1127 lines
35 KiB
Go
1127 lines
35 KiB
Go
package routers
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import (
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"bufio"
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"bytes"
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"context"
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"encoding/base64"
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"encoding/json"
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"errors"
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"fmt"
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"io"
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"net/http"
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"net/url"
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"ops/agents"
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"ops/models"
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"strings"
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"time"
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"github.com/gin-gonic/gin"
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)
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// SSE frame types sent to frontend
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type sseEvent struct {
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Type string `json:"type"`
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Text string `json:"text,omitempty"`
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Tool string `json:"tool,omitempty"`
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Stage string `json:"stage,omitempty"`
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Status string `json:"status,omitempty"`
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Message string `json:"message,omitempty"`
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Data map[string]interface{} `json:"data,omitempty"`
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Stats *tokenUsageStats `json:"stats,omitempty"`
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Error string `json:"error,omitempty"`
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}
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type tokenUsageStats struct {
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PromptTokens int `json:"prompt_tokens"`
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CompletionTokens int `json:"completion_tokens"`
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ToolPromptTokens int `json:"tool_prompt_tokens"`
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ToolCompletionTokens int `json:"tool_completion_tokens"`
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TotalTokens int `json:"total_tokens"`
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CompletionTokensPerSec float64 `json:"completion_tokens_per_sec"`
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PeakCompletionTokensPerSec float64 `json:"peak_completion_tokens_per_sec"`
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Estimated bool `json:"estimated"`
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}
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const maxImageDataSize = 4 * 1024 * 1024
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var allowedImageTypes = map[string]bool{
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"image/jpeg": true,
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"image/png": true,
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"image/webp": true,
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"image/gif": true,
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}
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// chatRequestFromFrontend is the expected POST body
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type chatRequest struct {
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Messages []chatMessage `json:"messages"`
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OpenAIName string `json:"openaiName,omitempty"`
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ConversationID uint `json:"conversationId,omitempty"`
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ClientLocalID string `json:"clientLocalId,omitempty"`
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SaveToServer bool `json:"saveToServer,omitempty"`
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}
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type chatMessage struct {
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Role string `json:"role"`
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Content string `json:"content"`
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ImageURL string `json:"image_url,omitempty"`
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ImageURLAlias string `json:"imageURL,omitempty"`
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}
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// openaiChatRequest is the request sent to the upstream OpenAI-compatible API
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type openaiChatRequest struct {
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Model string `json:"model"`
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Messages []openaiMessage `json:"messages"`
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Stream bool `json:"stream"`
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MaxTokens int `json:"max_tokens,omitempty"`
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Temperature float64 `json:"temperature,omitempty"`
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Tools []openaiTool `json:"tools,omitempty"`
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ToolChoice any `json:"tool_choice,omitempty"`
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}
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type openaiMessage struct {
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Role string `json:"role"`
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Content any `json:"content,omitempty"`
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Name string `json:"name,omitempty"`
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ToolCallID string `json:"tool_call_id,omitempty"`
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ToolCalls []openaiToolCall `json:"tool_calls,omitempty"`
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}
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type openaiTool struct {
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Type string `json:"type"`
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Function openaiFunctionDefinition `json:"function"`
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}
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type openaiFunctionDefinition struct {
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Name string `json:"name"`
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Description string `json:"description"`
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Parameters map[string]interface{} `json:"parameters"`
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}
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type openaiToolCall struct {
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ID string `json:"id,omitempty"`
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Type string `json:"type,omitempty"`
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Function openaiFunctionCall `json:"function"`
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}
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type openaiFunctionCall struct {
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Name string `json:"name"`
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Arguments string `json:"arguments"`
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}
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type openaiContentPart struct {
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Type string `json:"type"`
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Text string `json:"text,omitempty"`
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ImageURL *openaiImageURL `json:"image_url,omitempty"`
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}
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type openaiImageURL struct {
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URL string `json:"url"`
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Detail string `json:"detail,omitempty"`
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}
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type openaiResponseMessage struct {
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Role string `json:"role"`
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Content string `json:"content"`
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ToolCalls []openaiToolCall `json:"tool_calls,omitempty"`
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}
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// openaiStreamChunk is one SSE data line from the upstream
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type openaiStreamChunk struct {
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ID string `json:"id,omitempty"`
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Object string `json:"object,omitempty"`
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Created int64 `json:"created,omitempty"`
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Model string `json:"model,omitempty"`
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Choices []openaiChoice `json:"choices"`
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Usage *openaiUsage `json:"usage,omitempty"`
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}
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type openaiChatResponse struct {
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Choices []openaiResponseChoice `json:"choices"`
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Usage *openaiUsage `json:"usage,omitempty"`
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}
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type openaiResponseChoice struct {
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Message openaiResponseMessage `json:"message"`
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}
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type toolSelection struct {
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Name string `json:"name"`
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Reason string `json:"reason"`
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}
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type toolRoutingDecision struct {
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Tools []toolSelection `json:"tools"`
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Reason string `json:"reason"`
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}
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type toolRoutingResult struct {
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Decision toolRoutingDecision
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Selected []string
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Messages []openaiMessage
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Response string
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Usage *openaiUsage
