package api import ( "bufio" "bytes" "encoding/base64" "encoding/json" "errors" "fmt" "io" "io/ioutil" "net/http" "os" "path" "path/filepath" "strconv" "strings" "github.com/ggerganov/whisper.cpp/bindings/go/pkg/whisper" model "github.com/go-skynet/LocalAI/pkg/model" whisperutil "github.com/go-skynet/LocalAI/pkg/whisper" llama "github.com/go-skynet/go-llama.cpp" "github.com/gofiber/fiber/v2" "github.com/rs/zerolog/log" "github.com/valyala/fasthttp" ) // APIError provides error information returned by the OpenAI API. type APIError struct { Code any `json:"code,omitempty"` Message string `json:"message"` Param *string `json:"param,omitempty"` Type string `json:"type"` } type ErrorResponse struct { Error *APIError `json:"error,omitempty"` } type OpenAIUsage struct { PromptTokens int `json:"prompt_tokens"` CompletionTokens int `json:"completion_tokens"` TotalTokens int `json:"total_tokens"` } type Item struct { Embedding []float32 `json:"embedding"` Index int `json:"index"` Object string `json:"object,omitempty"` // Images URL string `json:"url,omitempty"` B64JSON string `json:"b64_json,omitempty"` } type OpenAIResponse struct { Created int `json:"created,omitempty"` Object string `json:"object,omitempty"` ID string `json:"id,omitempty"` Model string `json:"model,omitempty"` Choices []Choice `json:"choices,omitempty"` Data []Item `json:"data,omitempty"` Usage OpenAIUsage `json:"usage"` } type Choice struct { Index int `json:"index,omitempty"` FinishReason string `json:"finish_reason,omitempty"` Message *Message `json:"message,omitempty"` Delta *Message `json:"delta,omitempty"` Text string `json:"text,omitempty"` } type Message struct { Role string `json:"role,omitempty" yaml:"role"` Content string `json:"content,omitempty" yaml:"content"` } type OpenAIModel struct { ID string `json:"id"` Object string `json:"object"` } type OpenAIRequest struct { Model string `json:"model" yaml:"model"` // whisper File string `json:"file" validate:"required"` Language string `json:"language"` //whisper/image ResponseFormat string `json:"response_format"` // image Size string `json:"size"` // Prompt is read only by completion/image API calls Prompt interface{} `json:"prompt" yaml:"prompt"` // Edit endpoint Instruction string `json:"instruction" yaml:"instruction"` Input interface{} `json:"input" yaml:"input"` Stop interface{} `json:"stop" yaml:"stop"` // Messages is read only by chat/completion API calls Messages []Message `json:"messages" yaml:"messages"` Stream bool `json:"stream"` Echo bool `json:"echo"` // Common options between all the API calls TopP float64 `json:"top_p" yaml:"top_p"` TopK int `json:"top_k" yaml:"top_k"` Temperature float64 `json:"temperature" yaml:"temperature"` Maxtokens int `json:"max_tokens" yaml:"max_tokens"` N int `json:"n"` // Custom parameters - not present in the OpenAI API Batch int `json:"batch" yaml:"batch"` F16 bool `json:"f16" yaml:"f16"` IgnoreEOS bool `json:"ignore_eos" yaml:"ignore_eos"` RepeatPenalty float64 `json:"repeat_penalty" yaml:"repeat_penalty"` Keep int `json:"n_keep" yaml:"n_keep"` MirostatETA float64 `json:"mirostat_eta" yaml:"mirostat_eta"` MirostatTAU float64 `json:"mirostat_tau" yaml:"mirostat_tau"` Mirostat int `json:"mirostat" yaml:"mirostat"` FrequencyPenalty float64 `json:"frequency_penalty" yaml:"frequency_penalty"` TFZ float64 `json:"tfz" yaml:"tfz"` Seed int `json:"seed" yaml:"seed"` // Image (not supported by OpenAI) Mode int `json:"mode"` Step int `json:"step"` } func defaultRequest(modelFile string) OpenAIRequest { return OpenAIRequest{ TopP: 0.7, TopK: 80, Maxtokens: 512, Temperature: 0.9, Model: modelFile, } } // https://platform.openai.com/docs/api-reference/completions func completionEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { process := func(s string, req *OpenAIRequest, config *Config, loader *model.ModelLoader, responses chan OpenAIResponse) { ComputeChoices(s, req, config, loader, func(s string, c *[]Choice) {}, func(s string) bool { resp := OpenAIResponse{ Model: req.