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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: about 20 hours 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 (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-02T17:23:00.000Z (6 days ago)
- Last Synced: 2024-11-02T17:29:38.178Z (6 days ago)
- Topics: ai, chatgpt, embeddings, go, golang, index, llm, openai, pinecone, pipeline, prompt, vector
- Language: Go
- Homepage: https://lingoose.io
- Size: 1.17 MB
- Stars: 640
- Watchers: 10
- Forks: 53
- Open Issues: 11
-
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)
README
# 🪿 LinGoose [](https://github.com/henomis/lingoose/actions/workflows/checks.yml) [](https://godoc.org/github.com/henomis/lingoose) [](https://goreportcard.com/report/github.com/henomis/lingoose) [](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
[](https://twitter.com/simonevellei) [](https://github.com/henomis) [](https://www.linkedin.com/in/simonevellei/)
### Join the community
[](https://discord.gg/mcKEQTKqGS)
## License
© Simone Vellei, 2023~`time.Now()`
Released under the [MIT License](LICENSE)