, web reader, and search APIs without managing separate credentials, billing, or quota tracking for each service.
Embeddings convert text or images into dense vectors that you store in a vector database for approximate nearest-neighbor retrieval — this is the first-stage recall step. The reranker is a cross-encoder that takes a query and a shortlist of candidate documents (typically the top 50-100 from vector search) and scores them jointly, producing a much more accurate final ordering. Most production RAG pipelines use both: embeddings for fast recall, reranker for precision before passing context to the LLM.
Reader (r.jina.ai) is purpose-built to produce LLM-friendly output: it renders JavaScript, strips navigation, ads, cookie banners, and boilerplate, then returns clean Markdown with preserved structure (headings, lists, links, tables). Traditional scrapers return raw HTML that wastes context tokens and confuses models. Reader also handles PDF extraction, image captioning via vision models, and can be called with a single GET request — just prefix any URL with r.jina.ai/.
Yes. Most Jina embedding and reranker models are released with open weights on Hugging Face under Apache 2.0 or CC-BY-NC licenses (check each model card). You can run them locally with sentence-transformers, vLLM, or Text Embeddings Inference. The hosted API still tends to be cheaper than self-hosting for small to mid-scale workloads once you factor in GPU costs, but self-hosting is the right choice for air-gapped or strict-data-residency deployments.
DeepSearch is an agentic endpoint that takes a complex research question and autonomously runs multiple search-read-reason iterations until it produces a cited, grounded answer — similar in concept to Perplexity Pro or OpenAI Deep Research. Use it for questions requiring synthesis across multiple sources (market research, technical comparisons, fact-checking) rather than simple lookups. For single-shot queries, the Search API (s.jina.ai) is faster and cheaper.
Jina uses a unified token-based credit system: you purchase tokens and they are consumed by whichever endpoint you call, at different rates per service (embeddings are cheapest, DeepSearch most expensive per call due to multi-step reasoning). New API keys receive 10 million free tokens with no credit card required. Beyond that, you top up pay-as-you-go without monthly commitments, which is unusual in the embeddings market where most competitors require enterprise contracts at scale.
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Tutorial updated March 2026