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//! Rig is a Rust library for building LLM-powered applications that focuses on ergonomics and modularity.
//!
//! # Table of contents
//!
//! - [High-level features](#high-level-features)
//! - [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
//!
//! # Simple example:
//! ```
//! 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.model("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`).
//!
//! # Integrations
//! Rig currently has the following integration sub-libraries:
//! - MongoDB vector store: `rig-mongodb`

pub mod agent;
pub mod cli_chatbot;
pub mod completion;
pub mod embeddings;
pub mod extractor;
pub mod json_utils;
pub mod model;
pub mod providers;
pub mod rag;
pub mod tool;
pub mod vector_store;