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DOC: Restructure documentation around three user pathways (#1583)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎.pre-commit-config.yaml‎

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hooks:
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- id: nbstripout
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args: [--keep-output, '--extra-keys=metadata.kernelspec']
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files: ^doc/*\.(ipynb)$
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files: ^doc/.*\.ipynb$
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‎doc/code/converters/7_human_converter.ipynb‎

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"> ⚠️ **Deprecated:** The `HumanInTheLoopConverter` is deprecated and will be removed in PyRIT v0.13.0.\n",
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"> Use the React-based GUI (CoPyRIT) instead. See the [GUI documentation](../gui/0_gui.md) for details.\n",
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"> Use the React-based GUI (CoPyRIT) instead. See the [GUI documentation](../../gui/0_gui.md) for details.\n",
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"\n",
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"# 7. Human in the Loop Converter\n",
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‎doc/code/converters/7_human_converter.py‎

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# %% [markdown]
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# > ⚠️ **Deprecated:** The `HumanInTheLoopConverter` is deprecated and will be removed in PyRIT v0.13.0.
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# > Use the React-based GUI (CoPyRIT) instead. See the [GUI documentation](../gui/0_gui.md) for details.
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# > Use the React-based GUI (CoPyRIT) instead. See the [GUI documentation](../../gui/0_gui.md) for details.
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#
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# # 7. Human in the Loop Converter
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#

‎doc/code/datasets/5_simulated_conversation.ipynb‎

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"# 5. Simulated Conversations\n",
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"## Inspecting the Results\n",
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"## Replaying on a Different Target\n",
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"> **Note:** If the Crescendo result shows `backtrack_count: 0` even on failure, this is expected.\n",
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"## Key Parameters\n",
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‎doc/code/executor/benchmark/2_bias_benchmark.ipynb‎

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"# 2. Bias & Fairness Benchmark\n",
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"## Testing for Definitive YES/NO Answers\n",
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doc/code/architecture.md renamed to doc/code/framework.md

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# Framework
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Learn how to use PyRIT's components to build red teaming workflows.
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:::::{grid} 1 1 2 3
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:gutter: 3
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::::{card} 📦 Datasets
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:link: ./datasets/0_dataset
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Load, create, and manage seed datasets for red teaming campaigns.
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::::{card} ⚔️ Attacks & Executors
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:link: ./executor/0_executor
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Run single-turn and multi-turn attacks — Crescendo, TAP, Skeleton Key, and more.
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::::{card} 🔌 Targets
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:link: ./targets/0_prompt_targets
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Connect to OpenAI, Azure, Anthropic, HuggingFace, HTTP endpoints, and custom targets.
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::::{card} 🔄 Converters
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:link: ./converters/0_converters
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Transform prompts with text, audio, image, and video converters.
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Evaluate AI responses with true/false, Likert, classification, and custom scorers.
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Track conversations, scores, and attack results with SQLite or Azure SQL.
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Initialize PyRIT, configure defaults, and manage resiliency settings.
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Run standardized evaluation scenarios at scale across harm categories.
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Register and discover targets, scorers, and converters via class and instance registries.
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---
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The sections above link to detailed guides for each component. The architecture below explains how the pieces fit together — it's primarily aimed at contributors.
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# Architecture
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The main components of PyRIT are prompts, attacks, converters, targets, and scoring. The best way to contribute to PyRIT is by contributing to one of these components.

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