Using Golang for AI

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go mod init github.com/jdortiz/goai

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package main
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func main() {
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}

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type App struct {
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}

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func (app App) Start() error {
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        const serverAddr string = "0.0.0.0:3001"
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        log.Printf("Starting HTTP server: %s\n", serverAddr)
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        return http.ListenAndServe(serverAddr, nil)
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}

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app := App{}
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log.Fatal(app.Start())

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func (app App) imageSearch(w http.ResponseWriter, r *http.Request) {
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     log.Println("Image search invoked")
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}

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http.HandleFunc("POST /api/search", app.imageSearch)

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curl -IX POST localhost:3001/api/search

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type CelebMatchRequest struct {
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        Image64 string `json:"img"`
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}

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// Deserialize request
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var imgReq CelebMatchRequest
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err := json.NewDecoder(r.Body).Decode(&imgReq)
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if err != nil {
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        log.Println("ERR: parsing json data", err)
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        http.Error(w, err.Error(), http.StatusBadRequest)
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        return
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}

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// Split image into metadata and data
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imgParts := strings.Split(imgReq.Image64, ",")
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parts := len(imgParts)
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if parts != 2 {
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        log.Printf("ERR: expecting metadata and data. Got %d parts\n", parts)
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        http.Error(w, fmt.Sprintf("expecting metadata and data. Got %d parts", parts), http.StatusBadRequest)
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        return
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}

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// Receives a base64 encoded image
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func standardizeImage(imageB64 string) (*string, error) {
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}

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// Get the base64 decoder as an io.Reader and use it to decode the image from the data
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b64Decoder := base64.NewDecoder(base64.StdEncoding, strings.NewReader(imageB64))
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origImg, _, err := image.Decode(b64Decoder)
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if err != nil {
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        return nil, fmt.Errorf("standardizing image failed: %w", err)
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}

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// Resize to 800x600
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resizedImg := image.NewRGBA(image.Rect(0, 0, 800, 600))
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draw.NearestNeighbor.Scale(resizedImg, resizedImg.Rect, origImg, origImg.Bounds(), draw.Over, nil)

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// Reencode the image to JPEG format with Q=85
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var jpegToSend bytes.Buffer
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// Encode the image into the buffer
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if err = jpeg.Encode(&jpegToSend, resizedImg, &jpeg.Options{Quality: 85}); err != nil {
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        return nil, fmt.Errorf("standardizing image failed: %w", err)
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}
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// Re-encode to base64
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stdImgB64 := base64.StdEncoding.EncodeToString(jpegToSend.Bytes())
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return &stdImgB64, nil

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// Decode image from base 64, resize image to 800x600 with Q=85, and re-encode to base64
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stdImage, err := standardizeImage(imgParts[1])
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if err != nil {
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        log.Println("ERR:", err)
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        http.Error(w, "Error standardizing image", http.StatusInternalServerError)
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        return
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}

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go get golang.org/x/image/draw

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go get github.com/aws/aws-sdk-go-v2/config
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go get github.com/aws/aws-sdk-go-v2/service/bedrockruntime

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configur  *aws.Config

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func connectToAWS(ctx context.Context) (*aws.Config, error) {
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        const dfltRegion string = "us-east-1"
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        const credAccount string = "your-account-name"
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        // Load the Shared AWS Configuration (~/.aws/config)
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        cfg, err := config.LoadDefaultConfig(ctx,
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                config.WithSharedConfigProfile(credAccount), // this must be the name of the profile in ~/.aws/config and ~/.aws/credentials
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                config.WithRegion(dfltRegion),
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        )
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        return &cfg, err
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}

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func NewApp(ctx context.Context) (*App, error) {
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        cfg, err := connectToAWS(ctx)
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        if err != nil {
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                log.Println("ERR: Couldn't load default configuration. Have you set up your AWS account?", err)
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                return nil, err
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        }
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        return &App{
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                config:  cfg,
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        }, nil
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}

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bedrock *bedrockruntime.Client

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// Initialize bedrock client
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bedrockClient := bedrockruntime.NewFromConfig(*cfg)
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 return &App{
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         config:  cfg,
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         bedrock: bedrockClient,
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 }, nil

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ctx := context.Background()
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app, err := NewApp(ctx)
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if err != nil {
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        panic(err)
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}

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// Prepare request to titan-embed-img-v1
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func (app App) computeImageEmbedding(ctx context.Context, image string) ([]float64, error) {
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}

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const titanEmbedImgV1ModelId string = "amazon.titan-embed-image-v1"

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type EmbeddingConfig struct {
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        OutputEmbeddingLength int `json:"outputEmbeddingLength"`
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}
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type BedrockRequest struct {
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        InputImage      string          `json:"inputImage"`
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        EmbeddingConfig EmbeddingConfig `json:"embeddingConfig"`
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        InputText       *string         `json:"inputText,omitempty"`
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}

