up

main.go

@@ -1,6 +1,8 @@
 package main
 
 import (
+	"bufio"
+	"bytes"
 	"context"
 	"crypto/rand"
 	"encoding/base64"
@@ -46,12 +48,13 @@ const (
 )
 
 type OpenAIConfig struct {
-	Name    string `yaml:"name" json:"name"`
-	Active  bool   `yaml:"active,omitempty" json:"active"`
-	APIKey  string `yaml:"api_key" json:"-"`
-	BaseURL string `yaml:"base_url" json:"base_url"`
-	Model   string `yaml:"model" json:"model"`
-	Timeout int    `yaml:"timeout" json:"timeout"`
+	Name           string `yaml:"name" json:"name"`
+	Active         bool   `yaml:"active,omitempty" json:"active"`
+	APIKey         string `yaml:"api_key" json:"-"`
+	BaseURL        string `yaml:"base_url" json:"base_url"`
+	Model          string `yaml:"model" json:"model"`
+	Timeout        int    `yaml:"timeout" json:"timeout"`
+	ParseThinkTags *bool  `yaml:"parse_think_tags,omitempty" json:"parse_think_tags,omitempty"`
 }
 
 type OpenAIConfigs []OpenAIConfig
@@ -559,6 +562,44 @@ func publicOpenAIConfig(profile *OpenAIProfile, active bool) OpenAIConfig {
 	return config
 }
 
+func (s *ToolRouterState) RouterProfile(fallback *OpenAIProfile) *OpenAIProfile {
+	if s == nil || s.cfg == nil || s.ai == nil {
+		return fallback
+	}
+	name := strings.TrimSpace(s.cfg.OpenAIName)
+	if name == "" {
+		return fallback
+	}
+	profile, err := s.ai.GetProfile(name)
+	if err != nil {
+		return fallback
+	}
+	return profile
+}
+
+func isOllamaProfile(profile *OpenAIProfile) bool {
+	if profile == nil {
+		return false
+	}
+	u, err := url.Parse(strings.TrimSpace(profile.Config.BaseURL))
+	if err != nil {
+		return strings.Contains(profile.Config.BaseURL, ":11434")
+	}
+	host := strings.ToLower(u.Hostname())
+	port := u.Port()
+	return port == "11434" && (host == "127.0.0.1" || host == "localhost" || host == "::1")
+}
+
+func shouldParseThinkTags(profile *OpenAIProfile) bool {
+	if profile == nil {
+		return false
+	}
+	if profile.Config.ParseThinkTags != nil {
+		return *profile.Config.ParseThinkTags
+	}
+	return isOllamaProfile(profile)
+}
+
 // ─── 全局变量 ─────────────────────────────────────────────
 
 var (
@@ -810,6 +851,21 @@ func chatHandler(c *gin.Context) {
 	}
 	promptTokens := estimateChatMessagesTokens(req.Messages)
 