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: []Choice{{Text: s}}, Object: "text_completion", } log.Debug().Msgf("Sending goroutine: %s", s) responses <- resp return true }) close(responses) } return func(c *fiber.Ctx) error { model, input, err := readInput(c, o.loader, true) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("`input`: %+v", input) config, input, err := readConfig(model, input, cm, o.loader, o.debug, o.threads, o.ctxSize, o.f16) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("Parameter Config: %+v", config) if input.Stream { log.Debug().Msgf("Stream request received") c.Context().SetContentType("text/event-stream") //c.Response().Header.SetContentType(fiber.MIMETextHTMLCharsetUTF8) //c.Set("Content-Type", "text/event-stream") c.Set("Cache-Control", "no-cache") c.Set("Connection", "keep-alive") c.Set("Transfer-Encoding", "chunked") } templateFile := config.Model if config.TemplateConfig.Completion != "" { templateFile = config.TemplateConfig.Completion } if input.Stream { if len(config.PromptStrings) > 1 { return errors.New("cannot handle more than 1 `PromptStrings` when `Stream`ing") } predInput := config.PromptStrings[0] // A model can have a "file.bin.tmpl" file associated with a prompt template prefix templatedInput, err := o.loader.TemplatePrefix(templateFile, struct { Input string }{Input: predInput}) if err == nil { predInput = templatedInput log.Debug().Msgf("Template found, input modified to: %s", predInput) } responses := make(chan OpenAIResponse) go process(predInput, input, config, o.loader, responses) c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) { for ev := range responses { var buf bytes.Buffer enc := json.NewEncoder(&buf) enc.Encode(ev) log.Debug().Msgf("Sending chunk: %s", buf.String()) fmt.Fprintf(w, "data: %v\n", buf.String()) w.Flush() } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: []Choice{{FinishReason: "stop"}}, } respData, _ := json.Marshal(resp) w.WriteString(fmt.Sprintf("data: %s\n\n", respData)) w.WriteString("data: [DONE]\n\n") w.Flush() })) return nil } var result []Choice for _, i := range config.PromptStrings { // A model can have a "file.bin.tmpl" file associated with a prompt template prefix templatedInput, err := o.loader.TemplatePrefix(templateFile, struct { Input string }{Input: i}) if err == nil { i = templatedInput log.Debug().Msgf("Template found, input modified to: %s", i) } r, err := ComputeChoices(i, input, config, o.loader, func(s string, c *[]Choice) { *c = append(*c, Choice{Text: s}) }, nil) if err != nil { return err } result = append(result, r...) } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: result, Object: "text_completion", } jsonResult, _ := json.Marshal(resp) log.Debug().Msgf("Response: %s", jsonResult) // Return the prediction in the response body return c.JSON(resp) } } // https://platform.openai.com/docs/api-reference/embeddings func embeddingsEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { return func(c *fiber.Ctx) error { model, input, err := readInput(c, o.loader, true) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } config, input, err := readConfig(model, input, cm, o.loader, o.debug, o.threads, o.ctxSize, o.f16) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("Parameter Config: %+v", config) items := []Item{} for i, s := range config.InputToken { // get the model function to call for the result embedFn, err := ModelEmbedding("", s, o.loader, *config) if err != nil { return err } embeddings, err := embedFn() if err != nil { return err } items = append(items, Item{Embedding: embeddings, Index: i, Object: "embedding"}) } for i, s := range config.InputStrings { // get the model function to call for the result embedFn, err := ModelEmbedding(s, []int{}, o.loader, *config) if err != nil { return err } embeddings, err := embedFn() if err != nil { return err } items = append(items, Item{Embedding: embeddings, Index: i, Object: "embedding"}) } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Data: items, Object: "list", } jsonResult, _ := json.Marshal(resp) log.Debug().Msgf("Response: %s", jsonResult) // Return the prediction in the response body return c.JSON(resp) } } func chatEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { process := func(s string, req *OpenAIRequest, config *Config, loader *model.ModelLoader, responses chan OpenAIResponse) { initialMessage := OpenAIResponse{ Model: req.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: []Choice{{Delta: &Message{Role: "assistant"}}}, Object: "chat.completion.chunk", } responses <- initialMessage ComputeChoices(s, req, config, loader, func(s string, c *[]Choice) {}, func(s string) bool { resp := OpenAIResponse{ Model: req.