mirror of
https://github.com/mudler/LocalAI.git
synced 2024-06-07 19:40:48 +00:00
84 lines
2.5 KiB
Go
84 lines
2.5 KiB
Go
package openai
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import (
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"encoding/json"
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"fmt"
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"time"
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"github.com/go-skynet/LocalAI/core/backend"
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"github.com/go-skynet/LocalAI/core/config"
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"github.com/go-skynet/LocalAI/pkg/model"
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"github.com/go-skynet/LocalAI/core/schema"
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"github.com/google/uuid"
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"github.com/gofiber/fiber/v2"
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"github.com/rs/zerolog/log"
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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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// @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.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) func(c *fiber.Ctx) error {
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return func(c *fiber.Ctx) error {
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model, input, err := readRequest(c, ml, appConfig, true)
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if err != nil {
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return fmt.Errorf("failed reading parameters from request:%w", err)
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}
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config, input, err := mergeRequestWithConfig(model, input, cl, ml, appConfig.Debug, appConfig.Threads, appConfig.ContextSize, appConfig.F16)
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if err != nil {
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return fmt.Errorf("failed reading parameters from request:%w", err)
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}
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log.Debug().Msgf("Parameter Config: %+v", 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, schema.Item{Embedding: embeddings, Index: i, Object: "embedding"})
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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, schema.Item{Embedding: embeddings, Index: i, Object: "embedding"})
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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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log.Debug().Msgf("Response: %s", jsonResult)
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// Return the prediction in the response body
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return c.JSON(resp)
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}
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}
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