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}
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type openaiChoice struct {
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Index int `json:"index"`
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Delta openaiDelta `json:"delta"`
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Finish *string `json:"finish_reason,omitempty"`
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}
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type openaiDelta struct {
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Role string `json:"role,omitempty"`
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Content string `json:"content,omitempty"`
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ReasoningContent string `json:"reasoning_content,omitempty"`
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Reasoning string `json:"reasoning,omitempty"`
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Thinking string `json:"thinking,omitempty"`
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ToolCalls []openaiToolCall `json:"tool_calls,omitempty"`
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}
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type openaiUsage struct {
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PromptTokens int `json:"prompt_tokens"`
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CompletionTokens int `json:"completion_tokens"`
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TotalTokens int `json:"total_tokens"`
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}
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func ApiAIChat(r *gin.RouterGroup) {
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r.GET("/openai", handleOpenAIProfiles)
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r.POST("/chat", handleChat)
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conversations := r.Group("/conversations")
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conversations.POST("/list", handleAIChatConversationList)
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conversations.POST("/get", handleAIChatConversationGet)
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conversations.POST("/update", handleAIChatConversationUpdate)
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conversations.POST("/delete", handleAIChatConversationDelete)
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admin := r.Group("/admin")
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admin.POST("/config", handleAIChatAdminGetConfig)
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admin.POST("/config/update", handleAIChatAdminUpdateConfig)
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admin.POST("/refresh", handleAIChatAdminRefreshCache)
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}
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func handleOpenAIProfiles(ctx *gin.Context) {
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cfg := getAIChatConfig()
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active := ""
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profiles := make([]map[string]interface{}, 0, len(cfg.OpenAI))
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for _, profile := range cfg.OpenAI {
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if profile.Active {
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active = profile.Name
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}
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profiles = append(profiles, map[string]interface{}{
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"name": profile.Name,
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"active": profile.Active,
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"baseUrl": profile.BaseUrl,
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"model": profile.Model,
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"timeout": profile.Timeout,
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"maxTokens": profile.MaxTokens,
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"contextWindowTokens": profile.ContextWindowTokens,
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})
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}
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ReturnJson(ctx, "apiOK", gin.H{
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"enabled": cfg.Enabled,
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"active": active,
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"profiles": profiles,
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"toolRouter": gin.H{
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"enabled": cfg.ToolRouter.Enabled,
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"openaiName": cfg.ToolRouter.OpenAIName,
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"timeout": cfg.ToolRouter.Timeout,
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"maxTokens": cfg.ToolRouter.MaxTokens,
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},
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})
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}
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func handleChat(ctx *gin.Context) {
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data, cookieValue := SeparateData(ctx)
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if data == nil {
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sendSSEError(ctx, "请求数据为空")
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return
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}
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var req chatRequest
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if err := decodeJSON(data, &req); err != nil {
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sendSSEError(ctx, "解析消息失败: "+err.Error())
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return
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}
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if len(req.Messages) == 0 {
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sendSSEError(ctx, "消息不能为空")
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return
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}
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var currentUser *TabUser
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if cookieValue != "" {
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if user, err := AuthenticationAuthorityFromCookie(cookieValue); err == nil {
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currentUser = user
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}
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}
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// Check ai config
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cfg := getAIChatConfig()
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profile, ok := selectOpenAIProfile(cfg, req.OpenAIName)
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if !cfg.Enabled || !ok || profile.Model == "" || profile.ApiKey == "" {
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sendSSEError(ctx, "AI 聊天未配置，请在后台配置 API Key 和模型")
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return
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}
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chatMsgs := convertToChatMessages(req.Messages)
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// Set up SSE headers before routing/tools so progress can stream immediately.
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ctx.Writer.Header().Set("Content-Type", "text/event-stream")
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ctx.Writer.Header().Set("Cache-Control", "no-cache")
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ctx.Writer.Header().Set("Connection", "keep-alive")
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ctx.Writer.Header().Set("X-Accel-Buffering", "no")
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ctx.Writer.WriteHeader(http.StatusOK)
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flusher, _ := ctx.Writer.(http.Flusher)
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tracker := newTokenUsageTracker()
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traceEvents := []sseEvent{}
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emitTrace := func(tool, stage, status, message string, data map[string]interface{}) {
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event := sseEvent{Type: "trace", Tool: tool, Stage: stage, Status: status, Message: message, Data: data}
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traceEvents = append(traceEvents, event)
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sendSSE(ctx, flusher, event)
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}
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emitStats := func(stats tokenUsageStats) {
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sendSSE(ctx, flusher, sseEvent{Type: "stats", Stats: &stats})
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}
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conversation, persistErr := prepareAIChatPersistence(currentUser, req, profile.Name)
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if persistErr != nil {
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emitTrace("chat", "persist", "error", "聊天保存失败，将继续仅本次对话", map[string]interface{}{"error": persistErr.Error()})
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conversation = nil
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} else if conversation != nil {
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sendSSE(ctx, flusher, sseEvent{Type: "conversation", Data: map[string]interface{}{
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"id": conversation.ID,
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"title": conversation.Title,
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"clientLocalId": conversation.ClientLocalID,
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}})
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}
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toolConfigs := []agents.ToolConfig{}
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if cfg.ToolRouter.Enabled {
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toolConfigs = buildToolConfigs(cfg.ToolRouter.Tools)
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}
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// Build OpenAI-compatible request
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openaiMsgs, err := convertToOpenAIMessages(chatMsgs)
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if err != nil {
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sendSSE(ctx, flusher, sseEvent{Type: "error", Error: err.Error()})
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sendSSEDone(ctx, flusher)
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return
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}
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functionTools := buildFunctionTools(toolConfigs)
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if profile.SystemPrompt != "" {
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openaiMsgs = append([]openaiMessage{{Role: "system", Content: profile.SystemPrompt}}, openaiMsgs...)
