feat: inject metadata filters and add fallback reason codes [AC-IDSMETA-18, AC-IDSMETA-19, AC-IDSMETA-20]
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MerCry
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@ -653,6 +653,7 @@ class OrchestratorService:
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"""
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[AC-AISVC-16, AC-AISVC-17] RAG retrieval for evidence.
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Step 5-6: Multi-KB retrieval with target KBs from intent matching.
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[AC-IDSMETA-19] Inject metadata filters (grade/subject/scene) from context.
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"""
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# Skip if flow or intent already handled
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if ctx.diagnostics.get("flow_handled") or ctx.diagnostics.get("intent_handled"):
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@ -678,12 +679,24 @@ class OrchestratorService:
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retrieval_ctx.metadata["target_kb_ids"] = ctx.target_kb_ids
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logger.info(f"[AC-AISVC-16] Using target_kb_ids from intent: {ctx.target_kb_ids}")
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# [AC-IDSMETA-19] Inject metadata filters from context
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metadata_filters = await self._build_metadata_filters(ctx)
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if metadata_filters:
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retrieval_ctx.tag_filter = metadata_filters
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logger.info(
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f"[AC-IDSMETA-19] Injected metadata filters: "
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f"intent_id={ctx.intent_match.rule.id if ctx.intent_match else None}, "
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f"target_kbs={ctx.target_kb_ids}, "
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f"applied_metadata_filters={metadata_filters.fields}"
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)
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ctx.retrieval_result = await self._retriever.retrieve(retrieval_ctx)
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ctx.diagnostics["retrieval"] = {
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"hit_count": ctx.retrieval_result.hit_count,
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"max_score": ctx.retrieval_result.max_score,
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"is_empty": ctx.retrieval_result.is_empty,
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"applied_metadata_filters": metadata_filters.fields if metadata_filters else None,
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}
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logger.info(
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@ -708,6 +721,48 @@ class OrchestratorService:
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)
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ctx.diagnostics["retrieval_error"] = str(e)
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async def _build_metadata_filters(self, ctx: GenerationContext):
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"""
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[AC-IDSMETA-19] Build metadata filters from context.
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Sources:
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1. Intent rule metadata (if matched)
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2. Session metadata
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3. Request metadata
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4. Extracted slots from conversation
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Returns:
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TagFilter with at least grade, subject, scene if available
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"""
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from app.services.retrieval.metadata import TagFilter
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filter_fields = {}
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# 1. From intent rule metadata
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if ctx.intent_match and hasattr(ctx.intent_match.rule, 'metadata_') and ctx.intent_match.rule.metadata_:
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intent_metadata = ctx.intent_match.rule.metadata_
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for key in ['grade', 'subject', 'scene']:
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if key in intent_metadata:
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filter_fields[key] = intent_metadata[key]
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# 2. From session/request metadata
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if ctx.request_metadata:
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for key in ['grade', 'subject', 'scene']:
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if key in ctx.request_metadata and key not in filter_fields:
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filter_fields[key] = ctx.request_metadata[key]
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# 3. From merged context (extracted slots)
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if ctx.merged_context and hasattr(ctx.merged_context, 'slots'):
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slots = ctx.merged_context.slots or {}
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for key in ['grade', 'subject', 'scene']:
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if key in slots and key not in filter_fields:
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filter_fields[key] = slots[key]
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if not filter_fields:
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return None
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return TagFilter(fields=filter_fields)
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async def _build_system_prompt(self, ctx: GenerationContext) -> None:
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"""
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[AC-AISVC-56, AC-AISVC-84] Step 7: Build system prompt with template + behavior rules.
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@ -919,17 +974,66 @@ class OrchestratorService:
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def _fallback_response(self, ctx: GenerationContext) -> str:
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"""
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[AC-AISVC-17] Generate fallback response when LLM is unavailable.
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[AC-IDSMETA-20] Return fallback with structured reason code when no recall.
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"""
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if ctx.retrieval_result and not ctx.retrieval_result.is_empty:
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return (
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"根据知识库信息，我找到了一些相关内容，"
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"但暂时无法生成完整回复。建议您稍后重试或联系人工客服。"
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)
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# [AC-IDSMETA-20] Record structured fallback reason code
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fallback_reason_code = self._determine_fallback_reason_code(ctx)
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ctx.diagnostics["fallback_reason_code"] = fallback_reason_code
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logger.warning(
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f"[AC-IDSMETA-20] No recall, using fallback: "
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f"intent_id={ctx.intent_match.rule.id if ctx.intent_match else None}, "
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f"target_kbs={ctx.target_kb_ids}, "
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f"applied_metadata_filters={ctx.diagnostics.get('retrieval', {}).get('applied_metadata_filters')}, "
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f"fallback_reason_code={fallback_reason_code}"
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)
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return (
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"抱歉，我暂时无法处理您的请求。"
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"请稍后重试或联系人工客服获取帮助。"
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)
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def _determine_fallback_reason_code(self, ctx: GenerationContext) -> str:
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"""
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[AC-IDSMETA-20] Determine structured fallback reason code.
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Reason codes:
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- no_recall_after_metadata_filter: No results after applying metadata filters
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- no_recall_no_kb: No target knowledge bases configured
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- no_recall_kb_empty: Knowledge base is empty
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- no_recall_low_score: Results found but below threshold
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- no_recall_error: Retrieval error occurred
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"""
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retrieval_diag = ctx.diagnostics.get("retrieval", {})
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# Check for retrieval error
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if ctx.diagnostics.get("retrieval_error"):
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return "no_recall_error"
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# Check if metadata filters were applied
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if retrieval_diag.get("applied_metadata_filters"):
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return "no_recall_after_metadata_filter"
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# Check if target KBs were configured
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if not ctx.target_kb_ids:
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return "no_recall_no_kb"
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# Check if KB is empty (no candidates at all)
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if retrieval_diag.get("total_candidates", 0) == 0:
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return "no_recall_kb_empty"
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# Results found but filtered out by score threshold
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if retrieval_diag.get("total_candidates", 0) > 0 and retrieval_diag.get("filtered_hits", 0) == 0:
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return "no_recall_low_score"
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return "no_recall_unknown"
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def _calculate_confidence(self, ctx: GenerationContext) -> None:
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"""
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[AC-AISVC-17, AC-AISVC-18, AC-AISVC-19] Calculate confidence score.
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@ -6,7 +6,10 @@ Retrieval layer for AI Service.
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any
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from typing import Any, TYPE_CHECKING
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if TYPE_CHECKING:
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from app.services.retrieval.metadata import TagFilter
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logger = logging.getLogger(__name__)
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@ -23,6 +26,14 @@ class RetrievalContext:
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session_id: str | None = None
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channel_type: str | None = None
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metadata: dict[str, Any] | None = None
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tag_filter: "TagFilter | None" = None
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kb_ids: list[str] | None = None
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def get_tag_filter_dict(self) -> dict[str, str | list[str] | None] | None:
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"""获取标签过滤器的字典表示"""
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if self.tag_filter and not self.tag_filter.is_empty():
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return self.tag_filter.fields
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return None
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@dataclass
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@ -9,7 +9,7 @@ from datetime import date, datetime
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from enum import Enum
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from typing import Any
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from pydantic import BaseModel
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from pydantic import BaseModel, Field
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class RetrievalStrategy(str, Enum):
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@ -32,6 +32,39 @@ class ChunkMetadataModel(BaseModel):
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valid_until: str | None = None
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priority: int = 5
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keywords: list[str] = []
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grade: str = ""
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subject: str = ""
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type: str = ""
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class GradeEnum(str, Enum):
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"""年级枚举"""
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GRADE_7 = "初一"
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GRADE_8 = "初二"
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GRADE_9 = "初三"
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GRADE_10 = "高一"
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GRADE_11 = "高二"
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GRADE_12 = "高三"
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class SubjectEnum(str, Enum):
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"""学科枚举"""
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GENERAL = "通用"
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PHYSICS = "物理"
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CHINESE = "语文"
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MATH = "数学"
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ENGLISH = "英语"
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CHEMISTRY = "化学"
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BIOLOGY = "生物"
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class TypeEnum(str, Enum):
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"""内容类型枚举"""
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PAIN_POINT = "痛点"
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SUBJECT_FEATURE = "学科特点"
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ABILITY_REQUIREMENT = "能力要求"
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COURSE_VALUE = "课程价值"
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VIEWPOINT = "观点"
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@dataclass
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@ -50,6 +83,9 @@ class ChunkMetadata:
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valid_until: date | None = None
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priority: int = 5
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keywords: list[str] = field(default_factory=list)
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grade: str = ""
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subject: str = ""
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type: str = ""
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def to_dict(self) -> dict[str, Any]:
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"""Convert to dictionary for storage."""
