ai-chain-openai

Crates.ioai-chain-openai
lib.rsai-chain-openai
version0.14.2
sourcesrc
created_at2024-05-19 03:57:19.89097
updated_at2024-05-22 15:26:38.037061
descriptionA library implementing `ai-chains` for OpenAI's models. Chains can be use to apply the model series to complete complex tasks, such as text summation.
homepage
repositoryhttps://github.com/godlinchong/ai-chain
max_upload_size
id1244666
size177,191
linchong (louloulin)

documentation

README

ai-chain 🚀

ai-chain fork llm-chain with extensions

ai-chain is a collection of Rust crates designed to help you create advanced LLM applications such as chatbots, agents, and more. As a comprehensive LLM-Ops platform we have strong support for both cloud and locally-hosted LLMs. We also provide robust support for prompt templates and chaining together prompts in multi-step chains, enabling complex tasks that LLMs can't handle in a single step. We also provide vector store integrations making it easy to give your model long-term memory and subject matter knowledge. This empowers you to build sophisticated applications.

roadmap

changelog 2024-05-22 0.14.4

  • fix ai-chain executor costume bug
  • add qwen model support
  • other bug fix

changelog 2024-05-21 0.14.3

  • improve openai-compatible api

  • add glm llm

  • upgrade moonshot llm

Examples 💡

To help you get started, here is an example demonstrating how to use ai-chain. You can find more examples in the examples folder in the repository.

let exec = executor!()?;
let res = prompt!(
    "You are a robot assistant for making personalized greetings",
    "Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

➡️ tutorial: get started with ai-chain ➡️ quick-start: Create project based on our template

Features 🌟

  • Prompt templates: Create reusable and easily customizable prompt templates for consistent and structured interactions with LLMs.
  • Chains: Build powerful chains of prompts that allow you to execute more complex tasks, step by step, leveraging the full potential of LLMs.
  • ChatGPT support: Supports ChatGPT models, with plans to add OpenAI's other models in the future.
  • LLaMa support: Provides seamless integration with LLaMa models, enabling natural language understanding and generation tasks with Facebook's research models.
  • Alpaca support: Incorporates support for Stanford's Alpaca models, expanding the range of available language models for advanced AI applications.
  • llm.rs support: Use llms in rust without dependencies on C++ code with our support for llm.rs
  • Tools: Enhance your AI agents' capabilities by giving them access to various tools, such as running Bash commands, executing Python scripts, or performing web searches, enabling more complex and powerful interactions.
  • Extensibility: Designed with extensibility in mind, making it easy to integrate additional LLMs as the ecosystem grows.
  • Community-driven: We welcome and encourage contributions from the community to help improve and expand the capabilities of ai-chain.

Getting Started 🚀

To start using ai-chain, add it as a dependency in your Cargo.toml (you need Rust 1.65.0 or newer):

ai-chain-openai

  • cargo dependencies
[dependencies]
ai-chain = "0.14.2"
ai-chain-openai = "0.14.2"
  • coding
env::set_var("OPENAI_API_KEY", "sk-7LVW4lfKX3ZL01Iwuz8H0oZsUaLsEuO7ri9bfRKV36NrTE1A");
let exec = executor!()?;
let res = prompt!(
"You are a robot assistant for making personalized greetings",
"Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

ai-chain-moonshot

  • cargo dependencies
[dependencies]
ai-chain = "0.14.2"
ai-chain-moonshot = "0.14.2"
  • coding
env::set_var("OPENAI_API_KEY", "sk-7LVW4lfKX3ZL01Iwuz8H0oZsUaLsEuO7ri9bfRKV36NrTE1A");
let exec = executor!(mooonshot)?;
let res = prompt!(
    "You are a robot assistant for making personalized greetings",
    "Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

ai-chain-glm

  • cargo dependencies
[dependencies]
ai-chain = "0.14.2"
ai-chain-glm = "0.14.2"
  • coding
env::set_var("OPENAI_API_KEY", "sk-7LVW4lfKX3ZL01Iwuz8H0oZsUaLsEuO7ri9bfRKV36NrTE1A");
let exec = executor!(glm)?;
let res = prompt!(
    "You are a robot assistant for making personalized greetings",
    "Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

ai-chain-qwen

  • cargo dependencies
[dependencies]
ai-chain = "0.14.2"
ai-chain-qwen = "0.14.2"
  • coding
env::set_var("OPENAI_API_KEY", "sk-7LVW4lfKX3ZL01Iwuz8H0oZsUaLsEuO7ri9bfRKV36NrTE1A");
let exec = executor!(qwen)?;
let res = prompt!(
    "You are a robot assistant for making personalized greetings",
    "Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

The examples for `ai-chain-openai` or `ai-chain-moonshot` or others llms require you to set the `OPENAI_API_KEY` environment variable which you can do like this:

```bash
export OPENAI_API_KEY="sk-YOUR_OPEN_AI_KEY_HERE"
  • support costume llm
env::set_var("OPENAI_API_KEY", "sk-7LVW4lfKX3ZL01Iwuz8H0oZsUaLsEuO7ri9bfRKV36NrTE1A");
let exec = executor!(costume,ai_chain_qwen)?;
let res = prompt!(
    "You are a robot assistant for making personalized greetings",
    "Make a personalized greeting for Joe"
)
.run(parameters()!, &exec)
.await?;
println!("{}", res);

The examples for `ai-chain-openai` or `ai-chain-moonshot` or others llms require you to set the `OPENAI_API_KEY` environment variable which you can do like this:

```bash
export OPENAI_API_KEY="sk-YOUR_OPEN_AI_KEY_HERE"

Contributing 🤝

We warmly welcome contributions from everyone! If you're interested in helping improve ai-chain, please check out our CONTRIBUTING.md file for guidelines and best practices.

License 📄

ai-chain is licensed under the MIT License.

Connect with Us 🌐

Commit count: 36

cargo fmt