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// Prepare the request to bedrock
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payload := BedrockRequest{
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        InputImage: image,
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        EmbeddingConfig: EmbeddingConfig{
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                OutputEmbeddingLength: 1024,
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        },
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        InputText: nil,
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}

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bedrockBody, err := json.Marshal(payload)
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if err != nil {
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        return nil, fmt.Errorf("failed to get embedding from bedrock: %w", err)
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}

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bedrockReq := bedrockruntime.InvokeModelInput{
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        ModelId:     aws.String(titanEmbedImgV1ModelId),
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        Body:        bedrockBody,
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        ContentType: aws.String(contentTypeJson),
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}

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const contentTypeJson = "application/json"

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// Invoke model to obtain embedding for the image
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embeddingResp, err := app.bedrock.InvokeModel(ctx, &bedrockReq)
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if err != nil {
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        return nil, fmt.Errorf("failed to get embedding from bedrock: %w", err)
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}

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go get github.com/tidwall/gjson

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result := gjson.GetBytes(embeddingResp.Body, "embedding")
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var embedding []float64
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result.ForEach(func(key, value gjson.Result) bool {
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        embedding = append(embedding, value.Float())
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        return true
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})
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return embedding, nil

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// Compute the embedding using titan-embed-image-v1
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embedding, err := app.computeImageEmbedding(r.Context(), *stdImage)
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if err != nil {
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        log.Println("ERR:", err)
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        http.Error(w, "Error computing embedding", http.StatusInternalServerError)
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        return
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}

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go get github.com/joho/godotenv

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var uri string
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err := godotenv.Load()
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if err != nil {
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        log.Fatal("Unable to load .env file")
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}
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if uri = os.Getenv("MONGODB_URI"); uri == "" {
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        log.Fatal("You must set your 'MONGODB_URI' environment variable. See\n\t https://mongodb.prakticum-team.ru/proxy/docs.mongodb.com/drivers/go/current/usage-examples/")
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}

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go get go.mongodb.org/mongo-driver/v2

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client *mongo.Client

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func newDBClient(uri string) (*mongo.Client, error) {
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        // Use the SetServerAPIOptions() method to set the Stable API version to 1
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        serverAPI := options.ServerAPI(options.ServerAPIVersion1)
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        opts := options.Client().ApplyURI(uri).SetServerAPIOptions(serverAPI)
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        // Create a new client and connect to the server
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        client, err := mongo.Connect(opts)
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        if err != nil {
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                return nil, err
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        }
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        return client, nil
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}

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func NewApp(ctx context.Context, uri string) (*App, error) {

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app, err := NewApp(ctx, uri)

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client, err := newDBClient(uri)
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if err != nil {
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        log.Println("ERR: connecting to MongoDB cluster:", err)
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        return nil, err
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}
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return &App{
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            client:  client,
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            config:  cfg,
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            bedrock: bedrockClient,
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}, nil

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func (app *App) Close() {
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        if err := app.client.Disconnect(context.Background()); err != nil {
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                panic(err)
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        }
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}

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defer func() {
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        app.Close()
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}()

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func (app App) findSimilarImages(ctx context.Context, embedding []float64) ([]string, error) {
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}

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// Get celeb image collection
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imgCollection := app.client.Database("celebrity_matcher").Collection("celeb_images")

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// Aggregation pipeline to get the 3 closest images to the given embedding.
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vectorSchStage := bson.D{{"$vectorSearch", bson.D{{"index", "vector_index"},
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        {"path", "embeddings"},
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        {"queryVector", embedding},
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        {"numCandidates", 15},
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        {"limit", 3}}}}

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projectStage := bson.D{{"$project", bson.D{{"image", 1}}}}

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pipeline := mongo.Pipeline{vectorSchStage, projectStage}

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// Make query
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imgCursor, err := imgCollection.Aggregate(ctx, pipeline)
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if err != nil {
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        return nil, fmt.Errorf("failed to get similar images from the database: %w", err)
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}

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// Get all the result using the cursor
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similarImgs := []struct {
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        Id    bson.ObjectID `bson:"_id,omitempty"`
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        Image string        `bson:"image"`
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}{}
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if err = imgCursor.All(ctx, &similarImgs); err != nil {
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        return nil, fmt.Errorf("failed to get similar images from the database: %w", err)
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}

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// Return just the standardized images in an array
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var images []string
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var stdImage *string
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for _, item := range similarImgs {
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        stdImage, err = standardizeImage(item.Image)
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        if err != nil {
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                return nil, fmt.Errorf("failed to standardize similar images: %w", err)
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        }
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        images = append(images, *stdImage)
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}
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return images, nil