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}
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if len(functionTools) > 0 {
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toolNames := make([]string, 0, len(functionTools))
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for _, tool := range functionTools {
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toolNames = append(toolNames, tool.Function.Name)
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}
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emitTrace("function_tools", "prepare", "success", "已启用 Function Calling 工具", map[string]interface{}{"tools": toolNames})
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openaiMsgs = append([]openaiMessage{{Role: "system", Content: "可用工具使用规则：当用户询问“我是谁”“当前登录用户是谁”“我的用户信息”等当前身份问题时，调用 ops_ai_assistant_current_user；工具返回 loggedIn=true 时按工具结果回答当前用户信息，返回 loggedIn=false 时说明不知道并提示需要登录才能获取信息。当用户询问采购订单列表、采购订单详情、采购状态或数量统计时，调用 ops_ai_assistant_purchase_query；该工具只允许查询，禁止新增、修改、删除采购数据。当用户询问本月、今天、本周、下周等相对日期的日程时，先调用 time 获取明确 start_date/end_date，再调用 ops_ai_assistant_schedule_query 查询日程。用户询问有哪些日历或创建日程但未提供 calendar_id 时，调用 ops_ai_assistant_calendar_list 获取可用日历；用户明确要求新增/修改/删除日程时，分别调用 ops_ai_assistant_schedule_create、ops_ai_assistant_schedule_update、ops_ai_assistant_schedule_delete，写入工具必须基于用户明确指令和明确日期，不要自行猜测日历或事件 ID。不要臆造工具结果中不存在的信息。"}}, openaiMsgs...)
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var toolExecuted bool
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openaiMsgs, toolExecuted, err = runOpenAIToolLoop(ctx.Request.Context(), profile, openaiMsgs, functionTools, currentUser, tracker, emitTrace)
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if err != nil {
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emitTrace("model", "tool_call", "error", "工具调用失败，将继续普通回答", map[string]interface{}{"error": err.Error()})
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} else if toolExecuted {
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emitTrace("model", "tool_call", "success", "工具调用完成，准备生成最终回答", nil)
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}
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}
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apiReq := openaiChatRequest{
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Model: profile.Model,
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Messages: openaiMsgs,
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Stream: true,
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MaxTokens: profile.MaxTokens,
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Temperature: 0.7,
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}
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trimmedMessages, trimStats := trimOpenAIMessagesToContextWindow(apiReq.Messages, profile.ContextWindowTokens)
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apiReq.Messages = trimmedMessages
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if trimStats.RemovedMessages > 0 {
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emitTrace("model", "context_window", "success", "上下文窗口已裁剪旧消息", map[string]interface{}{
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"limit": trimStats.Limit,
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"before_tokens": trimStats.BeforeTokens,
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"after_tokens": trimStats.AfterTokens,
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"removed_messages": trimStats.RemovedMessages,
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})
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}
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modelPromptTokens := estimateOpenAIMessagesTokens(apiReq.Messages)
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completionTokens := 0
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modelUsageReceived := false
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assistantContent := strings.Builder{}
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reasoningContent := strings.Builder{}
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var finalStats *tokenUsageStats
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streamStarted := time.Now()
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windowStarted := streamStarted
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windowTokens := 0
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peakTokensPerSecond := 0.0
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emitTrace("model", "stream", "running", "正在请求模型回复", nil)
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err = streamOpenAI(ctx.Request.Context(), profile, apiReq, func(chunk openaiStreamChunk) {
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for _, choice := range chunk.Choices {
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reasoningText := choice.Delta.ReasoningContent
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if reasoningText == "" {
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reasoningText = choice.Delta.Reasoning
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}
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if reasoningText == "" {
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reasoningText = choice.Delta.Thinking
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}
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if reasoningText != "" {
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reasoningContent.WriteString(reasoningText)
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sendSSE(ctx, flusher, sseEvent{Type: "reasoning", Text: reasoningText})
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}
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if choice.Delta.Content != "" {
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assistantContent.WriteString(choice.Delta.Content)
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deltaTokens := estimateTokenCount(choice.Delta.Content)
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completionTokens += deltaTokens
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windowTokens += deltaTokens
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elapsedWindow := time.Since(windowStarted).Seconds()
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if elapsedWindow >= 1 {
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peakTokensPerSecond = maxFloat(peakTokensPerSecond, float64(windowTokens)/elapsedWindow)
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windowStarted = time.Now()
