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https://github.com/0xPlaygrounds/rig
⚙️🦀 Build portable, modular & lightweight Fullstack Agents
https://github.com/0xPlaygrounds/rig
agent ai artificial-intelligence automation large-language-model llm rust scalable-ai
Last synced: about 1 month ago
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⚙️🦀 Build portable, modular & lightweight Fullstack Agents
- Host: GitHub
- URL: https://github.com/0xPlaygrounds/rig
- Owner: 0xPlaygrounds
- License: mit
- Created: 2024-06-05T13:42:28.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-11T16:36:25.000Z (2 months ago)
- Last Synced: 2024-11-12T09:12:31.724Z (2 months ago)
- Topics: agent, ai, artificial-intelligence, automation, large-language-model, llm, rust, scalable-ai
- Language: Rust
- Homepage: https://rig.rs
- Size: 10.1 MB
- Stars: 153
- Watchers: 6
- Forks: 9
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
✨ If you would like to help spread the word about Rig, please consider starring the repo!
> [!WARNING]
> Here be dragons! As we plan to ship a torrent of features in the following months, future updates **will** contain **breaking changes**. With Rig evolving, we'll annotate changes and highlight migration paths as we encounter them.
## What is Rig?
Rig is a Rust library for building scalable, modular, and ergonomic **LLM-powered** applications.
More information about this crate can be found in the [crate documentation](https://docs.rs/rig-core/latest/rig/).
Help us improve Rig by contributing to our [Feedback form](https://bit.ly/Rig-Feeback-Form).
## Table of contents
- [What is Rig?](#what-is-rig)
- [Table of contents](#table-of-contents)
- [High-level features](#high-level-features)
- [Get Started](#get-started)
- [Simple example:](#simple-example)
- [Integrations](#integrations)
## High-level features
- Full support for LLM completion and embedding workflows
- Simple but powerful common abstractions over LLM providers (e.g. OpenAI, Cohere) and vector stores (e.g. MongoDB, in-memory)
- Integrate LLMs in your app with minimal boilerplate
## Get Started
```bash
cargo add rig-core
```
### Simple example:
```rust
use rig::{completion::Prompt, providers::openai};
#[tokio::main]
async fn main() {
// Create OpenAI client and model
// This requires the `OPENAI_API_KEY` environment variable to be set.
let openai_client = openai::Client::from_env();
let gpt4 = openai_client.agent("gpt-4").build();
// Prompt the model and print its response
let response = gpt4
.prompt("Who are you?")
.await
.expect("Failed to prompt GPT-4");
println!("GPT-4: {response}");
}
```
Note using `#[tokio::main]` requires you enable tokio's `macros` and `rt-multi-thread` features
or just `full` to enable all features (`cargo add tokio --features macros,rt-multi-thread`).
You can find more examples each crate's `examples` (ie. [`src/examples`](./src/examples)) directory. More detailed use cases walkthroughs are regularly published on our [Dev.to Blog](https://dev.to/0thtachi).
## Supported Integrations
| Model Providers | Vector Stores |
|:--------------:|:-------------:|
|
|
|
Vector stores are available as separate companion-crates:
- MongoDB vector store: [`rig-mongodb`](https://github.com/0xPlaygrounds/rig/tree/main/rig-mongodb)
- LanceDB vector store: [`rig-lancedb`](https://github.com/0xPlaygrounds/rig/tree/main/rig-lancedb)
- Neo4j vector store: [`rig-neo4j`](https://github.com/0xPlaygrounds/rig/tree/main/rig-neo4j)
- Qdrant vector store: [`rig-qdrant`](https://github.com/0xPlaygrounds/rig/tree/main/rig-qdrant)
