package openai

import (
	"encoding/json"
	"fmt"
	"strings"
	"time"

	"github.com/google/uuid"
	"github.com/labstack/echo/v4"
	"github.com/mudler/LocalAI/core/backend"
	"github.com/mudler/LocalAI/core/config"
	"github.com/mudler/LocalAI/core/http/middleware"
	"github.com/mudler/LocalAI/core/schema"
	"github.com/mudler/LocalAI/pkg/functions"

	"github.com/mudler/LocalAI/core/templates"
	"github.com/mudler/LocalAI/pkg/model"

	"github.com/mudler/xlog"
)

// 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.ModelConfigLoader, ml *model.ModelLoader, evaluator *templates.Evaluator, startupOptions *config.ApplicationConfig) echo.HandlerFunc {
	var id, textContentToReturn string
	var created int

	process := func(s string, req *schema.OpenAIRequest, config *config.ModelConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse, extraUsage bool) error {
		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"}, Index: 0, FinishReason: nil}},
		}
		responses <- initialMessage

		// Track accumulated content for reasoning extraction
		accumulatedContent := ""
		lastEmittedReasoning := ""
		lastEmittedCleanedContent := ""

		_, _, err := ComputeChoices(req, s, config, cl, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, tokenUsage backend.TokenUsage) bool {
			accumulatedContent += s
			// Extract reasoning from accumulated content
			currentReasoning, cleanedContent := functions.ExtractReasoning(accumulatedContent)

			// Calculate new reasoning delta (what we haven't emitted yet)
			var reasoningDelta *string
			if currentReasoning != lastEmittedReasoning {
				// Extract only the new part
				if len(currentReasoning) > len(lastEmittedReasoning) && strings.HasPrefix(currentReasoning, lastEmittedReasoning) {
					newReasoning := currentReasoning[len(lastEmittedReasoning):]
					reasoningDelta = &newReasoning
					lastEmittedReasoning = currentReasoning
				} else if currentReasoning != "" {
					// If reasoning changed in a non-append way, emit the full current reasoning
					reasoningDelta = &currentReasoning
					lastEmittedReasoning = currentReasoning
				}
			}

			// Calculate content delta from cleaned content
			var deltaContent string
			if len(cleanedContent) > len(lastEmittedCleanedContent) && strings.HasPrefix(cleanedContent, lastEmittedCleanedContent) {
				deltaContent = cleanedContent[len(lastEmittedCleanedContent):]
				lastEmittedCleanedContent = cleanedContent
			} else if cleanedContent != lastEmittedCleanedContent {
				// If cleaned content changed but not in a simple append, extract delta from cleaned content
				// This handles cases where thinking tags are removed mid-stream
				if lastEmittedCleanedContent == "" {
					deltaContent = cleanedContent
					lastEmittedCleanedContent = cleanedContent
				} else {
					// Content changed in non-append way, use the new cleaned content
					deltaContent = cleanedContent
					lastEmittedCleanedContent = cleanedContent
				}
			}
			// Only emit content if there's actual content (not just thinking tags)
			// If deltaContent is empty, we still emit the response but with empty content

			usage := schema.OpenAIUsage{
				PromptTokens:     tokenUsage.Prompt,
				CompletionTokens: tokenUsage.Completion,
				TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
			}
			if extraUsage {
				usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
				usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
			}

			delta := &schema.Message{}
			// Only include content if there's actual content (not just thinking tags)
			if deltaContent != "" {
				delta.Content = &deltaContent
			}
			if reasoningDelta != nil && *reasoningDelta != "" {
				delta.Reasoning = reasoningDelta
			}

			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: delta, Index: 0, FinishReason: nil}},
				Object:  "chat.completion.chunk",
				Usage:   usage,
			}

			responses <- resp
			return true
		})
		close(responses)
		return err
	}
	processTools := func(noAction string, prompt string, req *schema.OpenAIRequest, config *config.ModelConfig, loader *model.ModelLoader, responses chan schema.OpenAIResponse, extraUsage bool) error {
		result := ""
		lastEmittedCount := 0
		_, tokenUsage, err := ComputeChoices(req, prompt, config, cl, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
			result += s
			// Try incremental XML parsing for streaming support using iterative parser
			// This allows emitting partial tool calls as they're being generated
			cleanedResult := functions.CleanupLLMResult(result, config.FunctionsConfig)

