package main

import (
	"context"
	"errors"
	"fmt"
	"io"
	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	req := openai.ChatCompletionRequest{
		Model:     openai.GPT3Dot5Turbo,
		MaxTokens: 20,
		Messages: []openai.ChatCompletionMessage{
			{
				Role:    openai.ChatMessageRoleUser,
				Content: "Lorem ipsum",
			},
		},
		Stream: true,
	}
	stream, err := c.CreateChatCompletionStream(ctx, req)
	if err != nil {
		fmt.Printf("ChatCompletionStream error: %v\n", err)
		return
	}
	defer stream.Close()

	fmt.Printf("Stream response: ")
	for {
		response, err := stream.Recv()
		if errors.Is(err, io.EOF) {
			fmt.Println("\nStream finished")
			return
		}

		if err != nil {
			fmt.Printf("\nStream error: %v\n", err)
			return
		}

		fmt.Printf(response.Choices[0].Delta.Content)
	}
}

package main

import (
	"context"
	"fmt"
	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	req := openai.CompletionRequest{
		Model:     openai.GPT3Babbage002,
		MaxTokens: 5,
		Prompt:    "Lorem ipsum",
	}
	resp, err := c.CreateCompletion(ctx, req)
	if err != nil {
		fmt.Printf("Completion error: %v\n", err)
		return
	}
	fmt.Println(resp.Choices[0].Text)
}

package main

import (
	"errors"
	"context"
	"fmt"
	"io"
	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	req := openai.CompletionRequest{
		Model:     openai.GPT3Babbage002,
		MaxTokens: 5,
		Prompt:    "Lorem ipsum",
		Stream:    true,
	}
	stream, err := c.CreateCompletionStream(ctx, req)
	if err != nil {
		fmt.Printf("CompletionStream error: %v\n", err)
		return
	}
	defer stream.Close()

	for {
		response, err := stream.Recv()
		if errors.Is(err, io.EOF) {
			fmt.Println("Stream finished")
			return
		}

		if err != nil {
			fmt.Printf("Stream error: %v\n", err)
			return
		}


		fmt.Printf("Stream response: %v\n", response)
	}
}

package main

import (
	"context"
	"fmt"

	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	req := openai.AudioRequest{
		Model:    openai.Whisper1,
		FilePath: "recording.mp3",
	}
	resp, err := c.CreateTranscription(ctx, req)
	if err != nil {
		fmt.Printf("Transcription error: %v\n", err)
		return
	}
	fmt.Println(resp.Text)
}

package main

import (
	"context"
	"fmt"
	"os"

	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient(os.Getenv("OPENAI_KEY"))

	req := openai.AudioRequest{
		Model:    openai.Whisper1,
		FilePath: os.Args[1],
		Format:   openai.AudioResponseFormatSRT,
	}
	resp, err := c.CreateTranscription(context.Background(), req)
	if err != nil {
		fmt.Printf("Transcription error: %v\n", err)
		return
	}
	f, err := os.Create(os.Args[1] + ".srt")
	if err != nil {
		fmt.Printf("Could not open file: %v\n", err)
		return
	}
	defer f.Close()
	if _, err := f.WriteString(resp.Text); err != nil {
		fmt.Printf("Error writing to file: %v\n", err)
		return
	}
}

package main

import (
	"bytes"
	"context"
	"encoding/base64"
	"fmt"
	openai "github.com/sashabaranov/go-openai"
	"image/png"
	"os"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	// Sample image by link
	reqUrl := openai.ImageRequest{
		Prompt:         "Parrot on a skateboard performs a trick, cartoon style, natural light, high detail",
		Size:           openai.CreateImageSize256x256,
		ResponseFormat: openai.CreateImageResponseFormatURL,
		N:              1,
	}

	respUrl, err := c.CreateImage(ctx, reqUrl)
	if err != nil {
		fmt.Printf("Image creation error: %v\n", err)
		return
	}
	fmt.Println(respUrl.Data[0].URL)

