feat(llm): 大模型双协议(OpenAI+Anthropic)接入 + SSH凭证修复 + 容量预测AI解读
后端双协议: 新增 anthropic.go(Complete/Stream/工具转换) + client.go 工厂按 Protocol 路由; llm.Client 加 Stream; llm_chat 三方法收敛复用 llm.Client; resolveProtocol 按 provider 路由; Agent CLI 注入协议。编码套餐(GLM-5.2/Kimi/MiMo/Anthropic)走 Anthropic /v1/messages。 SSH凭证修复: applyCredential 正确解析凭证 JSON 的 private_key(旧实现把整个 JSON 当私钥 → ssh.ParsePrivateKey 失败 → ssh-key 登录不通); AssignCredential/批量导入/单台 CRUD 三处统一; 单台补齐 credential_id 字段(原被忽略)。 容量预测: 新增 AI 深度解读(buildCapacityInsightPrompt + PredictiveCapacityTab 按钮, 复用 aiApi.chat)。 其他: DeepSeek 模型名更新为 deepseek-v4-flash/pro; 智能助手去掉重复模型配置按钮; LLMConfig 精简生成参数(Claude Code 式)。 Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -166,6 +166,7 @@ func (ac *agentConn) startTask(sshCfg remoteexec.SSHConfig, llmCfg model.LLMConf
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// 构建引擎
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engine, err := agentcli.NewEngine(agentcli.EngineConfig{
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LLM: llm.Config{
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Protocol: resolveProtocol(llmCfg.Provider),
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Endpoint: resolveEndpoint(llmCfg.Provider, llmCfg.Endpoint),
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APIKey: llmCfg.APIKey, Model: llmCfg.Model,
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Temperature: llmCfg.Temperature, MaxTokens: llmCfg.MaxTokens,
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@@ -1,6 +1,7 @@
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package controller
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import (
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"encoding/json"
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"net/http"
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"strconv"
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@@ -43,15 +44,7 @@ func (h *CredentialHandler) AssignCredential(c *gin.Context) {
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return
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}
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if cred.SecretType == "ssh" {
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server.SSHKey = plain
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server.AuthType = "key"
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server.Password = ""
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} else {
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server.Password = plain
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server.AuthType = "password"
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server.SSHKey = ""
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}
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applyCredential(cred, plain, &server)
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if err := h.db.Save(&server).Error; err != nil {
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c.JSON(http.StatusInternalServerError, model.Response{Code: 500, Message: "分配失败: " + err.Error()})
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@@ -60,3 +53,30 @@ func (h *CredentialHandler) AssignCredential(c *gin.Context) {
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c.JSON(http.StatusOK, model.Response{Code: 200, Message: "凭据已关联"})
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}
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// applyCredential 将解密后的明文 plain 填充到 server 认证字段（统一入口，避免 plain JSON 误用） // Logan.Li
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// ssh 凭证 plain 是 JSON {"private_key":...,"ssh_user":...,"ssh_port":...}，须解析出 private_key（旧实现直接把
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// 整个 JSON 当私钥，导致 ssh.ParsePrivateKey 失败 → ssh-key 登录不通）；其余类型 plain 即密码。
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// 供 AssignCredential / 单台服务器 CRUD / 批量导入 三处复用。
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func applyCredential(cred model.Credential, plain string, server *model.Server) {
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if cred.SecretType == "ssh" {
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privateKey := plain
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var sshc model.SSHSecretContent
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if json.Unmarshal([]byte(plain), &sshc) == nil && sshc.PrivateKey != "" {
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privateKey = sshc.PrivateKey
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if sshc.SSHUser != "" {
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server.Username = sshc.SSHUser
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}
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if sshc.SSHPort > 0 {
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server.Port = sshc.SSHPort
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}
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}
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server.SSHKey = privateKey
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server.AuthType = "key"
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server.Password = ""
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} else {
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server.Password = plain
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server.AuthType = "password"
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server.SSHKey = ""
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}
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}
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+19
-17
@@ -122,23 +122,6 @@ func (h *LLMHandler) SaveLLMConfig(c *gin.Context) {
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}})
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}
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// OpenAI 兼容请求/响应结构（仅取必要字段） // Logan.Li
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type openaiChatRequest struct {
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Model string `json:"model"`
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Messages []model.ChatMessage `json:"messages"`
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Temperature float64 `json:"temperature"`
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MaxTokens int `json:"max_tokens,omitempty"`
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}
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type openaiChoice struct {
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Message model.ChatMessage `json:"message"`
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}
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type openaiChatResponse struct {
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Choices []openaiChoice `json:"choices"`
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Usage struct {
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TotalTokens int `json:"total_tokens"`
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} `json:"usage"`
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}
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// resolveEndpoint 根据提供商补全默认 endpoint // Logan.Li
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func resolveEndpoint(provider, endpoint string) string {
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if endpoint != "" {
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@@ -147,10 +130,20 @@ func resolveEndpoint(provider, endpoint string) string {
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switch provider {
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case "deepseek":
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return "https://api.deepseek.com/v1"
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case "glm": // 智谱 GLM 通用 OpenAI 兼容端点（编码套餐改用 https://open.bigmodel.cn/api/coding/paas/v4） // Logan.Li
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return "https://open.bigmodel.cn/api/paas/v4"
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case "qwen":
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return "https://dashscope.aliyuncs.com/compatible-mode/v1"
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case "moonshot":
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return "https://api.moonshot.cn/v1"
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case "kimi": // Kimi 编码套餐 OpenAI 兼容端点（注意：该端点有客户端白名单，通用调用可能被拒） // Logan.Li
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return "https://api.kimi.com/coding/v1"
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case "mimo": // 小米 MiMo OpenAI 兼容端点 // Logan.Li
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return "https://api.mimo.mi.com/v1"
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case "anthropic": // Anthropic 官方 Messages 协议（anthropic.go 客户端拼 /v1/messages） // Logan.Li
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return "https://api.anthropic.com"
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case "glm-coding": // 智谱 GLM 编码套餐 Anthropic 协议端点（模型 GLM-5.2） // Logan.Li
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return "https://open.bigmodel.cn/api/anthropic"
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case "ollama":
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return "http://localhost:11434/v1"
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default: // openai / custom
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@@ -158,6 +151,15 @@ func resolveEndpoint(provider, endpoint string) string {
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}
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}
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// resolveProtocol 根据提供商推断接入协议（决定走 OpenAI 还是 Anthropic 客户端） // Logan.Li
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// 编码套餐（智谱 GLM 编码 / Anthropic 官方）走 Anthropic Messages 协议；其余走 OpenAI 兼容协议。
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func resolveProtocol(provider string) string {
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if provider == "anthropic" || provider == "glm-coding" {
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return "anthropic"
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}
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return "openai"
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}
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// defaultSystemPrompt AIOps 运维助手默认系统提示词 // Logan.Li
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const defaultSystemPrompt = `你是 AIOps 智能运维助手，专注于企业级运维场景。你可以：
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1. 分析服务器、容器、K8s 集群的性能瓶颈与异常
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+50
-192
@@ -1,22 +1,41 @@
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package controller
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import (
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"bufio"
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"bytes"
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"encoding/json"
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"fmt"
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"io"
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"net/http"
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"strings"
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"time"
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"ezops/middleware"
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"ezops/model"
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"ezops/pkg/agentcli/llm"
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"github.com/gin-gonic/gin"
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)
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// Chat AI 对话代理（OpenAI 兼容接口） // Logan.Li
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// buildLLMClient 从数据库配置构造 LLM 客户端（按 provider 协议自动路由 OpenAI/Anthropic） // Logan.Li
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func (h *LLMHandler) buildLLMClient(cfg model.LLMConfig) llm.Client {
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return llm.NewClient(llm.Config{
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Protocol: resolveProtocol(cfg.Provider),
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Endpoint: resolveEndpoint(cfg.Provider, cfg.Endpoint),
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APIKey: cfg.APIKey,
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Model: cfg.Model,
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Temperature: cfg.Temperature,
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MaxTokens: cfg.MaxTokens,
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})
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}
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// toLLMMessages model.ChatMessage[] → llm.Message[]（智能助手纯对话，仅填 Role/Content） // Logan.Li
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func toLLMMessages(msgs []model.ChatMessage) []llm.Message {
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out := make([]llm.Message, len(msgs))
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for i, m := range msgs {
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out[i] = llm.Message{Role: m.Role, Content: m.Content}
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}
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return out
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}
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// Chat AI 对话代理（统一走 llm.Client，按配置协议自动适配 OpenAI/Anthropic） // Logan.Li
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// POST /api/v1/aiops/chat
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func (h *LLMHandler) Chat(c *gin.Context) {
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var cfg model.LLMConfig
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@@ -38,60 +57,22 @@ func (h *LLMHandler) Chat(c *gin.Context) {
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}
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messages := append([]model.ChatMessage{{Role: "system", Content: systemPrompt}}, req.Messages...)
