
lingoose
🪿 LinGoose is a Go framework for building awesome AI/LLM applications.
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LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.
README:
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 and sharing it with your friends!
mkdir example
cd example
go mod init example
- Create your first LinGoose application
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)
}
- Install the Go dependencies
go mod tidy
- Start the example application
export OPENAI_API_KEY=your-api-key
go run .
A goose fills its car with goose-line!
If you think you've found a bug, or something isn't behaving the way you think it should, please raise an issue on GitHub.
We welcome contributions, Read our Contribution Guidelines to learn more about contributing to LinGoose
- Anthropic's Claude Integration with Go and Lingoose
- Empowering Go: unveiling the synergy of AI and Q&A pipelines
- Leveraging Go and Redis for Efficient Retrieval Augmented Generation
© Simone Vellei, 2023~time.Now()
Released under the MIT License
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