100d1a06eb
- 后端 API（Flask + Gunicorn） - RAG 引擎（混合检索 + 云端 Reranker + 引用溯源） - 文档解析（MinerU + 多格式支持） - Docker 生产部署配置 - 排除前端项目、敏感配置、模型文件
138 lines
4.5 KiB
Python
138 lines
4.5 KiB
Python
"""
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Agentic RAG - 质量评估 Mixin
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包含置信度门控、质量评估、推理反思等方法
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"""
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import logging
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from .agentic_base import logger
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logger = logging.getLogger(__name__)
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class QualityMixin:
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"""质量评估方法"""
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def _check_confidence_gate(self, query: str, docs: list, verbose: bool = True,
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precomputed_scores: list = None):
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"""检查置信度门控
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Args:
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query: 用户查询
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docs: 文档列表
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verbose: 是否详细输出
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precomputed_scores: 预计算的 Rerank 分数（可选，避免重复推理）
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"""
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if not self.confidence_gate:
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return {"passed": True, "reason": "no_gate"}
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try:
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result = self.confidence_gate.evaluate(query, docs,
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precomputed_scores=precomputed_scores)
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return result
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except Exception as e:
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logger.warning(f"置信度门控检查失败: {e}")
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return {"passed": True, "reason": "error"}
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def _assess_quality(self, query: str, docs: list, metas: list = None,
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verbose: bool = True) -> dict:
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"""多维质量评估"""
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if not self.quality_assessor:
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return {"overall_score": 0.5, "dimensions": {}}
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try:
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result = self.quality_assessor.assess(query, docs, metas)
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return result
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except Exception as e:
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logger.warning(f"质量评估失败: {e}")
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return {"overall_score": 0.5, "dimensions": {}}
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def _reflect_on_answer(self, query: str, answer: str, contexts: list,
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verbose: bool = True) -> dict:
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"""推理反思"""
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if not self.reasoning_reflector:
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return {"needs_reflection": False, "issues": []}
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try:
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result = self.reasoning_reflector.reflect(query, answer, contexts)
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return result
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except Exception as e:
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logger.warning(f"推理反思失败: {e}")
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return {"needs_reflection": False, "issues": []}
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def _think(self, original_query: str, current_query: str,
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iteration: int, contexts: list, verbose: bool = True) -> dict:
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"""
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Agent 思考：决定下一步行动
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Returns:
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{
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"action": "answer" | "rewrite" | "search_web" | "decompose",
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"reason": "...",
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"rewrite_query": "..." # 如果 action == "rewrite"
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}
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"""
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from core.llm_utils import call_llm, parse_json_from_response
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from .agentic_base import MODEL
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# 构建思考提示
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context_summary = ""
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if contexts:
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for i, ctx in enumerate(contexts[:3], 1):
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meta = ctx.get('meta', {})
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source = meta.get('source', '未知')
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doc_preview = ctx.get('doc', '')[:100]
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context_summary += f"{i}. [{source}] {doc_preview}...\n"
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prompt = f"""你是一个 RAG 系统的决策 Agent，需要判断下一步行动。
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【原始问题】
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{original_query}
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【当前问题】
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{current_query}
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【迭代轮次】
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{iteration} / {self.max_iterations}
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【已检索到的上下文】
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{context_summary if context_summary else "（无）"}
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【可选行动】
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1. answer - 已有足够信息，可以回答
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2. rewrite - 查询不够清晰，需要重写
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3. search_web - 知识库信息不足，需要网络搜索
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4. decompose - 问题太复杂，需要分解
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【决策要求】
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- 如果上下文足够回答问题，选择 answer
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- 如果上下文不足且迭代未超限，选择 search_web 或 rewrite
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- 返回 JSON 格式
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请决策："""
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try:
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result = call_llm(
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self.client, prompt, MODEL,
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temperature=0.3,
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max_tokens=200
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)
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decision = parse_json_from_response(result) if result else {}
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# 默认决策
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if not decision or "action" not in decision:
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if contexts and len(contexts) >= 2:
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decision = {"action": "answer", "reason": "有足够上下文"}
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else:
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decision = {"action": "rewrite", "reason": "上下文不足"}
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return decision
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except Exception as e:
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logger.warning(f"Agent 思考失败: {e}")
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if contexts:
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return {"action": "answer", "reason": "默认回答"}
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return {"action": "rewrite", "reason": "默认重写"}
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