orch

Rust framework for LLM orchestration

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orch is a library for building language model powered applications and agents for the Rust programming language. It can be used for tasks such as text generation, streaming text generation, structured data generation, and embedding generation. The library provides functionalities for executing various language model tasks and can be integrated into different applications and contexts. It offers flexibility for developers to create language model-powered features and applications in Rust.

README:

orch

orch is a library for building language model powered applications and agents for the Rust programming language. It was primarily built for usage in magic-cli, but can be used in other contexts as well.

[!NOTE]

If the project gains traction, this can be compiled as an addon to other languages such as Python or a standalone WebAssembly module.

Installation

cargo add orch
cargo add orch_response

Alternatively, add orch as a dependency to your Cargo.toml file:

[dependencies]
orch = "*" # Substitute with the latest version
orch_response = "*" # Substitute with the latest version

Basic Usage

Simple Text Generation

use orch::execution::*;
use orch::lm::*;

#[tokio::main]
async fn main() {
  let lm = OllamaBuilder::new().try_build().unwrap();
  let executor = TextExecutorBuilder::new().with_lm(&lm).try_build().unwrap();
  let response = executor.execute("What is 2+2?").await.expect("Execution failed");
  println!("{}", response.content);
}

Streaming Text Generation

use orch::execution::*;
use orch::lm::*;
use tokio_stream::StreamExt;

#[tokio::main]
async fn main() {
  let lm = OllamaBuilder::new().try_build().unwrap();
  let executor = TextExecutorBuilder::new().with_lm(&lm).try_build().unwrap();
  let mut response = executor.execute_stream("What is 2+2?").await.expect("Execution failed");
  while let Some(chunk) = response.stream.next().await {
    match chunk {
      Ok(chunk) => print!("{chunk}"),
      Err(e) => {
        println!("Error: {e}");
        break;
      }
    }
  }
  println!();
}

Structured Data Generation

use orch::execution::*;
use orch::lm::*;
use orch::response::*;

#[derive(Variants, serde::Deserialize)]
pub enum ResponseVariants {
    Answer(AnswerResponseVariant),
    Fail(FailResponseVariant),
}

#[derive(Variant, serde::Deserialize)]
#[variant(
    variant = "Answer",
    scenario = "You know the capital city of the country",
    description = "Capital city of the country"
)]
pub struct AnswerResponseVariant {
    #[schema(
        description = "Capital city of the received country",
        example = "London"
    )]
    pub capital: String,
}

#[derive(Variant, serde::Deserialize)]
#[variant(
    variant = "Fail",
    scenario = "You don't know the capital city of the country",
    description = "Reason why the capital city is not known"
)]
pub struct FailResponseVariant {
    #[schema(
        description = "Reason why the capital city is not known",
        example = "Country 'foobar' does not exist"
    )]
    pub reason: String,
}

#[tokio::main]
async fn main() {
    let lm = OllamaBuilder::new().try_build().unwrap();
    let executor = StructuredExecutorBuilder::new()
    .with_lm(&lm)
    .with_preamble("You are a geography expert who helps users understand the capital city of countries around the world.")
		.with_options(Box::new(variants!(ResponseVariants)))
    .try_build()
    .unwrap();
    let response = executor
        .execute("What is the capital of Fooland?")
        .await
        .expect("Execution failed");

    println!("Response:");
    match response.content {
        ResponseVariants::Answer(answer) => {
            println!("Capital city: {}", answer.capital);
        }
        ResponseVariants::Fail(fail) => {
            println!("Model failed to generate a response: {}", fail.reason);
        }
    }
}

Embedding Generation

use orch::execution::*;
use orch::lm::*;

#[tokio::main]
async fn main() {
  let lm = OllamaBuilder::new().try_build().unwrap();
  let executor = TextExecutorBuilder::new()
    .with_lm(&lm)
    .try_build()
    .unwrap();
  let embedding = executor
    .generate_embedding("Phrase to generate an embedding for")
    .await
    .expect("Execution failed");

  println!("Embedding:");
  println!("{:?}", embedding);
}

More Examples

See the examples directory for usage examples.

For Tasks:

Click tags to check more tools for each tasks

generate text stream text generate structured data generate embedding execute language model tasks

For Jobs:

software developer data scientist machine learning engineer ai researcher natural language processing engineer

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