fix: 1.修改HTTP机器人回调 2.修改HTTP卡片回调 3.追加知识库命中判断
This commit is contained in:
fuzhongyun
parent
99865c2bc4
commit
b104572e1b
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@ -77,33 +77,23 @@ func (c *CallbackBiz) issueHandlingExtractContent(data chatbot.BotCallbackDataMo
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}
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// 解析 JSON 响应
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var resp struct {
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Items []struct {
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Question string `json:"question"`
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Answer string `json:"answer"`
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Confidence string `json:"confidence"`
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} `json:"items"`
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}
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if err := json.Unmarshal([]byte(generateResp.Response), &resp); err != nil {
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log.Errorf("解析 JSON 响应失败: %v", err)
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return
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}
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// 2.构建文本域内容
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cardContentTpl := "问题：%s \n答案：%s"
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var cardContentList []string
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for _, item := range resp.Items {
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cardContentList = append(cardContentList, fmt.Sprintf(cardContentTpl, item.Question, item.Answer))
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}
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cardContent := strings.Join(cardContentList, "\n\n")
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// 3.获取应用AppKey
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// 2.获取应用AppKey
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appKey, err := c.botConfigImpl.GetRobotAppKey(data.RobotCode)
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if err != nil {
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log.Errorf("获取应用配置失败: %v", err)
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return
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}
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// 4.创建并投放卡片
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// 3.创建并投放卡片
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outTrackId := constants.BuildCardOutTrackId(data.ConversationId, data.RobotCode) // 构建卡片 OutTrackId
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_, err = c.dingtalkCardClient.CreateAndDeliver(
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appKey,
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@ -114,13 +104,14 @@ func (c *CallbackBiz) issueHandlingExtractContent(data chatbot.BotCallbackDataMo
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CallbackRouteKey: tea.String(c.cfg.Dingtalk.Card.CallbackRouteKey),
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CardData: &card_1_0.CreateAndDeliverRequestCardData{
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CardParamMap: map[string]*string{
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"_CARD_DEBUG_TOOL_ENTRY": tea.String(c.cfg.Dingtalk.Card.DebugToolEntryShow), // 调试字段
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"title": tea.String("QA知识收集"),
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"button_display": tea.String("true"),
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"QA_details_now": tea.String(cardContent),
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"textarea_display": tea.String("normal"),
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"action_id": tea.String("collect_qa"),
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"tenant_id": tea.String(constants.KnowledgeTenantIdDefault),
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"_CARD_DEBUG_TOOL_ENTRY": tea.String(c.cfg.Dingtalk.Card.DebugToolEntryShow), // 调试字段
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"question": tea.String(resp.Question),
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"answer": tea.String(resp.Answer),
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},
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},
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ImGroupOpenSpaceModel: &card_1_0.CreateAndDeliverRequestImGroupOpenSpaceModel{
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@ -223,15 +214,21 @@ func (c *CallbackBiz) issueHandlingQueryKnowledgeBase(data chatbot.BotCallbackDa
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func (c *CallbackBiz) IssueHandlingCollectQA(data card.CardRequest) *card.CardResponse {
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// 确认提交，文本写入知识库
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if data.CardActionData.CardPrivateData.Params["submit"] == "submit" {
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content := data.CardActionData.CardPrivateData.Params["QA_details"].(string)
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question := data.CardActionData.CardPrivateData.Params["question_local"].(string)
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answer := data.CardActionData.CardPrivateData.Params["answer_local"].(string)
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tenantID := data.CardActionData.CardPrivateData.Params["tenant_id"].(string)
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// 协程执行耗时操作，防止阻塞
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util.SafeGo("inject_knowledge_base", func() {
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knowledgeBase := knowledge_base.New(c.cfg.KnowledgeConfig)
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err := knowledgeBase.IngestText(&knowledge_base.IngestTextRequest{
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err := knowledgeBase.IngestBatchQA(&knowledge_base.IngestBacthQARequest{
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TenantID: tenantID,
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Text: content,
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QAList: []*knowledge_base.QA{
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{
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Question: question,
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Answer: answer,
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},
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},
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})
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if err != nil {
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log.Errorf("注入知识库失败: %v", err)
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@ -219,12 +219,16 @@ func (d *DingTalkBotBiz) handleKnowledgeQA(ctx context.Context, requireData *ent
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log.Debugf("改写前后的Query: %s -> %s", requireData.Req.Text.Content, queryText)
