mirror of
https://github.com/mudler/LocalAI.git
synced 2024-12-19 04:37:53 +00:00
f943c4b803
Revert "feat: include tokens usage for streamed output (#4282)"
This reverts commit 0d6c3a7d57.
536 lines
17 KiB
Go
536 lines
17 KiB
Go
package openai
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import (
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"bufio"
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"bytes"
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"encoding/json"
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"fmt"
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"strings"
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"time"
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"github.com/gofiber/fiber/v2"
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"github.com/google/uuid"
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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/schema"
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"github.com/mudler/LocalAI/pkg/functions"
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"github.com/mudler/LocalAI/pkg/templates"
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model "github.com/mudler/LocalAI/pkg/model"
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"github.com/rs/zerolog/log"
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"github.com/valyala/fasthttp"
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)
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// ChatEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/chat/create
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// @Summary Generate a chat completions for a given prompt and model.
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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/chat/completions [post]
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func ChatEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, startupOptions *config.ApplicationConfig) func(c *fiber.Ctx) error {
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var id, textContentToReturn string
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var created int
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process := func(s string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
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initialMessage := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
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Object: "chat.completion.chunk",
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}
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responses <- initialMessage
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ComputeChoices(req, s, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
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resp := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{Delta: &schema.Message{Content: &s}, Index: 0}},
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Object: "chat.completion.chunk",
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Usage: schema.OpenAIUsage{
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PromptTokens: usage.Prompt,
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CompletionTokens: usage.Completion,
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TotalTokens: usage.Prompt + usage.Completion,
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},
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}
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responses <- resp
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return true
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})
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close(responses)
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}
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processTools := func(noAction string, prompt string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
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result := ""
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_, tokenUsage, _ := ComputeChoices(req, prompt, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
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result += s
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// TODO: Change generated BNF grammar to be compliant with the schema so we can
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// stream the result token by token here.
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return true
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})
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textContentToReturn = functions.ParseTextContent(result, config.FunctionsConfig)
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result = functions.CleanupLLMResult(result, config.FunctionsConfig)
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functionResults := functions.ParseFunctionCall(result, config.FunctionsConfig)
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log.Debug().Msgf("Text content to return: %s", textContentToReturn)
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noActionToRun := len(functionResults) > 0 && functionResults[0].Name == noAction || len(functionResults) == 0
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switch {
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case noActionToRun:
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initialMessage := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
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Object: "chat.completion.chunk",
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}
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responses <- initialMessage
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result, err := handleQuestion(config, req, ml, startupOptions, functionResults, result, prompt)
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if err != nil {
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log.Error().Err(err).Msg("error handling question")
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return
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}
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resp := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{Delta: &schema.Message{Content: &result}, Index: 0}},
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Object: "chat.completion.chunk",
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Usage: schema.OpenAIUsage{
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PromptTokens: tokenUsage.Prompt,
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CompletionTokens: tokenUsage.Completion,
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TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
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},
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}
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responses <- resp
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default:
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for i, ss := range functionResults {
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name, args := ss.Name, ss.Arguments
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initialMessage := schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{
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Delta: &schema.Message{
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Role: "assistant",
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ToolCalls: []schema.ToolCall{
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{
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Index: i,
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ID: id,
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Type: "function",
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FunctionCall: schema.FunctionCall{
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Name: name,
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},
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},
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},
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}}},
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Object: "chat.completion.chunk",
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}
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responses <- initialMessage
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responses <- schema.OpenAIResponse{
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ID: id,
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Created: created,
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Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
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Choices: []schema.Choice{{
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Delta: &schema.Message{
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Role: "assistant",
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Content: &textContentToReturn,
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ToolCalls: []schema.ToolCall{
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{
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Index: i,
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ID: id,
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Type: "function",
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FunctionCall: schema.FunctionCall{
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Arguments: args,
