LocalAI/core/http/endpoints/openai/chat.go

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package openai
import (
"bufio"
"bytes"
"encoding/json"
"fmt"
"strings"
"time"
"github.com/gofiber/fiber/v2"
"github.com/google/uuid"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/functions"
model "github.com/mudler/LocalAI/pkg/model"
"github.com/rs/zerolog/log"
"github.com/valyala/fasthttp"
)
// ChatEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/chat/create
// @Summary Generate a chat completions for a given prompt and model.
// @Param request body schema.OpenAIRequest true "query params"
// @Success 200 {object} schema.OpenAIResponse "Response"
// @Router /v1/chat/completions [post]
func ChatEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, startupOptions *config.ApplicationConfig) func(c *fiber.Ctx) error {
textContentToReturn := ""
id := uuid.New().String()
created := int(time.Now().Unix())
process := func(s string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
initialMessage := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
Object: "chat.completion.chunk",
}
responses <- initialMessage
ComputeChoices(req, s, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
resp := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{Delta: &schema.Message{Content: &s}, Index: 0}},
Object: "chat.completion.chunk",
Usage: schema.OpenAIUsage{
PromptTokens: usage.Prompt,
CompletionTokens: usage.Completion,
TotalTokens: usage.Prompt + usage.Completion,
},
}
responses <- resp
return true
})
close(responses)
}
processTools := func(noAction string, prompt string, req *schema.OpenAIRequest, config *config.BackendConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse) {
result := ""
_, tokenUsage, _ := ComputeChoices(req, prompt, config, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
result += s
// TODO: Change generated BNF grammar to be compliant with the schema so we can
// stream the result token by token here.
return true
})
textContentToReturn = functions.ParseTextContent(result, config.FunctionsConfig)
result = functions.CleanupLLMResult(result, config.FunctionsConfig)
results := functions.ParseFunctionCall(result, config.FunctionsConfig)
log.Debug().Msgf("Text content to return: %s", textContentToReturn)
noActionToRun := len(results) > 0 && results[0].Name == noAction || len(results) == 0
switch {
case noActionToRun:
initialMessage := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{Delta: &schema.Message{Role: "assistant", Content: &textContentToReturn}}},
Object: "chat.completion.chunk",
}
responses <- initialMessage
result, err := handleQuestion(config, req, ml, startupOptions, results, result, prompt)
if err != nil {
log.Error().Err(err).Msg("error handling question")
return
}
resp := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{Delta: &schema.Message{Content: &result}, Index: 0}},
Object: "chat.completion.chunk",
Usage: schema.OpenAIUsage{
PromptTokens: tokenUsage.Prompt,
CompletionTokens: tokenUsage.Completion,
TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
},
}
responses <- resp
default:
for i, ss := range results {
name, args := ss.Name, ss.Arguments
initialMessage := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{
Delta: &schema.Message{
Role: "assistant",
ToolCalls: []schema.ToolCall{
{
Index: i,
ID: id,
Type: "function",
FunctionCall: schema.FunctionCall{
Name: name,
},
},
},
}}},
Object: "chat.completion.chunk",
}
responses <- initialMessage
responses <- schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{{
Delta: &schema.Message{
Role: "assistant",
Content: &textContentToReturn,
ToolCalls: []schema.ToolCall{
{
Index: i,
ID: id,
Type: "function",
FunctionCall: schema.FunctionCall{
Arguments: args,
},
},
},
}}},
Object: "chat.completion.chunk",
}
}
}
close(responses)
}
return func(c *fiber.Ctx) error {
modelFile, input, err := readRequest(c, cl, ml, startupOptions, true)
if err != nil {
return fmt.Errorf("failed reading parameters from request:%w", err)
}
config, input, err := mergeRequestWithConfig(modelFile, input, cl, ml, startupOptions.Debug, startupOptions.Threads, startupOptions.ContextSize, startupOptions.F16)
if err != nil {
return fmt.Errorf("failed reading parameters from request:%w", err)
}
log.Debug().Msgf("Configuration read: %+v", config)
funcs := input.Functions
shouldUseFn := len(input.Functions) > 0 && config.ShouldUseFunctions()
// Allow the user to set custom actions via config file
// to be "embedded" in each model
noActionName := "answer"
noActionDescription := "use this action to answer without performing any action"