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// 构造 OpenAI 兼容请求
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payload := openaiChatRequest{
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Model: cfg.Model,
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Messages: messages,
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Temperature: cfg.Temperature,
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MaxTokens: cfg.MaxTokens,
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}
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body, _ := json.Marshal(payload)
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endpoint := resolveEndpoint(cfg.Provider, cfg.Endpoint)
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url := endpoint + "/chat/completions"
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httpReq, err := http.NewRequestWithContext(c.Request.Context(), http.MethodPost, url, bytes.NewReader(body))
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resp, err := h.buildLLMClient(cfg).Complete(c.Request.Context(), &llm.CompletionRequest{
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Messages: toLLMMessages(messages),
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})
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if err != nil {
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c.JSON(http.StatusOK, model.Response{Code: 500, Message: "构造请求失败: " + err.Error()})
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c.JSON(http.StatusOK, model.Response{Code: 502, Message: "LLM 调用失败: " + err.Error()})
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return
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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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resp, err := h.client.Do(httpReq)
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if err != nil {
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c.JSON(http.StatusOK, model.Response{Code: 502, Message: "LLM 服务连接失败，请检查网络或 Endpoint 配置: " + err.Error()})
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return
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}
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defer resp.Body.Close()
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raw, _ := io.ReadAll(resp.Body)
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if resp.StatusCode != http.StatusOK {
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// 提取上游错误信息
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msg := fmt.Sprintf("LLM 服务返回错误 (HTTP %d)", resp.StatusCode)
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if upErr := parseOpenAIError(raw); upErr != "" {
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msg += ": " + upErr
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}
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c.JSON(http.StatusOK, model.Response{Code: resp.StatusCode, Message: msg})
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return
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}
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var chatResp openaiChatResponse
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if err := json.Unmarshal(raw, &chatResp); err != nil || len(chatResp.Choices) == 0 {
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c.JSON(http.StatusOK, model.Response{Code: 500, Message: "解析 LLM 响应失败"})
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return
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}
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replyContent := chatResp.Choices[0].Message.Content
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replyContent := resp.Message.Content
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// 持久化对话历史（按用户隔离，成对写入 user + assistant） // Logan.Li
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// 仅在成功时入库；错误气泡为瞬时 UI 不持久化
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uid := middleware.GetUserID(c)
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if uid != 0 && len(req.Messages) > 0 {
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// 取最后一条用户消息（前端已附加当前提问）
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lastUser := req.Messages[len(req.Messages)-1]
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now := time.Now()
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// session_id：前端传入或后端生成（兼容旧版客户端） // Logan.Li
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sid := req.SessionID
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sid := req.SessionID // 前端传入或后端生成（兼容旧版客户端）
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if sid == "" {
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sid = fmt.Sprintf("sess-%d", now.UnixMilli())
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}
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@@ -104,15 +85,15 @@ func (h *LLMHandler) Chat(c *gin.Context) {
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c.JSON(http.StatusOK, model.Response{Code: 200, Data: model.ChatResponse{
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Content: replyContent,
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Model: cfg.Model,
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TokensUsed: chatResp.Usage.TotalTokens,
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TokensUsed: resp.Usage.TotalTokens,
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}})
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}
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// ChatStream AI 流式对话代理（SSE） // Logan.Li
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// POST /api/v1/aiops/chat/stream
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// 客户端用 EventSource/fetch-stream 消费，每条 SSE data 为 {"content": "..."}
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// 出错时发送 event: error，data 为 {"message": "..."}
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// 完成后服务端持久化 user + assistant 两条历史，event: done 结束
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// 客户端用 fetch-stream 消费，每条 SSE data 为 {"content": "..."}；出错发 event: error；完成发 event: done。
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// 上游协议差异（OpenAI delta / Anthropic content_block_delta）由 llm.Client.Stream 屏蔽，
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// controller 只负责 SSE 转发 + 客户端断开检测 + 历史持久化。
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func (h *LLMHandler) ChatStream(c *gin.Context) {
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var cfg model.LLMConfig
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if err := h.db.First(&cfg).Error; err != nil || !cfg.Enabled {
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@@ -136,127 +117,37 @@ func (h *LLMHandler) ChatStream(c *gin.Context) {
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c.Writer.Header().Set("Connection", "keep-alive")
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c.Writer.Header().Set("X-Accel-Buffering", "no") // Nginx 关闭缓冲
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c.Writer.WriteHeader(http.StatusOK)
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// 注册写入心跳（立即 flush 一个注释行让客户端确认连接建立）
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c.Writer.Flush()
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c.Writer.Flush() // 立即 flush 让客户端确认连接建立
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// 组装系统提示词 + 上下文
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systemPrompt := cfg.SystemPrompt