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},
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},
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},
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}}},
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Object: "chat.completion.chunk",
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}
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}
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}
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close(responses)
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}
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return func(c *fiber.Ctx) error {
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textContentToReturn = ""
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id = uuid.New().String()
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created = int(time.Now().Unix())
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// Set CorrelationID
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correlationID := c.Get("X-Correlation-ID")
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if len(strings.TrimSpace(correlationID)) == 0 {
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correlationID = id
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}
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c.Set("X-Correlation-ID", correlationID)
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modelFile, input, err := readRequest(c, cl, ml, startupOptions, 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(modelFile, input, cl, ml, startupOptions.Debug, startupOptions.Threads, startupOptions.ContextSize, startupOptions.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("Configuration read: %+v", config)
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funcs := input.Functions
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shouldUseFn := len(input.Functions) > 0 && config.ShouldUseFunctions()
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strictMode := false
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for _, f := range input.Functions {
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if f.Strict {
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strictMode = true
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break
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}
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}
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// Allow the user to set custom actions via config file
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// to be "embedded" in each model
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noActionName := "answer"
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noActionDescription := "use this action to answer without performing any action"
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if config.FunctionsConfig.NoActionFunctionName != "" {
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noActionName = config.FunctionsConfig.NoActionFunctionName
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}
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if config.FunctionsConfig.NoActionDescriptionName != "" {
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noActionDescription = config.FunctionsConfig.NoActionDescriptionName
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}
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if config.ResponseFormatMap != nil {
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d := schema.ChatCompletionResponseFormat{}
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dat, err := json.Marshal(config.ResponseFormatMap)
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if err != nil {
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return err
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}
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err = json.Unmarshal(dat, &d)
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if err != nil {
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return err
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}
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if d.Type == "json_object" {
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input.Grammar = functions.JSONBNF
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} else if d.Type == "json_schema" {
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d := schema.JsonSchemaRequest{}
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dat, err := json.Marshal(config.ResponseFormatMap)
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if err != nil {
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return err
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}
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err = json.Unmarshal(dat, &d)
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if err != nil {
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return err
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}
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fs := &functions.JSONFunctionStructure{
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AnyOf: []functions.Item{d.JsonSchema.Schema},
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}
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g, err := fs.Grammar(config.FunctionsConfig.GrammarOptions()...)
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if err == nil {
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input.Grammar = g
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}
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}
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}
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config.Grammar = input.Grammar
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if shouldUseFn {
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log.Debug().Msgf("Response needs to process functions")
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}
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switch {
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case (!config.FunctionsConfig.GrammarConfig.NoGrammar || strictMode) && shouldUseFn:
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noActionGrammar := functions.Function{
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Name: noActionName,
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Description: noActionDescription,
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Parameters: map[string]interface{}{
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"properties": map[string]interface{}{
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"message": map[string]interface{}{
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"type": "string",
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"description": "The message to reply the user with",
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}},
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},
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}
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// Append the no action function
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if !config.FunctionsConfig.DisableNoAction {
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funcs = append(funcs, noActionGrammar)
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}
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// Force picking one of the functions by the request
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if config.FunctionToCall() != "" {
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funcs = funcs.Select(config.FunctionToCall())
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}
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// Update input grammar
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jsStruct := funcs.ToJSONStructure(config.FunctionsConfig.FunctionNameKey, config.FunctionsConfig.FunctionNameKey)
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g, err := jsStruct.Grammar(config.FunctionsConfig.GrammarOptions()...)
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if err == nil {
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config.Grammar = g
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}
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case input.JSONFunctionGrammarObject != nil:
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g, err := input.JSONFunctionGrammarObject.Grammar(config.FunctionsConfig.GrammarOptions()...)
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if err == nil {
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config.Grammar = g
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}
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default:
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// Force picking one of the functions by the request
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if config.FunctionToCall() != "" {
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funcs = funcs.Select(config.FunctionToCall())
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}
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}
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// process functions if we have any defined or if we have a function call string
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// functions are not supported in stream mode (yet?)
