Examples
September 21, 2026 ยท View on GitHub
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.
New to SIE? Start with the quickstart notebook : encode, score, and extract in 5 minutes, then pick a project below.
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 |
| Rank exact primary-source passages | Testing a reranker on verbatim SEC, CMS, NTSB, and Supreme Court excerpts, where the closest wrong passage answers half the question | score | SIE endpoint with Qwen3 Reranker 4B; fetch.py pulls the recorded run, score.py reproduces the scores with no key | Runnable recorded example |
| Extract custom entities from primary sources | Changing zero-shot labels across financial, healthcare, rail-safety and legal text, with the two labels that came back empty left visible | extract | SIE endpoint with GLiNER multi v2.1; fetch.py pulls the recorded run, score.py reproduces the counts with no key | Runnable recorded example |
| Answer a question from a passage, and quote the sentence you used | Checking a generated answer for its citation, its length and whether it declines a question the passage cannot answer | chat/completions | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run, score.py reproduces the numbers with no key | Runnable recorded example |
| Write public status updates from internal incident reports | Checking a generated status update for leaked hosts, tickets, staff names and the wrong outage window | chat/completions | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run, score.py reproduces the number with no key | Runnable recorded example |
| Build a citable graph from filing text | Turning SEC, NHTSA and FDA paragraphs into entities and relations with offsets, and seeing where the relations go wrong | extract | SIE Cloud with GLiNER2; fetch.py pulls the recorded run, score.py reproduces the counts with no key | Runnable recorded example |
| Find the passage that answers a question | Ranking 253 regulation and documentation passages full of near misses, with query-side encoding | encode | SIE Cloud with Arctic Embed L v2.0; fetch.py pulls the recorded vectors, score.py ranks them with no key | Runnable recorded example |
| Pick the passage that meets every condition | Separating same-page near-duplicates with ColBERT late interaction, scored token by token | encode | SIE Cloud with GTE-ModernColBERT-v1; fetch.py pulls the recorded token vectors, score.py scores them with no key | Runnable recorded example |
| Fill a JSON Schema from a messy document | Reading NHTSA complaints, SEC officer filings and GSA listings into schema-valid JSON, with the two wrong fields left visible | chat/completions | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run, score.py reproduces the number with no key | Runnable recorded example |
| Route a request to the action it asks for | Scoring held-out SNIPS requests against seven action labels, and showing what happens when the labels overlap | extract | SIE Cloud with GLiClass; fetch.py pulls the recorded run, score.py reproduces the number with no key | Runnable recorded example |
| Check text for a planted instruction | Gating an agent on one label over twelve inputs from BIPIA, InjecAgent, LLMail-Inject, AgentDojo and XSTest, misses included | extract, chat_completions | SIE Cloud with GLiGuard and Granite Guardian; fetch.py pulls the recorded run, score.py reproduces both models' numbers with no key | Runnable recorded example |
| Expand a query into matchable terms | Comparing SPLADE, which adds terms the text never used, with bge-m3 sparse, which does not | encode | SIE Cloud with SPLADE++ and bge-m3; fetch.py pulls the recorded vectors, score.py reproduces the figures with no key | Runnable recorded example |
| Get the exact character offsets to mask | Naming your own ID types, measuring against published gold PII spans, and showing what a 384-word window costs | extract | SIE Cloud with GLiNER multi PII; fetch.py pulls the recorded run, score.py reproduces the figures with no key | Runnable recorded example |
| Read the numbers off a dashboard that has no API | Reading twelve Superset, Argo CD, Airflow, Kubernetes, GitLab and Jaeger screens into schema-valid JSON | generate | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run and the screenshots, score.py reproduces 328 of 335 with no key | Runnable recorded example |
| Read a scanned form into typed fields | Extracting typed fields from FAA, OSHA, NIST, USPS and invoice pages under a strict JSON schema | generate | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run and the page images, score.py reproduces 180 of 223 with no key | Runnable recorded example |
| Ask a question about an image | Putting one specific question to equipment photographs, hazmat placards, wiring drawings and analogue dials | generate | SIE Cloud with Qwen3.8 27B; fetch.py pulls the recorded run and the images, score.py reproduces 10 of 12 with no key | Runnable recorded example |
| Locate every object a prompt can name in a photo | Grounding open-vocabulary phrases to boxes on nine photos, with the two boxes it puts on the wrong object kept visible | extract | SIE Cloud with Grounding DINO; fetch.py pulls the recorded run, score.py reproduces the number with no key | Runnable recorded example |
| Sort product photos against labels you write yourself | Scoring damaged and whole parts against your own label wording, and the cheaper model that gets four of sixteen wrong | score, encode | SIE Cloud with Qwen3 VL Reranker and SigLIP 2; fetch.py pulls the recorded run, score.py reproduces the number with no key | Runnable recorded example |
| Find a spoken part number in a noisy recording | Transcribing twelve clips and searching them for the doses, figures and statute numbers that matter, the five misses kept | extract | SIE Cloud with Whisper large v3 turbo; fetch.py pulls the recorded run, score.py reproduces the word error rate with no key | Runnable recorded example |
| Find the image that matches a whole request | Ranking six product photos against one written query, where a colour-only and a category-only match sit just behind the right answer | encode | SIE Cloud with SigLIP so400m; fetch.py pulls the recorded run, score.py reproduces the ranking with no key | Runnable recorded example |
