Update RAG example to v0.2.x

[wizardlancet]

.1 Re-implement RAG agent with Agent-Lightning v2.x
2. Merge environments management into uv
3. Update sample dataset to a tiny dataset for future CI
4. Update readme

[Copilot]

Pull request overview

This pull request updates the RAG (Retrieval-Augmented Generation) example to be compatible with Agent-Lightning v2.x. The update includes migrating from shell-based training scripts to a Python-based training interface, consolidating environment management into uv, and using a smaller "tiny" dataset for easier testing and CI integration.

• Migrated from shell script configuration to Python-based training with train_rag.py
• Updated agent implementation to use v2.x API signatures (training_rollout_async and validation_rollout_async now accept Rollout parameter)
• Consolidated dependency management by removing conda-based environment setup in favor of uv-managed dependencies

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
pyproject.toml	Added rag dependency group with required packages (fastmcp, faiss-cpu, sentence-transformers) and conflict resolution overrides
wiki_retriever_mcp/wiki_retriever_mcp.py	Updated to use tiny dataset files and reduced top_k from 4 to 1 for demonstration purposes
wiki_retriever_mcp/wiki_retriever_install.sh	Removed conda-based installation script in favor of uv dependency management
train_rag.py	New Python-based training script with configurable training modes (fast/single_gpu) replacing the shell script approach
train.sh	Removed shell-based training script, replaced by train_rag.py
rag_run_dev.py	New development/testing script for running the RAG agent with a local vLLM server
rag_agent.py	Updated to v2.x API with new method signatures, improved logging, and type hints
README.md	Comprehensive documentation update with step-by-step setup instructions for the tiny dataset

Comments suppressed due to low confidence (2)

examples/rag/rag_run_dev.py:1

• Import of 'json' is not used.

import json

examples/rag/train_rag.py:13

• Import of 'os' is not used.

import os

---

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[Copilot]

The usage examples in the docstring reference train_rag_agent.py, but the actual filename is train_rag.py. Please update the docstring to match the correct filename.

Suggested change

	python train_rag_agent.py fast # Fast training for CI/testing
	python train_rag_agent.py single_gpu # Optimized for Single GPU (1.5B/7B models)
	python train_rag.py fast # Fast training for CI/testing
	python train_rag.py single_gpu # Optimized for Single GPU (1.5B/7B models)

[Copilot]

The val_resources is created as a plain dictionary but is passed to training_rollout_async which expects an agl.NamedResources object. This type mismatch could cause runtime errors. Consider wrapping it properly or ensuring it's the correct type.

Suggested change

	val_resources = {
	"main_llm": agl.LLM(
	endpoint=llm.endpoint,
	model=llm.model,
	sampling_parameters={"temperature": 0.7},
	)
	}
	val_resources = agl.NamedResources({
	"main_llm": agl.LLM(
	endpoint=llm.endpoint,
	model=llm.model,
	sampling_parameters={"temperature": 0.7},
	)
	})

[Copilot]

The description mentions wiki_retriever_install.sh as part of the wiki_retriever_mcp/ directory, but this file has been deleted in this PR. Please update the description to remove the reference to this file.

Suggested change

	| `wiki_retriever_mcp/` | Setup scripts and MCP server (`wiki_retriever_install.sh`, `wiki_retriever_mcp.py`) for Wikipedia retrieval |
	| `wiki_retriever_mcp/` | MCP server (`wiki_retriever_mcp.py`) for Wikipedia retrieval |

[Copilot]

The json module is imported but never used in this file. Consider removing this unused import.

Suggested change

# Copyright (c) Microsoft. All rights reserved.

[Copilot]

The os module is imported but never used in this file. Consider removing this unused import.

Suggested change

import os

[Copilot]

There's a path navigation inconsistency in the instructions. After step 2, users are left in the examples/rag/wiki_retriever_mcp directory, but step 3 says to cd examples/rag/wiki_retriever_mcp (which assumes they're at the repo root). Consider adding a comment to indicate the expected working directory at each step or adjusting the paths to be consistent.

Suggested change

	cd examples/rag/wiki_retriever_mcp
	# (Assuming you are still in examples/rag/wiki_retriever_mcp)

[ultmaster]

/ci

[github-actions]

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