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if strings.TrimSpace(systemPrompt) == "" {
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systemPrompt = defaultSystemPrompt
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}
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messages := append([]model.ChatMessage{{Role: "system", Content: systemPrompt}}, req.Messages...)
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// 构造 OpenAI 兼容流式请求
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payload := struct {
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Model string `json:"model"`
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Messages []model.ChatMessage `json:"messages"`
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Temperature float64 `json:"temperature"`
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MaxTokens int `json:"max_tokens,omitempty"`
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Stream bool `json:"stream"`
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}{
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Model: cfg.Model,
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Messages: messages,
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Temperature: cfg.Temperature,
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MaxTokens: cfg.MaxTokens,
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Stream: true,
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}
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body, _ := json.Marshal(payload)
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endpoint := resolveEndpoint(cfg.Provider, cfg.Endpoint)
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url := endpoint + "/chat/completions"
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httpReq, err := http.NewRequestWithContext(c.Request.Context(), http.MethodPost, url, bytes.NewReader(body))
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if err != nil {
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writeSSEError(c, 500, "构造请求失败: "+err.Error())
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return
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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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httpReq.Header.Set("Accept", "text/event-stream")
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// 流式请求不能用全局 120s 超时客户端，构造无超时 client 让 ctx 控制 // Logan.Li
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streamClient := &http.Client{Timeout: 0}
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resp, err := streamClient.Do(httpReq)
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if err != nil {
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writeSSEError(c, 502, "LLM 服务连接失败，请检查网络或 Endpoint 配置: "+err.Error())
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return
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}
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defer resp.Body.Close()
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if resp.StatusCode != http.StatusOK {
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raw, _ := io.ReadAll(resp.Body)
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msg := fmt.Sprintf("LLM 服务返回错误 (HTTP %d)", resp.StatusCode)
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if e := parseOpenAIError(raw); e != "" {
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msg += ": " + e
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}
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writeSSEError(c, resp.StatusCode, msg)
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return
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}
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// 逐行解析上游 SSE，转发增量内容给前端 // Logan.Li
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// 流式补全：逐 token 回调转发 SSE；ctx 取消（客户端断开）由 llm.Client 内部终止上游读取
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var contentBuilder strings.Builder
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reader := bufio.NewReader(resp.Body)
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clientGone := false
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for {
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// 客户端断开时停止读取（c.Request.Context().Done() 在客户端断开时触发） // Logan.Li
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select {
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case <-c.Request.Context().Done():
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clientGone = true
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default:
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}
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||||
if clientGone {
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break
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}
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line, readErr := reader.ReadString('\n')
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line = strings.TrimSpace(line)
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if line != "" {
|
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// 仅处理 data: 开头的行
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if strings.HasPrefix(line, "data:") {
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data := strings.TrimSpace(strings.TrimPrefix(line, "data:"))
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if data == "[DONE]" {
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// 上游结束，跳出循环准备持久化 + 发送 [DONE]
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break
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}
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// 解析 {"choices":[{"delta":{"content":"..."}}]}
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var chunk struct {
|
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Choices []struct {
|
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Delta struct {
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Content string `json:"content"`
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} `json:"delta"`
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} `json:"choices"`
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}
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if json.Unmarshal([]byte(data), &chunk) == nil && len(chunk.Choices) > 0 {
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delta := chunk.Choices[0].Delta.Content
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if delta != "" {
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_, streamErr := h.buildLLMClient(cfg).Stream(c.Request.Context(), &llm.CompletionRequest{
|
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Messages: toLLMMessages(messages),
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}, func(delta string) {
|
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contentBuilder.WriteString(delta)
|
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// 转发给前端：data: {"content":"..."}
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writeSSEChunk(c, delta)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
if readErr != nil {
|
||||
if readErr != io.EOF {
|
||||
// 读取出错（非正常结束）
|
||||
if contentBuilder.Len() == 0 {
|
||||
writeSSEError(c, 502, "LLM 流式响应读取失败: "+readErr.Error())
|
||||
return
|
||||
}
|
||||
// 已有部分内容则正常结束
|
||||
}
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
})
|
||||
fullContent := contentBuilder.String()
