llm-chain is the ultimate toolbox for developers looking to supercharge their applications with the power of Large Language Models (LLMs)! 🎉
This versatile crate lets you chain together LLMs, making it incredibly useful for:
- Effortlessly summarizing lengthy documents 📚
- Allow your bots to interact with the environment using tools.
- Seamlessly chaining together multiple prompts to tackle complex tasks.
- ChatGPT model support
- LLaMA model support
And that’s not all!
llm-chain is also your best friend when it comes to creating and managing prompts for LLMs. Say goodbye to hassle and bloated syntax! Quickly create and manage prompts with our intuitive templating system, and let
llm-chain handle the rest! 🤩
Keep in mind that this crate is a library, which means it doesn’t include any LLMs out of the box. But fear not! We also offer the llm-chain-openai crate, which brings the power of OpenAI’s LLMs right to your fingertips! 🪄 We recommend starting with that crate to make the most of
So, buckle up and dive into the amazing world of LLMs! Unlock the full potential of your applications with
llm-chain and watch them soar! 🌈💥
To help you get started, we’ve prepared a comprehensive tutorial that will guide you through the process of using
llm-chain. The tutorial covers everything from installation to advanced usage, so you’ll be well-equipped to make the most of this powerful toolbox.
Happy coding, and may your LLM adventures be both exciting and productive! 🥳🚀
pub use parameters::Parameters;
- Chains are multi-step modes of execution for LLMs such as Sequential or MapReduce.
- Utilities for working with executors
- A frame is the combination of a
Executor. It wraps common behavior used by different chain types.
- Parameters are used to pass data steps of the chain. This module implements them.
- Functions for parsing the output of LLMs, including YAML and Markdown.
- Module implementing prompts and prompt templates. Contains the
prompt!macro, Prompts and PromptTemplates.
- Schema for Documents that can be stored in vector stores.
- Envelope Serialization
- Steps are individual LLM invocations in a chain. They are a combination of a prompt and a configuration.
- Opinionated text summarization functionality
- Tokens Module
- Tool Access Module
- Traits Module
- A macro that creates a new executor for a specified model.
options!is a declarative macro that facilitates the creation of an
- A macro that creates a new
Parametersinstance with the provided key-value pairs.
- Creates a
ChatPromptbased on the number of arguments provided.