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windowTokens = 0
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} else if peakTokensPerSecond == 0 && elapsedWindow > 0.25 {
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peakTokensPerSecond = maxFloat(peakTokensPerSecond, float64(windowTokens)/elapsedWindow)
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}
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stats := tracker.setModelEstimate(modelPromptTokens, completionTokens).snapshot(tokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
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finalStats = &stats
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sendSSE(ctx, flusher, sseEvent{Type: "delta", Text: choice.Delta.Content, Stats: &stats})
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}
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}
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if chunk.Usage != nil {
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modelUsageReceived = true
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stats := tracker.setModelUsage(chunk.Usage).snapshot(tokensPerSecond(tracker.completionTokens, streamStarted), peakTokensPerSecond)
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finalStats = &stats
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emitStats(stats)
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}
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})
|
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if err != nil {
|
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errorText := "请求失败: " + err.Error()
|
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if conversation != nil && assistantContent.Len() > 0 {
|
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_ = persistAIChatAssistantMessage(conversation, assistantContent.String(), reasoningContent.String(), traceEvents, finalStats)
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}
|
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sendSSE(ctx, flusher, sseEvent{Type: "error", Error: errorText})
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sendSSEDone(ctx, flusher)
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return
|
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}
|
|
|
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if windowTokens > 0 {
|
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elapsedWindow := time.Since(windowStarted).Seconds()
|
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if elapsedWindow > 0 {
|
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peakTokensPerSecond = maxFloat(peakTokensPerSecond, float64(windowTokens)/elapsedWindow)
|
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}
|
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}
|
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emitTrace("model", "stream", "success", "模型回复完成", nil)
|
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if modelUsageReceived {
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stats := tracker.snapshot(tokensPerSecond(tracker.completionTokens, streamStarted), peakTokensPerSecond)
|
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finalStats = &stats
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emitStats(stats)
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} else {
|
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stats := tracker.setModelEstimate(modelPromptTokens, completionTokens).snapshot(tokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
|
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finalStats = &stats
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emitStats(stats)
|
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}
|
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if err := persistAIChatAssistantMessage(conversation, assistantContent.String(), reasoningContent.String(), traceEvents, finalStats); err != nil {
|
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sendSSE(ctx, flusher, sseEvent{Type: "trace", Tool: "chat", Stage: "persist", Status: "error", Message: "助手回复保存失败", Data: map[string]interface{}{"error": err.Error()}})
|
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}
|
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sendSSEDone(ctx, flusher)
|
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flusher.Flush()
|
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}
|
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|
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func callOpenAIChat(ctx context.Context, cfg models.ConfigsAIChatOpenAI_, req openaiChatRequest) (*openaiChatResponse, error) {
|
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bodyBytes, err := json.Marshal(req)
|
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if err != nil {
|
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return nil, fmt.Errorf("序列化请求失败: %w", err)
|
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}
|
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|
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url := strings.TrimRight(cfg.BaseUrl, "/") + "/chat/completions"
|
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httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(bodyBytes))
|
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if err != nil {
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return nil, fmt.Errorf("创建请求失败: %w", err)
|
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}
|
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httpReq.Header.Set("Content-Type", "application/json")
|
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httpReq.Header.Set("Authorization", "Bearer "+cfg.ApiKey)
|
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|
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client := &http.Client{Timeout: time.Duration(cfg.Timeout) * time.Second}
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resp, err := client.Do(httpReq)
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if err != nil {
|
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return nil, fmt.Errorf("连接上游服务失败: %w", err)
|
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}
|
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defer resp.Body.Close()
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|
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if resp.StatusCode != http.StatusOK {
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body, _ := io.ReadAll(resp.Body)
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return nil, fmt.Errorf("上游返回 %d: %s", resp.StatusCode, string(body))