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// 获取知识库结果
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isRetrieved, err := d.getKnowledgeAnswer(ctx, requireData, tenantId, queryText)
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isRetrieved, responseContent, err := d.getKnowledgeAnswer(ctx, requireData.Ch, tenantId, queryText)
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if err != nil {
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return err
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}
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if isRetrieved {
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return nil
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// 过一遍 LLM 判断是否真的命中知识库
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isRetrieved, err = d.handle.IsAnswerRelevant(ctx, queryText, responseContent)
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if err != nil {
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return err
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}
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}
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// 未匹配&全局 -> 明确具体系统
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@ -242,7 +246,7 @@ func (d *DingTalkBotBiz) handleKnowledgeQA(ctx context.Context, requireData *ent
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}
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// 获取知识库问答结果
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func (d *DingTalkBotBiz) getKnowledgeAnswer(ctx context.Context, requireData *entitys.RequireDataDingTalkBot, tenantId string, queryText string) (bool, error) {
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func (d *DingTalkBotBiz) getKnowledgeAnswer(ctx context.Context, ch chan entitys.Response, tenantId string, queryText string) (bool, string, error) {
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// 请求知识库工具
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knowledgeBase := knowledge_base.New(d.conf.KnowledgeConfig)
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knowledgeResp, err := knowledgeBase.Query(&knowledge_base.QueryRequest{
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@ -254,11 +258,11 @@ func (d *DingTalkBotBiz) getKnowledgeAnswer(ctx context.Context, requireData *en
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OnlyRAG: true,
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})
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if err != nil {
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return false, fmt.Errorf("请求知识库工具失败，err: %v", err)
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return false, "", fmt.Errorf("请求知识库工具失败，err: %v", err)
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}
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// 读取知识库SSE数据
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return d.groupConfigBiz.readKnowledgeSSE(knowledgeResp, requireData.Ch, true)
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return d.groupConfigBiz.readKnowledgeSSE(knowledgeResp, ch, true)
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}
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type resolveSystemAndIssueTypeResult struct {
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@ -207,7 +207,7 @@ func (r *Handle) ClassifyIssueType(ctx context.Context, issueTypes []string, sys
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- 当前输入未明确，但历史已有 → 继承历史类型
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- 当前输入未匹配，历史也没有 → 选择最接近的列表类型（尽量匹配意图）
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- 除非是闲聊（如“你好”“在吗”），禁止返回空值
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- 除非明确是需求，否则禁止返回“开发需求”类型
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- 除非明确是需求，否则禁止返回“开发需求”类型，疑问句式一定不能返回“开发需求”类型
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3. 特殊规则
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- 当前输入只包含系统名/模块名/参数名 → 视为问题补充，继承历史 issue_type_name
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@ -256,6 +256,56 @@ func (r *Handle) ClassifyIssueType(ctx context.Context, issueTypes []string, sys
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return &result, nil
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}
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type IsAnswerRelevant struct {
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Relevance string `json:"relevance"`
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Reason string `json:"reason"`
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}
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// 判断答案是否回答了问题
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func (r *Handle) IsAnswerRelevant(ctx context.Context, question string, answer string) (bool, error) {
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prompt := `## 角色
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你是一个答案评估专家，你的任务是判断给定的答案是否真正回答了用户的问题。你必须严格分析语义、意图和信息覆盖情况，避免只看关键词。
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## 输入
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- question: %s
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- answer: %s
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## 判断逻辑
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1. **直接回答**：答案明确提供了解决方案、步骤、结论或可执行信息 → 输出 True
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2. **未回答**：答案仅泛泛提示、缺少关键步骤或信息，或者只是提供背景、登录信息等无关内容 → 输出 False
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3. **部分回答**：答案提供了一部分可用信息，但未完全解决问题 → 输出 “Partial”
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## 输出要求
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输出严格 JSON 格式，只包含以下字段：
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{
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"relevance": "True / False / Partial",
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"reason": "简要说明为什么答案被认为回答或未回答问题"
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}`
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resp, err := r.Ollama.Generation(ctx, fmt.Sprintf(prompt, question, answer))
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if err != nil {
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return false, err
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}
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// 尝试清理 JSON 内容（有时模型会返回 markdown 块）
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resp = strings.TrimPrefix(resp, "```json")
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resp = strings.TrimSuffix(resp, "```")
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resp = strings.TrimSpace(resp)
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var result IsAnswerRelevant
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if err := json.Unmarshal([]byte(resp), &result); err != nil {
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return false, fmt.Errorf("解析分类结果失败: %w, 原文: %s", err, resp)
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}
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log.Debug("分析结果：%s，原因：%s", result.Relevance, result.Reason)