			// Determine XML format from config
			var xmlFormat *functions.XMLToolCallFormat
			if config.FunctionsConfig.XMLFormat != nil {
				xmlFormat = config.FunctionsConfig.XMLFormat
			} else if config.FunctionsConfig.XMLFormatPreset != "" {
				xmlFormat = functions.GetXMLFormatPreset(config.FunctionsConfig.XMLFormatPreset)
			}

			// Use iterative parser for streaming (partial parsing enabled)
			// Try XML parsing first
			partialResults, parseErr := functions.ParseXMLIterative(cleanedResult, xmlFormat, true)
			if parseErr == nil && len(partialResults) > 0 {
				// Emit new XML tool calls that weren't emitted before
				if len(partialResults) > lastEmittedCount {
					for i := lastEmittedCount; i < len(partialResults); i++ {
						toolCall := partialResults[i]
						initialMessage := schema.OpenAIResponse{
							ID:      id,
							Created: created,
							Model:   req.Model,
							Choices: []schema.Choice{{
								Delta: &schema.Message{
									Role: "assistant",
									ToolCalls: []schema.ToolCall{
										{
											Index: i,
											ID:    id,
											Type:  "function",
											FunctionCall: schema.FunctionCall{
												Name: toolCall.Name,
											},
										},
									},
								},
								Index:        0,
								FinishReason: nil,
							}},
							Object: "chat.completion.chunk",
						}
						select {
						case responses <- initialMessage:
						default:
						}
					}
					lastEmittedCount = len(partialResults)
				}
			} else {
				// Try JSON tool call parsing for streaming
				// Check if the result looks like JSON tool calls
				jsonResults, jsonErr := functions.ParseJSONIterative(cleanedResult, true)
				if jsonErr == nil && len(jsonResults) > 0 {
					// Check if these are tool calls (have "name" and optionally "arguments")
					for _, jsonObj := range jsonResults {
						if name, ok := jsonObj["name"].(string); ok && name != "" {
							// This looks like a tool call
							args := "{}"
							if argsVal, ok := jsonObj["arguments"]; ok {
								if argsStr, ok := argsVal.(string); ok {
									args = argsStr
								} else {
									argsBytes, _ := json.Marshal(argsVal)
									args = string(argsBytes)
								}
							}
							// Emit tool call
							initialMessage := schema.OpenAIResponse{
								ID:      id,
								Created: created,
								Model:   req.Model,
								Choices: []schema.Choice{{
									Delta: &schema.Message{
										Role: "assistant",
										ToolCalls: []schema.ToolCall{
											{
												Index: lastEmittedCount,
												ID:    id,
												Type:  "function",
												FunctionCall: schema.FunctionCall{
													Name:      name,
													Arguments: args,
												},
											},
										},
									},
									Index:        0,
									FinishReason: nil,
								}},
								Object: "chat.completion.chunk",
							}
							select {
							case responses <- initialMessage:
							default:
							}
							lastEmittedCount++
						}
					}
				}
			}
			return true
		})
		if err != nil {
			return err
		}
		// Extract reasoning before processing tool calls
		reasoning, cleanedResult := functions.ExtractReasoning(result)
		result = cleanedResult

		textContentToReturn = functions.ParseTextContent(result, config.FunctionsConfig)
		result = functions.CleanupLLMResult(result, config.FunctionsConfig)
		functionResults := functions.ParseFunctionCall(result, config.FunctionsConfig)
		xlog.Debug("Text content to return", "text", textContentToReturn)
		noActionToRun := len(functionResults) > 0 && functionResults[0].Name == noAction || len(functionResults) == 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"}, Index: 0, FinishReason: nil}},
				Object:  "chat.completion.chunk",
			}
			responses <- initialMessage

			result, err := handleQuestion(config, cl, req, ml, startupOptions, functionResults, result, prompt)
			if err != nil {
				xlog.Error("error handling question", "error", err)
				return err
			}
			usage := schema.OpenAIUsage{
				PromptTokens:     tokenUsage.Prompt,
				CompletionTokens: tokenUsage.Completion,
				TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
			}
			if extraUsage {
				usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
				usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
			}

			var deltaReasoning *string
			if reasoning != "" {
				deltaReasoning = &reasoning
			}
			delta := &schema.Message{Content: &result}
			if deltaReasoning != nil {
				delta.Reasoning = deltaReasoning
			}

