Crate anchor_chain

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§Anchor Chain

Anchor Chain is a Rust framework designed to simplify the orchestration of workflows involving Large Language Models (LLMs). Inspired by LangChain, Anchor Chain provides a set of easy-to-use and extensible building blocks that enable developers to create robust and efficient LLM-based applications quickly. The framework prioritizes type safety, processing efficiency, and flexibility through its carefully designed APIs and abstractions.

§Features

  • Statically Typed Chains: Anchor Chain leverages Rust’s type system to provide statically typed chains, catching potential type mismatches at compile time.

  • Async Runtime for Parallel Execution: Built with Rust’s async runtime, Anchor Chain allows for efficient parallel processing of nodes in complex chains.

  • Extensibility through the Node Trait: The Node trait allows developers to create custom nodes tailored to their specific use cases, enabling seamless integration into the chain.

  • Support for Popular LLMs: Anchor Chain provides built-in support for popular LLMs, such as OpenAI’s GPT models and Anthropic Claude, abstracting away API details to provide a common interface.

  • Parallel Node Execution: The ParallelNode struct enables parallel execution of multiple nodes, leveraging concurrency to improve overall chain performance.

§Getting Started

To get started with Anchor Chain, add the following dependency to your Cargo.toml file:

[dependencies]
anchor-chain = "0.1.0"

Then, you can create chains using the ChainBuilder and invoke them with the .process() function. Any node can be added to the chain using the link() function which will execute the node in the order it was added.

#[tokio::main]
async fn main() {
    use anchor_chain::{
        chain::ChainBuilder,
        models::openai::OpenAIModel,
    };

    let chain = ChainBuilder::new()
        .link(OpenAIModel::new_gpt3_5_turbo("You are a helpful assistant").await)
        .build();

    let result = chain
        .process("Write a hello world program in Rust")
        .await
        .expect("Error processing chain");

    println!("Result: {}", result);
}

Prompts can be constructed using the Prompt struct. Prompt uses Tera templating to allow for dynamic input substitution. Tera’s syntax is based on Jinja2 and Django templates. Context variables are passed to the prompt using a HashMap.

use std::collections::HashMap;

#[tokio::main]
async fn main() {
    use anchor_chain::{
        chain::ChainBuilder,
        models::openai::OpenAIModel,
        nodes::prompt::Prompt,
    };

    let chain = ChainBuilder::new()
        .link(Prompt::new("{{ input }}"))
        .link(OpenAIModel::new_gpt3_5_turbo("You are a helpful assistant").await)
        .build();

    let result = chain
        .process(HashMap::from([("input", "Write a hello world program in Rust")]))
        .await
        .expect("Error processing chain");

    println!("Result: {}", result);
}

For more examples please refer to the examples directory.

Re-exports§

Modules§

  • agents
  • chain
    Provides structures for creating and executing chains.
  • models
    Contains nodes that are designed to work with various LLM models.
  • node
    Module providing foundational structures for building chains.
  • nodes
    Various nodes that can be chained together to form an LLM process chain.
  • parallel_node
    Provides a structure for processing input through multiple nodes in parallel.
  • vector
    This module contains various nodes for working with Vector databases.

Structs§

Enums§