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feat: add AGENTS.md
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.gitignore

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.vscode/
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# AI Assistant Config Files
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.cursor/
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.clinerules/
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.roorules/
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AGENT.md
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CLAUDE.md
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GEMINI.md
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QWEN.md
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# Must-Gather Artifacts
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must-gather-collected/

AGENTS.md

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# Overview
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This is a testing repo for OpenDataHub and OpenShift AI, an MLOps platform for OpenShift.
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The tests contained in the repo are high-level integration tests at the Kubernetes API level.
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# Documentation
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All the general information about the repo is contained in the /docs directory.
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At the start of each session, consider if you need to consult any of these files in order to answer:
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- [Guidelines for Getting Started](./docs/GETTING_STARTED.md)
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- [Developer Guide](./docs/DEVELOPER_GUIDE.md)
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- [Style Guide](./docs/STYLE_GUIDE.md)
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# Specific Instructions
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- Avoid unnecessary complexity: Aim for the simplest solution that works, while keeping the code clean.
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- Avoid obvious comments: Only add comments to explain especially complex code blocks.
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- Maintain code consistency: Follow existing code patterns and architecture.
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- Maintain locality of behavior: Keep code close to where it's used.
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- Make small, focused changes, unless explicitly asked otherwise.
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- Keep security in mind: Avoid filtering sensitive information and running destructive commands.
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- When in doubt about something, ask the user.

docs/CONTRIBUTING.md

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## Pull requests
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Follow the guidelines in [Developer guide](DEVELOPER_GUIDE.md)
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## Adding new runtime
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To add a new runtime, you need to:
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1. Add a new file under [manifests](../utilities/manifests) directory.
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2. Add `<runtime>_INFERENCE_CONFIG` dict with:
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```code
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"support_multi_default_queries": True|False, # Optioanl, if set to True, `default_query_model` should contains a dict with corresponding inference_type
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"default_query_model": {
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"query_input": <default query to be sent to the model>,
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"query_output": <expected output>,
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"use_regex": True|False, # Optional, if set to True, `query_output` should be a regex
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},
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"<query type, for example: all-tokens>": {
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"<protocol, for example HTTP>": {
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"endpoint": "<model endpoint>",
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"header": "<model required headers>",
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"body": '{<model expected body}',
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"response_fields_map": {
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"response_output": <output field in response>,
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"response": <response field in response - optional>,
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},
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},
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```
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3. See [caikit_standalone](../utilities/manifests/caikit_standalone.py) for an example

docs/DEVELOPER_GUIDE.md

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```bash
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tox
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```
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## Adding new runtime
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To add a new runtime, you need to:
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1. Add a new file under [manifests](../utilities/manifests) directory.
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2. Add `<runtime>_INFERENCE_CONFIG` dict with:
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```code
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"support_multi_default_queries": True|False, # Optioanl, if set to True, `default_query_model` should contains a dict with corresponding inference_type
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"default_query_model": {
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"query_input": <default query to be sent to the model>,
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"query_output": <expected output>,
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"use_regex": True|False, # Optional, if set to True, `query_output` should be a regex
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},
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"<query type, for example: all-tokens>": {
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"<protocol, for example HTTP>": {
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"endpoint": "<model endpoint>",
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"header": "<model required headers>",
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"body": '{<model expected body}',
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"response_fields_map": {
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"response_output": <output field in response>,
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"response": <response field in response - optional>,
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},
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},
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```
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3. See [caikit_standalone](../utilities/manifests/caikit_standalone.py) for an example
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## AI Usage
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If using AI tooling to assist you in the process of writing or reviewing code:
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1. Understand what you are doing --as a developer, you are ultimately responsible for the code. Always assume the code produced by the AI tools is unsafe and incorrect, and always double-check it.
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2. We support [AGENTS.md](../AGENTS.md), an [open format](https://agents.md/) for guiding coding agents. If you use any proprietary tool that do not support `AGENTS.md` (e.g. Claude Code, Qwen Code, Gemini Code), you can create a symlink:
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```bash
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ln -s AGENTS.md CLAUDE.md
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```

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