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Orchid Ranker

Orchid is an adaptive-practice engine for learning products.

It ranks an eligible set of exercises, observes the learner's result, and adapts what it recommends next. The public interface is one class: AdaptiveRanker.

Install

pip install orchid-ranker

Complete example

import pandas as pd
from orchid_ranker import AdaptiveRanker

history = pd.DataFrame({
    "user_id":   ["a", "a", "a", "b", "b", "b"],
    "item_id":   [101, 102, 201, 101, 102, 201],
    "outcome":   [1,   1,   0,   1,   0,   0],
    "timestamp": [1,   2,   3,   1,   2,   3],
})

ranker = AdaptiveRanker().fit(history)
ranked = ranker.recommend("a", [101, 102, 201], top_k=2)

ranker.observe(
    user_id="a",
    item_id=ranked[0].item_id,
    outcome=1,
    timestamp=4,
)

Orchid chooses the appropriate starting learner: a transparent empirical baseline for sparse pilots and knowledge tracing only after data-support checks pass. Users do not select from a model catalog.

print(ranker.learning_readiness())

Where it fits

Orchid works best when:

  • learners complete multiple scored practice attempts;
  • an existing curriculum supplies multiple pedagogically valid next exercises;
  • the next exercise should react to prior attempts; and
  • success can be measured later through retained mastery, a post-test, or a certification outcome.

It is not a generic content feed, product recommender, LMS, or curriculum authoring system. Your learning product owns the content, eligibility rules, and learner experience; Orchid supplies adaptive practice sequencing and the decision evidence for a controlled evaluation.

Choose your path

If you want to… Start here
Try the learner loop on historical attempts Quickstart
Check whether your data and catalog are ready Adaptive-practice data readiness
Add durable decisions and delayed outcomes to an existing product Production serving
Run an evidence-oriented controlled pilot Pilot workflow
Look up a class or method API reference
Check compatibility guarantees API support policy

Start with the quickstart, then read how Orchid works, the small API reference, adaptive-practice data readiness, or how to run a learning-efficacy pilot. The pilot integration contract defines the handoff between Orchid and a learning platform. The end-to-end reference-pilot workflow turns that contract into a runnable sequence. The product roadmap describes the path from a single-course pilot to an evidence-backed integration. The design-partner council keeps that roadmap grounded in simulated customer review without claiming any real-company affiliation.