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

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

Configure environment (optional)
```
cp .env.example .env
# Edit .env to customize provider/model
```
See haiku.rag configuration for details.
Start the application
```
docker compose up --build
```
Access the interface
- Frontend: http://localhost:3000
- Backend health: http://localhost:8000/health

How It Works

Ask a question: Type your research question in the chat
Review the plan: The agent decomposes your question into 3 sub-questions
Approve or revise: Choose to approve the plan or request changes
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
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.

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