if config.FunctionsConfig.NoActionFunctionName != "" {
noActionName = config.FunctionsConfig.NoActionFunctionName
}
if config.FunctionsConfig.NoActionDescriptionName != "" {
noActionDescription = config.FunctionsConfig.NoActionDescriptionName
}
if config.ResponseFormatMap != nil {
d := schema.ChatCompletionResponseFormat{}
dat, _ := json.Marshal(config.ResponseFormatMap)
_ = json.Unmarshal(dat, &d)
if d.Type == "json_object" {
input.Grammar = functions.JSONBNF
}
}
config.Grammar = input.Grammar
if shouldUseFn {
log.Debug().Msgf("Response needs to process functions")
}
switch {
case !config.FunctionsConfig.GrammarConfig.NoGrammar && shouldUseFn:
noActionGrammar := functions.Function{
Name: noActionName,
Description: noActionDescription,
Parameters: map[string]interface{}{
"properties": map[string]interface{}{
"message": map[string]interface{}{
"type": "string",
"description": "The message to reply the user with",
}},
},
}
// Append the no action function
if !config.FunctionsConfig.DisableNoAction {
funcs = append(funcs, noActionGrammar)
}
// Force picking one of the functions by the request
if config.FunctionToCall() != "" {
funcs = funcs.Select(config.FunctionToCall())
}
// Update input grammar
jsStruct := funcs.ToJSONStructure(config.FunctionsConfig.FunctionNameKey, config.FunctionsConfig.FunctionNameKey)
g, err := jsStruct.Grammar(config.FunctionsConfig.GrammarOptions()...)
if err == nil {
config.Grammar = g
}
case input.JSONFunctionGrammarObject != nil:
g, err := input.JSONFunctionGrammarObject.Grammar(config.FunctionsConfig.GrammarOptions()...)
if err == nil {
config.Grammar = g
}
default:
// Force picking one of the functions by the request
if config.FunctionToCall() != "" {
funcs = funcs.Select(config.FunctionToCall())
}
}
// process functions if we have any defined or if we have a function call string
// functions are not supported in stream mode (yet?)
toStream := input.Stream
log.Debug().Msgf("Parameters: %+v", config)
var predInput string
// If we are using the tokenizer template, we don't need to process the messages
// unless we are processing functions
if !config.TemplateConfig.UseTokenizerTemplate || shouldUseFn {
suppressConfigSystemPrompt := false
mess := []string{}
for messageIndex, i := range input.Messages {
var content string
role := i.Role
// if function call, we might want to customize the role so we can display better that the "assistant called a json action"
// if an "assistant_function_call" role is defined, we use it, otherwise we use the role that is passed by in the request
if (i.FunctionCall != nil || i.ToolCalls != nil) && i.Role == "assistant" {
roleFn := "assistant_function_call"
r := config.Roles[roleFn]
if r != "" {
role = roleFn
}
}
r := config.Roles[role]
contentExists := i.Content != nil && i.StringContent != ""
fcall := i.FunctionCall
if len(i.ToolCalls) > 0 {
fcall = i.ToolCalls
}
// First attempt to populate content via a chat message specific template
if config.TemplateConfig.ChatMessage != "" {
chatMessageData := model.ChatMessageTemplateData{
SystemPrompt: config.SystemPrompt,
Role: r,
RoleName: role,
Content: i.StringContent,
FunctionCall: fcall,
FunctionName: i.Name,
LastMessage: messageIndex == (len(input.Messages) - 1),
Function: config.Grammar != "" && (messageIndex == (len(input.Messages) - 1)),
MessageIndex: messageIndex,
}
templatedChatMessage, err := ml.EvaluateTemplateForChatMessage(config.TemplateConfig.ChatMessage, chatMessageData)
if err != nil {
log.Error().Err(err).Interface("message", chatMessageData).Str("template", config.TemplateConfig.ChatMessage).Msg("error processing message with template, skipping")
} else {
if templatedChatMessage == "" {
log.Warn().Msgf("template \"%s\" produced blank output for %+v. Skipping!", config.TemplateConfig.ChatMessage, chatMessageData)
continue // TODO: This continue is here intentionally to skip over the line `mess = append(mess, content)` below, and to prevent the sprintf
}
log.Debug().Msgf("templated message for chat: %s", templatedChatMessage)
content = templatedChatMessage
}
}
marshalAnyRole := func(f any) {
j, err := json.Marshal(f)
if err == nil {
if contentExists {
content += "\n" + fmt.Sprint(r, " ", string(j))
} else {
content = fmt.Sprint(r, " ", string(j))
}
}
}
marshalAny := func(f any) {
j, err := json.Marshal(f)
if err == nil {
if contentExists {
content += "\n" + string(j)
} else {
content = string(j)
}
}
}
// If this model doesn't have such a template, or if that template fails to return a value, template at the message level.