|
||||
|
||||
// 持久化对话历史（仅在成功且非空时入库；错误/中断不入库避免污染历史） // Logan.Li
|
||||
// 出错且无任何内容 → 发 error 事件
|
||||
if streamErr != nil && fullContent == "" {
|
||||
writeSSEError(c, 502, "LLM 流式响应失败: "+streamErr.Error())
|
||||
return
|
||||
}
|
||||
|
||||
// 持久化 + 结束事件（仅在非空且客户端未断开时入库；错误/中断不入库避免污染历史） // Logan.Li
|
||||
clientGone := c.Request.Context().Err() != nil
|
||||
if fullContent != "" && !clientGone {
|
||||
uid := middleware.GetUserID(c)
|
||||
if uid != 0 {
|
||||
lastUser := req.Messages[len(req.Messages)-1]
|
||||
now := time.Now()
|
||||
// session_id：前端传入或后端生成（兼容旧版客户端） // Logan.Li
|
||||
sid := req.SessionID
|
||||
if sid == "" {
|
||||
sid = fmt.Sprintf("sess-%d", now.UnixMilli())
|
||||
@@ -266,7 +157,6 @@ func (h *LLMHandler) ChatStream(c *gin.Context) {
|
||||
{UserID: uid, SessionID: sid, Role: "assistant", Content: fullContent, Model: cfg.Model, CreatedAt: now.Add(time.Millisecond)},
|
||||
})
|
||||
}
|
||||
// 发送结束标志
|
||||
c.Writer.WriteString("event: done\ndata: {\"model\":\"" + cfg.Model + "\"}\n\n")
|
||||
c.Writer.Flush()
|
||||
} else if !clientGone {
|
||||
@@ -298,19 +188,6 @@ func writeSSEError(c *gin.Context, code int, message string) {
|
||||
c.Writer.Flush()
|
||||
}
|
||||
|
||||
// parseOpenAIError 从上游错误响应中提取 message 字段 // Logan.Li
|
||||
func parseOpenAIError(raw []byte) string {
|
||||
var m struct {
|
||||
Error struct {
|
||||
Message string `json:"message"`
|
||||
} `json:"error"`
|
||||
}
|
||||
if json.Unmarshal(raw, &m) == nil && m.Error.Message != "" {
|
||||
return m.Error.Message
|
||||
}
|
||||
return ""
|
||||
}
|
||||
|
||||
// TestLLMConnection 测试 LLM 连接（发送一条 ping 消息） // Logan.Li
|
||||
// POST /api/v1/aiops/llm-config/test
|
||||
func (h *LLMHandler) TestLLMConnection(c *gin.Context) {
|
||||
@@ -319,31 +196,12 @@ func (h *LLMHandler) TestLLMConnection(c *gin.Context) {
|
||||
c.JSON(http.StatusOK, model.Response{Code: 400, Message: "请先保存配置"})
|
||||
return
|
||||
}
|
||||
payload := openaiChatRequest{
|
||||
Model: cfg.Model,
|
||||
Messages: []model.ChatMessage{{Role: "user", Content: "ping"}},
|
||||
Temperature: 0,
|
||||
MaxTokens: 16,
|
||||
}
|
||||
body, _ := json.Marshal(payload)
|
||||
endpoint := resolveEndpoint(cfg.Provider, cfg.Endpoint)
|
||||
httpReq, _ := http.NewRequestWithContext(c.Request.Context(), http.MethodPost, endpoint+"/chat/completions", bytes.NewReader(body))
|
||||
httpReq.Header.Set("Content-Type", "application/json")
|
||||
httpReq.Header.Set("Authorization", "Bearer "+cfg.APIKey)
|
||||
resp, err := h.client.Do(httpReq)
|
||||
_, err := h.buildLLMClient(cfg).Complete(c.Request.Context(), &llm.CompletionRequest{
|
||||
Messages: []llm.Message{{Role: "user", Content: "ping"}},
|
||||
})
|
||||
if err != nil {
|
||||
c.JSON(http.StatusOK, model.Response{Code: 502, Message: "连接失败: " + err.Error()})
|
||||
return
|
||||
}
|
||||
defer resp.Body.Close()
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
raw, _ := io.ReadAll(resp.Body)
|
||||
msg := fmt.Sprintf("HTTP %d", resp.StatusCode)
|
||||
if e := parseOpenAIError(raw); e != "" {
|
||||
msg += ": " + e
|
||||
}
|
||||
c.JSON(http.StatusOK, model.Response{Code: resp.StatusCode, Message: msg})
|
||||
return
|
||||
}
|
||||
c.JSON(http.StatusOK, model.Response{Code: 200, Message: "连接成功"})
|
||||
}
|
||||
@@ -223,6 +223,21 @@ func (h *ServerHandler) CreateServer(c *gin.Context) {
|
||||
server.AuthType = "password"
|
||||
}
|
||||
|
||||
// 关联 SSH 凭证：解密后覆盖认证字段（同 AssignCredential，避免单台 CRUD 忽略 credential_id） // Logan.Li
|
||||
if req.CredentialID != nil && *req.CredentialID > 0 {
|
||||
var cred model.Credential
|
||||
if err := h.db.First(&cred, *req.CredentialID).Error; err != nil {
|
||||
c.JSON(http.StatusBadRequest, model.Response{Code: 400, Message: "凭据不存在"})
|
||||
return
|
||||
}
|
||||
plain, err := h.crypt.Decrypt(cred.Content)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusInternalServerError, model.Response{Code: 500, Message: "凭据解密失败"})
|
||||
return
|
||||
}
|
||||
applyCredential(cred, plain, &server)
|
||||
}
|
||||
|
||||
if err := h.db.Create(&server).Error; err != nil {
|
||||
c.JSON(http.StatusInternalServerError, model.Response{
|
||||
Code: 500,
|
||||
@@ -297,6 +312,25 @@ func (h *ServerHandler) UpdateServer(c *gin.Context) {
|
||||
if req.AuthType != "" {
|
||||
updates["auth_type"] = req.AuthType
|
||||
}
|
||||
// 关联 SSH 凭证：解密后覆盖认证字段（applyCredential 填充已 load 的 server，再同步到 updates） // Logan.Li
|
||||
if req.CredentialID != nil && *req.CredentialID > 0 {
|
||||
var cred model.Credential
|
||||
if err := h.db.First(&cred, *req.CredentialID).Error; err != nil {
|
||||
c.JSON(http.StatusBadRequest, model.Response{Code: 400, Message: "凭据不存在"})
|
||||
return
|
||||
}
|
||||
plain, err := h.crypt.Decrypt(cred.Content)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusInternalServerError, model.Response{Code: 500, Message: "凭据解密失败"})
|
||||
return
|
||||
}
|
||||
applyCredential(cred, plain, &server)
|
||||
updates["ssh_key"] = server.SSHKey
|
||||
updates["password"] = server.Password
|
||||
updates["auth_type"] = server.AuthType
|
||||
updates["username"] = server.Username
|
||||
updates["port"] = server.Port
|
||||
}
|
||||
if req.OS != "" {
|
||||
updates["os"] = req.OS
|
||||
}
|
||||
|
||||
@@ -30,26 +30,24 @@ func (h *ServerHandler) BatchCreateServers(c *gin.Context) {
|
||||
authType = "password"
|
||||
}
|
||||
|
||||
// 如果指定了凭据，提前解密 // Logan.Li
|
||||
var decryptedPwd, decryptedKey string
|
||||
// 如果指定了凭据，提前解密（plain 在循环内由 applyCredential 解析填充） // Logan.Li
|
||||
var (
|
||||
hasCred bool
|
||||
cred model.Credential
|
||||
credPlain string
|
||||
)
|
||||
if req.CredentialID != nil && *req.CredentialID > 0 {
|
||||
var cred model.Credential
|
||||
if err := h.db.First(&cred, *req.CredentialID).Error; err != nil {
|
||||
c.JSON(http.StatusBadRequest, model.Response{Code: 400, Message: "凭据不存在"})
|
||||
return
|
||||
}
|
||||
plain, err := h.crypt.Decrypt(cred.Content)
|
||||
p, err := h.crypt.Decrypt(cred.Content)
|
||||
if err != nil {
|
||||
c.JSON(http.StatusInternalServerError, model.Response{Code: 500, Message: "凭据解密失败"})
|
||||
return
|
||||
}
|
||||
if cred.SecretType == "ssh" {
|
||||
decryptedKey = plain
|
||||
authType = "key"
|
||||
} else {
|
||||
decryptedPwd = plain
|
||||
authType = "password"
|
||||
}
|
||||
credPlain = p
|
||||
hasCred = true
|
||||
}
|
||||
|
||||
result := model.BatchCreateResult{Errors: []string{}}
|
||||
@@ -84,9 +82,7 @@ func (h *ServerHandler) BatchCreateServers(c *gin.Context) {
|
||||
IP: ip,
|
||||
Port: port,
|
||||
Username: username,
|
||||
AuthType: authType,
|
||||
Password: decryptedPwd,
|
||||
SSHKey: decryptedKey,
|
||||
AuthType: authType, // 无凭证时用请求 authType（默认 password）
|
||||
OS: item.OS,
|
||||
CPU: item.CPU,
|
||||
Memory: item.Memory * 1024, // GB → MB
|
||||
@@ -95,6 +91,9 @@ func (h *ServerHandler) BatchCreateServers(c *gin.Context) {
|
||||
RegionID: req.RegionID,
|
||||
Status: "stopped",
|
||||
}
|
||||
if hasCred {
|
||||
applyCredential(cred, credPlain, &server) // 有凭证则覆盖认证字段（ssh→私钥/ssh_user/ssh_port） // Logan.Li
|
||||
}
|
||||
if err := h.db.Create(&server).Error; err != nil {
|
||||
result.Failed++
|
||||
result.Errors = append(result.Errors, fmt.Sprintf("%s(%s): %s", hostname, ip, err.Error()))
|
||||
|
||||
@@ -3,11 +3,11 @@ package model
|
||||
import "time"
|
||||
|
||||
// LLMConfig 大语言模型配置（单行配置，类似 LDAPConfig） // Logan.Li
|
||||
// 支持 OpenAI 兼容接口（OpenAI / DeepSeek / 通义千问 / Moonshot / Ollama / 自定义）
|
||||
// 支持 OpenAI 兼容接口（OpenAI / DeepSeek / 智谱GLM / 通义千问 / Moonshot / Kimi / 小米MiMo / Anthropic(需代理) / Ollama / 自定义）
