https://github.com/henomis/lingoose

🪿 LinGoose is a Go framework for building awesome AI/LLM applications.
https://github.com/henomis/lingoose

ai chatgpt embeddings go golang index llm openai pinecone pipeline prompt vector

Last synced: 10 days ago
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🪿 LinGoose is a Go framework for building awesome AI/LLM applications.

• Host: GitHub
• URL: https://github.com/henomis/lingoose
• Owner: henomis
• License: mit
• Created: 2023-04-17T05:51:56.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2025-03-20T12:30:37.000Z (about 1 month ago)
• Last Synced: 2025-04-06T23:04:14.933Z (17 days ago)
• Topics: ai, chatgpt, embeddings, go, golang, index, llm, openai, pinecone, pipeline, prompt, vector
• Language: Go
• Homepage: https://lingoose.io
• Size: 1.3 MB
• Stars: 753
• Watchers: 14
• Forks: 67
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Changelog: history/history.go
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-ChatGPT-repositories - lingoose - 🪿 LinGoose it's a Go framework for developing LLMs-based application using pipelines (Prompts)
• awesome - henomis/lingoose - 🪿 LinGoose is a Go framework for building awesome AI/LLM applications. (Go)
• awesome - henomis/lingoose - 🪿 LinGoose is a Go framework for building awesome AI/LLM applications. (Go)

README

        
![lingoose](docs/static/lingoose-small.png)

# 🪿 LinGoose [![Build Status](https://github.com/henomis/lingoose/actions/workflows/checks.yml/badge.svg)](https://github.com/henomis/lingoose/actions/workflows/checks.yml) [![GoDoc](https://godoc.org/github.com/henomis/lingoose?status.svg)](https://godoc.org/github.com/henomis/lingoose) [![Go Report Card](https://goreportcard.com/badge/github.com/henomis/lingoose)](https://goreportcard.com/report/github.com/henomis/lingoose) [![GitHub release](https://img.shields.io/github/release/henomis/lingoose.svg)](https://github.com/henomis/lingoose/releases)

## What is LinGoose?

[LinGoose](https://github.com/henomis/lingoose) is a Go framework for building awesome AI/LLM applications.


- **LinGoose is modular** — You can import only the modules you need to build your application.

- **LinGoose is an abstraction of features** — You can choose your preferred implementation of a feature and/or create your own.

- **LinGoose is a complete solution** — You can use LinGoose to build your AI/LLM application from the ground up.

> **Did you know?** A goose 🪿 fills its car 🚗 with goose-line ⛽!

🚀 Support the project by starring ⭐ the repository on [GitHub](https://github.com/henomis/lingoose) and sharing it with your friends!

## Quick start

1. [Initialise a new go module](https://golang.org/doc/tutorial/create-module)

```sh

mkdir example

cd example

go mod init example

```

2. Create your first LinGoose application

```go

package main

import (

	"context"

	"fmt"

	"github.com/henomis/lingoose/llm/openai"

	"github.com/henomis/lingoose/thread"

)

func main() {

	myThread := thread.New().AddMessage(

		thread.NewUserMessage().AddContent(

			thread.NewTextContent("Tell me a joke about geese"),

		),

	)

	err := openai.New().Generate(context.Background(), myThread)

	if err != nil {

		panic(err)

	}

	fmt.Println(myThread)

}

```

3. Install the Go dependencies

```sh

go mod tidy

```

4. Start the example application

```sh

export OPENAI_API_KEY=your-api-key

go run .

A goose fills its car with goose-line!

```

## Reporting Issues

If you think you've found a bug, or something isn't behaving the way you think it should, please raise an [issue](https://github.com/henomis/lingoose/issues) on GitHub.

## Contributing

We welcome contributions, Read our [Contribution Guidelines](https://github.com/henomis/lingoose/blob/main/CONTRIBUTING.md) to learn more about contributing to **LinGoose**

## Blog posts and articles

- [Anthropic's Claude Integration with Go and Lingoose](https://simonevellei.com/blog/posts/anthropic-claude-integration-with-go-and-lingoose/)

- [Empowering Go: unveiling the synergy of AI and Q&A pipelines](https://simonevellei.com/blog/posts/empowering-go-unveiling-the-synergy-of-ai-and-qa-pipelines/)

- [Leveraging Go and Redis for Efficient Retrieval Augmented Generation](https://simonevellei.com/blog/posts/leveraging-go-and-redis-for-efficient-retrieval-augmented-generation/)

## Connect with the author

[![Twitter](https://img.shields.io/twitter/follow/simonevellei?label=Follow:%20Simone%20Vellei&style=social)](https://twitter.com/simonevellei) [![GitHub](https://img.shields.io/badge/Follow-henomis-green?logo=github&link=https%3A%2F%2Fgithub.com%2Fhenomis)](https://github.com/henomis) [![Linkedin](https://img.shields.io/badge/Connect-Simone%20Vellei-blue?logo=linkedin&link=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fsimonevellei%2F)](https://www.linkedin.com/in/simonevellei/)

### Join the community

[![Discord](https://img.shields.io/badge/Discord-lingoose-blue?logo=discord&link=https%3A%2F%2Fdiscord.gg%2FmcKEQTKqGS)](https://discord.gg/mcKEQTKqGS)

## License

© Simone Vellei, 2023~`time.Now()`

Released under the [MIT License](LICENSE)