			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: delta, Index: 0, FinishReason: nil}},
				Object:  "chat.completion.chunk",
				Usage:   usage,
			}

			responses <- resp

		default:
			for i, ss := range functionResults {
				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,
									},
								},
							},
						},
						Index:        0,
						FinishReason: nil,
					}},
					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,
									},
								},
							},
						},
						Index:        0,
						FinishReason: nil,
					}},
					Object: "chat.completion.chunk",
				}
			}
		}

		close(responses)
		return err
	}

	return func(c echo.Context) error {
		textContentToReturn = ""
		id = uuid.New().String()
		created = int(time.Now().Unix())

		input, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_LOCALAI_REQUEST).(*schema.OpenAIRequest)
		if !ok || input.Model == "" {
			return echo.ErrBadRequest
		}

		extraUsage := c.Request().Header.Get("Extra-Usage") != ""

		config, ok := c.Get(middleware.CONTEXT_LOCALS_KEY_MODEL_CONFIG).(*config.ModelConfig)
		if !ok || config == nil {
			return echo.ErrBadRequest
		}

		xlog.Debug("Chat endpoint configuration read", "config", config)

		funcs := input.Functions
		shouldUseFn := len(input.Functions) > 0 && config.ShouldUseFunctions()
		strictMode := false

		for _, f := range input.Functions {
			if f.Strict {
				strictMode = true
				break
			}
		}

		// 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 we are using a response format, we need to generate a grammar for it
		if config.ResponseFormatMap != nil {
			d := schema.ChatCompletionResponseFormat{}
			dat, err := json.Marshal(config.ResponseFormatMap)
			if err != nil {
				return err
			}
			err = json.Unmarshal(dat, &d)
			if err != nil {
				return err
			}

			switch d.Type {
			case "json_object":
				input.Grammar = functions.JSONBNF
			case "json_schema":
				d := schema.JsonSchemaRequest{}
				dat, err := json.Marshal(config.ResponseFormatMap)
				if err != nil {
					return err
				}
				err = json.Unmarshal(dat, &d)
				if err != nil {
					return err
				}
				fs := &functions.JSONFunctionStructure{
					AnyOf: []functions.Item{d.JsonSchema.Schema},
				}
				g, err := fs.Grammar(config.FunctionsConfig.GrammarOptions()...)
				if err == nil {
					input.Grammar = g
				} else {
					xlog.Error("Failed generating grammar", "error", err)
				}
			}
		}

		config.Grammar = input.Grammar

		if shouldUseFn {
			xlog.Debug("Response needs to process functions")
		}

		switch {
		// Generates grammar with internal's LocalAI engine
		case (!config.FunctionsConfig.GrammarConfig.NoGrammar || strictMode) && 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 && !strictMode {
				funcs = append(funcs, noActionGrammar)
			}

			// Force picking one of the functions by the request
			if config.FunctionToCall() != "" {
				funcs = funcs.Select(config.FunctionToCall())
			}

			// Update input grammar or json_schema based on use_llama_grammar option
			jsStruct := funcs.ToJSONStructure(config.FunctionsConfig.FunctionNameKey, config.FunctionsConfig.FunctionNameKey)
			g, err := jsStruct.Grammar(config.FunctionsConfig.GrammarOptions()...)
			if err == nil {
				config.Grammar = g
			} else {
				xlog.Error("Failed generating grammar", "error", err)
			}
		case input.JSONFunctionGrammarObject != nil:
			g, err := input.JSONFunctionGrammarObject.Grammar(config.FunctionsConfig.GrammarOptions()...)
			if err == nil {
				config.Grammar = g
			} else {
				xlog.Error("Failed generating grammar", "error", err)
			}

