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.

Submit your project

We welcome community examples. To add yours:

Create a subdirectory in examples/ with a short name (e.g. wikipedia-search/, pdf-rag/).
Include a README covering what it does, how to run it, and which SIE features it uses.
Keep it self-contained: include requirements.txt or package.json, a docker-compose if needed, and sample data or instructions to fetch it.
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.