| Search document pages without flattening them to text | Ranking 712 report pages by what they look like, against a text baseline that buries the answer | encode | Self-hosted SIE with ColPali v1.3; fetch.py pulls the recorded run, score.py reproduces both rankings with no key | Runnable recorded example |
| Search licensed images with text | Recomputing a six-image hard-negative ranking from full SigLIP vectors | encode | SIE endpoint with SigLIP; standalone uv project | Runnable verified example |
| 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 | encode, score, generate | SIE endpoint, Shopify dataset prep via uv run scripts, standalone uv project | Runnable evaluation example |
| Read a page image, then read the Markdown | Measuring an OCR stage and a schema-filling stage separately, so a wrong field can be traced to the stage that lost it | extract, chat/completions | SIE Cloud with LightOnOCR-2-1B and two Qwen3 models; fetch.py pulls the recorded run, score.py reproduces the figures with no key | Runnable recorded example |
| Swap an OCR model with one identifier change | Driving recognition (VLM-OCR), structured extraction (Donut), and zero-shot NER (GLiNER) through the same extract call by swapping the model ID | extract | Docker Compose plus Node UI, no API key required, hosted version on Hugging Face Spaces | Runnable demo |
| A Stripe Link checkout with an SIE fraud-risk gate | Wiring all three SIE primitives into a pre-authorization fraud-risk gate that runs in the same round-trip as the Stripe PaymentIntent | extract, encode, score | Docker Compose plus Node UI; Stripe test-mode keys optional (runs in mock mode without them) | Runnable demo |
| Vision-first document RAG | Retrieving and answering questions over a multi-tenant page corpus by looking at page images (including scanned drawings) with OCR kept out of the score path | encode, chat/completions, score (optional) | GPU SIE deployment required: ColQwen2.5 retriever + Qwen3.5-4B answer model (runs on the generation bundle) | Runnable demo |
| Multi-model contract review with the OpenAI Agents SDK | Running an OpenAI Agents SDK agent whose every model call (triage, orchestration, vision, OCR, embeddings, rerank, entity extraction, text-to-SQL, reasoning, and a safety guardrail) is served by one SIE cluster, each step on the right catalog model, with per-model observability | generate, encode, score, extract | GPU SIE deployment required; standalone uv project; real contracts fetched from CUAD (CC BY 4.0) | Runnable demo |
| Turn difficult PDFs into Markdown | Preserving tables, reading order, headings, and form labels across real financial, academic, and government PDFs | extract | SIE endpoint with docling; fetch.py pulls the recorded run, verify-run checks 61 digests and relations offline; source PDFs fetched at run time | Runnable evaluation example |
| Review a published flood-insurance appeal | Separating FEMA's covered stone-removal scope from excluded barge, handling, disposal, and yard costs | extract, score, generate | GPU SIE deployment; standalone uv project; bundled public FEMA appeal and policy | Runnable agent example |
| Trace a restated filing figure | Following one reported figure through an original filing, corrective notice, and restatement while preserving source status | extract, encode, score | SIE endpoint; standalone uv project; public SEC facts and saved verified evidence | Runnable agent example |
| Reproduce CMS's L1851 documentation finding | Tracing a published six-month requirement against a seven-month face-to-face encounter and CMS's recoupment result | extract, encode, score | SIE endpoint; standalone uv project; exact CMS published example | Runnable agent example |
| Reconstruct a bearing failure | Turning the NTSB's three East Palestine detector readings into a cited temperature and alert sequence without adding a new causal claim | extract, encode, score | SIE endpoint; standalone uv project; exact NTSB illustrated report spread | Runnable agent example |
| Make a shelf gap auditable | Detecting one empty facing, deriving its notice and shelf-label crops by geometry, then preserving OCR evidence | extract | GPU SIE deployment; standalone uv project; CC0 supermarket shelf image and recorded direct-checkpoint evidence included | Runnable evaluation example |
| Turn threat reports into cited ATT&CK mapping suggestions | Mapping full reports against active ATT&CK 19.2, with a separate pinned AnnoCTR linking benchmark and analyst review for every suggestion | generate, extract, encode, score | GPU SIE deployment; standalone uv project; pinned MITRE ATT&CK and AnnoCTR sources | Runnable agent benchmark |
| A behavioural gate that catches hijacked AI agents by their actions, not their credentials | Judging a proposed AI agent action against that agent's own learned baseline in real time, before it reaches a downstream system | encode, score, extract | Docker Compose (gate + self-hosted SIE + n8n + mock downstream), no API key required | Runnable demo |
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:
- Create a subdirectory with a short, descriptive name (e.g.
wikipedia-search/,pdf-rag/) - 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.)
- Keep it self-contained - include a
requirements.txtorpackage.json, a docker-compose if needed, and sample data or instructions to fetch it - Open a PR against
main
Recorded evidence
Examples that reproduce a number published on a task page keep their code here
and their evidence in the public HuggingFace dataset
superlinked/sie-task-evidence,
one folder per task holding inputs/, calls.json and manifest.json. Each
example's fetch.py downloads that folder at a pinned dataset revision, never
main, so a later upload cannot change what the example scores.
The trade is deliberate: a reader cannot verify by cloning alone, and in return nobody needs an API key or any inference spend to re-derive a published number.
Review workflow
Maintainers apply the coderabbit-direct label to eligible PRs that change content under examples/** or the root README.md. The label opts the PR into CodeRabbit review and allows CodeRabbit to formally approve it once review comments are resolved and required checks pass.
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.