mirror of
https://github.com/mudler/LocalAI.git
synced 2024-12-22 14:02:24 +00:00
374 lines
13 KiB
Go
374 lines
13 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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"github.com/go-skynet/LocalAI/api/backend"
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config "github.com/go-skynet/LocalAI/api/config"
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"github.com/go-skynet/LocalAI/api/options"
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"github.com/go-skynet/LocalAI/api/schema"
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"github.com/go-skynet/LocalAI/pkg/grammar"
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model "github.com/go-skynet/LocalAI/pkg/model"
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"github.com/go-skynet/LocalAI/pkg/utils"
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"github.com/gofiber/fiber/v2"
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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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func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx) error {
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emptyMessage := ""
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process := func(s string, req *schema.OpenAIRequest, config *config.Config, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
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initialMessage := schema.OpenAIResponse{
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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: &emptyMessage}}},
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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, o, 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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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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return func(c *fiber.Ctx) error {
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processFunctions := false
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funcs := grammar.Functions{}
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modelFile, input, err := readInput(c, o, 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 := readConfig(modelFile, input, cm, o.Loader, o.Debug, o.Threads, o.ContextSize, o.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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// 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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// process functions if we have any defined or if we have a function call string
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if len(input.Functions) > 0 && config.ShouldUseFunctions() {
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log.Debug().Msgf("Response needs to process functions")
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processFunctions = true
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noActionGrammar := grammar.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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funcs = append(funcs, input.Functions...)
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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()
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config.Grammar = jsStruct.Grammar("")
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} else if input.JSONFunctionGrammarObject != nil {
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config.Grammar = input.JSONFunctionGrammarObject.Grammar("")
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}
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// functions are not supported in stream mode (yet?)
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toStream := input.Stream && !processFunctions
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log.Debug().Msgf("Parameters: %+v", config)
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var predInput string
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suppressConfigSystemPrompt := false
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mess := []string{}
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for messageIndex, i := range input.Messages {
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var content string
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role := i.Role
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// if function call, we might want to customize the role so we can display better that the "assistant called a json action"
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// if an "assistant_function_call" role is defined, we use it, otherwise we use the role that is passed by in the request
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if i.FunctionCall != nil && i.Role == "assistant" {
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roleFn := "assistant_function_call"
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r := config.Roles[roleFn]
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if r != "" {
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role = roleFn
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}
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}
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r := config.Roles[role]
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contentExists := i.Content != nil && *i.Content != ""
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// First attempt to populate content via a chat message specific template
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if config.TemplateConfig.ChatMessage != "" {
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chatMessageData := model.ChatMessageTemplateData{
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SystemPrompt: config.SystemPrompt,
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Role: r,
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RoleName: role,
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Content: *i.Content,
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MessageIndex: messageIndex,
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}
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templatedChatMessage, err := o.Loader.EvaluateTemplateForChatMessage(config.TemplateConfig.ChatMessage, chatMessageData)
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if err != nil {
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log.Error().Msgf("error processing message %+v using template \"%s\": %v. Skipping!", chatMessageData, config.TemplateConfig.ChatMessage, err)
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} else {
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if templatedChatMessage == "" {
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log.Warn().Msgf("template \"%s\" produced blank output for %+v. Skipping!", config.TemplateConfig.ChatMessage, chatMessageData)
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continue // TODO: This continue is here intentionally to skip over the line `mess = append(mess, content)` below, and to prevent the sprintf
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}
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log.Debug().Msgf("templated message for chat: %s", templatedChatMessage)
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content = templatedChatMessage
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}
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}
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// If this model doesn't have such a template, or if that template fails to return a value, template at the message level.
