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"
	reason "github.com/mudler/LocalAI/pkg/reasoning"

	"github.com/mudler/LocalAI/core/templates"
	pb "github.com/mudler/LocalAI/pkg/grpc/proto"
	"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}},
			Object:  "chat.completion.chunk",
		}
		responses <- initialMessage

		// Detect if thinking token is already in prompt or template
		// When UseTokenizerTemplate is enabled, predInput is empty, so we check the template
		var template string
		if config.TemplateConfig.UseTokenizerTemplate {
			template = config.GetModelTemplate()
		} else {
			template = s
		}
		thinkingStartToken := reason.DetectThinkingStartToken(template, &config.ReasoningConfig)

		// 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

			currentReasoning, cleanedContent := reason.ExtractReasoningWithConfig(accumulatedContent, thinkingStartToken, config.ReasoningConfig)

			// 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 {
		// Detect if thinking token is already in prompt or template
		var template string
		if config.TemplateConfig.UseTokenizerTemplate {
			template = config.GetModelTemplate()
		} else {
			template = prompt
		}
		thinkingStartToken := reason.DetectThinkingStartToken(template, &config.ReasoningConfig)

		result := ""
		lastEmittedCount := 0
		_, tokenUsage, chatDeltas, 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
		}
		// Try using pre-parsed tool calls from C++ autoparser (chat deltas)
		var functionResults []functions.FuncCallResults
		var reasoning string

		if deltaToolCalls := functions.ToolCallsFromChatDeltas(chatDeltas); len(deltaToolCalls) > 0 {
			xlog.Debug("[ChatDeltas] Using pre-parsed tool calls from C++ autoparser", "count", len(deltaToolCalls))
			functionResults = deltaToolCalls
			// Use content/reasoning from deltas too
			textContentToReturn = functions.ContentFromChatDeltas(chatDeltas)
			reasoning = functions.ReasoningFromChatDeltas(chatDeltas)
		} else {
			// Fallback: parse tool calls from raw text (no chat deltas from backend)
			xlog.Debug("[ChatDeltas] no pre-parsed tool calls, falling back to Go-side text parsing")
			reasoning, result = reason.ExtractReasoningWithConfig(result, thinkingStartToken, config.ReasoningConfig)
			textContentToReturn = functions.ParseTextContent(result, config.FunctionsConfig)
			result = functions.CleanupLLMResult(result, config.FunctionsConfig)
			functionResults = functions.ParseFunctionCall(result, config.FunctionsConfig)
		}
		xlog.Debug("[ChatDeltas] final tool call decision", "tool_calls", len(functionResults), "text_content", 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, 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
				toolCallID := ss.ID
				if toolCallID == "" {
					toolCallID = id
				}

				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:    toolCallID,
									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:    toolCallID,
									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

		xlog.Debug("Tool call routing decision",
			"shouldUseFn", shouldUseFn,
			"len(input.Functions)", len(input.Functions),
			"len(input.Tools)", len(input.Tools),
			"config.ShouldUseFunctions()", config.ShouldUseFunctions(),
			"config.FunctionToCall()", config.FunctionToCall(),
		)

		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:
			// Detect if thinking token is already in prompt or template
			var template string
			if config.TemplateConfig.UseTokenizerTemplate {
				template = config.GetModelTemplate() // TODO: this should be the parsed jinja template. But for now this is the best we can do.
			} else {
				template = predInput
			}
			thinkingStartToken := reason.DetectThinkingStartToken(template, &config.ReasoningConfig)

			xlog.Debug("Thinking start token", "thinkingStartToken", thinkingStartToken, "template", template)

			// When shouldUseFn, the callback just stores the raw text — tool parsing
			// is deferred to after ComputeChoices so we can check chat deltas first
			// and avoid redundant Go-side parsing.
			var cbRawResult, cbReasoning string
			var emptyRetryNeeded bool

			tokenCallback := func(s string, c *[]schema.Choice) {
				reasoning, s := reason.ExtractReasoningWithConfig(s, thinkingStartToken, config.ReasoningConfig)

				if !shouldUseFn {
					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
				}

