OpenAI

Overview

• Models: GPT-5, GPT-4.1, GPT-4o, GPT-4o-mini, GPT-4-turbo, GPT-3.5-turbo
• Embedding Models: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
• Features: Chat completions, streaming, function/tool calling, embeddings

Configuration

client, err := omnillm.NewClient(omnillm.ClientConfig{
    Providers: []omnillm.ProviderConfig{
        {Provider: omnillm.ProviderNameOpenAI, APIKey: "your-openai-api-key"},
    },
})

Available Models

Model	Context Window	Description
omnillm.ModelGPT5	200K	Latest GPT-5 model
omnillm.ModelGPT41	128K	GPT-4.1
omnillm.ModelGPT4o	128K	GPT-4o (recommended)
omnillm.ModelGPT4oMini	128K	GPT-4o Mini (cost-effective)
omnillm.ModelGPT4Turbo	128K	GPT-4 Turbo
omnillm.ModelGPT35Turbo	16K	GPT-3.5 Turbo

Tool Calling

OpenAI supports function/tool calling for agentic workflows:

response, err := client.CreateChatCompletion(ctx, &omnillm.ChatCompletionRequest{
    Model: omnillm.ModelGPT4o,
    Messages: []omnillm.Message{
        {Role: omnillm.RoleUser, Content: "What's the weather in Tokyo?"},
    },
    Tools: []omnillm.Tool{
        {
            Type: "function",
            Function: omnillm.ToolFunction{
                Name:        "get_weather",
                Description: "Get current weather for a location",
                Parameters: map[string]any{
                    "type": "object",
                    "properties": map[string]any{
                        "location": map[string]any{
                            "type":        "string",
                            "description": "City name",
                        },
                    },
                    "required": []string{"location"},
                },
            },
        },
    },
})

Embeddings

OpenAI provides text embedding models for semantic search and RAG workflows.

Available Embedding Models

Model	Dimensions	Description
text-embedding-3-small	512-1536	Efficient, cost-effective
text-embedding-3-large	256-3072	Higher quality
text-embedding-ada-002	1536	Legacy model

Basic Usage

embeddingProvider, err := omnillm.GetEmbeddingProvider(
    omnillm.ProviderNameOpenAI,
    omnillm.ProviderConfig{APIKey: "your-openai-api-key"},
)
if err != nil {
    log.Fatal(err)
}
defer embeddingProvider.Close()

resp, err := embeddingProvider.CreateEmbedding(ctx, &provider.EmbeddingRequest{
    Model: "text-embedding-3-small",
    Input: []string{"Hello world", "How are you?"},
})

Custom Dimensions

Reduce dimensions to save storage while maintaining quality:

dims := 512
resp, err := embeddingProvider.CreateEmbedding(ctx, &provider.EmbeddingRequest{
    Model:      "text-embedding-3-small",
    Input:      []string{"Hello world"},
    Dimensions: &dims,
})

See Embeddings Feature Guide for more details.

Custom Endpoint

Use a custom OpenAI-compatible endpoint:

client, err := omnillm.NewClient(omnillm.ClientConfig{
    Providers: []omnillm.ProviderConfig{
        {
            Provider: omnillm.ProviderNameOpenAI,
            APIKey:   "your-api-key",
            BaseURL:  "https://your-custom-endpoint.com/v1",
        },
    },
})