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: []Choice{{Delta: &Message{Content: s}}}, Object: "chat.completion.chunk", } log.Debug().Msgf("Sending goroutine: %s", s) responses <- resp return true }) close(responses) } return func(c *fiber.Ctx) error { model, input, err := readInput(c, o.loader, true) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } config, input, err := readConfig(model, input, cm, o.loader, o.debug, o.threads, o.ctxSize, o.f16) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("Parameter Config: %+v", config) var predInput string mess := []string{} for _, i := range input.Messages { var content string r := config.Roles[i.Role] if r != "" { content = fmt.Sprint(r, " ", i.Content) } else { content = i.Content } mess = append(mess, content) } predInput = strings.Join(mess, "\n") if input.Stream { log.Debug().Msgf("Stream request received") c.Context().SetContentType("text/event-stream") //c.Response().Header.SetContentType(fiber.MIMETextHTMLCharsetUTF8) // c.Set("Content-Type", "text/event-stream") c.Set("Cache-Control", "no-cache") c.Set("Connection", "keep-alive") c.Set("Transfer-Encoding", "chunked") } templateFile := config.Model if config.TemplateConfig.Chat != "" { templateFile = config.TemplateConfig.Chat } // A model can have a "file.bin.tmpl" file associated with a prompt template prefix templatedInput, err := o.loader.TemplatePrefix(templateFile, struct { Input string }{Input: predInput}) if err == nil { predInput = templatedInput log.Debug().Msgf("Template found, input modified to: %s", predInput) } if input.Stream { responses := make(chan OpenAIResponse) go process(predInput, input, config, o.loader, responses) c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) { for ev := range responses { var buf bytes.Buffer enc := json.NewEncoder(&buf) enc.Encode(ev) log.Debug().Msgf("Sending chunk: %s", buf.String()) fmt.Fprintf(w, "data: %v\n", buf.String()) w.Flush() } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: []Choice{{FinishReason: "stop"}}, } respData, _ := json.Marshal(resp) w.WriteString(fmt.Sprintf("data: %s\n\n", respData)) w.WriteString("data: [DONE]\n\n") w.Flush() })) return nil } result, err := ComputeChoices(predInput, input, config, o.loader, func(s string, c *[]Choice) { *c = append(*c, Choice{Message: &Message{Role: "assistant", Content: s}}) }, nil) if err != nil { return err } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: result, Object: "chat.completion", } respData, _ := json.Marshal(resp) log.Debug().Msgf("Response: %s", respData) // Return the prediction in the response body return c.JSON(resp) } } func editEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { return func(c *fiber.Ctx) error { model, input, err := readInput(c, o.loader, true) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } config, input, err := readConfig(model, input, cm, o.loader, o.debug, o.threads, o.ctxSize, o.f16) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("Parameter Config: %+v", config) templateFile := config.Model if config.TemplateConfig.Edit != "" { templateFile = config.TemplateConfig.Edit } var result []Choice for _, i := range config.InputStrings { // A model can have a "file.bin.tmpl" file associated with a prompt template prefix templatedInput, err := o.loader.TemplatePrefix(templateFile, struct { Input string Instruction string }{Input: i}) if err == nil { i = templatedInput log.Debug().Msgf("Template found, input modified to: %s", i) } r, err := ComputeChoices(i, input, config, o.loader, func(s string, c *[]Choice) { *c = append(*c, Choice{Text: s}) }, nil) if err != nil { return err } result = append(result, r...) } resp := &OpenAIResponse{ Model: input.Model, // we have to return what the user sent here, due to OpenAI spec. Choices: result, Object: "edit", } jsonResult, _ := json.Marshal(resp) log.Debug().Msgf("Response: %s", jsonResult) // Return the prediction in the response body return c.JSON(resp) } } // https://platform.openai.com/docs/api-reference/images/create /* * curl http://localhost:8080/v1/images/generations \ -H "Content-Type: application/json" \ -d '{ "prompt": "A cute baby sea otter", "n": 1, "size": "512x512" }' * */ func imageEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { return func(c *fiber.Ctx) error { m, input, err := readInput(c, o.loader, false) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } if m == "" { m = model.StableDiffusionBackend } log.Debug().Msgf("Loading