126 lines
3.3 KiB
Go
126 lines
3.3 KiB
Go
package eino
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import (
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"context"
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"errors"
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"fmt"
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"io"
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"github.com/cloudwego/eino-ext/components/model/qwen"
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"github.com/cloudwego/eino/components/prompt"
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"github.com/cloudwego/eino/schema"
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"github.com/gogf/gf/v2/frame/g"
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"github.com/gogf/gf/v2/os/glog"
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"github.com/gogf/gf/v2/util/gconv"
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)
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var globalChatModel *qwen.ChatModel
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func init() {
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ctx := context.Background()
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apiKey := g.Cfg().MustGet(ctx, "eino.chatmodel.apiKey").String()
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model := g.Cfg().MustGet(ctx, "eino.chatmodel.model").String()
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var err error
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globalChatModel, err = qwen.NewChatModel(ctx, &qwen.ChatModelConfig{
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APIKey: apiKey,
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Model: model,
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BaseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
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Temperature: gconv.PtrFloat32(0.7), // 客服最佳
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MaxTokens: gconv.PtrInt(1024), // 最长回答
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TopP: gconv.PtrFloat32(1.0),
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})
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if err != nil {
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glog.Errorf(ctx, "初始化大模型失败: %v", err)
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}
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return
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}
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// NewChatModel 只处理逻辑，不复用创建模型
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func NewChatModel(ctx context.Context, content string, docs []*schema.Document) (replyMsg *schema.Message, sources []string, err error) {
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// 1. 构建参考知识
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knowledge, sources := buildKnowledgeAndSources(docs)
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// 2. 构建提示词
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msgs, err := buildPromptMessages(ctx, knowledge, content)
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if err != nil {
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return
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}
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// 3. 🔥 直接使用全局单例，不重复创建
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replyMsg, err = streamGenerateAnswer(ctx, globalChatModel, msgs)
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return
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}
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// buildKnowledgeAndSources 拼接参考知识 + 提取文档来源
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func buildKnowledgeAndSources(docs []*schema.Document) (string, []string) {
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var knowledge string
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var sources []string
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for i, doc := range docs {
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knowledge += fmt.Sprintf("[参考%d] %s\n", i+1, doc.Content)
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// 提取 document_id
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if docID, ok := doc.MetaData["document_id"].(int64); ok && docID > 0 {
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sources = append(sources, gconv.String(docID))
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}
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}
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return knowledge, sources
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}
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// buildPromptMessages 构建提示词模板
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func buildPromptMessages(ctx context.Context, knowledge string, question string) (msgs []*schema.Message, err error) {
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promptTpl := prompt.FromMessages(
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schema.FString,
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&schema.Message{
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Role: schema.System,
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// Content: `你是专业的客服助手，语气友好。
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//如果参考知识中有相关信息，请优先依据参考知识回答。
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//如果没有相关信息，就正常回答，不要说无法回答。
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//
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//参考知识：
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//{knowledge}`,
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Content: `你是专业的客服助手，语气友好。
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请根据参考知识回答用户问题，无法回答则说：抱歉，我暂时无法回答这个问题。
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参考知识：
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{knowledge}`,
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},
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&schema.Message{
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Role: schema.User,
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Content: "{question}",
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},
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)
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return promptTpl.Format(ctx, map[string]any{
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"knowledge": knowledge,
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"question": question,
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})
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}
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// streamGenerateAnswer 流式生成
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func streamGenerateAnswer(ctx context.Context, chatModel *qwen.ChatModel, msgs []*schema.Message) (reply *schema.Message, err error) {
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sr, err := chatModel.Stream(ctx, msgs)
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if err != nil {
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return nil, fmt.Errorf("stream failed: %w", err)
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}
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var chunks []*schema.Message
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for {
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chunk, err := sr.Recv()
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if errors.Is(err, io.EOF) {
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break
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}
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if err != nil {
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return nil, fmt.Errorf("stream recv failed: %w", err)
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}
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chunks = append(chunks, chunk)
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}
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return schema.ConcatMessages(chunks)
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}
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