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// Find similar images using vector search in MongoDB
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images, err := app.findSimilarImages(r.Context(), embedding)
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if err != nil {
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        log.Println("ERR:", err)
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        http.Error(w, "Error getting similar images", http.StatusInternalServerError)
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        return
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}

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// https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
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func (app App) getImageSimilaritiesDescription(ctx context.Context, imgB64 string, similarImgB64 []string) (*string, error) {
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}

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const claude3SonnetV1ModelId string = "anthropic.claude-3-sonnet-20240229-v1:0"

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type ClaudeBodyMsgSource struct {
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        Type      string  `json:"type"`
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        MediaType *string `json:"media_type,omitempty"`
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        Data      *string `json:"data,omitempty"`
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}
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type ClaudeBodyMsgContent struct {
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        Type   string               `json:"type"`
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        Source *ClaudeBodyMsgSource `json:"source,omitempty"`
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        Text   *string              `json:"text,omitempty"`
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}
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type ClaudeBodyMsg struct {
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        Role    string                 `json:"role"`
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        Content []ClaudeBodyMsgContent `json:"content"`
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}
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type ClaudeBody struct {
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        AnthropicVersion string          `json:"anthropic_version"`
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        MaxTokens        int             `json:"max_tokens"`
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        System           string          `json:"system"`
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        Messages         []ClaudeBodyMsg `json:"messages"`
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}

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// Prepare the request to bedrock
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const mediaTypeImage = "image/jpeg"
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prompt := "Please let the user know how their first image is similar to the other 3 and which one is the most similar?"
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payload := ClaudeBody{
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        AnthropicVersion: "bedrock-2023-05-31",
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        MaxTokens:        1000,
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        System:           "Please act as face comparison analyzer.",
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        Messages: []ClaudeBodyMsg{
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                        {
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                            Role: "user",
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                            Content: []ClaudeBodyMsgContent{
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                                    {
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                                            Type: "image",
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                                            Source: &ClaudeBodyMsgSource{
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                                                    Type:      "base64",
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                                                    MediaType: aws.String(mediaTypeImage),
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                                                    Data:      &imgB64,
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                                            },
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                                    },
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                                    {
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                                            Type: "image",
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                                            Source: &ClaudeBodyMsgSource{
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                                                    Type:      "base64",
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                                                    MediaType: aws.String(mediaTypeImage),
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                                                    Data:      &similarImgB64[0],
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                                            },
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                                    },
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                                    {
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                                            Type: "image",
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                                            Source: &ClaudeBodyMsgSource{
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                                                    Type:      "base64",
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                                                    MediaType: aws.String(mediaTypeImage),
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                                                    Data:      &similarImgB64[1],
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                                            },
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                                    },
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                                    {
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                                            Type: "image",
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                                            Source: &ClaudeBodyMsgSource{
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                                                    Type:      "base64",
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                                                    MediaType: aws.String(mediaTypeImage),
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                                                    Data:      &similarImgB64[2],
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                                            },
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                                    },
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                                    {
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                                            Type: "text",
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                                            Text: &prompt,
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                                    },
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                            },
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                    },
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                },
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}

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bedrockBody, err := json.Marshal(payload)
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if err != nil {
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        return nil, fmt.Errorf("failed to get embedding from bedrock: %w", err)
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}
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bedrockReq := bedrockruntime.InvokeModelInput{
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        ModelId:     aws.String(claude3SonnetV1ModelId),
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        Body:        bedrockBody,
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        ContentType: aws.String(contentTypeJson),
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        Accept:      aws.String(contentTypeJson),
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}

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// Invoke the model with the request
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bedrockResp, err := app.bedrock.InvokeModel(ctx, &bedrockReq)
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if err != nil {
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        return nil, fmt.Errorf("failed to get embedding from bedrock: %w", err)
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}

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description := gjson.GetBytes(bedrockResp.Body, "content.0.text").String()
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return &description, nil

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description, err := app.getImageSimilaritiesDescription(r.Context(), *stdImage, images)
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if err != nil {
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        log.Println("ERR: failed to describe similarities with images", err)
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        http.Error(w, "Error describing similarities with images", http.StatusInternalServerError)
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        return
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}

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type CelebMatchResponse struct {
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        Description string   `json:"description"`
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        Images      []string `json:"images"`
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}

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response := CelebMatchResponse{
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        Description: *description,
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        Images:      images,
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}

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jData, err := json.Marshal(response)
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if err != nil {
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        log.Fatalln("error serializing json", err)
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}
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// Set response headers and return JSON
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w.Header().Set("Content-Type", contentTypeJson)
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w.Header().Set("Content-Length", strconv.Itoa(len(jData)))
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w.WriteHeader(http.StatusOK)
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w.Write(jData)