	// Example image as base64
	reqBase64 := openai.ImageRequest{
		Prompt:         "Portrait of a humanoid parrot in a classic costume, high detail, realistic light, unreal engine",
		Size:           openai.CreateImageSize256x256,
		ResponseFormat: openai.CreateImageResponseFormatB64JSON,
		N:              1,
	}

	respBase64, err := c.CreateImage(ctx, reqBase64)
	if err != nil {
		fmt.Printf("Image creation error: %v\n", err)
		return
	}

	imgBytes, err := base64.StdEncoding.DecodeString(respBase64.Data[0].B64JSON)
	if err != nil {
		fmt.Printf("Base64 decode error: %v\n", err)
		return
	}

	r := bytes.NewReader(imgBytes)
	imgData, err := png.Decode(r)
	if err != nil {
		fmt.Printf("PNG decode error: %v\n", err)
		return
	}

	file, err := os.Create("example.png")
	if err != nil {
		fmt.Printf("File creation error: %v\n", err)
		return
	}
	defer file.Close()

	if err := png.Encode(file, imgData); err != nil {
		fmt.Printf("PNG encode error: %v\n", err)
		return
	}

	fmt.Println("The image was saved as example.png")
}

package main

import (
	"context"
	"encoding/base64"
	"fmt"
	"os"

	openai "github.com/sashabaranov/go-openai"
)

func main() {
	c := openai.NewClient("your token")
	ctx := context.Background()

	req := openai.ImageRequest{
		Prompt:            "Parrot on a skateboard performing a trick. Large bold text \"SKATE MASTER\" banner at the bottom of the image. Cartoon style, natural light, high detail, 1:1 aspect ratio.",
		Background:        openai.CreateImageBackgroundOpaque,
		Model:             openai.CreateImageModelGptImage1,
		Size:              openai.CreateImageSize1024x1024,
		N:                 1,
		Quality:           openai.CreateImageQualityLow,
		OutputCompression: 100,
		OutputFormat:      openai.CreateImageOutputFormatJPEG,
		// Moderation: 		 openai.CreateImageModerationLow,
		// User: 					 "",
	}

	resp, err := c.CreateImage(ctx, req)
	if err != nil {
		fmt.Printf("Image creation Image generation with GPT Image 1error: %v\n", err)
		return
	}

	fmt.Println("Image Base64:", resp.Data[0].B64JSON)

	// Decode the base64 data
	imgBytes, err := base64.StdEncoding.DecodeString(resp.Data[0].B64JSON)
	if err != nil {
		fmt.Printf("Base64 decode error: %v\n", err)
		return
	}

	// Write image to file
	outputPath := "generated_image.jpg"
	err = os.WriteFile(outputPath, imgBytes, 0644)
	if err != nil {
		fmt.Printf("Failed to write image file: %v\n", err)
		return
	}

	fmt.Printf("The image was saved as %s\n", outputPath)
}

config := openai.DefaultConfig("token")
proxyUrl, err := url.Parse("http://localhost:{port}")
if err != nil {
	panic(err)
}
transport := &http.Transport{
	Proxy: http.ProxyURL(proxyUrl),
}
config.HTTPClient = &http.Client{
	Transport: transport,
}

c := openai.NewClientWithConfig(config)

See also: https://pkg.go.dev/github.com/sashabaranov/go-openai#ClientConfig

package main

import (
	"bufio"
	"context"
	"fmt"
	"os"
	"strings"

	"github.com/sashabaranov/go-openai"
)

func main() {
	client := openai.NewClient("your token")
	messages := make([]openai.ChatCompletionMessage, 0)
	reader := bufio.NewReader(os.Stdin)
	fmt.Println("Conversation")
	fmt.Println("---------------------")

	for {
		fmt.Print("-> ")
		text, _ := reader.ReadString('\n')
		// convert CRLF to LF
		text = strings.Replace(text, "\n", "", -1)
		messages = append(messages, openai.ChatCompletionMessage{
			Role:    openai.ChatMessageRoleUser,
			Content: text,
		})

		resp, err := client.CreateChatCompletion(
			context.Background(),
			openai.ChatCompletionRequest{
				Model:    openai.GPT3Dot5Turbo,
				Messages: messages,
			},
		)

		if err != nil {
			fmt.Printf("ChatCompletion error: %v\n", err)
			continue
		}

		content := resp.Choices[0].Message.Content
		messages = append(messages, openai.ChatCompletionMessage{
			Role:    openai.ChatMessageRoleAssistant,
			Content: content,
		})
		fmt.Println(content)
	}
}

package main

import (
	"context"
	"fmt"

	openai "github.com/sashabaranov/go-openai"
)

func main() {
	config := openai.DefaultAzureConfig("your Azure OpenAI Key", "https://your Azure OpenAI Endpoint")
	// If you use a deployment name different from the model name, you can customize the AzureModelMapperFunc function
	// config.AzureModelMapperFunc = func(model string) string {
	// 	azureModelMapping := map[string]string{
	// 		"gpt-3.5-turbo": "your gpt-3.5-turbo deployment name",
	// 	}
	// 	return azureModelMapping[model]
	// }

	client := openai.NewClientWithConfig(config)
	resp, err := client.CreateChatCompletion(
		context.Background(),
		openai.ChatCompletionRequest{
			Model: openai.GPT3Dot5Turbo,
			Messages: []openai.ChatCompletionMessage{
				{
					Role:    openai.ChatMessageRoleUser,
					Content: "Hello Azure OpenAI!",
				},
			},
		},
	)
	if err != nil {
		fmt.Printf("ChatCompletion error: %v\n", err)
		return
	}

	fmt.Println(resp.Choices[0].Message.Content)
}

package main

import (
	"context"
	"log"
	openai "github.com/sashabaranov/go-openai"