+	if isOllamaProfile(profile) && hasImageMessage(req.Messages) {
+		emitTrace("model", "request", "running", "正在通过 Ollama 原生接口调用视觉模型", nil)
+		err = streamOllamaChat(ctx, profile, messages, promptTokens, usage, emit, func(content string) {
+			if req.ConversationID != "" {
+				if err := saveConversationMessages(req.ConversationID, req.Messages, content); err != nil {
+					fmt.Fprintln(os.Stderr, "保存对话失败:", err)
+				}
+			}
+		})
+		if err != nil {
+			emitError(err)
+		}
+		return
+	}
+
 	emitTrace("model", "request", "running", "正在调用模型生成回答", nil)
 	stream, err := profile.Client.CreateChatCompletionStream(ctx, model.CreateChatCompletionRequest{
 		Model:     profile.Config.Model,
@@ -829,9 +885,56 @@ func chatHandler(c *gin.Context) {
 	windowStarted := streamStarted
 	windowTokens := 0
 	peakTokensPerSecond := 0.0
+	parseThinkTags := shouldParseThinkTags(profile)
+	thinkParser := &thinkTagParser{}
+	emitDelta := func(delta string) {
+		if delta == "" {
+			return
+		}
+		now := time.Now()
+		deltaTokens := estimateTokenCount(delta)
+		windowTokens += deltaTokens
+		windowElapsed := now.Sub(windowStarted).Seconds()
+		if windowElapsed >= 1 {
+			windowSpeed := float64(windowTokens) / windowElapsed
+			if windowSpeed > peakTokensPerSecond {
+				peakTokensPerSecond = windowSpeed
+			}
+			windowStarted = now
+			windowTokens = 0
+		} else if peakTokensPerSecond == 0 && windowElapsed > 0.25 {
+			peakTokensPerSecond = float64(windowTokens) / windowElapsed
+		}
+		full.WriteString(delta)
+		completionTokens += deltaTokens
+		usage.setModel(promptTokens, completionTokens)
+		stats := usage.snapshot(tokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
+		emit(chatSSEFrame{Type: "delta", Text: delta, Stats: &stats})
+	}
+	emitModelContent := func(delta string) {
+		if delta == "" {
+			return
+		}
+		if !parseThinkTags {
+			emitDelta(delta)
+			return
+		}
+		visible, reasoning := thinkParser.Accept(delta)
+		if reasoning != "" {
+			emit(chatSSEFrame{Type: "reasoning", Text: reasoning})
+		}
+		emitDelta(visible)
+	}
 	for {
 		resp, err := stream.Recv()
 		if errors.Is(err, io.EOF) {
+			if parseThinkTags {
+				visible, reasoning := thinkParser.Flush()
+				if reasoning != "" {
+					emit(chatSSEFrame{Type: "reasoning", Text: reasoning})
+				}
+				emitDelta(visible)
+			}
 			usage.setModel(promptTokens, completionTokens)
 			if windowTokens > 0 {
 				windowElapsed := time.Since(windowStarted).Seconds()
@@ -862,28 +965,7 @@ func chatHandler(c *gin.Context) {
 			return
 		}
 		if len(resp.Choices) > 0 {
-			delta := resp.Choices[0].Delta.Content
-			if delta != "" {
-				now := time.Now()
-				deltaTokens := estimateTokenCount(delta)
-				windowTokens += deltaTokens
-				windowElapsed := now.Sub(windowStarted).Seconds()
-				if windowElapsed >= 1 {
-					windowSpeed := float64(windowTokens) / windowElapsed
-					if windowSpeed > peakTokensPerSecond {
-						peakTokensPerSecond = windowSpeed
-					}
-					windowStarted = now
-					windowTokens = 0
-				} else if peakTokensPerSecond == 0 && windowElapsed > 0.25 {
-					peakTokensPerSecond = float64(windowTokens) / windowElapsed
-				}
-				full.WriteString(delta)
-				completionTokens += deltaTokens
-				usage.setModel(promptTokens, completionTokens)
-				stats := usage.snapshot(tokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
-				emit(chatSSEFrame{Type: "delta", Text: delta, Stats: &stats})
-			}
+			emitModelContent(resp.Choices[0].Delta.Content)
 			// 思考过程 reasoning_content 单独事件推送
 			if resp.Choices[0].Delta.ReasoningContent != nil && *resp.Choices[0].Delta.ReasoningContent != "" {
 				emit(chatSSEFrame{Type: "reasoning", Text: *resp.Choices[0].Delta.ReasoningContent})
@@ -1035,13 +1117,27 @@ func availableAgentTools(profile *OpenAIProfile, emit func(chatSSEFrame)) []agen
 }
 
