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Pull Request Overview
This PR introduces a new generic RAG agent sample that demonstrates multi-source information retrieval combining context grounding indexes, web search, and optional MCP tool integration. The agent uses a LlamaIndex workflow with ReAct reasoning and includes human-in-the-loop feedback capabilities.
Key changes:
- Implementation of a flexible RAG agent with multi-knowledge base support using UiPath Context Grounding
- Integration of Tavily web search for real-time information and optional MCP server connectivity for dynamic tool loading
- Human-in-the-loop workflow with iterative feedback and answer refinement
Reviewed Changes
Copilot reviewed 16 out of 17 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
| main.py | Core agent implementation with workflow steps for indexing, querying, formatting, and human confirmation |
| uipath.json | Agent schema definition with input/output contracts and resource bindings |
| pyproject.toml | Project dependencies including UiPath SDK, LlamaIndex, Tavily, and MCP tools |
| README.md | Comprehensive documentation covering features, setup, configuration, and usage examples |
| input.json | Sample input demonstrating multi-tool query capabilities |
| sample_data/primary_data/*.txt | Sample company policy documents for primary knowledge base |
| sample_data/secondary_data/*.txt | Sample user preference data for secondary knowledge base |
| resume.json | Workflow resume state file |
| llama_index.json | LlamaIndex workflow configuration |
| agent.mermaid | Workflow visualization diagram |
| AGENTS.md, CLAUDE.md, .agent/*.md | Agent development documentation and references |
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Pull Request Overview
Copilot reviewed 16 out of 17 changed files in this pull request and generated no new comments.
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This PR introduces a generic agent built using UiPath Llamaindex. The agent serves as a template for building agents and is suitable for expansions.