model: %+v", m) config, input, err := readConfig(m, input, cm, o.loader, o.debug, 0, 0, false) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } log.Debug().Msgf("Parameter Config: %+v", config) // XXX: Only stablediffusion is supported for now if config.Backend == "" { config.Backend = model.StableDiffusionBackend } sizeParts := strings.Split(input.Size, "x") if len(sizeParts) != 2 { return fmt.Errorf("Invalid value for 'size'") } width, err := strconv.Atoi(sizeParts[0]) if err != nil { return fmt.Errorf("Invalid value for 'size'") } height, err := strconv.Atoi(sizeParts[1]) if err != nil { return fmt.Errorf("Invalid value for 'size'") } b64JSON := false if input.ResponseFormat == "b64_json" { b64JSON = true } var result []Item for _, i := range config.PromptStrings { n := input.N if input.N == 0 { n = 1 } for j := 0; j < n; j++ { prompts := strings.Split(i, "|") positive_prompt := prompts[0] negative_prompt := "" if len(prompts) > 1 { negative_prompt = prompts[1] } mode := 0 step := 15 if input.Mode != 0 { mode = input.Mode } if input.Step != 0 { step = input.Step } tempDir := "" if !b64JSON { tempDir = o.imageDir } // Create a temporary file outputFile, err := ioutil.TempFile(tempDir, "b64") if err != nil { return err } outputFile.Close() output := outputFile.Name() + ".png" // Rename the temporary file err = os.Rename(outputFile.Name(), output) if err != nil { return err } baseURL := c.BaseURL() fn, err := ImageGeneration(height, width, mode, step, input.Seed, positive_prompt, negative_prompt, output, o.loader, *config) if err != nil { return err } if err := fn(); err != nil { return err } item := &Item{} if b64JSON { defer os.RemoveAll(output) data, err := os.ReadFile(output) if err != nil { return err } item.B64JSON = base64.StdEncoding.EncodeToString(data) } else { base := filepath.Base(output) item.URL = baseURL + "/generated-images/" + base } result = append(result, *item) } } resp := &OpenAIResponse{ Data: result, } jsonResult, _ := json.Marshal(resp) log.Debug().Msgf("Response: %s", jsonResult) // Return the prediction in the response body return c.JSON(resp) } } // https://platform.openai.com/docs/api-reference/audio/create func transcriptEndpoint(cm *ConfigMerger, o *Option) func(c *fiber.Ctx) error { return func(c *fiber.Ctx) error { m, input, err := readInput(c, o.loader, false) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } config, input, err := readConfig(m, input, cm, o.loader, o.debug, o.threads, o.ctxSize, o.f16) if err != nil { return fmt.Errorf("failed reading parameters from request:%w", err) } // retrieve the file data from the request file, err := c.FormFile("file") if err != nil { return err } f, err := file.Open() if err != nil { return err } defer f.Close() dir, err := os.MkdirTemp("", "whisper") if err != nil { return err } defer os.RemoveAll(dir) dst := filepath.Join(dir, path.Base(file.Filename)) dstFile, err := os.Create(dst) if err != nil { return err } if _, err := io.Copy(dstFile, f); err != nil { log.Debug().Msgf("Audio file copying error %+v - %+v - err %+v", file.Filename, dst, err) return err } log.Debug().Msgf("Audio file copied to: %+v", dst) whisperModel, err := o.loader.BackendLoader(model.WhisperBackend, config.Model, []llama.ModelOption{}, uint32(config.Threads)) if err != nil { return err } if whisperModel == nil { return fmt.Errorf("could not load whisper model") } w, ok := whisperModel.(whisper.Model) if !ok { return fmt.Errorf("loader returned non-whisper object") } tr, err := whisperutil.Transcript(w, dst, input.Language, uint(config.Threads)) if err != nil { return err } log.Debug().Msgf("Trascribed: %+v", tr) // TODO: handle different outputs here return c.Status(http.StatusOK).JSON(fiber.Map{"text": tr}) } } func listModels(loader *model.ModelLoader, cm *ConfigMerger) func(ctx *fiber.Ctx) error { return func(c *fiber.Ctx) error { models, err := loader.ListModels() if err != nil { return err } var mm map[string]interface{} = map[string]interface{}{} dataModels := []OpenAIModel{} for _, m := range models { mm[m] = nil dataModels = append(dataModels, OpenAIModel{ID: m, Object: "model"}) } for _, k := range cm.ListConfigs() { if _, exists := mm[k]; !exists { dataModels = append(dataModels, OpenAIModel{ID: k, Object: "model"}) } } return c.JSON(struct { Object string `json:"object"` Data []OpenAIModel `json:"data"` }{ Object: "list", Data: dataModels, }) } }