|
||||
type LLMConfig struct {
|
||||
ID uint `gorm:"primarykey" json:"id"`
|
||||
Provider string `gorm:"size:50;not null;default:openai" json:"provider"` // openai/deepseek/qwen/moonshot/ollama/custom
|
||||
Model string `gorm:"size:100;not null;default:gpt-4o-mini" json:"model"` // 模型名，如 gpt-4o-mini / deepseek-chat
|
||||
Provider string `gorm:"size:50;not null;default:openai" json:"provider"` // openai/deepseek/glm/qwen/moonshot/kimi/mimo/anthropic/ollama/custom
|
||||
Model string `gorm:"size:100;not null;default:gpt-4o-mini" json:"model"` // 模型名，如 gpt-4o-mini / deepseek-v4-flash
|
||||
APIKey string `gorm:"size:500" json:"api_key"` // API Key（GET 时会被脱敏）
|
||||
Endpoint string `gorm:"size:500" json:"endpoint"` // 基础地址，如 https://api.openai.com/v1
|
||||
Temperature float64 `gorm:"default:0.7" json:"temperature"` // 采样温度 0-2
|
||||
|
||||
@@ -137,6 +137,7 @@ type CreateServerRequest struct {
|
||||
Password string `json:"password"`
|
||||
SSHKey string `json:"ssh_key"`
|
||||
AuthType string `json:"auth_type" binding:"omitempty,oneof=password key"`
|
||||
CredentialID *uint `json:"credential_id,omitempty"` // 关联 SSH 凭证，非空则后端解密覆盖认证字段
|
||||
OS string `json:"os"`
|
||||
CPU int `json:"cpu"`
|
||||
Memory int64 `json:"memory"`
|
||||
@@ -158,6 +159,7 @@ type UpdateServerRequest struct {
|
||||
Password string `json:"password"`
|
||||
SSHKey string `json:"ssh_key"`
|
||||
AuthType string `json:"auth_type" binding:"omitempty,oneof=password key"`
|
||||
CredentialID *uint `json:"credential_id,omitempty"` // 关联 SSH 凭证，非空则后端解密覆盖认证字段
|
||||
OS string `json:"os"`
|
||||
CPU int `json:"cpu"`
|
||||
Memory int64 `json:"memory"`
|
||||
|
||||
@@ -0,0 +1,337 @@
|
||||
// anthropic.go - Anthropic Messages 协议 LLM 客户端实现 // Logan.Li
|
||||
// 支持官方 Claude（api.anthropic.com）及智谱 GLM 编码套餐（open.bigmodel.cn/api/anthropic）等
|
||||
// 所有 Anthropic /v1/messages 兼容端点。与 openai.go 实现同一 Client 接口，由 client.go 按 Protocol 路由。
|
||||
// 鉴权统一用 Authorization: Bearer（智谱编码套餐实测通过；官方 Claude 较新版本亦支持）+ anthropic-version。
|
||||
package llm
|
||||
|
||||
import (
|
||||
"bufio"
|
||||
"bytes"
|
||||
"context"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"net/http"
|
||||
"strings"
|
||||
"time"
|
||||
)
|
||||
|
||||
// AnthropicClient Anthropic Messages 协议客户端 // Logan.Li
|
||||
type AnthropicClient struct {
|
||||
cfg Config
|
||||
client *http.Client
|
||||
}
|
||||
|
||||
// NewAnthropicClient 创建 Anthropic 兼容客户端 // Logan.Li
|
||||
func NewAnthropicClient(cfg Config) Client {
|
||||
timeout := cfg.Timeout
|
||||
if timeout <= 0 {
|
||||
timeout = 180 * time.Second
|
||||
}
|
||||
return &AnthropicClient{cfg: cfg, client: &http.Client{Timeout: timeout}}
|
||||
}
|
||||
|
||||
// Name 返回客户端名称 // Logan.Li
|
||||
func (c *AnthropicClient) Name() string { return "anthropic(" + c.cfg.Model + ")" }
|
||||
|
||||
// ---- 请求体结构（Anthropic Messages 格式） // Logan.Li ----
|
||||
|
||||
// anthropicMessage Anthropic 消息：content 可为字符串或 block 数组，用 RawMessage 原样嵌入。
|
||||
type anthropicMessage struct {
|
||||
Role string `json:"role"`
|
||||
Content json.RawMessage `json:"content"`
|
||||
}
|
||||
|
||||
// anthropicRequest Anthropic /v1/messages 请求体 // Logan.Li
|
||||
type anthropicRequest struct {
|
||||
Model string `json:"model"`
|
||||
MaxTokens int `json:"max_tokens"`
|
||||
System string `json:"system,omitempty"` // system 抽到顶层（不在 messages 内）
|
||||
Messages []anthropicMessage `json:"messages"`
|
||||
Tools []anthropicTool `json:"tools,omitempty"`
|
||||
Temperature float64 `json:"temperature,omitempty"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
}
|
||||
|
||||
// anthropicTool Anthropic 工具定义（input_schema，对应 OpenAI 的 parameters） // Logan.Li
|
||||
type anthropicTool struct {
|
||||
Name string `json:"name"`
|
||||
Description string `json:"description"`
|
||||
InputSchema map[string]interface{} `json:"input_schema"`
|
||||
}
|
||||
|
||||
// 响应 content block：text 或 tool_use（互斥字段） // Logan.Li
|
||||
type anthropicBlock struct {
|
||||
Type string `json:"type"` // text | tool_use
|
||||
Text string `json:"text,omitempty"`
|
||||
ID string `json:"id,omitempty"` // tool_use
|
||||
Name string `json:"name,omitempty"` // tool_use
|
||||
Input json.RawMessage `json:"input,omitempty"` // tool_use
|
||||
}
|
||||
|
||||
// 请求侧 content block（assistant 输出 tool_use / user 输出 tool_result） // Logan.Li
|
||||
type textBlock struct {
|
||||
Type string `json:"type"` // 固定 "text"
|
||||
Text string `json:"text"`
|
||||
}
|
||||
type toolUseBlock struct {
|
||||
Type string `json:"type"` // 固定 "tool_use"
|
||||
ID string `json:"id"`
|
||||
Name string `json:"name"`
|
||||
Input json.RawMessage `json:"input"`
|
||||
}
|
||||
type toolResultBlock struct {
|
||||
Type string `json:"type"` // 固定 "tool_result"
|
||||
ToolUseID string `json:"tool_use_id"`
|
||||
Content string `json:"content"`
|
||||
}
|
||||
|
||||
// anthropicResponse Anthropic /v1/messages 响应体 // Logan.Li
|
||||
type anthropicResponse struct {
|
||||
ID string `json:"id"`
|
||||
Model string `json:"model"`
|
||||
Content []anthropicBlock `json:"content"`
|
||||
Usage struct {
|
||||
InputTokens int `json:"input_tokens"`
|
||||
OutputTokens int `json:"output_tokens"`
|
||||
} `json:"usage"`
|
||||
Error *struct {
|
||||
Type string `json:"type"`
|
||||
Message string `json:"message"`
|
||||
} `json:"error,omitempty"`
|
||||
}
|
||||
|
||||
// buildMessages 把 OpenAI 风格 Message[] 转成 Anthropic messages + 顶层 system // Logan.Li
|
||||
// - system role 抽到顶层 system 字段
|
||||
// - tool role（工具结果）→ user 消息的 tool_result 块
|
||||
// - assistant 带 tool_calls → content 含 tool_use 块
|
||||
func (c *AnthropicClient) buildMessages(req *CompletionRequest) (string, []anthropicMessage) {
|
||||
var system string
|
||||
var msgs []anthropicMessage
|
||||
for _, m := range req.Messages {
|
||||
switch m.Role {
|
||||
case "system":
|
||||
if system == "" {
|
||||
system = m.Content
|
||||
} else {
|
||||
system += "\n" + m.Content
|
||||
}
|
||||
case "tool":
|
||||
raw, _ := json.Marshal([]toolResultBlock{{
|
||||
Type: "tool_result", ToolUseID: m.ToolCallID, Content: m.Content,
|
||||
}})
|
||||
msgs = append(msgs, anthropicMessage{Role: "user", Content: raw})
|
||||
case "assistant":
|
||||
// assistant 消息可能含文本 + tool_use 计划，组装成 content block 数组
|
||||
blocks := make([]interface{}, 0, 1+len(m.ToolCalls))
|
||||
if m.Content != "" {
|
||||
blocks = append(blocks, textBlock{Type: "text", Text: m.Content})
|
||||
}
|
||||
for _, tc := range m.ToolCalls {
|
||||
args := json.RawMessage("{}")
|
||||
if strings.TrimSpace(tc.Function.Arguments) != "" {
|
||||
args = json.RawMessage(tc.Function.Arguments)
|
||||
}
|
||||
blocks = append(blocks, toolUseBlock{
|
||||
Type: "tool_use", ID: tc.ID, Name: tc.Function.Name, Input: args,
|
||||
})
|
||||
}
|
||||
raw, _ := json.Marshal(blocks)
|
||||
msgs = append(msgs, anthropicMessage{Role: "assistant", Content: raw})
|
||||
default: // user
|
||||
raw, _ := json.Marshal(m.Content)
|
||||
msgs = append(msgs, anthropicMessage{Role: "user", Content: raw})
|
||||
}
|
||||
}
|
||||
return system, msgs
|
||||
}
|
||||
|
||||
// buildTools OpenAI Tool[](parameters) → Anthropic [](input_schema) // Logan.Li
|
||||
func buildTools(tools []Tool) []anthropicTool {
|
||||
out := make([]anthropicTool, 0, len(tools))
|
||||
for _, t := range tools {
|
||||
out = append(out, anthropicTool{
|
||||
Name: t.Function.Name, Description: t.Function.Description, InputSchema: t.Function.Parameters,
|
||||
})