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if result.Relevance == "True" {
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return true, nil
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}
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return false, nil
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}
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func (r *Handle) handleOtherTask(ctx context.Context, requireData *entitys.RequireData) (err error) {
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entitys.ResText(requireData.Ch, "", requireData.Match.Reasoning)
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return
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@ -507,7 +507,7 @@ func (g *GroupConfigBiz) handleKnowledge(ctx context.Context, rec *entitys.Recog
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}
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// 读取知识库SSE数据
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isRetrieved, err = g.readKnowledgeSSE(knowledgeResp, rec.Ch, true)
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isRetrieved, _, err = g.readKnowledgeSSE(knowledgeResp, rec.Ch, true)
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if err != nil {
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return
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}
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@ -522,16 +522,16 @@ func (g *GroupConfigBiz) handleKnowledge(ctx context.Context, rec *entitys.Recog
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}
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// 读取知识库 SSE 数据
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func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entitys.Response, useParagraphMode bool) (isRetrieved bool, err error) {
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func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entitys.Response, useParagraphMode bool) (isRetrieved bool, allContent string, err error) {
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scanner := bufio.NewScanner(resp)
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var buffer strings.Builder
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var allContentBuilder strings.Builder
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for scanner.Scan() {
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line := scanner.Text()
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delta, done, err := knowledge_base.ParseOpenAIStreamData(line)
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if err != nil {
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return false, fmt.Errorf("解析SSE数据失败: %w", err)
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return false, "", fmt.Errorf("解析SSE数据失败: %w", err)
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}
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if done {
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break
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@ -544,7 +544,7 @@ func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entit
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if delta.XRagStatus == constants.KnowledgeRagStatusMiss {
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var missContent string = "知识库未检测到匹配信息。"
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entitys.ResStream(channel, "", missContent)
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return false, nil
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return false, missContent, nil
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}
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// 推理内容
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if delta.ReasoningContent != "" {
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@ -555,6 +555,7 @@ func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entit
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if delta.Content != "" && useParagraphMode {
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// 存入缓冲区
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buffer.WriteString(delta.Content)
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allContentBuilder.WriteString(delta.Content)
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content := buffer.String()
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// 检查是否有换行符，按段落输出
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@ -572,10 +573,11 @@ func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entit
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// 输出内容 - 逐字
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if delta.Content != "" && !useParagraphMode {
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entitys.ResStream(channel, "", delta.Content)
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allContentBuilder.WriteString(delta.Content)
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}
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}
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if err := scanner.Err(); err != nil {
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return true, fmt.Errorf("读取SSE流中断: %w", err)
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return true, "", fmt.Errorf("读取SSE流中断: %w", err)
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}
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// 发送缓冲区剩余内容（仅在段落模式下需要）
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@ -583,7 +585,7 @@ func (g *GroupConfigBiz) readKnowledgeSSE(resp io.ReadCloser, channel chan entit
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entitys.ResStream(channel, "", buffer.String())
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}
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return true, nil
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return true, allContentBuilder.String(), nil
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}
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// 询问是否创建群聊处理问题
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@ -70,6 +70,19 @@ func (r *OllamaService) Chat(ctx context.Context, messages []api.Message) (strin
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return res.Message.Content, nil
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}
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func (r *OllamaService) Generation(ctx context.Context, prompt string) (string, error) {
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res, err := r.client.Generation(ctx, &api.GenerateRequest{
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Model: r.config.Ollama.GenerateModel,
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Stream: new(bool),
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Prompt: prompt,
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Think: &api.ThinkValue{Value: false},
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})
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if err != nil {
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return "", err
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}