 func runAgentToolLoop(ctx context.Context, profile *OpenAIProfile, chatMessages []ChatMessage, emit func(chatSSEFrame)) ([]*model.ChatCompletionMessage, error) {
-	messages, err := buildArkMessages(chatMessages)
+	finalMessages, err := buildArkMessages(chatMessages)
 	if err != nil {
 		return nil, err
 	}
-	tools := availableAgentTools(profile, emit)
+	routerProfile := profile
+	if toolRouterState != nil {
+		routerProfile = toolRouterState.RouterProfile(profile)
+	}
+	tools := availableAgentTools(routerProfile, emit)
 	if len(tools) == 0 {
-		return messages, nil
+		return finalMessages, nil
+	}
+	decisionMessages := append([]*model.ChatCompletionMessage(nil), finalMessages...)
+	if hasImageMessage(chatMessages) {
+		decisionMessages, err = buildToolDecisionMessages(chatMessages)
+		if err != nil {
+			return nil, err
+		}
+		if emit != nil {
+			emit(chatSSEFrame{Type: "trace", Tool: "agent_tools", Stage: "prepare", Status: "success", Message: "检测到图片输入，工具判断阶段将使用纯文本上下文"})
+		}
 	}
 	toolByName := make(map[string]agentTool, len(tools))
 	definitions := make([]*model.Tool, 0, len(tools))
@@ -1059,28 +1155,30 @@ func runAgentToolLoop(ctx context.Context, profile *OpenAIProfile, chatMessages
 		emit(chatSSEFrame{Type: "trace", Tool: "agent_tools", Stage: "prepare", Status: "success", Message: "已准备可用工具", Data: map[string]any{"tools": availableNames, "tool_descriptions": toolDescriptions}})
 	}
 	if prompt := strings.TrimSpace(toolRouterState.cfg.SystemPrompt); prompt != "" {
-		messages = append([]*model.ChatCompletionMessage{{Role: model.ChatMessageRoleSystem, Content: stringContent(prompt)}}, messages...)
+		systemMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleSystem, Content: stringContent(prompt)}
+		finalMessages = append([]*model.ChatCompletionMessage{systemMessage}, finalMessages...)
+		decisionMessages = append([]*model.ChatCompletionMessage{systemMessage}, decisionMessages...)
 	}
 	for i := 0; i < maxAgentToolIterations; i++ {
 		if emit != nil {
 			emit(chatSSEFrame{Type: "trace", Tool: "agent_tools", Stage: "request", Status: "running", Message: fmt.Sprintf("正在进行第 %d 轮工具判断", i+1), Data: map[string]any{"iteration": i + 1, "max_iterations": maxAgentToolIterations, "tools": availableNames}})
 		}
-		resp, err := toolRouterState.complete(ctx, profile, model.CreateChatCompletionRequest{
-			Model:             profile.Config.Model,
-			Messages:          messages,
+		resp, err := toolRouterState.complete(ctx, routerProfile, model.CreateChatCompletionRequest{
+			Model:             routerProfile.Config.Model,
+			Messages:          decisionMessages,
 			MaxTokens:         intPtr(toolRouterState.cfg.MaxTokens),
 			Tools:             definitions,
 			ToolChoice:        model.ToolChoiceStringTypeAuto,
 			ParallelToolCalls: boolPtr(false),
 		}, time.Duration(toolRouterState.cfg.Timeout)*time.Second)
 		if err != nil {
-			return messages, err
+			return finalMessages, err
 		}
 		if tracker := tokenUsageFromContext(ctx); tracker != nil {
 			tracker.addTool(resp.Usage.PromptTokens, resp.Usage.CompletionTokens)
 		}
 		if len(resp.Choices) == 0 {
-			return messages, nil
+			return finalMessages, nil
 		}
 		choice := resp.Choices[0]
 		decisionPreview := chatMessageContentString(choice.Message.Content)
@@ -1095,7 +1193,7 @@ func runAgentToolLoop(ctx context.Context, profile *OpenAIProfile, chatMessages
 			if emit != nil {
 				emit(chatSSEFrame{Type: "trace", Tool: "agent_tools", Stage: "request", Status: "success", Message: "模型未请求工具，进入回答生成"})
 			}
-			return messages, nil
+			return finalMessages, nil
 		}
 		callNames := make([]string, 0, len(calls))
 		for _, call := range calls {
@@ -1106,14 +1204,89 @@ func runAgentToolLoop(ctx context.Context, profile *OpenAIProfile, chatMessages
 		if emit != nil {
 			emit(chatSSEFrame{Type: "trace", Tool: "agent_tools", Stage: "tool_calls", Status: "running", Message: fmt.Sprintf("模型请求调用 %d 个工具", len(calls)), Data: map[string]any{"tools": callNames, "iteration": i + 1}})
 		}
-		messages = append(messages, &model.ChatCompletionMessage{Role: model.ChatMessageRoleAssistant, ToolCalls: calls, Content: choice.Message.Content})
+		assistantMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleAssistant, ToolCalls: calls, Content: choice.Message.Content}
+		finalMessages = append(finalMessages, assistantMessage)
+		decisionMessages = append(decisionMessages, assistantMessage)
 		for _, call := range calls {
 			result := executeAgentToolCall(ctx, call, toolByName, emit)
-			messages = append(messages, &model.ChatCompletionMessage{Role: model.ChatMessageRoleTool, ToolCallID: call.ID, Content: stringContent(result)})
+			toolMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleTool, ToolCallID: call.ID, Content: stringContent(result)}
+			finalMessages = append(finalMessages, toolMessage)
+			decisionMessages = append(decisionMessages, toolMessage)
 		}
 	}
-	messages = append(messages, &model.ChatCompletionMessage{Role: model.ChatMessageRoleSystem, Content: stringContent("工具调用轮数已达到上限。请基于已有工具结果回答，并说明可能未完成全部工具调用。")})
-	return messages, nil
+	limitMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleSystem, Content: stringContent("工具调用轮数已达到上限。请基于已有工具结果回答，并说明可能未完成全部工具调用。")}
+	finalMessages = append(finalMessages, limitMessage)
+	return finalMessages, nil
+}
+
+type thinkTagParser struct {
+	inThink bool
+	buffer  string
+}
+
+const (
+	thinkOpenTag  = "<think>"
+	thinkCloseTag = "</think>"
+)
+
+func (p *thinkTagParser) Accept(delta string) (visible string, reasoning string) {
+	p.buffer += delta
+	for p.buffer != "" {
+		if p.inThink {
+			idx := strings.Index(p.buffer, thinkCloseTag)
+			if idx >= 0 {
+				reasoning += p.buffer[:idx]
+				p.buffer = p.buffer[idx+len(thinkCloseTag):]
+				p.inThink = false
+				continue
+			}
+			keep := tagPrefixSuffixLen(p.buffer, thinkCloseTag)
+			if len(p.buffer) > keep {
+				reasoning += p.buffer[:len(p.buffer)-keep]
+				p.buffer = p.buffer[len(p.buffer)-keep:]
+			}
+			return visible, reasoning
+		}
+
+		idx := strings.Index(p.buffer, thinkOpenTag)
+		if idx >= 0 {
+			visible += p.buffer[:idx]
+			p.buffer = p.buffer[idx+len(thinkOpenTag):]
+			p.inThink = true
+			continue
+		}
+		keep := tagPrefixSuffixLen(p.buffer, thinkOpenTag)
+		if len(p.buffer) > keep {
+			visible += p.buffer[:len(p.buffer)-keep]
+			p.buffer = p.buffer[len(p.buffer)-keep:]
+		}
+		return visible, reasoning
+	}
+	return visible, reasoning
+}
+
+func (p *thinkTagParser) Flush() (visible string, reasoning string) {
+	if p.inThink {
+		reasoning = p.buffer
+	} else {
+		visible = p.buffer
+	}
+	p.buffer = ""
+	p.inThink = false
+	return visible, reasoning
+}
+
+func tagPrefixSuffixLen(text, tag string) int {
+	limit := len(tag) - 1
+	if len(text) < limit {
+		limit = len(text)
+	}
+	for i := limit; i > 0; i-- {
+		if strings.HasPrefix(tag, text[len(text)-i:]) {
+			return i
+		}
+	}
+	return 0
 }
 