)

func main() {
	client := openai.NewClient("your-token")

	// Create an EmbeddingRequest for the user query
	queryReq := openai.EmbeddingRequest{
		Input: []string{"How many chucks would a woodchuck chuck"},
		Model: openai.AdaEmbeddingV2,
	}

	// Create an embedding for the user query
	queryResponse, err := client.CreateEmbeddings(context.Background(), queryReq)
	if err != nil {
		log.Fatal("Error creating query embedding:", err)
	}

	// Create an EmbeddingRequest for the target text
	targetReq := openai.EmbeddingRequest{
		Input: []string{"How many chucks would a woodchuck chuck if the woodchuck could chuck wood"},
		Model: openai.AdaEmbeddingV2,
	}

	// Create an embedding for the target text
	targetResponse, err := client.CreateEmbeddings(context.Background(), targetReq)
	if err != nil {
		log.Fatal("Error creating target embedding:", err)
	}

	// Now that we have the embeddings for the user query and the target text, we
	// can calculate their similarity.
	queryEmbedding := queryResponse.Data[0]
	targetEmbedding := targetResponse.Data[0]

	similarity, err := queryEmbedding.DotProduct(&targetEmbedding)
	if err != nil {
		log.Fatal("Error calculating dot product:", err)
	}

	log.Printf("The similarity score between the query and the target is %f", similarity)
}

package main

import (
	"context"
	"fmt"

	openai "github.com/sashabaranov/go-openai"
)

func main() {

	config := openai.DefaultAzureConfig("your Azure OpenAI Key", "https://your Azure OpenAI Endpoint")
	config.APIVersion = "2023-05-15" // optional update to latest API version

	//If you use a deployment name different from the model name, you can customize the AzureModelMapperFunc function
	//config.AzureModelMapperFunc = func(model string) string {
	//    azureModelMapping := map[string]string{
	//        "gpt-3.5-turbo":"your gpt-3.5-turbo deployment name",
	//    }
	//    return azureModelMapping[model]
	//}

	input := "Text to vectorize"

	client := openai.NewClientWithConfig(config)
	resp, err := client.CreateEmbeddings(
		context.Background(),
		openai.EmbeddingRequest{
			Input: []string{input},
			Model: openai.AdaEmbeddingV2,
		})

	if err != nil {
		fmt.Printf("CreateEmbeddings error: %v\n", err)
		return
	}

	vectors := resp.Data[0].Embedding // []float32 with 1536 dimensions

	fmt.Println(vectors[:10], "...", vectors[len(vectors)-10:])
}

It is now possible for chat completion to choose to call a function for more information (see developer docs here).

In order to describe the type of functions that can be called, a JSON schema must be provided. Many JSON schema libraries exist and are more advanced than what we can offer in this library, however we have included a simple jsonschema package for those who want to use this feature without formatting their own JSON schema payload.

The developer documents give this JSON schema definition as an example:

{
  "name":"get_current_weather",
  "description":"Get the current weather in a given location",
  "parameters":{
    "type":"object",
    "properties":{
        "location":{
          "type":"string",
          "description":"The city and state, e.g. San Francisco, CA"
        },
        "unit":{
          "type":"string",
          "enum":[
              "celsius",
              "fahrenheit"
          ]
        }
    },
    "required":[
        "location"
    ]
  }
}

Using the jsonschema package, this schema could be created using structs as such:

FunctionDefinition{
  Name: "get_current_weather",
  Parameters: jsonschema.Definition{
    Type: jsonschema.Object,
    Properties: map[string]jsonschema.Definition{
      "location": {
        Type: jsonschema.String,
        Description: "The city and state, e.g. San Francisco, CA",
      },
      "unit": {
        Type: jsonschema.String,
        Enum: []string{"celsius", "fahrenheit"},
      },
    },
    Required: []string{"location"},
  },
}

The Parameters field of a FunctionDefinition can accept either of the above styles, or even a nested struct from another library (as long as it can be marshalled into JSON).