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return res.Response, nil
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}
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//func (r *OllamaService) RecognizeWithImg(ctx context.Context, imgByte []api.ImageData, ch chan entitys.Response) (desc api.GenerateResponse, err error) {
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// if imgByte == nil {
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// return
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@ -126,8 +126,7 @@ const IssueHandlingExtractContentPrompt string = `你是一个【问题与答案
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当用户输入为【多条群聊聊天记录】时：
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- 结合问题主题，判断聊天记录中正在讨论或试图解决的问题
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- 一个群聊中可能包含多个相互独立的问题，但它们都围绕着一个主题，一般为用户提出的第一个问题，尽可能总结为一个问题
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- 若确实问题很独立，需要分别识别，对每个问题，整理出清晰、可复用的“问题描述”和“对应答案”
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- 一个群聊中可能包含多个相互独立的问题，但它们都围绕着一个主题，一般为用户提出的第一个问题。尽可能总结为一个问题、一个答案
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生成答案时的原则：
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- 答案必须来源于聊天内容中已经给出的信息或共识
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@ -141,24 +140,19 @@ const IssueHandlingExtractContentPrompt string = `你是一个【问题与答案
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- JSON 结构必须严格符合以下约定
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JSON 结构约定：
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{
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"items": [
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{
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"question": "清晰、独立、可复用的问题描述",
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"answer": "基于聊天内容整理出的答案；如无结论则为“暂无明确结论”",
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"confidence": "high | medium | low"
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}
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]
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}
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字段说明：
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- items：问题与答案列表；若未识别到有效问题，则返回空数组 []
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- question：抽象后的标准问题表述，不包含具体聊天语句
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- answer：整理后的答案，不得引入聊天之外的信息
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- confidence：根据聊天中信息的一致性和明确程度给出判断
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如果无法从输入中识别出任何有效问题，返回：
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{ "items": [] }
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{ "confidence": "low" }
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用户输入：
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%s
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@ -3,6 +3,7 @@ package knowledge_base
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import (
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"ai_scheduler/internal/config"
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"ai_scheduler/internal/pkg/l_request"
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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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@ -110,3 +111,53 @@ func (c *Client) IngestText(req *IngestTextRequest) error {
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return nil
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}
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// IngestBatchQA 向知识库中注入问答对
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func (c *Client) IngestBatchQA(req *IngestBacthQARequest) error {
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if req == nil {
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return fmt.Errorf("req is nil")
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}
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if req.TenantID == "" {
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return fmt.Errorf("tenantID is empty")
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}
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for _, item := range req.QAList {
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if item.Question == "" {
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return fmt.Errorf("question is empty")
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}
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if item.Answer == "" {
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return fmt.Errorf("answer is empty")
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}
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}
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data := []map[string]string{}
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for _, item := range req.QAList {
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data = append(data, map[string]string{
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"question": item.Question,
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"answer": item.Answer,
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})
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}
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jsonByte, err := json.Marshal(data)
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if err != nil {
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return err
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}
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baseURL := strings.TrimRight(c.cfg.BaseURL, "/")
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rsp, err := (&l_request.Request{
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Method: "POST",
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Url: baseURL + "/ingest/batch_qa",
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Headers: map[string]string{
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"Content-Type": "application/json",
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"X-Tenant-ID": req.TenantID,
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},
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JsonByte: jsonByte,
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}).Send()
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if err != nil {
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return err
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}
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if rsp.StatusCode != http.StatusOK {
|
||||
return fmt.Errorf("knowledge base returned status %d: %s", rsp.StatusCode, rsp.Text)
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
|
|
|||
|
|
@ -13,3 +13,13 @@ type IngestTextRequest struct {
|
|||
TenantID string // 租户 ID
|
||||
Text string // 要注入的文本内容
|
||||
}
|
||||
|
||||
type IngestBacthQARequest struct {
|
||||
TenantID string // 租户 ID
|
||||
QAList []*QA // 问答对列表
|
||||
}
|
||||
|
||||
type QA struct {
|
||||
Question string // 问题
|
||||
Answer string // 答案
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue