feat(AC-AISVC-93): 完整流程测试12步执行时间线与步骤详情
改进内容: - 每个步骤添加详细的input_data和output_data - InputScanner: 显示用户输入文本 - FlowEngine: 显示会话ID和流程名称 - IntentRouter: 显示查询和匹配结果 - QueryRewriter: 显示查询和重写状态 - MultiKBRetrieval: 显示查询、top_k、命中数、最高分、top_hits详情 - PromptBuilder: 显示模板ID、行为规则、prompt预览 - LLMGenerate: 显示模型名称(deepseek-chat)、回复长度、回复预览 - OutputFilter: 显示文本长度、是否过滤、触发词 - Confidence: 显示回复长度、命中数、置信度、是否转人工 - Memory: 显示会话ID、保存状态 - Response: 显示置信度、是否转人工、回复预览 修复问题: - OrchestratorService没有返回execution_steps - 前端字段名与后端不一致(camelCase vs snake_case) - RetrievalResult.evidence -> RetrievalResult.hits - LLM模型名称显示unknown -> 显示实际模型名称
This commit is contained in:
MerCry
parent
6b21ba8351
commit
aa02ab79d2
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@ -23,18 +23,18 @@ export interface FlowExecutionStep {
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}
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export interface FlowExecutionResponse {
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testId: string
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sessionId: string
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test_id: string
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session_id: string
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status: string
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steps: FlowExecutionStep[]
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finalResponse: {
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final_response: {
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reply: string
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confidence: number | null
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should_transfer: boolean
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sources?: any[]
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} | null
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totalDurationMs: number
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createdAt: string
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total_duration_ms: number
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created_at: string
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}
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export interface FlowTestRecord {
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@ -152,7 +152,7 @@
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<el-tag :type="getStatusType(flowTestResult.status)" size="small">
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{{ flowTestResult.status }}
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</el-tag>
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<span class="duration">{{ flowTestResult.totalDurationMs }}ms</span>
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<span class="duration">{{ flowTestResult.total_duration_ms }}ms</span>
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</div>
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</div>
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</template>
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@ -197,14 +197,14 @@
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</el-timeline-item>
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</el-timeline>
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<el-divider content-position="left" v-if="flowTestResult.finalResponse">最终响应</el-divider>
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<div v-if="flowTestResult.finalResponse" class="final-response">
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<div class="response-content">{{ flowTestResult.finalResponse.reply }}</div>
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<el-divider content-position="left" v-if="flowTestResult.final_response">最终响应</el-divider>
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<div v-if="flowTestResult.final_response" class="final-response">
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<div class="response-content">{{ flowTestResult.final_response.reply }}</div>
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<div class="response-meta">
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<span v-if="flowTestResult.finalResponse.confidence">
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置信度: {{ (flowTestResult.finalResponse.confidence * 100).toFixed(1) }}%
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<span v-if="flowTestResult.final_response.confidence">
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置信度: {{ (flowTestResult.final_response.confidence * 100).toFixed(1) }}%
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</span>
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<el-tag v-if="flowTestResult.finalResponse.should_transfer" type="warning" size="small">
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<el-tag v-if="flowTestResult.final_response.should_transfer" type="warning" size="small">
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需转人工
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</el-tag>
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</div>
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@ -116,6 +116,7 @@ class GenerationContext:
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behavior_rules: list[str] = field(default_factory=list)
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diagnostics: dict[str, Any] = field(default_factory=dict)
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execution_steps: list[dict[str, Any]] = field(default_factory=list)
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class OrchestratorService:
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@ -204,6 +205,28 @@ class OrchestratorService:
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self._behavior_rule_service = behavior_rule_service
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self._output_filter = output_filter
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def _record_step(
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self,
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ctx: GenerationContext,
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step_no: int,
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name: str,
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status: str = "success",
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duration_ms: int = 0,
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input_data: Any = None,
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output_data: Any = None,
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error: str | None = None,
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) -> None:
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"""Record execution step for flow test visualization."""
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ctx.execution_steps.append({
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"step": step_no,
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"name": name,
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"status": status,
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"duration_ms": duration_ms,
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"input": input_data,
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"output": output_data,
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"error": error,
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})
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async def generate(
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self,
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tenant_id: str,
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@ -242,42 +265,110 @@ class OrchestratorService:
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)
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try:
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import time
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# Step 1: InputScanner - Scan user input for forbidden words
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step_start = time.time()
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await self._scan_input(ctx)
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self._record_step(ctx, 1, "InputScanner", "success", int((time.time() - step_start) * 1000),
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input_data={"text": ctx.current_message[:200]},
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output_data=ctx.diagnostics.get("input_scan"))
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# Load local history and merge context (original pipeline)
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await self._load_local_history(ctx)
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await self._merge_context(ctx, request.history)
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# Step 2: FlowEngine - Check if session has active script flow
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step_start = time.time()
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await self._check_active_flow(ctx)
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self._record_step(ctx, 2, "FlowEngine", "success", int((time.time() - step_start) * 1000),
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input_data={"session_id": ctx.session_id},
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output_data={"active_flow": bool(ctx.active_flow), "flow_name": getattr(ctx.active_flow, 'flow_name', None) if ctx.active_flow else None})
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# Step 3: IntentRouter - Match intent rules and route
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step_start = time.time()
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await self._match_intent(ctx)
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intent_output = {"matched": bool(ctx.intent_match)}
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if ctx.intent_match:
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intent_output["rule_name"] = getattr(ctx.intent_match, 'rule_name', None)
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intent_output["confidence"] = getattr(ctx.intent_match, 'confidence', None)
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self._record_step(ctx, 3, "IntentRouter", "success", int((time.time() - step_start) * 1000),
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input_data={"query": ctx.current_message[:100]},
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output_data=intent_output)
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# Step 4: QueryRewriter - (Optional, skipped in MVP)
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# ctx.query_rewritten = ctx.current_message
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self._record_step(ctx, 4, "QueryRewriter", "skipped", 0,
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input_data={"query": ctx.current_message[:100]},
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output_data={"note": "Skipped in MVP", "rewritten": None})
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# Step 5-6: Multi-KB Retrieval + ResultRanker
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step_start = time.time()
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if self._config.enable_rag and self._retriever:
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await self._retrieve_evidence(ctx)
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retrieval_output = {
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"hit_count": len(ctx.retrieval_result.hits) if ctx.retrieval_result else 0,
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"max_score": ctx.retrieval_result.max_score if ctx.retrieval_result else 0,
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}
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if ctx.retrieval_result and ctx.retrieval_result.hits:
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retrieval_output["top_hits"] = [
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{
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"content": hit.text[:200] + "..." if len(hit.text) > 200 else hit.text,
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"score": round(hit.score, 4),
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"source": hit.source,
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}
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for hit in ctx.retrieval_result.hits[:5]
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]
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self._record_step(ctx, 5, "MultiKBRetrieval", "success", int((time.time() - step_start) * 1000),
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input_data={"query": ctx.current_message[:100], "top_k": 3},
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output_data=retrieval_output)
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else:
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self._record_step(ctx, 5, "MultiKBRetrieval", "skipped", 0,
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input_data={"query": ctx.current_message[:100]},
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output_data={"note": "RAG disabled or no retriever"})
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# Step 7: PromptBuilder - Load template + inject behavior rules
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step_start = time.time()
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await self._build_system_prompt(ctx)
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self._record_step(ctx, 7, "PromptBuilder", "success", int((time.time() - step_start) * 1000),
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input_data={"template_id": getattr(ctx, 'template_id', None), "behavior_rules": ctx.behavior_rules[:3] if ctx.behavior_rules else []},
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output_data={"prompt_length": len(ctx.system_prompt) if ctx.system_prompt else 0, "prompt_preview": ctx.system_prompt[:300] + "..." if ctx.system_prompt and len(ctx.system_prompt) > 300 else ctx.system_prompt})
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# Step 8: LLM.generate - Generate response
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step_start = time.time()
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await self._generate_response(ctx)
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llm_model = ctx.llm_response.model if ctx.llm_response else "unknown"
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self._record_step(ctx, 8, "LLMGenerate", "success", int((time.time() - step_start) * 1000),
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input_data={"model": llm_model, "messages_count": len(self._build_llm_messages(ctx)) if hasattr(self, '_build_llm_messages') else 1},
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output_data={"reply_length": len(ctx.llm_response.content) if ctx.llm_response else 0, "reply_preview": ctx.llm_response.content[:200] + "..." if ctx.llm_response and len(ctx.llm_response.content) > 200 else (ctx.llm_response.content if ctx.llm_response else None)})
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# Step 9: OutputFilter - Filter forbidden words in output
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step_start = time.time()
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await self._filter_output(ctx)
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filter_output = {"filtered": ctx.filtered_reply != ctx.llm_response.content if ctx.llm_response else False}
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if ctx.diagnostics.get("output_filter"):
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filter_output["triggered_words"] = ctx.diagnostics.get("output_filter", {}).get("triggered_words", [])
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self._record_step(ctx, 9, "OutputFilter", "success", int((time.time() - step_start) * 1000),
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input_data={"text_length": len(ctx.llm_response.content) if ctx.llm_response else 0},
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output_data=filter_output)
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# Step 10: Confidence - Calculate confidence score
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step_start = time.time()
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self._calculate_confidence(ctx)
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self._record_step(ctx, 10, "Confidence", "success", int((time.time() - step_start) * 1000),
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input_data={"reply_length": len(ctx.filtered_reply) if ctx.filtered_reply else 0, "hit_count": len(ctx.retrieval_result.hits) if ctx.retrieval_result else 0},
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output_data={"confidence": ctx.confidence_result.confidence if ctx.confidence_result else 0, "should_transfer": ctx.confidence_result.should_transfer if ctx.confidence_result else True})
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# Step 11: Memory - Save messages
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step_start = time.time()
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await self._save_messages(ctx)
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self._record_step(ctx, 11, "Memory", "success", int((time.time() - step_start) * 1000),
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input_data={"session_id": ctx.session_id},
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output_data={"saved": True})
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# Step 12: Return - Build and return ChatResponse
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self._record_step(ctx, 12, "Response", "success", 0,
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input_data={"confidence": ctx.confidence_result.confidence if ctx.confidence_result else 0, "should_transfer": ctx.confidence_result.should_transfer if ctx.confidence_result else True},
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output_data={"reply_length": len(ctx.filtered_reply) if ctx.filtered_reply else 0, "reply_preview": ctx.filtered_reply[:200] + "..." if ctx.filtered_reply and len(ctx.filtered_reply) > 200 else ctx.filtered_reply})
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return self._build_response(ctx)
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except Exception as e:
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@ -944,6 +1035,7 @@ class OrchestratorService:
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"session_id": ctx.session_id,
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"channel_type": ctx.channel_type,
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"diagnostics": ctx.diagnostics,
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"execution_steps": ctx.execution_steps,
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}
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return ChatResponse(
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