examples

Examples

A project gallery of full end-to-end applications built with SIE. Each project lives in its own subdirectory. Clone it, run it, learn from it.

Gallery

Use this table to pick the right starting point. "Runnable" means the example has code, sample data or data-fetch instructions, and a documented local path. "Advanced" examples may require a custom SIE image or third-party service keys.

Example	Best for	SIE primitives	Setup	Status
Self-hosted product search in 5 min	Showing the fastest local product-search path with extraction, embeddings, and reranking	extract, encode, score	Local SIE Docker image, Python or TypeScript app	Runnable
Find the best retrieval strategy for your RAG	Picking a production RAG retrieval pipeline by evals on real financial documents	encode, score	SIE endpoint, Turbopuffer key, optional SIE API key for auth-enabled clusters	Runnable benchmark
Find SOTA embedding models by MTEB task	Searching ~14K HF embedding models ranked by task-specific MTEB scores	encode, score	Backend seed script plus Vite frontend; falls back without a live SIE endpoint	Runnable
Private fine-tuned compliance RAG	Hot-loading a domain LoRA encoder and a custom token-pruning adapter on SIE	encode, score, extract	Custom SIE Docker image, GPU recommended	Advanced runnable example
Build a multimodal wine recommender with OCR	Combining preference-based retrieval with OCR-driven label detection in one UI	encode, score, extract	Docker Compose app plus local SIE endpoint; API key optional for unauthenticated SIE	Runnable demo
Build a multi-modal product classifier with embeddings	Evaluating text, image, NLI, and reranking approaches for hierarchical product taxonomy classification	extract, encode, score	SIE endpoint, Shopify dataset prep via uv run scripts, standalone uv project	Runnable evaluation example

For docs publishing, lead with the quickest runnable demos, then use the benchmark and evaluation examples for deeper technical users.

Submit your project

We welcome contributions. To add your project to the gallery:

1. Create a subdirectory with a short, descriptive name (e.g. wikipedia-search/, pdf-rag/)
2. Include a README that covers:
   • What the project does
   • How to run it (docker compose up, a script, etc.)
   • Which SIE features it uses (encode, score, extract, cluster, etc.)
3. Keep it self-contained - include a requirements.txt or package.json, a docker-compose if needed, and sample data or instructions to fetch it
4. Open a PR against main

Projects can be anything: a search engine, a RAG pipeline, a benchmark, a migration guide, a CLI tool. If it uses SIE, it belongs here.

Links

• SIE overview
• API reference
• Deployment guide
• All models