gollm: Go Large Language Model
Simplify your AI development with a unified Go package for multiple Large Language Model providers.
What are the key features of gollm?
The key features of gollm are:
- Unified API: gollm supports multiple LLM providers, including OpenAI, Anthropic, and Groq, allowing seamless integration with various language models.
- Easy Provider Switching: You can switch between different LLM providers and models with simple function calls, facilitating experimentation and optimization.
- Flexible Configuration: gollm offers multiple configuration options, including environment variables, code-based settings, and configuration files to suit various project needs.
- Advanced Prompt Engineering: Create sophisticated prompts with context, directives, and examples to guide LLM responses effectively.
- Structured Output and Validation: Generate and validate JSON schemas for LLM responses, ensuring consistency and reliability in output.
- Provider Comparison Tools: Easily compare responses from different LLM providers and models for the same prompt, helping you choose the best option for your use case.
What can you do with gollm?
With gollm, you can implement the following real-world applications:
- Content Creation Workflows:
- Generate research summaries
- Create article ideas and outlines
- Refine and polish written content
- Complex Reasoning Tasks:
- Break down complex problems using the ChainOfThought function
- Perform step-by-step analysis of intricate issues
- Structured Data Generation:
- Create complex data structures with customizable JSON schemas
- Validate LLM-generated data against predefined schemas
- Model Performance Analysis:
- Compare different models' performance for specific tasks
- Optimize your AI pipeline by selecting the best-performing models
How do you get started with gollm?
To get started with gollm, follow these steps:
- Install gollm:
go get github.com/teilomillet/gollm - Import gollm in your Go project:
import "github.com/teilomillet/gollm" - Set up your LLM client:
llm, err := gollm.NewLLM( gollm.SetProvider("openai"), gollm.SetModel("gpt-4o-mini"), gollm.SetMaxTokens(100), gollm.SetAPIKey("your-api-key-here"), ) - Generate text using the LLM:
prompt := gollm.NewPrompt("Tell me a short joke about programming.") response, err := llm.Generate(context.Background(), prompt)
Frequently Asked Questions
What is a Language Model (LLM)?
A Large Language Model (LLM) is an advanced AI system trained on vast amounts of text data. LLMs can understand and generate human-like text, perform various language tasks, and assist with complex reasoning and problem-solving.
How can I implement LLMs in a Go project?
To implement LLMs in a Go project, you can use libraries like gollm that provide APIs for interacting with various LLM providers. Here's a basic implementation steps:
- Install gollm:
go get github.com/teilomillet/gollm - Import gollm in your Go file
- Set up the LLM client with your chosen provider and API key
- Create prompts and generate responses using the LLM
What are the benefits of using LLMs in a Go project?
Using LLMs in a Go project offers several benefits:
- Enhanced natural language processing capabilities
- Automated content generation and summarization
- Improved search and recommendation systems
- Advanced data analysis and pattern recognition
- Streamlined customer support through chatbots
What is gollm?
gollm is a Go package that provides a unified interface for interacting with multiple Language Model (LLM) providers. It simplifies the process of working with different AI models in Go applications.
Which LLM providers does gollm support?
gollm currently supports OpenAI, Anthropic, and Groq. This allows you to work with popular models like GPT-4, GPT-4o-mini, Claude, and llama-3.1.
How does gollm handle API rate limits?
gollm includes built-in retry mechanisms with customizable delays to handle API rate limits and transient errors gracefully.
Can I use gollm for structured data generation?
Yes, gollm provides functionality to generate and validate JSON schemas for structured outputs, ensuring consistency and reliability in LLM responses.
Is gollm suitable for production use?
Yes, gollm is designed for production use. It includes features like robust error handling, retries, and configuration options that make it suitable for production environments.
How do I choose the right LLM for my Go project?
Choosing the right LLM depends on your specific needs. Consider factors such as:
- The complexity of your tasks
- Required response speed
- Cost considerations
- Specific capabilities of different models
gollm allows you to easily compare different models, helping you make an informed decision.
How can I optimize LLM performance in my Go application?
To optimize LLM performance in your Go application:
- Use efficient prompts that clearly define the task
- Implement caching for common queries
- Use gollm's provider comparison tools to select the best-performing model for your use case
- Adjust token limits and other parameters to balance between quality and speed
- Implement proper error handling and retries to manage API limitations
What are some common use cases for LLMs in Go projects?
Common use cases for LLMs in Go projects include:
- Natural language processing and understanding
- Automated content generation and summarization
- Sentiment analysis and opinion mining
- Chatbots and conversational AI
- Code generation and analysis
- Data extraction and structuring from unstructured text