 func executeAgentToolCall(ctx context.Context, call *model.ToolCall, tools map[string]agentTool, emit func(chatSSEFrame)) string {
@@ -1155,6 +1328,223 @@ func executeAgentToolCall(ctx context.Context, call *model.ToolCall, tools map[s
 	return result
 }
 
+type ollamaChatRequest struct {
+	Model    string              `json:"model"`
+	Messages []ollamaChatMessage `json:"messages"`
+	Stream   bool                `json:"stream"`
+	Options  map[string]int      `json:"options,omitempty"`
+}
+
+type ollamaChatMessage struct {
+	Role    string   `json:"role"`
+	Content string   `json:"content"`
+	Images  []string `json:"images,omitempty"`
+}
+
+type ollamaChatResponse struct {
+	Message struct {
+		Role     string `json:"role"`
+		Content  string `json:"content"`
+		Thinking string `json:"thinking"`
+	} `json:"message"`
+	Done            bool   `json:"done"`
+	PromptEvalCount int    `json:"prompt_eval_count"`
+	EvalCount       int    `json:"eval_count"`
+	DoneReason      string `json:"done_reason"`
+}
+
+func streamOllamaChat(ctx context.Context, profile *OpenAIProfile, messages []*model.ChatCompletionMessage, promptTokens int, usage *tokenUsageTracker, emit func(chatSSEFrame), onDone func(string)) error {
+	requestMessages, err := buildOllamaMessages(messages)
+	if err != nil {
+		return err
+	}
+	baseURL, err := ollamaBaseURL(profile)
+	if err != nil {
+		return err
+	}
+	body, err := json.Marshal(ollamaChatRequest{
+		Model:    profile.Config.Model,
+		Messages: requestMessages,
+		Stream:   true,
+		Options:  map[string]int{"num_predict": 4096},
+	})
+	if err != nil {
+		return err
+	}
+	req, err := http.NewRequestWithContext(ctx, http.MethodPost, strings.TrimRight(baseURL, "/")+"/api/chat", bytes.NewReader(body))
+	if err != nil {
+		return err
+	}
+	req.Header.Set("Content-Type", "application/json")
+	resp, err := http.DefaultClient.Do(req)
+	if err != nil {
+		return err
+	}
+	defer resp.Body.Close()
+	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
+		data, _ := io.ReadAll(io.LimitReader(resp.Body, 4096))
+		return fmt.Errorf("Ollama 原生接口调用失败: %s %s", resp.Status, strings.TrimSpace(string(data)))
+	}
+
+	emit(chatSSEFrame{Type: "trace", Tool: "model", Stage: "stream", Status: "running", Message: "Ollama 视觉模型已开始输出"})
+	parseThinkTags := shouldParseThinkTags(profile)
+	thinkParser := &thinkTagParser{}
+	var full strings.Builder
+	completionTokens := 0
+	streamStarted := time.Now()
+	peakTokensPerSecond := 0.0
+	emitDelta := func(delta string) {
+		if delta == "" {
+			return
+		}
+		full.WriteString(delta)
+		completionTokens += estimateTokenCount(delta)
+		usage.setModel(promptTokens, completionTokens)
+		currentSpeed := tokensPerSecond(completionTokens, streamStarted)
+		if currentSpeed > peakTokensPerSecond {
+			peakTokensPerSecond = currentSpeed
+		}
+		stats := usage.snapshot(currentSpeed, peakTokensPerSecond)
+		emit(chatSSEFrame{Type: "delta", Text: delta, Stats: &stats})
+	}
+	emitContent := func(delta string) {
+		if delta == "" {
+			return
+		}
+		if !parseThinkTags {
+			emitDelta(delta)
+			return
+		}
+		visible, reasoning := thinkParser.Accept(delta)
+		if reasoning != "" {
+			emit(chatSSEFrame{Type: "reasoning", Text: reasoning})
+		}
+		emitDelta(visible)
+	}
+
+	scanner := bufio.NewScanner(resp.Body)
+	scanner.Buffer(make([]byte, 0, 64*1024), 10*1024*1024)
+	for scanner.Scan() {
+		line := strings.TrimSpace(scanner.Text())
+		if line == "" {
+			continue
+		}
+		var chunk ollamaChatResponse