Open-AI maintains clear documentation on how to handle API errors

example:

e := &openai.APIError{}
if errors.As(err, &e) {
  switch e.HTTPStatusCode {
    case 401:
      // invalid auth or key (do not retry)
    case 429:
      // rate limiting or engine overload (wait and retry) 
    case 500:
      // openai server error (retry)
    default:
      // unhandled
  }
}

package main

import (
	"context"
	"fmt"
	"github.com/sashabaranov/go-openai"
)

func main() {
	client := openai.NewClient("your token")
	ctx := context.Background()

	// create a .jsonl file with your training data for conversational model
	// {"prompt": "<prompt text>", "completion": "<ideal generated text>"}
	// {"prompt": "<prompt text>", "completion": "<ideal generated text>"}
	// {"prompt": "<prompt text>", "completion": "<ideal generated text>"}

	// chat models are trained using the following file format:
	// {"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris, as if everyone doesn't know that already."}]}
	// {"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "Who wrote 'Romeo and Juliet'?"}, {"role": "assistant", "content": "Oh, just some guy named William Shakespeare. Ever heard of him?"}]}
	// {"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "How far is the Moon from Earth?"}, {"role": "assistant", "content": "Around 384,400 kilometers. Give or take a few, like that really matters."}]}

	// you can use openai cli tool to validate the data
	// For more info - https://platform.openai.com/docs/guides/fine-tuning

	file, err := client.CreateFile(ctx, openai.FileRequest{
		FilePath: "training_prepared.jsonl",
		Purpose:  "fine-tune",
	})
	if err != nil {
		fmt.Printf("Upload JSONL file error: %v\n", err)
		return
	}

	// create a fine tuning job
	// Streams events until the job is done (this often takes minutes, but can take hours if there are many jobs in the queue or your dataset is large)
	// use below get method to know the status of your model
	fineTuningJob, err := client.CreateFineTuningJob(ctx, openai.FineTuningJobRequest{
		TrainingFile: file.ID,
		Model:        "davinci-002", // gpt-3.5-turbo-0613, babbage-002.
	})
	if err != nil {
		fmt.Printf("Creating new fine tune model error: %v\n", err)
		return
	}

	fineTuningJob, err = client.RetrieveFineTuningJob(ctx, fineTuningJob.ID)
	if err != nil {
		fmt.Printf("Getting fine tune model error: %v\n", err)
		return
	}
	fmt.Println(fineTuningJob.FineTunedModel)

	// once the status of fineTuningJob is `succeeded`, you can use your fine tune model in Completion Request or Chat Completion Request

	// resp, err := client.CreateCompletion(ctx, openai.CompletionRequest{
	//	 Model:  fineTuningJob.FineTunedModel,
	//	 Prompt: "your prompt",
	// })
	// if err != nil {
	//	 fmt.Printf("Create completion error %v\n", err)
	//	 return
	// }
	//
	// fmt.Println(resp.Choices[0].Text)
}

package main

import (
	"context"
	"fmt"
	"log"

	"github.com/sashabaranov/go-openai"
	"github.com/sashabaranov/go-openai/jsonschema"
)

func main() {
	client := openai.NewClient("your token")
	ctx := context.Background()

	type Result struct {
		Steps []struct {
			Explanation string `json:"explanation"`
			Output      string `json:"output"`
		} `json:"steps"`
		FinalAnswer string `json:"final_answer"`
	}
	var result Result
	schema, err := jsonschema.GenerateSchemaForType(result)
	if err != nil {
		log.Fatalf("GenerateSchemaForType error: %v", err)
	}
	resp, err := client.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
		Model: openai.GPT4oMini,
		Messages: []openai.ChatCompletionMessage{
			{
				Role:    openai.ChatMessageRoleSystem,
				Content: "You are a helpful math tutor. Guide the user through the solution step by step.",
			},
			{
				Role:    openai.ChatMessageRoleUser,
				Content: "how can I solve 8x + 7 = -23",
			},
		},
		ResponseFormat: &openai.ChatCompletionResponseFormat{
			Type: openai.ChatCompletionResponseFormatTypeJSONSchema,
			JSONSchema: &openai.ChatCompletionResponseFormatJSONSchema{
				Name:   "math_reasoning",
				Schema: schema,
				Strict: true,
			},
		},
	})
	if err != nil {
		log.Fatalf("CreateChatCompletion error: %v", err)
	}
	err = schema.Unmarshal(resp.Choices[0].Message.Content, &result)
	if err != nil {
		log.Fatalf("Unmarshal schema error: %v", err)
	}
	fmt.Println(result)
}