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// buildRequest 构造 Anthropic 请求体（Complete 与 Stream 共用） // Logan.Li
|
||||
func (c *AnthropicClient) buildRequest(req *CompletionRequest, stream bool) ([]byte, error) {
|
||||
system, msgs := c.buildMessages(req)
|
||||
payload := anthropicRequest{
|
||||
Model: c.cfg.Model, MaxTokens: c.cfg.MaxTokens, System: system,
|
||||
Messages: msgs, Tools: buildTools(req.Tools),
|
||||
Temperature: c.cfg.Temperature, Stream: stream,
|
||||
}
|
||||
if payload.MaxTokens <= 0 {
|
||||
payload.MaxTokens = 4096
|
||||
}
|
||||
body, err := json.Marshal(payload)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("序列化请求失败: %w", err)
|
||||
}
|
||||
return body, nil
|
||||
}
|
||||
|
||||
// newHTTPRequest 构造 POST /v1/messages 请求（Bearer + anthropic-version） // Logan.Li
|
||||
func (c *AnthropicClient) newHTTPRequest(ctx context.Context, body []byte) (*http.Request, error) {
|
||||
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, c.cfg.Endpoint+"/v1/messages", bytes.NewReader(body))
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("构造请求失败: %w", err)
|
||||
}
|
||||
httpReq.Header.Set("Content-Type", "application/json")
|
||||
httpReq.Header.Set("Authorization", "Bearer "+c.cfg.APIKey)
|
||||
httpReq.Header.Set("anthropic-version", "2023-06-01")
|
||||
return httpReq, nil
|
||||
}
|
||||
|
||||
// Complete 一次性补全（Anthropic /v1/messages） // Logan.Li
|
||||
// 响应 content blocks：text 拼为 Content，tool_use 转为 ToolCalls；usage 映射。
|
||||
func (c *AnthropicClient) Complete(ctx context.Context, req *CompletionRequest) (*CompletionResponse, error) {
|
||||
body, err := c.buildRequest(req, false)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
httpReq, err := c.newHTTPRequest(ctx, body)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
resp, err := c.client.Do(httpReq)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("LLM 连接失败: %w", err)
|
||||
}
|
||||
defer resp.Body.Close()
|
||||
raw, _ := io.ReadAll(resp.Body)
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
return nil, fmt.Errorf("LLM 返回错误 (HTTP %d): %s", resp.StatusCode, extractErrorMessage(raw))
|
||||
}
|
||||
var parsed anthropicResponse
|
||||
if err := json.Unmarshal(raw, &parsed); err != nil {
|
||||
return nil, fmt.Errorf("解析 LLM 响应失败: %w", err)
|
||||
}
|
||||
if parsed.Error != nil && parsed.Error.Message != "" {
|
||||
return nil, fmt.Errorf("LLM 错误: %s", parsed.Error.Message)
|
||||
}
|
||||
|
||||
var textBuf strings.Builder
|
||||
var toolCalls []ToolCall
|
||||
for _, b := range parsed.Content {
|
||||
switch b.Type {
|
||||
case "text":
|
||||
textBuf.WriteString(b.Text)
|
||||
case "tool_use":
|
||||
args := string(b.Input)
|
||||
if strings.TrimSpace(args) == "" {
|
||||
args = "{}"
|
||||
}
|
||||
toolCalls = append(toolCalls, ToolCall{
|
||||
ID: b.ID, Type: "function",
|
||||
Function: FunctionCall{Name: b.Name, Arguments: args},
|
||||
})
|
||||
}
|
||||
}
|
||||
return &CompletionResponse{
|
||||
ID: parsed.ID,
|
||||
Model: parsed.Model,
|
||||
Message: Message{
|
||||
Role: "assistant",
|
||||
Content: textBuf.String(),
|
||||
ToolCalls: toolCalls,
|
||||
},
|
||||
Usage: Usage{
|
||||
PromptTokens: parsed.Usage.InputTokens,
|
||||
CompletionTokens: parsed.Usage.OutputTokens,
|
||||
TotalTokens: parsed.Usage.InputTokens + parsed.Usage.OutputTokens,
|
||||
},
|
||||
}, nil
|
||||
}
|
||||
|
||||
// Stream 流式补全（Anthropic SSE） // Logan.Li
|
||||
// 逐 token 回调 text_delta；usage 从 message_start(input) + message_delta(output) 取。
|
||||
// 注：流式主要用于纯对话（智能助手），tool_use 流式增量在此不组装（Agent CLI 走 Complete）。
|
||||
func (c *AnthropicClient) Stream(ctx context.Context, req *CompletionRequest, onDelta func(string)) (*CompletionResponse, error) {
|
||||
body, err := c.buildRequest(req, true)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
httpReq, err := c.newHTTPRequest(ctx, body)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
httpReq.Header.Set("Accept", "text/event-stream")
|
||||
|
||||
streamClient := &http.Client{Timeout: 0} // 流式由 ctx 控制超时
|
||||
resp, err := streamClient.Do(httpReq)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("LLM 连接失败: %w", err)
|
||||
}
|
||||
defer resp.Body.Close()
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
raw, _ := io.ReadAll(resp.Body)
|
||||
return nil, fmt.Errorf("LLM 返回错误 (HTTP %d): %s", resp.StatusCode, extractErrorMessage(raw))
|
||||
}
|
||||
|
||||
var contentBuilder strings.Builder
|
||||
var usage Usage
|
||||
var eventType string
|
||||
reader := bufio.NewReader(resp.Body)
|
||||
for {
|
||||
line, readErr := reader.ReadString('\n')
|
||||
trimmed := strings.TrimSpace(line)
|
||||
switch {
|
||||
case strings.HasPrefix(trimmed, "event:"):
|
||||
eventType = strings.TrimSpace(strings.TrimPrefix(trimmed, "event:"))
|
||||
case strings.HasPrefix(trimmed, "data:"):
|
||||
data := strings.TrimSpace(strings.TrimPrefix(trimmed, "data:"))
|
||||
if data != "" && data != "[DONE]" {
|
||||
switch eventType {
|
||||
case "content_block_delta":
|
||||
var d struct {
|
||||
Delta struct {
|
||||
Type string `json:"type"`
|
||||
Text string `json:"text"`
|
||||
} `json:"delta"`
|
||||
}
|
||||
if json.Unmarshal([]byte(data), &d) == nil && d.Delta.Type == "text_delta" && d.Delta.Text != "" {
|
||||
contentBuilder.WriteString(d.Delta.Text)
|
||||
if onDelta != nil {
|
||||
onDelta(d.Delta.Text)
|
||||
}
|
||||
}
|
||||
case "message_start":
|
||||
var d struct {
|
||||
Message struct {
|
||||
Usage struct {
|
||||
InputTokens int `json:"input_tokens"`
|
||||
} `json:"usage"`
|
||||
} `json:"message"`
|
||||
}
|
||||
if json.Unmarshal([]byte(data), &d) == nil {
|
||||
usage.PromptTokens = d.Message.Usage.InputTokens
|
||||
}
|
||||
case "message_delta":
|
||||
var d struct {
|
||||
Usage struct {
|
||||
OutputTokens int `json:"output_tokens"`
|
||||
} `json:"usage"`
|
||||
}
|
||||
if json.Unmarshal([]byte(data), &d) == nil {
|
||||
usage.CompletionTokens = d.Usage.OutputTokens
|
||||
usage.TotalTokens = usage.PromptTokens + usage.CompletionTokens
|
||||
}
|
||||
}
|
||||
}
|
||||
eventType = "" // 消费完 data 重置，防默认事件串用
|
||||
}
|
||||
if readErr != nil {
|
||||
break
|
||||
}
|
||||
}
|
||||
return &CompletionResponse{
|
||||
Model: c.cfg.Model,
|
||||
Message: Message{Role: "assistant", Content: contentBuilder.String()},
|
||||
Usage: usage,
|
||||
}, nil
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
// client.go - LLM 客户端工厂（按 Config.Protocol 路由到具体实现） // Logan.Li
|
||||
// 上层（controller/agentcli）统一调 NewClient，无需感知协议差异。
|
||||
// 新增协议只需在此分支 + 实现对应 Client。
|
||||
package llm
|
||||
|
||||
// NewClient 按 Config.Protocol 选择 LLM 客户端实现 // Logan.Li
|
||||
// "anthropic" → Anthropic Messages 协议（Claude 官方 / 智谱 GLM 编码套餐等）；
|
||||
// 其余（含空值）→ OpenAI 兼容协议。
|
||||
func NewClient(cfg Config) Client {
|
||||
if cfg.Protocol == "anthropic" {
|
||||
return NewAnthropicClient(cfg)
|
||||
}
|
||||
return NewOpenAIClient(cfg)
|
||||
}
|
||||
@@ -4,17 +4,20 @@
|
||||
package llm
|
||||
|
||||
import (
|
||||
"bufio"
|
||||
"bytes"
|
||||
"context"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"net/http"
|
||||
"strings"
|
||||
"time"
|
||||
)
|
||||
|
||||
// Config LLM 客户端配置（endpoint 已由上层解析为完整 base url） // Logan.Li
|
||||
type Config struct {
|
||||
Protocol string // "openai" | "anthropic"，由 client.go 工厂决定具体实现
|
||||
Endpoint string // 完整 base url，如 https://api.deepseek.com/v1
|
||||
APIKey string // Bearer token
|
||||
Model string // 模型名，如 deepseek-chat / gpt-4o-mini
|
||||
@@ -29,8 +32,9 @@ type OpenAIClient struct {
|
||||
client *http.Client
|
||||
}
|
||||
|
||||
// NewClient 创建 OpenAI 兼容客户端 // Logan.Li