if content == "" {
if r != "" {
if contentExists {
content = fmt.Sprint(r, i.StringContent)
}
if i.FunctionCall != nil {
marshalAnyRole(i.FunctionCall)
}
if i.ToolCalls != nil {
marshalAnyRole(i.ToolCalls)
}
} else {
if contentExists {
content = fmt.Sprint(i.StringContent)
}
if i.FunctionCall != nil {
marshalAny(i.FunctionCall)
}
if i.ToolCalls != nil {
marshalAny(i.ToolCalls)
}
}
// Special Handling: System. We care if it was printed at all, not the r branch, so check seperately
if contentExists && role == "system" {
suppressConfigSystemPrompt = true
}
}
mess = append(mess, content)
}
joinCharacter := "\n"
if config.TemplateConfig.JoinChatMessagesByCharacter != nil {
joinCharacter = *config.TemplateConfig.JoinChatMessagesByCharacter
}
predInput = strings.Join(mess, joinCharacter)
log.Debug().Msgf("Prompt (before templating): %s", predInput)
templateFile := ""
// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
if ml.ExistsInModelPath(fmt.Sprintf("%s.tmpl", config.Model)) {
templateFile = config.Model
}
if config.TemplateConfig.Chat != "" && !shouldUseFn {
templateFile = config.TemplateConfig.Chat
}
if config.TemplateConfig.Functions != "" && shouldUseFn {
templateFile = config.TemplateConfig.Functions
}
if templateFile != "" {
templatedInput, err := ml.EvaluateTemplateForPrompt(model.ChatPromptTemplate, templateFile, model.PromptTemplateData{
SystemPrompt: config.SystemPrompt,
SuppressSystemPrompt: suppressConfigSystemPrompt,
Input: predInput,
Functions: funcs,
})
if err == nil {
predInput = templatedInput
log.Debug().Msgf("Template found, input modified to: %s", predInput)
} else {
log.Debug().Msgf("Template failed loading: %s", err.Error())
}
}
log.Debug().Msgf("Prompt (after templating): %s", predInput)
if shouldUseFn && config.Grammar != "" {
log.Debug().Msgf("Grammar: %+v", config.Grammar)
}
}
switch {
case toStream:
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")
responses := make(chan schema.OpenAIResponse)
if !shouldUseFn {
go process(predInput, input, config, ml, responses)
} else {
go processTools(noActionName, predInput, input, config, ml, responses)
}
c.Context().SetBodyStreamWriter(fasthttp.StreamWriter(func(w *bufio.Writer) {
usage := &schema.OpenAIUsage{}
toolsCalled := false
for ev := range responses {
usage = &ev.Usage // Copy a pointer to the latest usage chunk so that the stop message can reference it
if len(ev.Choices[0].Delta.ToolCalls) > 0 {
toolsCalled = true
}
var buf bytes.Buffer
enc := json.NewEncoder(&buf)
enc.Encode(ev)
log.Debug().Msgf("Sending chunk: %s", buf.String())
_, err := fmt.Fprintf(w, "data: %v\n", buf.String())
if err != nil {
log.Debug().Msgf("Sending chunk failed: %v", err)
input.Cancel()
}
w.Flush()
}
finishReason := "stop"
if toolsCalled {
finishReason = "tool_calls"
} else if toolsCalled && len(input.Tools) == 0 {
finishReason = "function_call"
}
resp := &schema.OpenAIResponse{
ID: id,
Created: created,
Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: []schema.Choice{
{
FinishReason: finishReason,
Index: 0,
Delta: &schema.Message{Content: &textContentToReturn},
}},
Object: "chat.completion.chunk",
Usage: *usage,
}
respData, _ := json.Marshal(resp)
w.WriteString(fmt.Sprintf("data: %s\n\n", respData))
w.WriteString("data: [DONE]\n\n")
w.Flush()
}))
return nil
// no streaming mode
default:
result, tokenUsage, err := ComputeChoices(input, predInput, config, startupOptions, ml, func(s string, c *[]schema.Choice) {
if !shouldUseFn {
// no function is called, just reply and use stop as finish reason
*c = append(*c, schema.Choice{FinishReason: "stop", Index: 0, Message: &schema.Message{Role: "assistant", Content: &s}})
return
}
textContentToReturn = functions.ParseTextContent(s, config.FunctionsConfig)