				// Store raw text for deferred tool parsing
				cbRawResult = s
				cbReasoning = reasoning
			}

			const maxEmptyRetries = 5
			var result []schema.Choice
			var tokenUsage backend.TokenUsage
			var err error

			var chatDeltas []*pb.ChatDelta
			for attempt := 0; attempt <= maxEmptyRetries; attempt++ {
				emptyRetryNeeded = false
				result, tokenUsage, chatDeltas, err = ComputeChoices(
					input,
					predInput,
					config,
					cl,
					startupOptions,
					ml,
					tokenCallback,
					nil,
				)
				if err != nil {
					break
				}

				// Tool parsing is deferred here (only when shouldUseFn)
				if shouldUseFn {
					var funcResults []functions.FuncCallResults

					// Try pre-parsed tool calls from C++ autoparser first
					if deltaToolCalls := functions.ToolCallsFromChatDeltas(chatDeltas); len(deltaToolCalls) > 0 {
						xlog.Debug("[ChatDeltas] non-SSE: using C++ autoparser tool calls, skipping Go-side parsing", "count", len(deltaToolCalls))
						funcResults = deltaToolCalls
						textContentToReturn = functions.ContentFromChatDeltas(chatDeltas)
						cbReasoning = functions.ReasoningFromChatDeltas(chatDeltas)
					} else {
						// Fallback: parse tool calls from raw text
						xlog.Debug("[ChatDeltas] non-SSE: no chat deltas, falling back to Go-side text parsing")
						textContentToReturn = functions.ParseTextContent(cbRawResult, config.FunctionsConfig)
						cbRawResult = functions.CleanupLLMResult(cbRawResult, config.FunctionsConfig)
						funcResults = functions.ParseFunctionCall(cbRawResult, config.FunctionsConfig)
					}

					noActionsToRun := len(funcResults) > 0 && funcResults[0].Name == noActionName || len(funcResults) == 0

					switch {
					case noActionsToRun:
						if cbRawResult == "" && textContentToReturn == "" {
							xlog.Warn("Backend returned empty content in tool-calling context, will retry")
							emptyRetryNeeded = true
							continue
						}
						qResult, qErr := handleQuestion(config, funcResults, cbRawResult, predInput)
						if qErr != nil {
							xlog.Error("error handling question", "error", qErr)
							emptyRetryNeeded = true
							continue
						}

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

						for _, ss := range funcResults {
							name, args := ss.Name, ss.Arguments
							toolCallID := ss.ID
							if toolCallID == "" {
								toolCallID = id
							}
							if len(input.Tools) > 0 {
								toolChoice.Message.Content = textContentToReturn
								toolChoice.Message.ToolCalls = append(toolChoice.Message.ToolCalls,
									schema.ToolCall{
										ID:   toolCallID,
										Type: "function",
										FunctionCall: schema.FunctionCall{
											Name:      name,
											Arguments: args,
										},
									},
								)
							} else {
								// Deprecated function_call format
								functionCallReason := FinishReasonFunctionCall
								message := &schema.Message{
									Role:    "assistant",
									Content: &textContentToReturn,
									FunctionCall: map[string]interface{}{
										"name":      name,
										"arguments": args,
									},
								}
								if cbReasoning != "" {
									message.Reasoning = &cbReasoning
								}
								result = append(result, schema.Choice{
									FinishReason: &functionCallReason,
									Message:      message,
								})
							}
						}

						if len(input.Tools) > 0 {
							result = append(result, toolChoice)
						}
					}
				}

				if !emptyRetryNeeded {
					break
				}
				xlog.Warn("Retrying prediction due to empty backend response", "attempt", attempt+1, "maxRetries", maxEmptyRetries)
			}
			if err != nil {
				return err
			}

			if emptyRetryNeeded {
				xlog.Warn("All retries exhausted, backend still returning empty content")
				stopReason := FinishReasonStop
				empty := ""
				result = append(result, schema.Choice{
					FinishReason: &stopReason,
					Index:        0,
					Message:      &schema.Message{Role: "assistant", Content: &empty},
				})
			}

			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, 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")

	return "", fmt.Errorf("no action received from LLM, without a message, computing a reply")
}
