haiku.rag/examples/ag-ui-research

Interactive Research Assistant

Research assistant powered by haiku.rag, Pydantic AI, and AG-UI. Ask complex questions and watch the research process unfold in real-time.

Watch demo video

Features

• Multi-step research workflow: Question decomposition, search, analysis, and synthesis
• Human-in-the-loop: Approve or revise research plans before execution
• Live state synchronization: Real-time updates of research progress between backend and frontend
• Context expansion: Automatically expands top search results for better context
• Rich reporting: Generates structured reports with findings, conclusions, and citations

Quick Start

Prerequisites

• Docker and Docker Compose
• A haiku.rag database with indexed documents
• Ollama (or configure another LLM provider)

Setup

1. Prepare your knowledge base

   mkdir -p data
   haiku-rag add "Your documents here" --db data/haiku_rag.lancedb
   # Or add from files
   haiku-rag add-src document.pdf --db data/haiku_rag.lancedb
   

2. Configure environment (optional)

   cp .env.example .env
   # Edit .env to customize provider/model
   

   See haiku.rag configuration for details.

3. Start the application

   docker compose up --build
   

4. Access the interface

   • Frontend: http://localhost:3000
   • Backend health: http://localhost:8000/health

How It Works

1. Ask a question: Type your research question in the chat
2. Review the plan: The agent decomposes your question into 3 sub-questions
3. Approve or revise: Choose to approve the plan or request changes
4. Watch it work: The agent automatically:
   • Searches the knowledge base for each sub-question
   • Extracts key insights from search results
   • Evaluates overall confidence in findings
5. Get your report: Receive a structured research report with citations

Architecture

• Backend (Python): Pydantic AI agent with haiku.rag integration

  • agent.py: Research agent with tool definitions
  • main.py: Starlette app serving AG-UI protocol

• Frontend (Next.js): CopilotKit/AG-UI interface

  • Real-time state synchronization with backend
  • Interactive approval workflow
  • Collapsible research plan and insights display

Configuration

Environment variables (see .env.example):

• DB_PATH: Path to haiku.rag database (default: haiku_rag.lancedb)
• QA_PROVIDER: LLM provider (default: ollama)
• QA_MODEL: Model name (default: gpt-oss:latest)
• OLLAMA_BASE_URL: Ollama endpoint (default: http://host.docker.internal:11434)

For other providers (OpenAI, Anthropic, etc.), see haiku.rag configuration docs.