|
||||
func NewClient(cfg Config) Client {
|
||||
// NewOpenAIClient 创建 OpenAI 兼容客户端 // Logan.Li
|
||||
// 顶层工厂在 client.go（按 Protocol 路由到 OpenAI / Anthropic 实现）。
|
||||
func NewOpenAIClient(cfg Config) Client {
|
||||
timeout := cfg.Timeout
|
||||
if timeout <= 0 {
|
||||
timeout = 180 * time.Second
|
||||
@@ -52,6 +56,7 @@ type openaiRequest struct {
|
||||
ToolChoice string `json:"tool_choice,omitempty"` // "auto" 让模型自主决定
|
||||
Temperature float64 `json:"temperature,omitempty"`
|
||||
MaxTokens int `json:"max_tokens,omitempty"`
|
||||
Stream bool `json:"stream,omitempty"` // 流式补全（Stream 方法置 true）
|
||||
}
|
||||
|
||||
// openaiResponse OpenAI chat/completions 响应体 // Logan.Li
|
||||
@@ -131,6 +136,88 @@ func (c *OpenAIClient) Complete(ctx context.Context, req *CompletionRequest) (*C
|
||||
}, nil
|
||||
}
|
||||
|
||||
// Stream 流式补全（OpenAI 兼容 SSE），逐 token 回调 onDelta，返回聚合响应 // Logan.Li
|
||||
// 解析 data: {"choices":[{"delta":{"content":"..."}}]} 增量；[DONE] 结束；末块 usage 若有则取。
|
||||
func (c *OpenAIClient) Stream(ctx context.Context, req *CompletionRequest, onDelta func(string)) (*CompletionResponse, error) {
|
||||
payload := openaiRequest{
|
||||
Model: c.cfg.Model,
|
||||
Messages: req.Messages,
|
||||
Tools: req.Tools,
|
||||
ToolChoice: "auto",
|
||||
Temperature: c.cfg.Temperature,
|
||||
MaxTokens: c.cfg.MaxTokens,
|
||||
Stream: true,
|
||||
}
|
||||
if payload.MaxTokens <= 0 {
|
||||
payload.MaxTokens = 4096
|
||||
}
|
||||
body, err := json.Marshal(payload)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("序列化请求失败: %w", err)
|
||||
}
|
||||
url := c.cfg.Endpoint + "/chat/completions"
|
||||
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, url, bytes.NewReader(body))
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("构造请求失败: %w", err)
|
||||
}
|
||||
httpReq.Header.Set("Content-Type", "application/json")
|
||||
httpReq.Header.Set("Authorization", "Bearer "+c.cfg.APIKey)
|
||||
httpReq.Header.Set("Accept", "text/event-stream")
|
||||
|
||||
streamClient := &http.Client{Timeout: 0} // 流式由 ctx 控制超时
|
||||
resp, err := streamClient.Do(httpReq)
|
||||
if err != nil {
|
||||
return nil, fmt.Errorf("LLM 连接失败: %w", err)
|
||||
}
|
||||
defer resp.Body.Close()
|
||||
if resp.StatusCode != http.StatusOK {
|
||||
raw, _ := io.ReadAll(resp.Body)
|
||||
return nil, fmt.Errorf("LLM 返回错误 (HTTP %d): %s", resp.StatusCode, extractErrorMessage(raw))
|
||||
}
|
||||
|
||||
var contentBuilder strings.Builder
|
||||
var usage Usage
|
||||
reader := bufio.NewReader(resp.Body)
|
||||
for {
|
||||
line, readErr := reader.ReadString('\n')
|
||||
line = strings.TrimSpace(line)
|
||||
if line != "" && strings.HasPrefix(line, "data:") {
|
||||
data := strings.TrimSpace(strings.TrimPrefix(line, "data:"))
|
||||
if data == "[DONE]" {
|
||||
break
|
||||
}
|
||||
var chunk struct {
|
||||
Choices []struct {
|
||||
Delta struct {
|
||||
Content string `json:"content"`
|
||||
} `json:"delta"`
|
||||
} `json:"choices"`
|
||||
Usage *Usage `json:"usage"`
|
||||
}
|
||||
if json.Unmarshal([]byte(data), &chunk) == nil {
|
||||
if len(chunk.Choices) > 0 && chunk.Choices[0].Delta.Content != "" {
|
||||
delta := chunk.Choices[0].Delta.Content
|
||||
contentBuilder.WriteString(delta)
|
||||
if onDelta != nil {
|
||||
onDelta(delta)
|
||||
}
|
||||
}
|
||||
if chunk.Usage != nil {
|
||||
usage = *chunk.Usage
|
||||
}
|
||||
}
|
||||
}
|
||||
if readErr != nil {
|
||||
break
|
||||
}
|
||||
}
|
||||
return &CompletionResponse{
|
||||
Model: c.cfg.Model,
|
||||
Message: Message{Role: "assistant", Content: contentBuilder.String()},
|
||||
Usage: usage,
|
||||
}, nil
|
||||
}
|
||||
|
||||
// extractErrorMessage 从上游错误响应提取 message 字段 // Logan.Li
|
||||
func extractErrorMessage(raw []byte) string {
|
||||
var m struct {
|
||||
|
||||
@@ -65,9 +65,13 @@ type Usage struct {
|
||||
}
|
||||
|
||||
// Client LLM 客户端接口 // Logan.Li
|
||||
// 实现按协议分两种：OpenAI 兼容（openai.go）与 Anthropic 兼容（anthropic.go），
|
||||
// 由 client.go 工厂按 Config.Protocol 路由。上层（controller/agentcli）只面向本接口。
|
||||
type Client interface {
|
||||
// Complete 一次性补全（返回思考文本 + 工具调用计划）。
|
||||
Complete(ctx context.Context, req *CompletionRequest) (*CompletionResponse, error)
|
||||
// Stream 流式补全，逐 token 回调 onDelta；返回最终聚合响应（含完整 content + usage）。
|
||||
Stream(ctx context.Context, req *CompletionRequest, onDelta func(string)) (*CompletionResponse, error)
|
||||
// Name 返回客户端名称（用于日志/审计）。
|
||||
Name() string
|
||||
}
|
||||
@@ -27,16 +27,6 @@ import {
|
||||
} from 'lucide-react';
|
||||
import type { JsonData } from '@/types/common';
|
||||
|
||||
// LLM 提供商列表（OpenAI 兼容接口） // Logan.Li
|
||||
const LLM_PROVIDERS: { id: string; name: string; defaultEndpoint: string; models: string[] }[] = [
|
||||
{ id: 'openai', name: 'OpenAI', defaultEndpoint: 'https://api.openai.com/v1', models: ['gpt-4o-mini', 'gpt-4o', 'gpt-4-turbo'] },
|
||||
{ id: 'deepseek', name: 'DeepSeek', defaultEndpoint: 'https://api.deepseek.com/v1', models: ['deepseek-chat', 'deepseek-reasoner'] },
|
||||
{ id: 'qwen', name: '通义千问 (DashScope)', defaultEndpoint: 'https://dashscope.aliyuncs.com/compatible-mode/v1', models: ['qwen-plus', 'qwen-turbo', 'qwen-max'] },
|
||||
{ id: 'moonshot', name: 'Moonshot 月之暗面', defaultEndpoint: 'https://api.moonshot.cn/v1', models: ['moonshot-v1-8k', 'moonshot-v1-32k'] },
|
||||
{ id: 'ollama', name: 'Ollama 本地', defaultEndpoint: 'http://localhost:11434/v1', models: ['qwen2.5:7b', 'llama3.2', 'deepseek-r1:7b'] },
|
||||
{ id: 'custom', name: '自定义 (OpenAI 兼容)', defaultEndpoint: '', models: [] },
|
||||
];
|
||||
|
||||
const defaultConfig: LLMConfigData = {
|
||||
provider: 'openai',
|
||||
model: 'gpt-4o-mini',
|
||||
@@ -323,15 +313,6 @@ export function AIOpsAssistant() {
|
||||
{config.model}
|
||||
</Badge>
|
||||
) : null}
|
||||
<Button
|
||||
variant="outline"
|
||||
size="sm"
|
||||
className="border-slate-700 text-slate-300 hover:text-purple-400"
|
||||
onClick={() => navigate('/ai-llm-config')}
|
||||
>
|
||||
<Settings className="h-4 w-4 mr-1" />
|
||||
模型配置
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
@@ -13,19 +13,27 @@ import { toast } from 'sonner';
|
||||
import { Loader2, Save, Wifi, KeyRound, Brain } from 'lucide-react';
|
||||
import { aiApi, type LLMConfigData } from '@/api';
|
||||
|
||||
// OpenAI 兼容提供商列表 // Logan.Li
|
||||
// 提供商列表（后端按 provider 自动路由协议） // Logan.Li
|
||||
// Anthropic 协议（glm-coding / anthropic）：端点不含 /v1，后端拼 /v1/messages。
|
||||
// OpenAI 协议（其余）：端点含 /v1，后端拼 /chat/completions。
|
||||
const PROVIDERS = [
|
||||
{ id: 'openai', name: 'OpenAI', endpoint: 'https://api.openai.com/v1', models: 'gpt-4o-mini, gpt-4o' },
|
||||
{ id: 'deepseek', name: 'DeepSeek', endpoint: 'https://api.deepseek.com/v1', models: 'deepseek-chat, deepseek-reasoner' },
|
||||
{ id: 'openai', name: 'OpenAI', endpoint: 'https://api.openai.com/v1', models: 'gpt-4o-mini, gpt-4o, gpt-4.1' },
|
||||
{ id: 'deepseek', name: 'DeepSeek', endpoint: 'https://api.deepseek.com/v1', models: 'deepseek-v4-flash, deepseek-v4-pro' },
|
||||
{ id: 'glm', name: '智谱 GLM (通用)', endpoint: 'https://open.bigmodel.cn/api/paas/v4', models: 'glm-4.6, glm-4.5, glm-4-flash' },
|
||||
{ id: 'glm-coding', name: '智谱 GLM (编码套餐)', endpoint: 'https://open.bigmodel.cn/api/anthropic', models: 'GLM-5.2' },
|
||||
{ id: 'qwen', name: '通义千问 (DashScope)', endpoint: 'https://dashscope.aliyuncs.com/compatible-mode/v1', models: 'qwen-plus, qwen-turbo, qwen-max' },
|
||||
{ id: 'moonshot', name: 'Moonshot 月之暗面', endpoint: 'https://api.moonshot.cn/v1', models: 'moonshot-v1-8k, moonshot-v1-32k' },
