30-Second Quick Start

CLI (no config file needed):

cargo install --path graphrag-cli           # one-time install
graphrag index ./mydoc.txt                  # builds ./graphrag-data
graphrag ask "What is the main topic?"      # answers from the graph

Add --ollama to either command for LLM-quality entity extraction (requires ollama serve running locally).

Library (Rust):

use graphrag::GraphRAG;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let mut g = GraphRAG::quick_start("Plato's Symposium full text here...").await?;
    println!("{}", g.ask("Who is Diotima?").await?);
    Ok(())
}

Both flows use sensible defaults — hash-fallback embeddings, pattern-based entity extraction, persistent workspace. Opt into Ollama / GLiNER / custom chunking with the builder when you need more.

System Requirements

• Rust 1.85+ with wasm32-unknown-unknown target
• Node.js 18+ (for WASM builds)
• Git for cloning

Install Visual Studio Build Tools with C++ support

Install Rust with Windows target support

rustup target add wasm32-unknown-unknown

---

## Deployment Options

GraphRAG-rs supports **three deployment architectures** - choose based on your needs:

---

# Install trunk for WASM builds
cargo install trunk wasm-bindgen-cli

---

# Optional: Install globally
cargo install --path .

Quick Start (5 Lines!)

The fastest way to get started with GraphRAG:

use graphrag_core::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    let mut graphrag = GraphRAG::quick_start("Your document text").await?;
    let answer = graphrag.ask("What is this about?").await?;
    println!("{}", answer);
    Ok(())
}

backend = "jina" # Cost-optimized ($0.02/1M)

License

MIT License - see LICENSE for details.