mirror of
https://github.com/mudler/LocalAI
synced 2026-04-21 13:27:21 +00:00
108 lines
3.5 KiB
Go
108 lines
3.5 KiB
Go
package openai
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import (
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"encoding/base64"
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"encoding/binary"
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"encoding/json"
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"math"
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"time"
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"github.com/labstack/echo/v4"
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"github.com/mudler/LocalAI/core/backend"
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"github.com/mudler/LocalAI/core/config"
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"github.com/mudler/LocalAI/core/http/middleware"
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"github.com/mudler/LocalAI/pkg/model"
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"github.com/google/uuid"
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"github.com/mudler/LocalAI/core/schema"
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"github.com/mudler/xlog"
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)
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// floatsToBase64 packs a float32 slice as little-endian bytes and returns a base64 string.
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// This matches the OpenAI API encoding_format=base64 contract expected by the Node.js SDK.
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func floatsToBase64(floats []float32) string {
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buf := make([]byte, len(floats)*4)
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for i, f := range floats {
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binary.LittleEndian.PutUint32(buf[i*4:], math.Float32bits(f))
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}
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return base64.StdEncoding.EncodeToString(buf)
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}
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// embeddingItem builds a schema.Item for an embedding, encoding as base64 when requested.
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// The OpenAI Node.js SDK (v4+) sends encoding_format=base64 by default and expects a base64
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// string in the response; returning a float array causes Buffer.from(array,'base64') to
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// interpret each float as a single byte, yielding dims/4 values in Qdrant.
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func embeddingItem(embeddings []float32, index int, encodingFormat string) schema.Item {
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if encodingFormat == "base64" {
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return schema.Item{EmbeddingBase64: floatsToBase64(embeddings), Index: index, Object: "embedding"}
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}
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return schema.Item{Embedding: embeddings, Index: index, Object: "embedding"}
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}
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// EmbeddingsEndpoint is the OpenAI Embeddings API endpoint https://platform.openai.com/docs/api-reference/embeddings
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// @Summary Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
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// @Tags embeddings
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// @Param request body schema.OpenAIRequest true "query params"
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// @Success 200 {object} schema.OpenAIResponse "Response"
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// @Router /v1/embeddings [post]
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func EmbeddingsEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) echo.HandlerFunc {
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return func(c echo.Context) error {
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input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.OpenAIRequest)
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if !ok || input.Model == "" {
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return echo.ErrBadRequest
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}
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config, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
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if !ok || config == nil {
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return echo.ErrBadRequest
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}
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xlog.Debug("Parameter Config", "config", config)
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items := []schema.Item{}
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for i, s := range config.InputToken {
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// get the model function to call for the result
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embedFn, err := backend.ModelEmbedding("", s, ml, *config, appConfig)
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if err != nil {
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return err
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}
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embeddings, err := embedFn()
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if err != nil {
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return err
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}
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items = append(items, embeddingItem(embeddings, i, input.EncodingFormat))
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}
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for i, s := range config.InputStrings {
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// get the model function to call for the result
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embedFn, err := backend.ModelEmbedding(s, []int{}, ml, *config, appConfig)
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if err != nil {
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return err
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}
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embeddings, err := embedFn()
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if err != nil {
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return err
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}
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items = append(items, embeddingItem(embeddings, i, input.EncodingFormat))
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}
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id := uuid.New().String()
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created := int(time.Now().Unix())
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resp := &schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
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Data: items,
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Object: "list",
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}
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jsonResult, _ := json.Marshal(resp)
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xlog.Debug("Response", "response", string(jsonResult))
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// Return the prediction in the response body
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return c.JSON(200, resp)
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}
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}
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