feat(regulatory): 增加条件字段LLM复核

review_agent/regulatory_review/services/llm_review.py

@@ -0,0 +1,175 @@
+from __future__ import annotations
+
+import json
+import re
+from collections.abc import Callable
+from typing import Any
+
+from review_agent.llm import LLMConfigurationError, LLMRequestError, generate_completion
+
+
+FIELD_LABELS = ["产品名称", "型号规格", "预期用途", "管理类别", "分类编码", "注册类型", "临床评价路径"]
+CompletionFunc = Callable[[list[dict[str, str]]], str]
+
+
+def review_condition_fields(
+    *,
+    text: str,
+    rule_fields: dict[str, str],
+    file_context: str = "",
+    completion_func: Callable[..., str] | None = None,
+) -> dict[str, Any]:
+    llm_fields: dict[str, str] = {}
+    status = "skipped"
+    error_message = ""
+    try:
+        raw = (completion_func or generate_completion)(_condition_messages(text, rule_fields, file_context), temperature=0.0)
+        payload = _parse_json_object(raw)
+        llm_fields = _clean_fields(payload.get("fields") or payload)
+        status = "success"
+    except (LLMConfigurationError, LLMRequestError, json.JSONDecodeError, TypeError, ValueError) as exc:
+        status = "failed"
+        error_message = str(exc)
+
+    selected_fields, selected_sources = _select_fields(rule_fields, llm_fields)
+    return {
+        "status": status,
+        "error_message": error_message,
+        "rule_fields": _clean_fields(rule_fields),
+        "llm_fields": llm_fields,
+        "selected_fields": selected_fields,
+        "selected_sources": selected_sources,
+    }
+
+
+def review_workflow_payload(
+    *,
+    stage: str,
+    payload: dict[str, Any],
+    completion_func: Callable[..., str] | None = None,
+) -> dict[str, Any]:
+    try:
+        raw = (completion_func or generate_completion)(_workflow_messages(stage, payload), temperature=0.0)
+        parsed = _parse_json_object(raw)
+        return {
+            "status": "success",
+            "stage": stage,
+            "result": parsed,
+            "error_message": "",
+        }
+    except (LLMConfigurationError, LLMRequestError, json.JSONDecodeError, TypeError, ValueError) as exc:
+        return {
+            "status": "failed",
+            "stage": stage,
+            "result": {},
+            "error_message": str(exc),
+        }
+
+
+def _condition_messages(text: str, rule_fields: dict[str, str], file_context: str) -> list[dict[str, str]]:
+    return [
+        {
+            "role": "system",
+            "content": (
+                "你是NMPA注册资料字段复核助手。请从附件文本中提取最合理的字段值，"
+                "只返回JSON，格式为 {\"fields\": {\"产品名称\": \"...\"}}。"
+                "产品名称应包含完整名称、检测对象和方法学括号；不要把章节标题当产品名称。"
+            ),
+        },
+        {
+            "role": "user",
+            "content": json.dumps(
+                {
+                    "file_context": file_context,
+                    "rule_fields": rule_fields,
+                    "text": text[:4000],
+                    "allowed_fields": FIELD_LABELS,
+                },
+                ensure_ascii=False,
+            ),
+        },
+    ]
+
+
+def _workflow_messages(stage: str, payload: dict[str, Any]) -> list[dict[str, str]]:
+    return [
+        {
+            "role": "system",
+            "content": (
+                "你是NMPA法规核查复核助手。请复核当前流程节点的规则结果，"
+                "指出可能误判、漏判和更合理的建议。只返回JSON。"
+            ),
+        },
+        {
+            "role": "user",
+            "content": json.dumps({"stage": stage, "payload": payload}, ensure_ascii=False)[:6000],
+        },
+    ]
+
+
+def _parse_json_object(raw: str) -> dict[str, Any]:
+    value = (raw or "").strip()
+    if value.startswith("```"):
+        value = re.sub(r"^```(?:json)?\s*", "", value)
+        value = re.sub(r"\s*```$", "", value)
+    start = value.find("{")
+    end = value.rfind("}")
+    if start >= 0 and end >= start:
+        value = value[start : end + 1]
+    parsed = json.loads(value)
+    if not isinstance(parsed, dict):
+        raise ValueError("LLM复核结果不是JSON对象。")
+    return parsed
+
+
+def _clean_fields(fields: dict[str, Any]) -> dict[str, str]:
+    clean = {}
+    for label in FIELD_LABELS:
+        value = fields.get(label)
+        if not isinstance(value, str):
+            continue
+        normalized = " ".join(value.strip().split())
+        if normalized:
+            clean[label] = normalized
+    return clean
+
+
+def _select_fields(rule_fields: dict[str, str], llm_fields: dict[str, str]) -> tuple[dict[str, str], dict[str, str]]:
+    rule_clean = _clean_fields(rule_fields)
+    selected = {}
+    sources = {}
+    for label in FIELD_LABELS:
+        rule_value = rule_clean.get(label, "")
+        llm_value = llm_fields.get(label, "")
+        value, source = _select_field(label, rule_value, llm_value)
+        if value:
+            selected[label] = value
+            sources[label] = source
+    return selected, sources
+
+
+def _select_field(label: str, rule_value: str, llm_value: str) -> tuple[str, str]:
+    if _invalid_field_value(llm_value):
+        return rule_value, "rule" if rule_value else ""
+    if not rule_value:
+        return llm_value, "llm" if llm_value else ""
+    if not llm_value:
+        return rule_value, "rule"
+    if label == "产品名称" and _better_product_name(llm_value, rule_value):
+        return llm_value, "llm"
+    if len(llm_value) > len(rule_value) * 1.35 and rule_value in llm_value:
+        return llm_value, "llm"
+    return rule_value, "rule"
+
+
+def _better_product_name(candidate: str, current: str) -> bool:
+    if current and current in candidate and len(candidate) > len(current):
+        return True
+    product_keywords = ["试剂盒", "检测试剂", "荧光PCR法", "PCR法", "核酸检测"]
+    return len(candidate) > len(current) and any(keyword in candidate for keyword in product_keywords)
+
+
+def _invalid_field_value(value: str) -> bool:
+    if not value:
+        return True
+    return any(keyword in value for keyword in ["第1章", "第2章", "第3章", "监管信息", "综述资料", "章节目录"])