|
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}
|
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|
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var result openaiChatResponse
|
|
if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
|
|
return nil, fmt.Errorf("解析响应失败: %w", err)
|
|
}
|
|
return &result, nil
|
|
}
|
|
|
|
func streamOpenAI(ctx context.Context, cfg models.ConfigsAIChatOpenAI_, req openaiChatRequest, onData func(openaiStreamChunk)) error {
|
|
bodyBytes, err := json.Marshal(req)
|
|
if err != nil {
|
|
return fmt.Errorf("序列化请求失败: %w", err)
|
|
}
|
|
|
|
url := strings.TrimRight(cfg.BaseUrl, "/") + "/chat/completions"
|
|
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(bodyBytes))
|
|
if err != nil {
|
|
return fmt.Errorf("创建请求失败: %w", err)
|
|
}
|
|
httpReq.Header.Set("Content-Type", "application/json")
|
|
httpReq.Header.Set("Authorization", "Bearer "+cfg.ApiKey)
|
|
httpReq.Header.Set("Accept", "text/event-stream")
|
|
|
|
client := &http.Client{Timeout: time.Duration(cfg.Timeout) * time.Second}
|
|
resp, err := client.Do(httpReq)
|
|
if err != nil {
|
|
return fmt.Errorf("连接上游服务失败: %w", err)
|
|
}
|
|
defer resp.Body.Close()
|
|
|
|
if resp.StatusCode != http.StatusOK {
|
|
body, _ := io.ReadAll(resp.Body)
|
|
return fmt.Errorf("上游返回 %d: %s", resp.StatusCode, string(body))
|
|
}
|
|
|
|
scanner := bufio.NewScanner(resp.Body)
|
|
scanner.Buffer(make([]byte, 0, 64*1024), 256*1024)
|
|
|
|
for scanner.Scan() {
|
|
line := scanner.Text()
|
|
|
|
if strings.TrimSpace(line) == "" {
|
|
continue
|
|
}
|
|
if !strings.HasPrefix(line, "data: ") {
|
|
continue
|
|
}
|
|
|
|
payload := strings.TrimPrefix(line, "data: ")
|
|
payload = strings.TrimSpace(payload)
|
|
|
|
if payload == "[DONE]" {
|
|
continue
|
|
}
|
|
|
|
var chunk openaiStreamChunk
|
|
if err := json.Unmarshal([]byte(payload), &chunk); err != nil {
|
|
continue
|
|
}
|
|
|
|
onData(chunk)
|
|
}
|
|
|
|
if err := scanner.Err(); err != nil {
|
|
return fmt.Errorf("读取流失败: %w", err)
|
|
}
|
|
|
|
return nil
|
|
}
|
|
|
|
// Initialize with system prompt if present
|
|
func sendSSE(ctx *gin.Context, flusher http.Flusher, event sseEvent) {
|
|
data, err := json.Marshal(event)
|
|
if err != nil {
|
|
return
|
|
}
|
|
_, _ = fmt.Fprintf(ctx.Writer, "data: %s\n\n", string(data))
|
|
flusher.Flush()
|
|
}
|
|
|
|
func sendSSEDone(ctx *gin.Context, flusher http.Flusher) {
|
|
_, _ = fmt.Fprint(ctx.Writer, "data: [DONE]\n\n")
|
|
flusher.Flush()
|
|
}
|
|
|
|
func sendSSEError(ctx *gin.Context, message string) {
|
|
ctx.Writer.Header().Set("Content-Type", "text/event-stream")
|
|
ctx.Writer.Header().Set("Cache-Control", "no-cache")
|
|
ctx.Writer.Header().Set("Connection", "keep-alive")
|
|
ctx.Writer.WriteHeader(http.StatusOK)
|
|
flusher, _ := ctx.Writer.(http.Flusher)
|
|
|
|
sendSSE(ctx, flusher, sseEvent{
|
|
Type: "error",
|
|
Error: message,
|
|
})
|
|
|
|
sendSSEDone(ctx, flusher)
|
|
flusher.Flush()
|
|
}
|
|
|
|
func convertToChatMessages(msgs []chatMessage) []agents.ChatMessage {
|
|
result := make([]agents.ChatMessage, 0, len(msgs))
|
|
for _, m := range msgs {
|
|
imageURL := m.ImageURL
|
|
if imageURL == "" {
|
|
imageURL = m.ImageURLAlias
|
|
}
|
|
result = append(result, agents.ChatMessage{Role: m.Role, Content: m.Content, ImageURL: imageURL})
|
|
}
|
|
return result
|
|
}
|
|
|
|
func convertToOpenAIMessages(msgs []agents.ChatMessage) ([]openaiMessage, error) {
|
|
result := make([]openaiMessage, 0, len(msgs))
|
|
for _, m := range msgs {
|
|
content, err := buildOpenAIContent(m)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
result = append(result, openaiMessage{Role: m.Role, Content: content})
|
|
}
|
|
return result, nil
|
|
}
|
|
|
|
func buildOpenAIContent(m agents.ChatMessage) (any, error) {
|
|
if strings.TrimSpace(m.ImageURL) == "" {
|
|
return m.Content, nil
|
|
}
|
|
|
|
imageURL, err := normalizeImageURL(m.ImageURL)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
parts := []openaiContentPart{
|
|
{
|
|
Type: "image_url",
|
|
ImageURL: &openaiImageURL{
|
|
URL: imageURL,
|
|
Detail: "auto",
|
|
},
|
|
},
|
|
}
|
|
if m.Content != "" {
|
|
parts = append(parts, openaiContentPart{Type: "text", Text: m.Content})
|
|
}
|
|
return parts, nil
|
|
}
|
|
|
|
func normalizeImageURL(raw string) (string, error) {
|
|
value := strings.TrimSpace(raw)
|
|
if value == "" {
|
|
return "", errors.New("图片地址为空")
|
|
}
|
|
|
|
if strings.HasPrefix(strings.ToLower(value), "data:") {
|
|
return normalizeImageDataURI(value)
|
|
}
|
|
|
|
parsed, err := url.Parse(value)
|
|
if err != nil || parsed.Host == "" || (parsed.Scheme != "http" && parsed.Scheme != "https") {
|
|
return "", errors.New("图片地址无效，仅支持 http/https URL 或 base64 data URI")
|
|
}
|
|
return value, nil
|
|
}
|
|
|
|
func normalizeImageDataURI(raw string) (string, error) {
|
|
commaIndex := strings.Index(raw, ",")
|
|
if commaIndex == -1 {
|
|
return "", errors.New("图片 data URI 格式无效")
|
|
}
|
|
|
|
metadata := strings.TrimSpace(raw[len("data:"):commaIndex])
|
|
payload := strings.TrimSpace(raw[commaIndex+1:])
|
|
if metadata == "" || payload == "" {
|
|
return "", errors.New("图片 data URI 格式无效")
|
|
}
|
|
|
|
metadataParts := strings.Split(metadata, ";")
|
|
mimeType := strings.ToLower(strings.TrimSpace(metadataParts[0]))
|
|
if !allowedImageTypes[mimeType] {
|
|
return "", errors.New("图片格式不支持，仅支持 jpeg/png/webp/gif")
|
|
}
|
|
|
|
hasBase64 := false
|
|
for _, part := range metadataParts[1:] {
|
|
if strings.EqualFold(strings.TrimSpace(part), "base64") {
|
|
hasBase64 = true
|
|
break
|
|
}
|
|
}
|
|
if !hasBase64 {
|
|
return "", errors.New("图片 data URI 必须使用 base64 编码")
|
|
}
|
|
|
|
if len(payload) > maxImageDataSize*4/3+16 {
|
|
return "", errors.New("图片过大，请选择小于 4MB 的图片")
|
|
}
|
|
decoded, err := base64.StdEncoding.DecodeString(payload)
|
|
if err != nil {
|
|
return "", errors.New("图片 base64 数据无效")
|
|
}
|
|
if len(decoded) > maxImageDataSize {
|
|
return "", errors.New("图片过大，请选择小于 4MB 的图片")
|
|
}
|
|
|
|
return "data:" + mimeType + ";base64," + payload, nil
|
|
}
|
|
|
|
type contextWindowTrimStats struct {
|
|
Enabled bool
|
|
Limit int
|
|
BeforeTokens int
|
|
AfterTokens int
|
|
RemovedMessages int
|
|
}
|
|
|
|
func trimOpenAIMessagesToContextWindow(messages []openaiMessage, maxTokens int) ([]openaiMessage, contextWindowTrimStats) {
|
|
stats := contextWindowTrimStats{Enabled: maxTokens > 0, Limit: maxTokens}
|
|
if maxTokens <= 0 || len(messages) == 0 {
|
|
stats.BeforeTokens = estimateOpenAIMessagesTokens(messages)
|
|
stats.AfterTokens = stats.BeforeTokens
|
|
return messages, stats
|
|
}
|
|
|
|
result := append([]openaiMessage(nil), messages...)