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@ -64,6 +100,9 @@ class ChunkMetadata:
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"valid_until": self.valid_until.isoformat() if self.valid_until else None,
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"priority": self.priority,
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"keywords": self.keywords,
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"grade": self.grade,
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"subject": self.subject,
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"type": self.type,
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}
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@classmethod
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@ -80,6 +119,9 @@ class ChunkMetadata:
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valid_until=date.fromisoformat(data["valid_until"]) if data.get("valid_until") else None,
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priority=data.get("priority", 5),
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keywords=data.get("keywords", []),
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grade=data.get("grade", ""),
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subject=data.get("subject", ""),
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type=data.get("type", ""),
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)
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@ -142,6 +184,103 @@ class MetadataFilter:
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return {"must": conditions}
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class TagFilterModel(BaseModel):
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"""Pydantic model for tag filter API serialization."""
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fields: dict[str, str | list[str] | None] = Field(default_factory=dict, description="动态过滤字段")
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@dataclass
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class TagFilter:
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"""
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动态标签过滤器
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用于在 RAG 检索时根据元数据字段进行过滤
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支持任意动态字段，不再硬编码 grade/subject/type
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示例:
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- 教育行业: {"grade": "初一", "type": "痛点"}
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- 医疗行业: {"department": "内科", "disease_type": "慢性病"}
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"""
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fields: dict[str, str | list[str] | None] = field(default_factory=dict)
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def is_empty(self) -> bool:
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"""检查过滤器是否为空"""
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if not self.fields:
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return True
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return all(v is None or v == "" or (isinstance(v, list) and len(v) == 0) for v in self.fields.values())
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def to_qdrant_filter(self) -> dict[str, Any] | None:
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"""转换为 Qdrant 过滤格式"""
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conditions = []
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for field_name, field_value in self.fields.items():
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if field_value is None or field_value == "":
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continue
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if isinstance(field_value, list):
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if len(field_value) == 0:
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continue
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conditions.append({
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"key": f"metadata.{field_name}",
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"match": {"any": field_value}
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})
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else:
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conditions.append({
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"key": f"metadata.{field_name}",
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"match": {"value": field_value}
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})
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if not conditions:
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return None
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return {"must": conditions}
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@classmethod
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def from_dict(cls, data: dict[str, Any]) -> "TagFilter":
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"""从字典创建"""
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return cls(fields=dict(data))
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@classmethod
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def from_model(cls, model: TagFilterModel) -> "TagFilter":
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"""从 Pydantic 模型创建"""
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return cls(fields=dict(model.fields))
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def merge_with_context(self, context: dict[str, Any]) -> "TagFilter":
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"""
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与上下文合并，支持模板变量替换
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例如:
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tag_filter = TagFilter(fields={"grade": "${context.grade}", "type": "痛点"})
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context = {"grade": "初一"}
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result = tag_filter.merge_with_context(context)
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# result = TagFilter(fields={"grade": "初一", "type": "痛点"})
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"""
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def resolve_value(value: str | list | None) -> str | list | None:
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if value is None:
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return None
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if isinstance(value, list):
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return [resolve_value(v) for v in value]
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if isinstance(value, str):
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if value.startswith("${context.") and value.endswith("}"):
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key = value[10:-1]
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return context.get(key)
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return value
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return str(value)
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resolved_fields = {}
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for field_name, field_value in self.fields.items():
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resolved = resolve_value(field_value)
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if resolved is not None and resolved != "":
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resolved_fields[field_name] = resolved
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elif field_name in context:
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resolved_fields[field_name] = context[field_name]
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return TagFilter(fields=resolved_fields)
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def get_field(self, field_name: str) -> str | list[str] | None:
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"""获取指定字段的值"""
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return self.fields.get(field_name)
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@dataclass
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class KnowledgeChunk:
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"""
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