		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

		xlog.Debug("Parameters", "config", 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 {
			predInput = evaluator.TemplateMessages(*input, input.Messages, config, funcs, shouldUseFn)

			xlog.Debug("Prompt (after templating)", "prompt", predInput)
			if config.Grammar != "" {
				xlog.Debug("Grammar", "grammar", config.Grammar)
			}
		}

		switch {
		case toStream:

			xlog.Debug("Stream request received")
			c.Response().Header().Set("Content-Type", "text/event-stream")
			c.Response().Header().Set("Cache-Control", "no-cache")
			c.Response().Header().Set("Connection", "keep-alive")
			c.Response().Header().Set("X-Correlation-ID", id)

			responses := make(chan schema.OpenAIResponse)
			ended := make(chan error, 1)

			go func() {
				if !shouldUseFn {
					ended <- process(predInput, input, config, ml, responses, extraUsage)
				} else {
					ended <- processTools(noActionName, predInput, input, config, ml, responses, extraUsage)
				}
			}()

			usage := &schema.OpenAIUsage{}
			toolsCalled := false

		LOOP:
			for {
				select {
				case <-input.Context.Done():
					// Context was cancelled (client disconnected or request cancelled)
					xlog.Debug("Request context cancelled, stopping stream")
					input.Cancel()
					break LOOP
				case ev := <-responses:
					if len(ev.Choices) == 0 {
						xlog.Debug("No choices in the response, skipping")
						continue
					}
					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
					}
					respData, err := json.Marshal(ev)
					if err != nil {
						xlog.Debug("Failed to marshal response", "error", err)
						input.Cancel()
						continue
					}
					xlog.Debug("Sending chunk", "chunk", string(respData))
					_, err = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", string(respData))
					if err != nil {
						xlog.Debug("Sending chunk failed", "error", err)
						input.Cancel()
						return err
					}
					c.Response().Flush()
				case err := <-ended:
					if err == nil {
						break LOOP
					}
					xlog.Error("Stream ended with error", "error", err)

					stopReason := FinishReasonStop
					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: &stopReason,
								Index:        0,
								Delta:        &schema.Message{Content: "Internal error: " + err.Error()},
							}},
						Object: "chat.completion.chunk",
						Usage:  *usage,
					}
					respData, marshalErr := json.Marshal(resp)
					if marshalErr != nil {
						xlog.Error("Failed to marshal error response", "error", marshalErr)
						// Send a simple error message as fallback
						fmt.Fprintf(c.Response().Writer, "data: {\"error\":\"Internal error\"}\n\n")
					} else {
						fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
					}
					fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
					c.Response().Flush()

					return nil
				}
			}

			finishReason := FinishReasonStop
			if toolsCalled && len(input.Tools) > 0 {
				finishReason = FinishReasonToolCalls
			} else if toolsCalled {
				finishReason = FinishReasonFunctionCall
			}

			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{},
					}},
				Object: "chat.completion.chunk",
				Usage:  *usage,
			}
			respData, _ := json.Marshal(resp)

			fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
			fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
			c.Response().Flush()
			xlog.Debug("Stream ended")
			return nil

		// no streaming mode
		default:

			tokenCallback := func(s string, c *[]schema.Choice) {
				// Extract reasoning from the response
				reasoning, cleanedS := functions.ExtractReasoning(s)
				s = cleanedS

				if !shouldUseFn {
					// no function is called, just reply and use stop as finish reason
					stopReason := FinishReasonStop
					message := &schema.Message{Role: "assistant", Content: &s}
					if reasoning != "" {
						message.Reasoning = &reasoning
					}
					*c = append(*c, schema.Choice{FinishReason: &stopReason, Index: 0, Message: message})
					return
				}

				textContentToReturn = functions.ParseTextContent(s, config.FunctionsConfig)
				s = functions.CleanupLLMResult(s, config.FunctionsConfig)
				results := functions.ParseFunctionCall(s, config.FunctionsConfig)
				xlog.Debug("Text content to return", "text", textContentToReturn)
				noActionsToRun := len(results) > 0 && results[0].Name == noActionName || len(results) == 0

				switch {
				case noActionsToRun:
					result, err := handleQuestion(config, cl, input, ml, startupOptions, results, s, predInput)
					if err != nil {
						xlog.Error("error handling question", "error", err)
						return
					}

					stopReason := FinishReasonStop
					message := &schema.Message{Role: "assistant", Content: &result}
					if reasoning != "" {
						message.Reasoning = &reasoning
					}
					*c = append(*c, schema.Choice{
						FinishReason: &stopReason,
						Message:      message})
				default:
					toolCallsReason := FinishReasonToolCalls
					toolChoice := schema.Choice{
						FinishReason: &toolCallsReason,
						Message: &schema.Message{
							Role: "assistant",
						},
					}
					if reasoning != "" {
						toolChoice.Message.Reasoning = &reasoning
					}