|
|
stats.BeforeTokens = estimateOpenAIMessagesTokens(result)
|
|
stats.AfterTokens = stats.BeforeTokens
|
|
if stats.BeforeTokens <= maxTokens {
|
|
return result, stats
|
|
}
|
|
|
|
for stats.AfterTokens > maxTokens {
|
|
startIndex := 0
|
|
if len(result) > 0 && result[0].Role == "system" {
|
|
startIndex = 1
|
|
}
|
|
latestUserIndex := latestUserMessageIndex(result)
|
|
removeIndex := -1
|
|
for i := startIndex; i < len(result); i++ {
|
|
if i == latestUserIndex {
|
|
continue
|
|
}
|
|
if result[i].Role == "system" {
|
|
continue
|
|
}
|
|
removeIndex = i
|
|
break
|
|
}
|
|
if removeIndex == -1 {
|
|
break
|
|
}
|
|
|
|
removeCount := 1
|
|
if result[removeIndex].Role == "user" {
|
|
nextIndex := removeIndex + 1
|
|
if nextIndex < len(result) && nextIndex != latestUserIndex && result[nextIndex].Role == "assistant" {
|
|
removeCount = 2
|
|
}
|
|
}
|
|
result = append(result[:removeIndex], result[removeIndex+removeCount:]...)
|
|
stats.RemovedMessages += removeCount
|
|
stats.AfterTokens = estimateOpenAIMessagesTokens(result)
|
|
}
|
|
|
|
return result, stats
|
|
}
|
|
|
|
func latestUserMessageIndex(messages []openaiMessage) int {
|
|
for i := len(messages) - 1; i >= 0; i-- {
|
|
if messages[i].Role == "user" {
|
|
return i
|
|
}
|
|
}
|
|
return -1
|
|
}
|
|
|
|
type tokenUsageTracker struct {
|
|
promptTokens int
|
|
completionTokens int
|
|
toolPromptTokens int
|
|
toolCompletionTokens int
|
|
estimated bool
|
|
}
|
|
|
|
func newTokenUsageTracker() *tokenUsageTracker {
|
|
return &tokenUsageTracker{estimated: true}
|
|
}
|
|
|
|
func (t *tokenUsageTracker) addToolUsage(usage *openaiUsage, estimatedPromptTokens, estimatedCompletionTokens int) {
|
|
if usage != nil {
|
|
t.toolPromptTokens += usage.PromptTokens
|
|
t.toolCompletionTokens += usage.CompletionTokens
|
|
return
|
|
}
|
|
t.toolPromptTokens += estimatedPromptTokens
|
|
t.toolCompletionTokens += estimatedCompletionTokens
|
|
t.estimated = true
|
|
}
|
|
|
|
func (t *tokenUsageTracker) setModelEstimate(promptTokens, completionTokens int) *tokenUsageTracker {
|
|
t.promptTokens = promptTokens
|
|
t.completionTokens = completionTokens
|
|
t.estimated = true
|
|
return t
|
|
}
|
|
|
|
func (t *tokenUsageTracker) setModelUsage(usage *openaiUsage) *tokenUsageTracker {
|
|
if usage == nil {
|
|
return t
|
|
}
|
|
t.promptTokens = usage.PromptTokens
|
|
t.completionTokens = usage.CompletionTokens
|
|
return t
|
|
}
|
|
|
|
func (t *tokenUsageTracker) snapshot(completionTokensPerSec, peakCompletionTokensPerSec float64) tokenUsageStats {
|
|
totalTokens := t.promptTokens + t.completionTokens + t.toolPromptTokens + t.toolCompletionTokens
|
|
return tokenUsageStats{
|
|
PromptTokens: t.promptTokens,
|
|
CompletionTokens: t.completionTokens,
|
|
ToolPromptTokens: t.toolPromptTokens,
|
|
ToolCompletionTokens: t.toolCompletionTokens,
|
|
TotalTokens: totalTokens,
|
|
CompletionTokensPerSec: completionTokensPerSec,
|
|
PeakCompletionTokensPerSec: peakCompletionTokensPerSec,
|
|
Estimated: t.estimated,
|
|
}
|
|
}
|
|
|
|
func estimateOpenAIMessagesTokens(messages []openaiMessage) int {
|
|
total := 0
|
|
for _, message := range messages {
|
|
total += estimateTokenCount(message.Role) + 4
|
|
total += estimateOpenAIContentTokens(message.Content)
|
|
}
|
|
return total
|
|
}
|
|
|
|
func estimateOpenAIContentTokens(content any) int {
|
|
switch value := content.(type) {
|
|
case string:
|
|
return estimateTokenCount(value)
|
|
case []openaiContentPart:
|
|
total := 0
|
|
for _, part := range value {
|
|
switch part.Type {
|
|
case "text":
|
|
total += estimateTokenCount(part.Text)
|
|
case "image_url":
|
|
total += 85
|
|
}
|
|
}
|
|
return total
|
|
case []interface{}:
|
|
data, err := json.Marshal(value)
|
|
if err != nil {
|
|
return 0
|
|
}
|
|
return estimateTokenCount(string(data))
|
|
default:
|
|
data, err := json.Marshal(value)
|
|
if err != nil {
|
|
return 0
|
|
}
|
|
return estimateTokenCount(string(data))
|
|
}
|
|
}
|
|
|
|
func estimateTokenCount(text string) int {
|
|
