Adding `anchor_chain` library as a dependency

Run this command in a terminal, in your project"s directory:

cargo add anchor-chain

To add it manually, edit your project"s Cargo.toml file and add to the [dependencies] section:

anchor-chain = "0.4.2"

The anchor_chain library will be automatically available globally. Read the anchor_chain library documentation.

Back to the crate overview.

Readme

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 LLM-based applications quickly. The framework prioritizes type safety, processing efficiency, and composability by design.

Anchor Chain is currently under active development and its API is subject to change. The framework has not yet reached a stable 1.0 release, and backward compatibility is not guaranteed for versions prior to 1.0.

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              )_)  )_)  )_)
             )___))___))___)
            )____)____)_____)
          _____|____|____|____
---------\                 0 /--------
  ^^^^^ ^^^^^^^^^^^^^^^^^^^0^
                           0
                           0
                           0
                           |
                         \_⟂_/

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"s 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.
Tracing Support: Anchor Chain integrates with the tracing crate to provide detailed logs and diagnostics, helping developers understand the execution flow of their chains.
OpenSearch Integration: Anchor Chain supports indexing and searching documents with OpenSearch vector indexes, enabling Retrieval-Augmented Generation (RAG) workflows.

Supported Models

Currently, Anchor chain supports OpenAI"s GPT3.5 Turbo, GPT4 Turbo, and GPT3.5 Instruct through the use of the async-openai crate as well as Claude 3 Sonnet through the AWS Bedrock API. There are plans to add support for Mistral and other models supported by AWS Bedrock as well as support for connecting to a locally running Ollama or llama.cpp model through the provided REST APIs.

Why Anchor Chain?

Anchor Chain addresses some of the challenges developers face when working with LangChain, such as the lack of documentation, ambiguous APIs, and the lack of type safety. These issues can lead to time-consuming trial and error when building LLM-based applications.

By leveraging Rust"s expressive type system, Anchor Chain provides statically typed chains that offer clear compile-time feedback and in-editor type hints. This helps catch potential errors early in the development process and facilitates a more efficient workflow.

Additionally, Anchor Chain"s built-in support for async runtimes enables efficient parallel processing of nodes in complex chains. This can significantly reduce the overall execution time of LLM-based workflows, making Anchor Chain an attractive choice for performance-critical applications.

Getting Started

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

[dependencies]
anchor-chain = "0.1.1"

Then, you can create chains using the ChainBuilder and invoke them with the .process() function:

use anchor_chain::{
    chain::ChainBuilder,
    nodes::prompt::Prompt,
    models::openai::OpenAIModel,
};
use std::collections::HashMap;

#[tokio::main]
async fn main() {
    let chain = ChainBuilder::new()
        .link(Prompt::new("{{ input }}"))
        .link(OpenAIModel::new_gpt4_turbo("You are a helpful assistant".to_string()).await)
        .build();

    let output = chain
        .process(HashMap::from([(
            "input".to_string(),
            "Write a hello world program in Rust".to_string(),
        )]))
        .await
        .expect("Error processing chain");
    println!("Output:\n{}", output);
}

For more examples and detailed documentation, please refer to the examples directory and the API documentation.

Contributing

Contributions to Anchor Chain are welcome! If you encounter any issues, have suggestions for improvements, or would like to contribute new features, please open an issue or submit a pull request on the GitHub repository.

TODO

While Anchor Chain is usable today, it"s still a work in progress. Below are a list of future features that are planned for the 1.0 release:

Support for Mistral through AWS Bedrock
Support for Ollama REST API
Add error handling strategy that will use a backup model if the primary fails

Other potential features for future releases:

Add feature flag for Askama to support statically typed prompts
Create a node to categorize and log input for better input observability
Output validation node that can attempt to fix unexpected or incomplete outputs
Support for rustformers/llm to utilize local models

License

Anchor Chain is released under the MIT License.

Adding anchor_chain library as a dependency

Adding `anchor_chain` library as a dependency