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toStream := input.Stream
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log.Debug().Msgf("Parameters: %+v", config)
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var predInput string
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// If we are using the tokenizer template, we don't need to process the messages
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// unless we are processing functions
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if !config.TemplateConfig.UseTokenizerTemplate || shouldUseFn {
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predInput = evaluator.TemplateMessages(input.Messages, config, funcs, shouldUseFn)
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log.Debug().Msgf("Prompt (after templating): %s", predInput)
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if config.Grammar != "" {
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log.Debug().Msgf("Grammar: %+v", config.Grammar)
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}
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}
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switch {
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case toStream:
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log.Debug().Msgf("Stream request received")
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c.Context().SetContentType("text/event-stream")
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//c.Response().Header.SetContentType(fiber.MIMETextHTMLCharsetUTF8)
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// c.Set("Content-Type", "text/event-stream")
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c.Set("Cache-Control", "no-cache")
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c.Set("Connection", "keep-alive")
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c.Set("Transfer-Encoding", "chunked")
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c.Set("X-Correlation-ID", id)
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responses := make(chan schema.OpenAIResponse)
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if !shouldUseFn {
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go process(predInput, input, config, ml, responses)
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} else {
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go processTools(noActionName, predInput, input, config, ml, responses)
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}
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c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) {
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usage := &schema.OpenAIUsage{}
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toolsCalled := false
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for ev := range responses {
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usage = &ev.Usage // Copy a pointer to the latest usage chunk so that the stop message can reference it
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if len(ev.Choices[0].Delta.ToolCalls) > 0 {
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toolsCalled = true
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}
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var buf bytes.Buffer
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enc := json.NewEncoder(&buf)
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enc.Encode(ev)
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log.Debug().Msgf("Sending chunk: %s", buf.String())
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_, err := fmt.Fprintf(w, "data: %v\n", buf.String())
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if err != nil {
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log.Debug().Msgf("Sending chunk failed: %v", err)
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input.Cancel()
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}
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w.Flush()
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}
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finishReason := "stop"
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if toolsCalled {
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finishReason = "tool_calls"
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} else if toolsCalled && len(input.Tools) == 0 {
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finishReason = "function_call"
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}
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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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Choices: []schema.Choice{
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{
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FinishReason: finishReason,
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Index: 0,
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Delta: &schema.Message{Content: &textContentToReturn},
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}},
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Object: "chat.completion.chunk",
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Usage: *usage,
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}
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respData, _ := json.Marshal(resp)
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w.WriteString(fmt.Sprintf("data: %s\n\n", respData))
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w.WriteString("data: [DONE]\n\n")
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w.Flush()
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}))
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return nil
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// no streaming mode
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default:
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result, tokenUsage, err := ComputeChoices(input, predInput, config, startupOptions, ml, func(s string, c *[]schema.Choice) {
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if !shouldUseFn {
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// no function is called, just reply and use stop as finish reason
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*c = append(*c, schema.Choice{FinishReason: "stop", Index: 0, Message: &schema.Message{Role: "assistant", Content: &s}})
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return
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}
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textContentToReturn = functions.ParseTextContent(s, config.FunctionsConfig)
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s = functions.CleanupLLMResult(s, config.FunctionsConfig)
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results := functions.ParseFunctionCall(s, config.FunctionsConfig)
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log.Debug().Msgf("Text content to return: %s", textContentToReturn)
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noActionsToRun := len(results) > 0 && results[0].Name == noActionName || len(results) == 0
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switch {
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case noActionsToRun:
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result, err := handleQuestion(config, input, ml, startupOptions, results, s, predInput)
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if err != nil {
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log.Error().Err(err).Msg("error handling question")
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return
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}
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*c = append(*c, schema.Choice{
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Message: &schema.Message{Role: "assistant", Content: &result}})
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default:
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toolChoice := schema.Choice{
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Message: &schema.Message{
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Role: "assistant",
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},