text = strings.TrimSpace(text)
|
|
if text == "" {
|
|
return 0
|
|
}
|
|
|
|
tokens := 0
|
|
asciiRunes := 0
|
|
flushASCII := func() {
|
|
if asciiRunes > 0 {
|
|
tokens += (asciiRunes + 3) / 4
|
|
asciiRunes = 0
|
|
}
|
|
}
|
|
|
|
for _, r := range text {
|
|
if r <= 127 {
|
|
if r == ' ' || r == '\n' || r == '\t' || r == '\r' {
|
|
flushASCII()
|
|
continue
|
|
}
|
|
asciiRunes++
|
|
continue
|
|
}
|
|
flushASCII()
|
|
tokens++
|
|
}
|
|
flushASCII()
|
|
if tokens == 0 {
|
|
return 1
|
|
}
|
|
return tokens
|
|
}
|
|
|
|
func tokensPerSecond(tokens int, start time.Time) float64 {
|
|
elapsed := time.Since(start).Seconds()
|
|
if tokens <= 0 || elapsed <= 0 {
|
|
return 0
|
|
}
|
|
return float64(tokens) / elapsed
|
|
}
|
|
|
|
func maxFloat(a, b float64) float64 {
|
|
if b > a {
|
|
return b
|
|
}
|
|
return a
|
|
}
|
|
|
|
func buildToolConfigs(configs []models.ConfigsAIChatTool_) []agents.ToolConfig {
|
|
result := make([]agents.ToolConfig, 0, len(configs))
|
|
for _, c := range configs {
|
|
result = append(result, agents.ToolConfig{
|
|
Name: c.Name,
|
|
Enabled: c.Enabled,
|
|
Description: c.Description,
|
|
})
|
|
}
|
|
return result
|
|
}
|
|
|
|
func buildFunctionTools(configs []agents.ToolConfig) []openaiTool {
|
|
schemas := agents.FunctionToolSchemas(configs)
|
|
tools := make([]openaiTool, 0, len(schemas))
|
|
for _, schema := range schemas {
|
|
tools = append(tools, openaiTool{
|
|
Type: "function",
|
|
Function: openaiFunctionDefinition{
|
|
Name: schema.Name,
|
|
Description: schema.Description,
|
|
Parameters: schema.Parameters,
|
|
},
|
|
})
|
|
}
|
|
return tools
|
|
}
|
|
|
|
func parseJSONTraceValue(raw string) interface{} {
|
|
value := strings.TrimSpace(raw)
|
|
if value == "" {
|
|
return ""
|
|
}
|
|
var parsed interface{}
|
|
if err := json.Unmarshal([]byte(value), &parsed); err != nil {
|
|
return value
|
|
}
|
|
return parsed
|
|
}
|
|
|
|
func runOpenAIToolLoop(ctx context.Context, profile models.ConfigsAIChatOpenAI_, messages []openaiMessage, tools []openaiTool, currentUser *TabUser, tracker *tokenUsageTracker, trace agents.TraceFunc) ([]openaiMessage, bool, error) {
|
|
toolExecuted := false
|
|
for round := 0; round < 5; round++ {
|
|
if trace != nil {
|
|
trace("model", "tool_call", "running", "正在请求模型决定是否调用工具", map[string]interface{}{"round": round + 1})
|
|
}
|
|
req := openaiChatRequest{
|
|
Model: profile.Model,
|
|
Messages: messages,
|
|
Stream: false,
|
|
MaxTokens: profile.MaxTokens,
|
|
Temperature: 0.1,
|
|
Tools: tools,
|
|
ToolChoice: "auto",
|
|
}
|
|
resp, err := callOpenAIChat(ctx, profile, req)
|
|
if err != nil {
|
|
return messages, toolExecuted, err
|
|
}
|
|
responseText := ""
|
|
if len(resp.Choices) == 0 {
|
|
return messages, toolExecuted, nil
|
|
}
|
|
message := resp.Choices[0].Message
|
|
responseText = message.Content
|
|
tracker.addToolUsage(resp.Usage, estimateOpenAIMessagesTokens(messages), estimateTokenCount(responseText))
|
|
if len(message.ToolCalls) == 0 {
|
|
return messages, toolExecuted, nil
|
|
}
|
|
|
|
toolExecuted = true
|
|
messages = append(messages, openaiMessage{Role: "assistant", Content: message.Content, ToolCalls: message.ToolCalls})
|
|
for _, toolCall := range message.ToolCalls {
|
|
toolName := strings.TrimSpace(toolCall.Function.Name)
|
|
parsedArgs := parseJSONTraceValue(toolCall.Function.Arguments)
|
|
if trace != nil {
|
|
trace(toolName, "call", "running", "模型调用工具："+toolName, map[string]interface{}{
|
|
"tool": toolName,
|
|
"arguments": parsedArgs,
|
|
})
|
|
}
|
|
resultJSON, err := executeAIFunctionTool(ctx, toolName, []byte(toolCall.Function.Arguments), currentUser)
|
|
status := "success"
|
|
if err != nil {
|
|
status = "error"
|
|
resultJSON, _ = json.Marshal(map[string]interface{}{"ok": false, "error": err.Error()})
|
|
}
|
|
if trace != nil {
|
|
data := map[string]interface{}{
|
|
"tool": toolName,
|
|
"result": parseJSONTraceValue(string(resultJSON)),
|
|
}
|
|
if len(resultJSON) > 1200 {
|
|
data["result"] = string(resultJSON[:1200]) + "..."