+		if err := json.Unmarshal([]byte(line), &chunk); err != nil {
+			return fmt.Errorf("解析 Ollama 流失败: %w", err)
+		}
+		if chunk.Message.Thinking != "" {
+			emit(chatSSEFrame{Type: "reasoning", Text: chunk.Message.Thinking})
+		}
+		emitContent(chunk.Message.Content)
+		if chunk.Done {
+			if chunk.PromptEvalCount > 0 || chunk.EvalCount > 0 {
+				usage.setModel(chunk.PromptEvalCount, chunk.EvalCount)
+			}
+			break
+		}
+	}
+	if err := scanner.Err(); err != nil {
+		return err
+	}
+	if parseThinkTags {
+		visible, reasoning := thinkParser.Flush()
+		if reasoning != "" {
+			emit(chatSSEFrame{Type: "reasoning", Text: reasoning})
+		}
+		emitDelta(visible)
+	}
+	if onDone != nil {
+		onDone(full.String())
+	}
+	finalStats := usage.snapshot(tokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
+	emit(chatSSEFrame{Type: "stats", Stats: &finalStats})
+	emit(chatSSEFrame{Type: "trace", Tool: "model", Stage: "stream", Status: "success", Message: "回答生成完成"})
+	return nil
+}
+
+func buildOllamaMessages(messages []*model.ChatCompletionMessage) ([]ollamaChatMessage, error) {
+	result := make([]ollamaChatMessage, 0, len(messages))
+	for _, msg := range messages {
+		if msg == nil {
+			continue
+		}
+		role := string(msg.Role)
+		if msg.Role == model.ChatMessageRoleTool {
+			role = string(model.ChatMessageRoleUser)
+		}
+		item := ollamaChatMessage{Role: role}
+		if msg.Content == nil {
+			if len(msg.ToolCalls) > 0 {
+				continue
+			}
+			result = append(result, item)
+			continue
+		}
+		if msg.Content.StringValue != nil {
+			item.Content = *msg.Content.StringValue
+			if msg.Role == model.ChatMessageRoleTool {
+				item.Content = "工具结果：\n" + item.Content
+			}
+			result = append(result, item)
+			continue
+		}
+		for _, part := range msg.Content.ListValue {
+			if part == nil {
+				continue
+			}
+			switch part.Type {
+			case model.ChatCompletionMessageContentPartTypeText:
+				if part.Text != "" {
+					if item.Content != "" {
+						item.Content += "\n"
+					}
+					item.Content += part.Text
+				}
+			case model.ChatCompletionMessageContentPartTypeImageURL:
+				if part.ImageURL == nil {
+					continue
+				}
+				image, err := ollamaImagePayload(part.ImageURL.URL)
+				if err != nil {
+					return nil, err
+				}
+				item.Images = append(item.Images, image)
+			}
+		}
+		result = append(result, item)
+	}
+	return result, nil
+}
+
+func ollamaImagePayload(raw string) (string, error) {
+	raw = strings.TrimSpace(raw)
+	if strings.HasPrefix(strings.ToLower(raw), "data:") {
+		comma := strings.Index(raw, ",")
+		if comma < 0 {
+			return "", errors.New("图片 base64 数据格式错误")
+		}
+		return strings.TrimSpace(raw[comma+1:]), nil
+	}
+	return raw, nil
+}
+
+func ollamaBaseURL(profile *OpenAIProfile) (string, error) {
+	if profile == nil {
+		return "", errors.New("Ollama 配置为空")
+	}
+	u, err := url.Parse(strings.TrimSpace(profile.Config.BaseURL))
+	if err != nil {
+		return "", err
+	}
+	if strings.TrimRight(u.Path, "/") == "/v1" {
+		u.Path = strings.TrimSuffix(strings.TrimRight(u.Path, "/"), "/v1")
+	}
+	u.RawQuery = ""
+	u.Fragment = ""
+	return strings.TrimRight(u.String(), "/"), nil
+}
+
 func completeText(ctx context.Context, profile *OpenAIProfile, chatMessages []ChatMessage, maxTokens int) (string, error) {
 	return completeTextWithTimeout(ctx, profile, chatMessages, maxTokens, time.Duration(profile.Config.Timeout)*time.Second)
 }
@@ -1178,10 +1568,23 @@ func completeTextWithTimeout(ctx context.Context, profile *OpenAIProfile, chatMe
 
