Build Local Semantic Search into Websites Using the 5MB ternlight WebAssembly Model

ternlight is a lightweight semantic embeddings model that runs in a compact 5–7 MB WebAssembly bundle. According to the official GitHub repository and demo site, the engine, model, and tokenizer are packaged into a single file, enabling local semantic search on CPU without any external API calls. This design makes it suitable for privacy-sensitive environments and offline use cases.

📑Table of Contents

1. Overview of ternlight and the 5MB Model Features
2. Technical Mechanism of WebAssembly Embeddings
3. npm Package Installation and the Two Tiers (base / mini)
4. Real-World Integration Example: Django Docs + Private Knowledge Base Search
5. Performance Benchmarks and Limitations
6. Next Actions and Considerations for Readers
7. Frequently Asked Questions (FAQ)
8. Summary

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Overview of ternlight and the 5MB Model Features

The model is released under the MIT license, with the full training pipeline available in the repository. Its primary advantages are small size and execution speed. WebAssembly allows instant search functionality without additional servers or external APIs. One documented example embeds the entire Django documentation together with a private knowledge base for instant retrieval.

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Technical Mechanism of WebAssembly Embeddings

WebAssembly provides near-native performance in browser and server-side environments. ternlight leverages this to perform embedding generation efficiently on CPU. Quantization techniques keep the model size small while supporting cosine similarity calculations. Implementation details, including training code, are available in the GitHub repository.

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npm Package Installation and the Two Tiers (base / mini)

Two packages are available on npm:

Package	Size	Time per embedding	Vector dimension	Characteristics
@ternlight/base	~7 MB	~5 ms	Higher precision	Suitable when higher expressiveness is needed
@ternlight/mini	~5 MB	~2.5 ms	256 projected to 384	Lightweight, fast, compatibility-focused

Installation is straightforward: npm install @ternlight/mini. Sample code from the official repository demonstrates easy embedding into static sites or documentation search.

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Real-World Integration Example: Django Docs + Private Knowledge Base Search

The official sources describe embedding both the full Django documentation and a private knowledge base. By including the WASM model, users can perform instant searches within the page without relying on external services. The absence of API keys or third-party dependencies makes it attractive for projects with strict security policies.

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Performance Benchmarks and Limitations

Benchmarks indicate embedding generation takes approximately 2.5–5 ms, which is sufficient for real-time search. However, the limited vector dimensionality means that very large datasets may require attention to precision and scalability. While local execution provides privacy benefits, evaluate the impact of quantization and training data before production use.

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Next Actions and Considerations for Readers

1. Clone the official GitHub repository: https://github.com/soycaporal/ternlight
2. Run npm install @ternlight/mini to install the package.
3. Refer to the demo site (https://ternlight-demo.vercel.app/) and embed the model into a static site or documentation search.
4. Benchmark embedding latency and cosine similarity quality on your own dataset.
5. Assess privacy requirements and data scale before considering production deployment.

Note that Hatena bookmark counts are only a discovery signal; always verify with independent technical sources.

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Frequently Asked Questions (FAQ)

Q: Can ternlight be used commercially?

Yes, the MIT license permits commercial use, modification, and redistribution. Always review the license terms.

Q: Does it really work completely locally without any API?

Yes. The WebAssembly bundle handles embedding generation and search entirely on CPU. No external API calls occur.

Q: Are there use cases beyond Django docs?

It can be applied to private internal documentation, technical blog search, or site-wide search on static sites. Extend the samples provided in the GitHub repository as needed.

Q: How does its accuracy compare to larger models?

As a lightweight model, it may not match state-of-the-art accuracy on extremely large datasets, but it delivers practical quality for everyday documentation search and small-to-medium knowledge bases.

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Summary

The 5 MB-class ternlight WebAssembly model offers a practical way to add local semantic search to websites. With the GitHub repository and npm packages well maintained, the barrier from cloning to working prototype is low. It balances privacy and performance effectively. Readers are encouraged to start with the official repository and demo, then validate suitability for their specific use case through benchmarking. Base decisions on independent technical sources rather than discovery signals alone.

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New related information:

• The published news article adds new operational context for 5MB WebAssembly ternlightモデルによるローカルsemantic searchの実装ガイド, especially around ai organization, ai ready, ai-native. devgent source Evidence: https://www.meti.go.jp/shingikai/mono_info_service/ai_shakai_jisso/20260331_report.html, https://forbesjapan.com/articles/detail/91384, https://www.forbes.com/councils/forbestechcouncil/2026/06/25/the-ai-native-organization-individual-transformation-is-not-enough