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if content == "" {
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if r != "" {
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if contentExists {
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content = fmt.Sprint(r, " ", *i.Content)
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}
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if i.FunctionCall != nil {
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j, err := json.Marshal(i.FunctionCall)
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if err == nil {
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if contentExists {
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content += "\n" + fmt.Sprint(r, " ", string(j))
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} else {
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content = fmt.Sprint(r, " ", string(j))
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}
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}
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}
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} else {
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if contentExists {
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content = fmt.Sprint(*i.Content)
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}
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if i.FunctionCall != nil {
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j, err := json.Marshal(i.FunctionCall)
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if err == nil {
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if contentExists {
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content += "\n" + string(j)
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} else {
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content = string(j)
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}
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}
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}
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}
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// Special Handling: System. We care if it was printed at all, not the r branch, so check seperately
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if contentExists && role == "system" {
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suppressConfigSystemPrompt = true
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}
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}
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mess = append(mess, content)
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}
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predInput = strings.Join(mess, "\n")
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log.Debug().Msgf("Prompt (before templating): %s", predInput)
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if 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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}
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templateFile := config.Model
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if config.TemplateConfig.Chat != "" && !processFunctions {
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templateFile = config.TemplateConfig.Chat
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}
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if config.TemplateConfig.Functions != "" && processFunctions {
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templateFile = config.TemplateConfig.Functions
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}
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// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
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templatedInput, err := o.Loader.EvaluateTemplateForPrompt(model.ChatPromptTemplate, templateFile, model.PromptTemplateData{
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SystemPrompt: config.SystemPrompt,
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SuppressSystemPrompt: suppressConfigSystemPrompt,
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Input: predInput,
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Functions: funcs,
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})
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if err == nil {
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predInput = templatedInput
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log.Debug().Msgf("Template found, input modified to: %s", predInput)
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} else {
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log.Debug().Msgf("Template failed loading: %s", err.Error())
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}
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log.Debug().Msgf("Prompt (after templating): %s", predInput)
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if processFunctions {
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log.Debug().Msgf("Grammar: %+v", config.Grammar)
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}
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if toStream {
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responses := make(chan schema.OpenAIResponse)
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go process(predInput, input, config, o.Loader, responses)
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c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) {
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usage := &schema.OpenAIUsage{}
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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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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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break
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}
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w.Flush()
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}
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resp := &schema.OpenAIResponse{
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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: "stop",
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Index: 0,
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Delta: &schema.Message{Content: &emptyMessage},
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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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}
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result, tokenUsage, err := ComputeChoices(input, predInput, config, o, o.Loader, func(s string, c *[]schema.Choice) {
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if processFunctions {
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// As we have to change the result before processing, we can't stream the answer (yet?)
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ss := map[string]interface{}{}
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// This prevent newlines to break JSON parsing for clients
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s = utils.EscapeNewLines(s)
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json.Unmarshal([]byte(s), &ss)
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log.Debug().Msgf("Function return: %s %+v", s, ss)
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// The grammar defines the function name as "function", while OpenAI returns "name"
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func_name := ss["function"]
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// Similarly, while here arguments is a map[string]interface{}, OpenAI actually want a stringified object
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args := ss["arguments"] // arguments needs to be a string, but we return an object from the grammar result (TODO: fix)
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d, _ := json.Marshal(args)
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ss["arguments"] = string(d)
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ss["name"] = func_name
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// if do nothing, reply with a message
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if func_name == noActionName {
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log.Debug().Msgf("nothing to do, computing a reply")
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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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json.Unmarshal([]byte(d), &arguments)
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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, predInput, message)
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log.Debug().Msgf("Reply received from LLM(finetuned): %s", message)
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*c = append(*c, schema.Choice{Message: &schema.Message{Role: "assistant", Content: &message}})
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return
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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) another computation
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config.Grammar = ""
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predFunc, err := backend.ModelInference(input.Context, predInput, o.Loader, *config, o, nil)
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if err != nil {
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log.Error().Msgf("inference error: %s", err.Error())
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return
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}
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prediction, err := predFunc()
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if err != nil {
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log.Error().Msgf("inference error: %s", err.Error())
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return
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}
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fineTunedResponse := backend.Finetune(*config, predInput, prediction.Response)
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*c = append(*c, schema.Choice{Message: &schema.Message{Role: "assistant", Content: &fineTunedResponse}})
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} else {
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// otherwise reply with the function call
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*c = append(*c, schema.Choice{
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FinishReason: "function_call",
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Message: &schema.Message{Role: "assistant", FunctionCall: ss},
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})
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}
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return
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}
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*c = append(*c, schema.Choice{FinishReason: "stop", Index: 0, Message: &schema.Message{Role: "assistant", Content: &s}})
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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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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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