|
|
data["truncated"] = true
|
|
}
|
|
trace(toolName, "execute", status, "工具执行完成："+toolName, data)
|
|
}
|
|
messages = append(messages, openaiMessage{Role: "tool", ToolCallID: toolCall.ID, Name: toolName, Content: string(resultJSON)})
|
|
}
|
|
}
|
|
return messages, toolExecuted, fmt.Errorf("工具调用超过最大轮数")
|
|
}
|
|
|
|
func executeAIFunctionTool(ctx context.Context, name string, rawArgs []byte, currentUser *TabUser) ([]byte, error) {
|
|
runtime := agents.FunctionToolRuntime{}
|
|
if currentUser != nil {
|
|
runtime.UserID = currentUser.ID
|
|
runtime.UserName = currentUser.Name
|
|
runtime.UserEmail = currentUser.Email
|
|
runtime.UserType = currentUser.Type
|
|
if userInfo := GetUserInfoFromUserID(currentUser.ID); userInfo != nil {
|
|
runtime.UserInfo = &agents.CurrentUserInfo{
|
|
ID: userInfo.ID,
|
|
UserID: userInfo.UserID,
|
|
FirstName: userInfo.FirstName,
|
|
Username: userInfo.Username,
|
|
Birthdate: userInfo.Birthdate.Format("2006-01-02"),
|
|
Gender: userInfo.Gender,
|
|
AvatarPath: userInfo.AvatarPath,
|
|
Region: userInfo.Region,
|
|
Language: userInfo.Language,
|
|
}
|
|
}
|
|
}
|
|
return agents.ExecuteFunctionTool(ctx, runtime, name, rawArgs)
|
|
}
|
|
|
|
func selectOpenAIProfile(cfg models.ConfigsAIChat_, name string) (models.ConfigsAIChatOpenAI_, bool) {
|
|
if name != "" {
|
|
for _, p := range cfg.OpenAI {
|
|
if p.Name == name {
|
|
return p, true
|
|
}
|
|
}
|
|
return models.ConfigsAIChatOpenAI_{}, false
|
|
}
|
|
for _, p := range cfg.OpenAI {
|
|
if p.Active {
|
|
return p, true
|
|
}
|
|
}
|
|
if len(cfg.OpenAI) > 0 {
|
|
return cfg.OpenAI[0], true
|
|
}
|
|
return models.ConfigsAIChatOpenAI_{}, false
|
|
}
|
|
|
|
func routeTools(ctx context.Context, profile models.ConfigsAIChatOpenAI_, router models.ConfigsAIChatToolRouter_, messages []agents.ChatMessage) (*toolRoutingResult, error) {
|
|
lastUserContent := strings.TrimSpace(agents.LastUserContent(messages))
|
|
if lastUserContent == "" {
|
|
return nil, nil
|
|
}
|
|
|
|
openaiMsgs := []openaiMessage{{Role: "user", Content: lastUserContent}}
|
|
|
|
toolNames := make([]string, 0, len(router.Tools))
|
|
for _, t := range router.Tools {
|
|
if t.Enabled {
|
|
toolNames = append(toolNames, t.Name+" - "+t.Description)
|
|
}
|
|
}
|
|
if len(toolNames) == 0 {
|
|
return nil, nil
|
|
}
|
|
|
|
sysPrompt := "请根据用户的最新一条消息，判断需要启用哪些工具来完成用户需求。\n可选工具：\n" + strings.Join(toolNames, "\n") + "\n\n回复格式要求：\n```json\n{\"tools\":[{\"name\":\"工具名称\",\"reason\":\"选择原因\"}],\"reason\":\"整体判断理由\"}\n```\n仅输出 JSON 代码块。如果没有需要启用的工具，返回 {\"tools\":[]}。"
|
|
openaiMsgs = append([]openaiMessage{{Role: "system", Content: sysPrompt}}, openaiMsgs...)
|
|
|
|
req := openaiChatRequest{
|
|
Model: profile.Model,
|
|
Messages: openaiMsgs,
|
|
Stream: false,
|
|
MaxTokens: router.MaxTokens,
|
|
Temperature: 0.1,
|
|
}
|
|
|
|
bodyBytes, err := json.Marshal(req)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
url := strings.TrimRight(profile.BaseUrl, "/") + "/chat/completions"
|
|
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(bodyBytes))
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
httpReq.Header.Set("Content-Type", "application/json")
|
|
httpReq.Header.Set("Authorization", "Bearer "+profile.ApiKey)
|
|
client := &http.Client{Timeout: time.Duration(router.Timeout) * time.Second}
|
|
resp, err := client.Do(httpReq)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
defer resp.Body.Close()
|
|
|
|
if resp.StatusCode != http.StatusOK {
|
|
body, _ := io.ReadAll(resp.Body)
|
|
return nil, fmt.Errorf("工具路由返回 %d: %s", resp.StatusCode, string(body))
|
|
}
|
|
|
|
var result openaiChatResponse
|
|
if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
|
|
return nil, err
|
|
}
|
|
if len(result.Choices) == 0 {
|
|
return &toolRoutingResult{Messages: openaiMsgs, Usage: result.Usage}, nil
|
|
}
|
|
|
|
response := result.Choices[0].Message.Content
|
|
decision := extractToolRoutingDecision(response)
|
|
selected := make([]string, 0, len(decision.Tools))
|
|
for _, t := range decision.Tools {
|
|
name := strings.TrimSpace(t.Name)
|
|
if name != "" {
|
|
selected = append(selected, name)
|
|
}
|
|
}
|
|
return &toolRoutingResult{Decision: decision, Selected: selected, Messages: openaiMsgs, Response: response, Usage: result.Usage}, nil
|
|
}
|
|
|
|
func extractToolRoutingDecision(response string) toolRoutingDecision {
|
|
start := strings.Index(response, "{")
|
|
end := strings.LastIndex(response, "}")
|
|
if start == -1 || end == -1 || end <= start {
|
|
return toolRoutingDecision{}
|
|
}
|
|
var parsed toolRoutingDecision
|
|
if err := json.Unmarshal([]byte(response[start:end+1]), &parsed); err != nil {
|
|
return toolRoutingDecision{}
|
|
}
|
|
return parsed
|
|
}
|
|
|
|
func filterToolConfigs(configs []agents.ToolConfig, selected []string) []agents.ToolConfig {
|
|
if len(selected) == 0 {
|
|
return []agents.ToolConfig{}
|
|
}
|
|
selectedSet := make(map[string]bool, len(selected))
|
|
for _, s := range selected {
|
|
selectedSet[s] = true
|
|
}
|
|
filtered := make([]agents.ToolConfig, 0, len(configs))
|
|
for _, c := range configs {
|
|
if selectedSet[c.Name] {
|
|
filtered = append(filtered, c)
|
|
}
|
|
}
|
|
return filtered
|
|
}
|