🤖 Self-hosted, community-driven, local OpenAI-compatible API with Keycloak Auth Flak app as frontend. 🏠
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FlaskAI/api/api.go

290 lines
6.9 KiB

package api
import (
"fmt"
"strings"
"sync"
model "github.com/go-skynet/llama-cli/pkg/model"
gptj "github.com/go-skynet/go-gpt4all-j.cpp"
llama "github.com/go-skynet/go-llama.cpp"
"github.com/gofiber/fiber/v2"
"github.com/gofiber/fiber/v2/middleware/cors"
"github.com/gofiber/fiber/v2/middleware/recover"
)
type OpenAIResponse struct {
Created int `json:"created,omitempty"`
Object string `json:"chat.completion,omitempty"`
ID string `json:"id,omitempty"`
Model string `json:"model,omitempty"`
Choices []Choice `json:"choices,omitempty"`
}
type Choice struct {
Index int `json:"index,omitempty"`
FinishReason string `json:"finish_reason,omitempty"`
Message *Message `json:"message,omitempty"`
Text string `json:"text,omitempty"`
}
type Message struct {
Role string `json:"role,omitempty"`
Content string `json:"content,omitempty"`
}
type OpenAIModel struct {
ID string `json:"id"`
Object string `json:"object"`
}
type OpenAIRequest struct {
Model string `json:"model"`
// Prompt is read only by completion API calls
Prompt string `json:"prompt"`
// Messages is read only by chat/completion API calls
Messages []Message `json:"messages"`
Echo bool `json:"echo"`
// Common options between all the API calls
TopP float64 `json:"top_p"`
TopK int `json:"top_k"`
Temperature float64 `json:"temperature"`
Maxtokens int `json:"max_tokens"`
N int `json:"n"`
// Custom parameters - not present in the OpenAI API
Batch int `json:"batch"`
F16 bool `json:"f16kv"`
IgnoreEOS bool `json:"ignore_eos"`
Seed int `json:"seed"`
}
// https://platform.openai.com/docs/api-reference/completions
func openAIEndpoint(chat bool, loader *model.ModelLoader, threads, ctx int, f16 bool, defaultMutex *sync.Mutex, mutexMap *sync.Mutex, mutexes map[string]*sync.Mutex) func(c *fiber.Ctx) error {
return func(c *fiber.Ctx) error {
var err error
var model *llama.LLama
var gptModel *gptj.GPTJ
input := new(OpenAIRequest)
// Get input data from the request body
if err := c.BodyParser(input); err != nil {
return err
}
if input.Model == "" {
return fmt.Errorf("no model specified")
} else {
// Try to load the model with both
var llamaerr error
llamaOpts := []llama.ModelOption{}
if ctx != 0 {
llamaOpts = append(llamaOpts, llama.SetContext(ctx))
}
if f16 {
llamaOpts = append(llamaOpts, llama.EnableF16Memory)
}
model, llamaerr = loader.LoadLLaMAModel(input.Model, llamaOpts...)
if llamaerr != nil {
gptModel, err = loader.LoadGPTJModel(input.Model)
if err != nil {
return fmt.Errorf("llama: %s gpt: %s", llamaerr.Error(), err.Error()) // llama failed first, so we want to catch both errors
}
}
}
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
if input.Model != "" {
mutexMap.Lock()
l, ok := mutexes[input.Model]
if !ok {
m := &sync.Mutex{}
mutexes[input.Model] = m
l = m
}
mutexMap.Unlock()
l.Lock()
defer l.Unlock()
} else {
defaultMutex.Lock()
defer defaultMutex.Unlock()
}
// Set the parameters for the language model prediction
topP := input.TopP
if topP == 0 {
topP = 0.7
}
topK := input.TopK
if topK == 0 {
topK = 80
}
temperature := input.Temperature
if temperature == 0 {
temperature = 0.9
}
tokens := input.Maxtokens
if tokens == 0 {
tokens = 512
}
predInput := input.Prompt
if chat {
mess := []string{}
for _, i := range input.Messages {
mess = append(mess, i.Content)
}
predInput = strings.Join(mess, "\n")
}
// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
templatedInput, err := loader.TemplatePrefix(input.Model, struct {
Input string
}{Input: predInput})
if err == nil {
predInput = templatedInput
}
result := []Choice{}
n := input.N
if input.N == 0 {
n = 1
}
var predFunc func() (string, error)
switch {
case gptModel != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []gptj.PredictOption{
gptj.SetTemperature(temperature),
gptj.SetTopP(topP),
gptj.SetTopK(topK),
gptj.SetTokens(tokens),
gptj.SetThreads(threads),
}
if input.Batch != 0 {
predictOptions = append(predictOptions, gptj.SetBatch(input.Batch))
}
if input.Seed != 0 {
predictOptions = append(predictOptions, gptj.SetSeed(input.Seed))
}
return gptModel.Predict(
predInput,
predictOptions...,
)
}
case model != nil:
predFunc = func() (string, error) {
// Generate the prediction using the language model
predictOptions := []llama.PredictOption{
llama.SetTemperature(temperature),
llama.SetTopP(topP),
llama.SetTopK(topK),
llama.SetTokens(tokens),
llama.SetThreads(threads),
}
if input.Batch != 0 {
predictOptions = append(predictOptions, llama.SetBatch(input.Batch))
}
if input.F16 {
predictOptions = append(predictOptions, llama.EnableF16KV)
}
if input.IgnoreEOS {
predictOptions = append(predictOptions, llama.IgnoreEOS)
}
if input.Seed != 0 {
predictOptions = append(predictOptions, llama.SetSeed(input.Seed))
}
return model.Predict(
predInput,
predictOptions...,
)
}
}
for i := 0; i < n; i++ {
var prediction string
prediction, err := predFunc()
if err != nil {
return err
}
if input.Echo {
prediction = predInput + prediction
}
if chat {
result = append(result, Choice{Message: &Message{Role: "assistant", Content: prediction}})
} else {
result = append(result, Choice{Text: prediction})
}
}
// Return the prediction in the response body
return c.JSON(OpenAIResponse{
Model: input.Model,
Choices: result,
})
}
}
func Start(loader *model.ModelLoader, listenAddr string, threads, ctxSize int, f16 bool) error {
app := fiber.New()
// Default middleware config
app.Use(recover.New())
app.Use(cors.New())
// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
var mutex = &sync.Mutex{}
mu := map[string]*sync.Mutex{}
var mumutex = &sync.Mutex{}
// openAI compatible API endpoint
app.Post("/v1/chat/completions", openAIEndpoint(true, loader, threads, ctxSize, f16, mutex, mumutex, mu))
app.Post("/v1/completions", openAIEndpoint(false, loader, threads, ctxSize, f16, mutex, mumutex, mu))
app.Get("/v1/models", func(c *fiber.Ctx) error {
models, err := loader.ListModels()
if err != nil {
return err
}
dataModels := []OpenAIModel{}
for _, m := range models {
dataModels = append(dataModels, OpenAIModel{ID: m, Object: "model"})
}
return c.JSON(struct {
Object string `json:"object"`
Data []OpenAIModel `json:"data"`
}{
Object: "list",
Data: dataModels,
})
})
// Start the server
app.Listen(listenAddr)
return nil
}