|
||||
{ id: 'moonshot', name: 'Moonshot (Kimi)', endpoint: 'https://api.moonshot.cn/v1', models: 'kimi-latest, moonshot-v1-8k, moonshot-v1-32k' },
|
||||
{ id: 'kimi', name: 'Kimi 编码套餐', endpoint: 'https://api.kimi.com/coding/v1', models: 'kimi-for-coding' },
|
||||
{ id: 'mimo', name: '小米 MiMo', endpoint: 'https://api.mimo.mi.com/v1', models: 'mimo-v2-flash, mimo-v2' },
|
||||
{ id: 'anthropic', name: 'Anthropic Claude', endpoint: 'https://api.anthropic.com', models: 'claude-sonnet-4-6, claude-opus-4-8, claude-haiku-4-5' },
|
||||
{ id: 'ollama', name: 'Ollama 本地', endpoint: 'http://localhost:11434/v1', models: 'qwen2.5:7b, llama3.2' },
|
||||
{ id: 'custom', name: '自定义 (OpenAI 兼容)', endpoint: '', models: '' },
|
||||
];
|
||||
|
||||
// 生成参数（温度/max_tokens/系统提示词）已按 Claude Code 模式精简：UI 不再暴露，走内置默认。 // Logan.Li
|
||||
const defaultCfg: LLMConfigData = {
|
||||
provider: 'deepseek', model: 'deepseek-chat', api_key: '',
|
||||
endpoint: 'https://api.deepseek.com/v1', temperature: 0.3, max_tokens: 4096,
|
||||
provider: 'deepseek', model: 'deepseek-v4-flash', api_key: '',
|
||||
endpoint: 'https://api.deepseek.com/v1', temperature: 0.7, max_tokens: 8192,
|
||||
system_prompt: '', enabled: false,
|
||||
};
|
||||
|
||||
@@ -105,36 +113,11 @@ export default function LLMConfigPage() {
|
||||
</CardContent>
|
||||
</Card>
|
||||
|
||||
<Card className="bg-slate-900/60 border-slate-800">
|
||||
<CardHeader><CardTitle className="text-slate-200 text-base">生成参数</CardTitle></CardHeader>
|
||||
<CardContent className="space-y-4">
|
||||
<div className="grid grid-cols-2 gap-4">
|
||||
<div className="space-y-1.5">
|
||||
<Label className="text-slate-300">温度（Temperature）</Label>
|
||||
<Input type="number" step="0.1" min="0" max="2" value={cfg.temperature}
|
||||
onChange={(e) => set('temperature', Number(e.target.value))}
|
||||
className="bg-slate-800 border-slate-700 text-slate-200" />
|
||||
</div>
|
||||
<div className="space-y-1.5">
|
||||
<Label className="text-slate-300">最大输出 Token</Label>
|
||||
<Input type="number" min="256" value={cfg.max_tokens}
|
||||
onChange={(e) => set('max_tokens', Number(e.target.value))}
|
||||
className="bg-slate-800 border-slate-700 text-slate-200" />
|
||||
</div>
|
||||
</div>
|
||||
<div className="space-y-1.5">
|
||||
<Label className="text-slate-300">系统提示词（可选，留空使用内置运维助手角色）</Label>
|
||||
<textarea value={cfg.system_prompt} onChange={(e) => set('system_prompt', e.target.value)} rows={3}
|
||||
className="w-full bg-slate-800 border border-slate-700 rounded-md px-3 py-2 text-sm text-slate-200 resize-none" />
|
||||
</div>
|
||||
<div className="flex items-center justify-between">
|
||||
<Label className="text-slate-300">启用 LLM 服务</Label>
|
||||
<Switch checked={cfg.enabled} onCheckedChange={(v) => set('enabled', v)} />
|
||||
</div>
|
||||
</CardContent>
|
||||
</Card>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
<div className="flex items-center gap-2 text-sm text-slate-300">
|
||||
<Switch checked={cfg.enabled} onCheckedChange={(v) => set('enabled', v)} />
|
||||
<span>启用 LLM 服务</span>
|
||||
</div>
|
||||
<Button onClick={save} disabled={saving} className="bg-blue-600 hover:bg-blue-700">
|
||||
{saving ? <Loader2 className="w-4 h-4 mr-1 animate-spin" /> : <Save className="w-4 h-4 mr-1" />} 保存配置
|
||||
</Button>
|
||||
|
||||
@@ -1,7 +1,11 @@
|
||||
import { HardDrive, Database, Zap, Loader2 } from 'lucide-react';
|
||||
import { useState } from 'react';
|
||||
import { HardDrive, Database, Zap, Loader2, Sparkles } from 'lucide-react';
|
||||
import { Badge } from '@/components/ui/badge';
|
||||
import { Button } from '@/components/ui/button';
|
||||
import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
|
||||
import type { CapacityForecast } from './predictUtils';
|
||||
import { toast } from 'sonner';
|
||||
import { aiApi } from '@/api';
|
||||
import { buildCapacityInsightPrompt, type CapacityForecast } from './predictUtils';
|
||||
|
||||
export function PredictiveCapacityTab({
|
||||
forecasts,
|
||||
@@ -10,6 +14,25 @@ export function PredictiveCapacityTab({
|
||||
forecasts: CapacityForecast[];
|
||||
loading: boolean;
|
||||
}) {
|
||||
const [insight, setInsight] = useState('');
|
||||
const [analyzing, setAnalyzing] = useState(false);
|
||||
|
||||
// AI 深度解读：把容量预测硬数据喂给 LLM，生成综合风险评估与运维建议 // Logan.Li
|
||||
// 复用 aiApi.chat（双协议后自动支持 GLM-5.2 等）；失败仅 toast，下方逐条模板建议仍可读。
|
||||
const analyze = async () => {
|
||||
const prompt = buildCapacityInsightPrompt(forecasts);
|
||||
if (!prompt) return;
|
||||
setAnalyzing(true);
|
||||
try {
|
||||
const res = await aiApi.chat([{ role: 'user', content: prompt }]);
|
||||
setInsight(res?.content || '');
|
||||
} catch {
|
||||
toast.error('AI 解读失败，请检查大模型配置或稍后重试');
|
||||
} finally {
|
||||
setAnalyzing(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<Card className="bg-slate-800 border-slate-700">
|
||||
<CardHeader className="pb-3">
|
||||
@@ -18,7 +41,22 @@ export function PredictiveCapacityTab({
|
||||
<HardDrive className="h-5 w-5 text-amber-400" />
|
||||
容量规划预警
|
||||
</CardTitle>
|
||||
<span className="text-xs text-slate-500">基于日均增长率推算阈值触及时间</span>
|
||||
{!loading && forecasts.length > 0 && (
|
||||
<Button
|
||||
variant="outline"
|
||||
size="sm"
|
||||
onClick={analyze}
|
||||
disabled={analyzing}
|
||||
className="border-purple-500/30 text-purple-300 hover:text-purple-200 h-7"
|
||||
>
|
||||
{analyzing ? (
|
||||
<Loader2 className="h-3.5 w-3.5 mr-1 animate-spin" />
|
||||
) : (
|
||||
<Sparkles className="h-3.5 w-3.5 mr-1" />
|
||||
)}
|
||||
AI 深度解读
|
||||
</Button>
|
||||
)}
|
||||
</div>
|
||||
</CardHeader>
|
||||
<CardContent>
|
||||
@@ -30,6 +68,16 @@ export function PredictiveCapacityTab({
|
||||
<div className="text-center py-12 text-slate-500">暂无容量数据</div>
|
||||
) : (
|
||||
<div className="space-y-4">
|
||||
{/* AI 深度解读结果（点击按钮后展示） */}
|
||||
{insight && (
|
||||
<div className="p-4 rounded-lg border border-purple-500/30 bg-purple-500/5">
|
||||
<div className="flex items-center gap-2 mb-2">
|
||||
<Sparkles className="h-4 w-4 text-purple-400" />
|
||||
<span className="text-sm font-medium text-purple-300">AI 深度解读</span>
|
||||
</div>
|
||||
<p className="text-sm text-slate-300 whitespace-pre-wrap break-words">{insight}</p>
|
||||
</div>
|
||||
)}
|
||||
{forecasts.map((forecast, idx) => (
|
||||
<div key={idx} className="p-4 rounded-lg border border-slate-700 bg-slate-800/50">
|
||||
<div className="flex items-center justify-between mb-3">
|
||||
|
||||
@@ -319,3 +319,26 @@ export function trendColorClass(trend: string): string {
|
||||
return 'text-slate-400';
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建容量预测「AI 深度解读」的 prompt —— 把硬数据序列化为结构化上下文喂给 LLM。 // Logan.Li
|
||||
* 纯函数，供 PredictiveCapacityTab 调 aiApi.chat 使用。无数据返回空串（调用方据此禁用按钮）。
|
||||
*/
|
||||
export function buildCapacityInsightPrompt(forecasts: CapacityForecast[]): string {
|
||||
if (forecasts.length === 0) return '';
|
||||
const lines = forecasts.map((f) => {
|
||||
const risk = f.daysUntilThreshold >= 999 ? '增长平缓，暂无触顶风险' : `${f.daysUntilThreshold} 天后触及阈值`;
|
||||
return `- ${f.resource}：当前 ${f.currentUsage}% → 7天后预测 ${f.predictedUsage}%（阈值 ${f.threshold}%），${risk}`;
|
||||
});
|
||||
return [
|
||||
'你是 AIOps 容量规划专家。基于以下服务器资源容量预测数据，给出综合风险评估与可执行的运维建议：',
|
||||
'',
|
||||
'【容量预测数据】',
|
||||
...lines,
|
||||
'',
|
||||
'要求：',
|
||||
'1. 指出最紧急的风险资源（触及阈值最快者优先）',
|
||||
'2. 给出针对性处置建议（扩容 / 清理 / 优化，具体到操作命令或步骤）',
|
||||
'3. 简洁中文，Markdown 无序列表，不超过 200 字',
|
||||
].join('\n');
|
||||
}
|
||||
Reference in new issue
Block a user