Overview

Full applications built on SIE, with the pipelines, models, and evaluation results documented in each one. Every project is self-contained: clone it, run it, learn from it.

Find the best retrieval strategy for your RAG Head-to-head retrieval ablation across 7 encoder, reranker, and multi-vector pipelines on 1,854 SEC 10-K queries, ranked by NDCG@10.

Private fine-tuned compliance RAG Domain-tuned LoRA encoder and a custom cross-encoder that reranks and prunes context in one forward pass, all served from one SIE cluster.

Self-hosted product search in 5 min A full Amazon-style product search engine running on a laptop in 5 minutes. Uses all three SIE primitives (extract, encode, score) through three SDK calls.

Build a multimodal wine recommender with OCR A demo app that pairs preference-based wine retrieval with OCR-based label detection. Shows extract, encode, and score wired into one user-facing flow.

Find SOTA embedding models by MTEB task Describe your task in plain language and search across ~14K Hugging Face embedding models, ranked by task-specific MTEB scores.

Build a multi-modal product classifier with embeddings A structured evaluation of NLI, text retrieval, image retrieval, and cross-encoder reranking on Shopify's hierarchical product taxonomy.

Swap an OCR model with one identifier change A multi-model OCR demo: recognition VLM, end-to-end document model, and zero-shot NER all driven by the same extract call. Only the model ID changes between calls.

A Stripe Link checkout with an SIE fraud-risk gate extract + encode + score score every order against a corpus of past fraud patterns before the Stripe PaymentIntent is created. The risk band annotates the Stripe Link payment button, in the same UI, in the same request.

Submit your project

We welcome community examples. To add yours:

1. Create a subdirectory in examples/ with a short name (e.g. wikipedia-search/, pdf-rag/).
2. Include a README covering what it does, how to run it, and which SIE features it uses.
3. Keep it self-contained: include 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.