Concurrent Processing with Rig
This example demonstrates how to use Rig, a powerful Rust library for building LLM-powered applications, to perform concurrent processing of LLM tasks. This approach significantly improves performance when dealing with multiple LLM queries, making it ideal for batch processing or high-throughput scenarios.
Prerequisites
Before you begin, ensure you have the following installed:
- Rust (latest stable version)
- Cargo (Rust’s package manager)
You’ll also need an OpenAI or Cohere API key. If you don’t have one, you can sign up at OpenAI’s website or Cohere’s website
Setup
-
Create a new Rust project:
cargo new rig-concurrent-processing cd rig-concurrent-processing
-
Add the following dependencies to your
Cargo.toml
:[dependencies] rig-core = "0.1.0" 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:
- OpenAI client initialization.
- Creation of a shared GPT-3.5-turbo model instance.
- Spawning of multiple concurrent tasks using Tokio.
- Concurrent execution of LLM queries.
- Collection and display of results.
Running the Example
- Copy the provided code into your
src/main.rs
file. - Run the example using:
cargo run
Customization
You can easily modify this example to suit your specific use case:
- Change the number of concurrent tasks by adjusting the loop range.
- Modify the prompt to generate different types of content.
- Experiment with different OpenAI models by changing the model name.
Performance Considerations
- Be mindful of OpenAI’s rate limits when increasing concurrency.
- Monitor system resource usage to optimize the number of concurrent tasks.
- Consider implementing error handling and retry logic for production use.
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.