 	promptTokens := estimateChatMessagesTokens(chatMessages)
 	completionTokens := 0
+	parseThinkTags := shouldParseThinkTags(profile)
+	thinkParser := &thinkTagParser{}
 	var b strings.Builder
+	appendVisible := func(delta string) {
+		if delta == "" {
+			return
+		}
+		b.WriteString(delta)
+		completionTokens += estimateTokenCount(delta)
+	}
 	for {
 		resp, err := stream.Recv()
 		if errors.Is(err, io.EOF) {
+			if parseThinkTags {
+				visible, _ := thinkParser.Flush()
+				appendVisible(visible)
+			}
 			if tracker := tokenUsageFromContext(ctx); tracker != nil {
 				tracker.addTool(promptTokens, completionTokens)
 			}
@@ -1192,8 +1595,12 @@ func completeTextWithTimeout(ctx context.Context, profile *OpenAIProfile, chatMe
 		}
 		if len(resp.Choices) > 0 {
 			delta := resp.Choices[0].Delta.Content
-			b.WriteString(delta)
-			completionTokens += estimateTokenCount(delta)
+			if parseThinkTags {
+				visible, _ := thinkParser.Accept(delta)
+				appendVisible(visible)
+			} else {
+				appendVisible(delta)
+			}
 		}
 	}
 }
@@ -1368,6 +1775,33 @@ func buildArkMessages(chatMessages []ChatMessage) ([]*model.ChatCompletionMessag
 	return messages, nil
 }
 
