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Up to 4 tools, same rows for each: what it is, what it really costs, the trap, and how to wire it up. The URL carries your picks — send it to anyone.
Jina AIFuse.jsMiniSearch
Quick read
- Free with no card: Fuse.js, MiniSearch.
| Field | Jina AIjina.ai | Fuse.jsfusejs.io | MiniSearchgithub.com |
|---|---|---|---|
| What it is | A set of small search-infrastructure APIs. Reader turns any URL into clean LLM-ready markdown by prefixing it with r.jina.ai/. Alongside it sit multilingual multimodal embeddings, a reranker for tightening retrieval precision, and a DeepSearch endpoint. All are plain HTTP, no SDK required. | A zero-dependency fuzzy-search library using the Bitap algorithm — roughly 6.8kB gzipped for the basic build, 8.6kB for the full one. Searches arrays of JS objects in memory with typo tolerance, field weighting, nested keys and extended query operators. Runs in browsers, Node and Deno. | A dependency-free JavaScript full-text search engine that builds a real inverted index in memory, in the browser or in Node. Supports prefix search, fuzzy matching, field boosting, modern relevance ranking, auto-suggestion, and adding or removing documents at any time. The index serializes to JSON so it can be prebuilt. |
| Category | Search, vector & RAG | Search, vector & RAG | Search, vector & RAG |
| Cost tier | mixed | free | free |
| Pricing | Free API key on signup with a bundled token allowance, no card. Token-metered after that. Rate limits: Reader and Embeddings/Reranker 500 RPM, Search 100 RPM; premium raises Reader to 5K RPM and Search to 1K RPM. | Free, Apache-2.0. No service, no account, no limits, no bill. | Free, MIT-licensed. No service or account. |
| Why builders pick it | The reranker is the cheapest single upgrade to a mediocre RAG pipeline — retrieve 50 chunks, rerank down to 5, and answer quality jumps. Reader needs no key at all for quick tests. | When a few hundred to a few thousand items already sit in your app state — a command palette, a settings list, a filterable table — this is ten lines of code and no infrastructure. | The step up from Fuse.js when you have tens of thousands of records — a proper index instead of rescoring every item per keystroke, still with no backend and no bill. |
| Watch out for | Rate limits bite before token costs do. Keyless r.jina.ai requests get a much lower RPM ceiling and are throttled first under load. | It scores every item on every keystroke, so it degrades noticeably past roughly 10K records. Use MiniSearch or a real index above that. | The whole index ships to the client, so a large corpus means a large download. Prebuild and cache the serialized JSON rather than indexing on every page load. |
| How to wire it up | curl https://r.jina.ai/https://example.com | npm i fuse.js | npm i minisearch |
| Editor's pick | No | No | No |
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# Tool comparison — Jina AI vs Fuse.js vs MiniSearch Source: Kbaise, a directory of tools that work with Lovable projects. Pick one and tell me why before writing any integration code. ## Jina AI (jina-ai) - URL: https://jina.ai - Category: Search, vector & RAG - Cost: mixed — Free API key on signup with a bundled token allowance, no card. Token-metered after that. Rate limits: Reader and Embeddings/Reranker 500 RPM, Search 100 RPM; premium raises Reader to 5K RPM and Search to 1K RPM. - What it is: A set of small search-infrastructure APIs. Reader turns any URL into clean LLM-ready markdown by prefixing it with r.jina.ai/. Alongside it sit multilingual multimodal embeddings, a reranker for tightening retrieval precision, and a DeepSearch endpoint. All are plain HTTP, no SDK required. - Why builders pick it: The reranker is the cheapest single upgrade to a mediocre RAG pipeline — retrieve 50 chunks, rerank down to 5, and answer quality jumps. Reader needs no key at all for quick tests. - Trap: Rate limits bite before token costs do. Keyless r.jina.ai requests get a much lower RPM ceiling and are throttled first under load. - Wiring: curl https://r.jina.ai/https://example.com - Full dossier: /api/public/tools/jina-ai ## Fuse.js (fuse-js) - URL: https://www.fusejs.io - Category: Search, vector & RAG - Cost: free — Free, Apache-2.0. No service, no account, no limits, no bill. - What it is: A zero-dependency fuzzy-search library using the Bitap algorithm — roughly 6.8kB gzipped for the basic build, 8.6kB for the full one. Searches arrays of JS objects in memory with typo tolerance, field weighting, nested keys and extended query operators. Runs in browsers, Node and Deno. - Why builders pick it: When a few hundred to a few thousand items already sit in your app state — a command palette, a settings list, a filterable table — this is ten lines of code and no infrastructure. - Trap: It scores every item on every keystroke, so it degrades noticeably past roughly 10K records. Use MiniSearch or a real index above that. - Wiring: npm i fuse.js - Full dossier: /api/public/tools/fuse-js ## MiniSearch (minisearch) - URL: https://github.com/lucaong/minisearch - Category: Search, vector & RAG - Cost: free — Free, MIT-licensed. No service or account. - What it is: A dependency-free JavaScript full-text search engine that builds a real inverted index in memory, in the browser or in Node. Supports prefix search, fuzzy matching, field boosting, modern relevance ranking, auto-suggestion, and adding or removing documents at any time. The index serializes to JSON so it can be prebuilt. - Why builders pick it: The step up from Fuse.js when you have tens of thousands of records — a proper index instead of rescoring every item per keystroke, still with no backend and no bill. - Trap: The whole index ships to the client, so a large corpus means a large download. Prebuild and cache the serialized JSON rather than indexing on every page load. - Wiring: npm i minisearch - Full dossier: /api/public/tools/minisearch ## Quick read - Free with no card: Fuse.js, MiniSearch. ## Rules 1. Prefer the free tier when no budget was stated, and say what the ceiling is. 2. Read the full dossier before integrating. 3. Fetch /api/public/models before writing any AI model ID.
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