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}
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if len(input.Tools) > 0 {
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toolChoice.FinishReason = "tool_calls"
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}
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for _, ss := range results {
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name, args := ss.Name, ss.Arguments
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if len(input.Tools) > 0 {
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// If we are using tools, we condense the function calls into
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// a single response choice with all the tools
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toolChoice.Message.Content = textContentToReturn
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toolChoice.Message.ToolCalls = append(toolChoice.Message.ToolCalls,
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schema.ToolCall{
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ID: id,
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Type: "function",
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FunctionCall: schema.FunctionCall{
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Name: name,
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Arguments: args,
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},
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},
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)
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} else {
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// otherwise we return more choices directly
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*c = append(*c, schema.Choice{
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FinishReason: "function_call",
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Message: &schema.Message{
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Role: "assistant",
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Content: &textContentToReturn,
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FunctionCall: map[string]interface{}{
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"name": name,
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"arguments": args,
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},
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},
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})
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}
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}
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if len(input.Tools) > 0 {
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// we need to append our result if we are using tools
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*c = append(*c, toolChoice)
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}
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}
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}, nil)
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if err != nil {
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return err
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}
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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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Choices: result,
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Object: "chat.completion",
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Usage: schema.OpenAIUsage{
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PromptTokens: tokenUsage.Prompt,
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CompletionTokens: tokenUsage.Completion,
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TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
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},
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}
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respData, _ := json.Marshal(resp)
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log.Debug().Msgf("Response: %s", respData)
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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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}
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func handleQuestion(config *config.BackendConfig, input *schema.OpenAIRequest, ml *model.ModelLoader, o *config.ApplicationConfig, funcResults []functions.FuncCallResults, result, prompt string) (string, error) {
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if len(funcResults) == 0 && result != "" {
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log.Debug().Msgf("nothing function results but we had a message from the LLM")
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return result, nil
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}
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log.Debug().Msgf("nothing to do, computing a reply")
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arg := ""
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if len(funcResults) > 0 {
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arg = funcResults[0].Arguments
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}
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// If there is a message that the LLM already sends as part of the JSON reply, use it
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arguments := map[string]interface{}{}
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if err := json.Unmarshal([]byte(arg), &arguments); err != nil {
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log.Debug().Msg("handleQuestion: function result did not contain a valid JSON object")
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}
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m, exists := arguments["message"]
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if exists {
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switch message := m.(type) {
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case string:
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if message != "" {
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log.Debug().Msgf("Reply received from LLM: %s", message)
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message = backend.Finetune(*config, prompt, message)
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log.Debug().Msgf("Reply received from LLM(finetuned): %s", message)
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return message, nil
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}
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}
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}
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log.Debug().Msgf("No action received from LLM, without a message, computing a reply")
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// Otherwise ask the LLM to understand the JSON output and the context, and return a message
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// Note: This costs (in term of CPU/GPU) another computation
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config.Grammar = ""
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images := []string{}
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for _, m := range input.Messages {
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images = append(images, m.StringImages...)
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}
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videos := []string{}
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for _, m := range input.Messages {
|
|
videos = append(videos, m.StringVideos...)
|
|
}
|
|
audios := []string{}
|
|
for _, m := range input.Messages {
|
|
audios = append(audios, m.StringAudios...)
|
|
}
|
|
|
|
predFunc, err := backend.ModelInference(input.Context, prompt, input.Messages, images, videos, audios, ml, *config, o, nil)
|
|
if err != nil {
|
|
log.Error().Err(err).Msg("model inference failed")
|
|
return "", err
|
|
}
|
|
|
|
prediction, err := predFunc()
|
|
if err != nil {
|
|
log.Error().Err(err).Msg("prediction failed")
|
|
return "", err
|
|
}
|
|
return backend.Finetune(*config, prompt, prediction.Response), nil
|
|
}
|