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- One notebook covering all framework adapters: LangChain, DSPy, Ragas, LlamaIndex — one section per adapter
- All examples runnable with stubs, no live API calls
Acceptance criteria ...
documentation
priority:low
task
Changes
- Notebook covering the full calibration workflow: QA dataset → generate answers (LiteLLMBackend) → rate answers → fit
detector → push weights to HF Hub via skops
Acceptance criteria ...
documentation
priority:low
task
Changes
- End-to-end notebook: raw API response → LogProbParser → EntropyFeatureExtractor →
HallucinationDetector.from_pretrained → predict_proba
- Uses fixtures — no live API calls required ...
documentation
priority:low
task
# Use the vehicle dimensions to plot the bounding box
bbox_corners_xyz_ego_frame = np.array(
[
[dimensions.length / 2, dimensions.width / 2, 0],
[dimensions.length / 2, -dimensions.width ...

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