s = functions.CleanupLLMResult(s, config.FunctionsConfig)
results := functions.ParseFunctionCall(s, config.FunctionsConfig)
log.Debug().Msgf("Text content to return: %s", textContentToReturn)
noActionsToRun := len(results) > 0 && results[0].Name == noActionName || len(results) == 0
switch {
case noActionsToRun:
result, err := handleQuestion(config, input, ml, startupOptions, results, s, predInput)
if err != nil {
log.Error().Err(err).Msg("error handling question")
return
}
*c = append(*c, schema.Choice{
Message: &schema.Message{Role: "assistant", Content: &result}})
default:
toolChoice := schema.Choice{
Message: &schema.Message{
Role: "assistant",
},
}
if len(input.Tools) > 0 {
toolChoice.FinishReason = "tool_calls"
}
for _, ss := range results {
name, args := ss.Name, ss.Arguments
if len(input.Tools) > 0 {
// If we are using tools, we condense the function calls into
// a single response choice with all the tools
toolChoice.Message.Content = textContentToReturn
toolChoice.Message.ToolCalls = append(toolChoice.Message.ToolCalls,
schema.ToolCall{
ID: id,
Type: "function",
FunctionCall: schema.FunctionCall{
Name: name,
Arguments: args,
},
},
)
} else {
// otherwise we return more choices directly
*c = append(*c, schema.Choice{
FinishReason: "function_call",
Message: &schema.Message{
Role: "assistant",
Content: &textContentToReturn,
FunctionCall: map[string]interface{}{
"name": name,
"arguments": args,
},
},
})
}
}
if len(input.Tools) > 0 {
// we need to append our result if we are using tools
*c = append(*c, toolChoice)
}
}
}, nil)
if err != nil {
return err
}
resp := &schema.OpenAIResponse{
ID: id,
Created: created,
Model: input.Model, // we have to return what the user sent here, due to OpenAI spec.
Choices: result,
Object: "chat.completion",
Usage: schema.OpenAIUsage{
PromptTokens: tokenUsage.Prompt,
CompletionTokens: tokenUsage.Completion,
TotalTokens: tokenUsage.Prompt + tokenUsage.Completion,
},
}
respData, _ := json.Marshal(resp)
log.Debug().Msgf("Response: %s", respData)
// Return the prediction in the response body
return c.JSON(resp)
}
}
}
func handleQuestion(config *config.BackendConfig, input *schema.OpenAIRequest, ml *model.ModelLoader, o *config.ApplicationConfig, funcResults []functions.FuncCallResults, result, prompt string) (string, error) {
if len(funcResults) == 0 && result != "" {
log.Debug().Msgf("nothing function results but we had a message from the LLM")
return result, nil
}
log.Debug().Msgf("nothing to do, computing a reply")
arg := ""
if len(funcResults) > 0 {
arg = funcResults[0].Arguments
}
// If there is a message that the LLM already sends as part of the JSON reply, use it
arguments := map[string]interface{}{}
if err := json.Unmarshal([]byte(arg), &arguments); err != nil {
log.Debug().Msg("handleQuestion: function result did not contain a valid JSON object")
}
m, exists := arguments["message"]
if exists {
switch message := m.(type) {
case string:
if message != "" {
log.Debug().Msgf("Reply received from LLM: %s", message)
message = backend.Finetune(*config, prompt, message)
log.Debug().Msgf("Reply received from LLM(finetuned): %s", message)
return message, nil
}
}
}
log.Debug().Msgf("No action received from LLM, without a message, computing a reply")
// Otherwise ask the LLM to understand the JSON output and the context, and return a message
// Note: This costs (in term of CPU/GPU) another computation
config.Grammar = ""
images := []string{}
for _, m := range input.Messages {
images = append(images, m.StringImages...)
}
predFunc, err := backend.ModelInference(input.Context, prompt, input.Messages, images, 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
}