+func hasImageMessage(messages []ChatMessage) bool {
+	for _, msg := range messages {
+		if strings.TrimSpace(msg.ImageURL) != "" || strings.TrimSpace(msg.ImageURLAlias) != "" {
+			return true
+		}
+	}
+	return false
+}
+
+func buildToolDecisionMessages(chatMessages []ChatMessage) ([]*model.ChatCompletionMessage, error) {
+	messages := make([]*model.ChatCompletionMessage, 0, len(chatMessages))
+	for _, m := range chatMessages {
+		content := m.Content
+		if strings.TrimSpace(m.ImageURL) != "" || strings.TrimSpace(m.ImageURLAlias) != "" {
+			content = strings.TrimSpace(content)
+			placeholder := "[用户上传了一张图片。工具判断阶段不读取图片内容；如果问题主要依赖识图，应不要调用工具，交给最终多模态模型回答。]"
+			if content == "" {
+				content = placeholder
+			} else {
+				content += "\n\n" + placeholder
+			}
+		}
+		messages = append(messages, &model.ChatCompletionMessage{Role: m.Role, Content: stringContent(content)})
+	}
+	return messages, nil
+}
+
 func buildArkMessage(m ChatMessage) (*model.ChatCompletionMessage, error) {
 	msg := &model.ChatCompletionMessage{Role: m.Role}
 
@@ -1388,11 +1822,12 @@ func buildArkMessage(m ChatMessage) (*model.ChatCompletionMessage, error) {
 	}
 
 	// 有图片时：文字内容可有可无（图片 caption 场景），均构造多模态消息
-	// 若无文字，则只传图片 part；若同时有图片和文字，先图后文
-	parts := []*model.ChatCompletionMessageContentPart{imagePart(imageURL)}
+	// 若无文字，则只传图片 part；若同时有图片和文字，先文后图
+	parts := make([]*model.ChatCompletionMessageContentPart, 0, 2)
 	if m.Content != "" {
 		parts = append(parts, textPart(m.Content))
 	}
+	parts = append(parts, imagePart(imageURL))
 	msg.Content = &model.ChatCompletionMessageContent{ListValue: parts}
 	return msg, nil
 }