					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 (deprecated)
							functionCallReason := FinishReasonFunctionCall
							message := &schema.Message{
								Role:    "assistant",
								Content: &textContentToReturn,
								FunctionCall: map[string]interface{}{
									"name":      name,
									"arguments": args,
								},
							}
							if reasoning != "" {
								message.Reasoning = &reasoning
							}
							*c = append(*c, schema.Choice{
								FinishReason: &functionCallReason,
								Message:      message,
							})
						}
					}

					if len(input.Tools) > 0 {
						// we need to append our result if we are using tools
						*c = append(*c, toolChoice)
					}
				}

			}

			// Echo properly supports context cancellation via c.Request().Context()
			// No workaround needed!

			result, tokenUsage, err := ComputeChoices(
				input,
				predInput,
				config,
				cl,
				startupOptions,
				ml,
				tokenCallback,
				nil,
			)
			if err != nil {
				return err
			}
			usage := schema.OpenAIUsage{
				PromptTokens:     tokenUsage.Prompt,
				CompletionTokens: tokenUsage.Completion,
				TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
			}
			if extraUsage {
				usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
				usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
			}

			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:   usage,
			}
			respData, _ := json.Marshal(resp)
			xlog.Debug("Response", "response", string(respData))

			// Return the prediction in the response body
			return c.JSON(200, resp)
		}
	}
}

func handleQuestion(config *config.ModelConfig, cl *config.ModelConfigLoader, input *schema.OpenAIRequest, ml *model.ModelLoader, o *config.ApplicationConfig, funcResults []functions.FuncCallResults, result, prompt string) (string, error) {

	if len(funcResults) == 0 && result != "" {
		xlog.Debug("nothing function results but we had a message from the LLM")

		return result, nil
	}

	xlog.Debug("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 {
		xlog.Debug("handleQuestion: function result did not contain a valid JSON object")
	}
	m, exists := arguments["message"]
	if exists {
		switch message := m.(type) {
		case string:
			if message != "" {
				xlog.Debug("Reply received from LLM", "message", message)
				message = backend.Finetune(*config, prompt, message)
				xlog.Debug("Reply received from LLM(finetuned)", "message", message)

				return message, nil
			}
		}
	}

	xlog.Debug("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...)
	}
	videos := []string{}
	for _, m := range input.Messages {
		videos = append(videos, m.StringVideos...)
	}
	audios := []string{}
	for _, m := range input.Messages {
		audios = append(audios, m.StringAudios...)
	}

	// Serialize tools and tool_choice to JSON strings
	toolsJSON := ""
	if len(input.Tools) > 0 {
		toolsBytes, err := json.Marshal(input.Tools)
		if err == nil {
			toolsJSON = string(toolsBytes)
		}
	}
	toolChoiceJSON := ""
	if input.ToolsChoice != nil {
		toolChoiceBytes, err := json.Marshal(input.ToolsChoice)
		if err == nil {
			toolChoiceJSON = string(toolChoiceBytes)
		}
	}

	// Extract logprobs from request
	// According to OpenAI API: logprobs is boolean, top_logprobs (0-20) controls how many top tokens per position
	var logprobs *int
	var topLogprobs *int
	if input.Logprobs.IsEnabled() {
		// If logprobs is enabled, use top_logprobs if provided, otherwise default to 1
		if input.TopLogprobs != nil {
			topLogprobs = input.TopLogprobs
			// For backend compatibility, set logprobs to the top_logprobs value
			logprobs = input.TopLogprobs
		} else {
			// Default to 1 if logprobs is true but top_logprobs not specified
			val := 1
			logprobs = &val
			topLogprobs = &val
		}
	}

	// Extract logit_bias from request
	// According to OpenAI API: logit_bias is a map of token IDs (as strings) to bias values (-100 to 100)
	var logitBias map[string]float64
	if len(input.LogitBias) > 0 {
		logitBias = input.LogitBias
	}

	predFunc, err := backend.ModelInference(input.Context, prompt, input.Messages, images, videos, audios, ml, config, cl, o, nil, toolsJSON, toolChoiceJSON, logprobs, topLogprobs, logitBias)
	if err != nil {
		xlog.Error("model inference failed", "error", err)
		return "", err
	}

	prediction, err := predFunc()
	if err != nil {
		xlog.Error("prediction failed", "error", err)
		return "", err
	}
	return backend.Finetune(*config, prompt, prediction.Response), nil
}