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Entity Extraction with Rig

This example demonstrates how to leverage Rig, a Rust library for building LLM-powered applications, to extract named entities from text. Whether you're new to Rig or looking to explore its capabilities, this example provides a great starting point for understanding how to work with custom data structures and AI-powered extraction.

Prerequisites

Before you begin, make sure you have the following installed:

  • Rust (latest stable version)
  • Cargo (Rust's package manager)

You'll also need an OpenAI API key. If you don't have one, you can sign up at OpenAI's website.

Setup

  • Create a new Rust project:

    • cargo new rig-entity-extraction
    • cd rig-entity-extraction

  • Add the following dependencies to your Cargo.toml:

[dependencies]
rig-core = "0.1.0"
serde = { version = "1.0", features = ["derive"] }
schemars = "0.8"
tokio = { version = "1.0", features = ["full"] }
  • Set your OpenAI API key as an environment variable:
    • export OPENAI_API_KEY=your_api_key_here

Code Overview

The main components of this example are:

  • Custom data structures (EntityType, Entity, ExtractedEntities) for representing extracted entities.
  • An OpenAI client initialization.
  • An extractor setup using GPT-4 model.
  • A sample text for entity extraction.
  • The extraction process and result handling.

Running the Example

  • Copy the provided code into your src/main.rs file.
  • Run the example using: cargo run

Customization

Feel free to modify the sample_text or adjust the EntityType enum to suit your specific use case. You can also experiment with different OpenAI models by changing the model name in the extractor setup.

Troubleshooting

If you encounter any issues:

  • Ensure your OpenAI API key is correctly set.
  • Check that all dependencies are properly installed.
  • Verify that you're using a compatible Rust version.

For more detailed information, refer to the Rig documentation.