初始化提交

backend/app/modules/imports/parsing.py

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+"""导入资料结构解析（纯函数，无 IO）。
+
+按 ``material_type`` / ``format`` 把原始资料切片为条目草稿。只做结构解析
+（消息数组、段落、字段），语义抽取（档案 / 记忆 / 人格候选）留给后续
+模型驱动阶段。领域层禁止 IO，本模块仅产出数据结构。
+"""
+
+import json
+from dataclasses import dataclass, field
+from typing import Any
+
+# 5a 不切片的媒体资料（仅存元数据与授权记录，spec §18 仅预留）。
+_NON_TEXT_MATERIAL_TYPES = {"voice_sample"}
+
+
+@dataclass
+class ParsedItemDraft:
+    """解析产出的条目草稿；落库前由 service 补 sequence / id。"""
+
+    item_type: str
+    role: str
+    content: str
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+def parse_import(
+    material_type: str, fmt: str, *, content: str | None = None
+) -> list[ParsedItemDraft]:
+    """按格式解析文本资料；媒体资料返回空。"""
+    if material_type in _NON_TEXT_MATERIAL_TYPES:
+        return []
+
+    if fmt == "json" and content:
+        return _parse_json(content)
+
+    if content:
+        return _parse_paragraphs(content)
+
+    return []
+
+
+def _parse_json(content: str) -> list[ParsedItemDraft]:
+    try:
+        data = json.loads(content)
+    except json.JSONDecodeError:
+        return _parse_paragraphs(content)
+
+    if (
+        isinstance(data, list)
+        and data
+        and isinstance(data[0], dict)
+        and ("role" in data[0] or "content" in data[0])
+    ):
+        return _parse_messages(data)
+    if isinstance(data, dict):
+        return _parse_fields(data)
+    return _parse_paragraphs(content)
+
+
+def _parse_messages(messages: list[dict[str, Any]]) -> list[ParsedItemDraft]:
+    drafts: list[ParsedItemDraft] = []
+    for msg in messages:
+        if not isinstance(msg, dict):
+            continue
+        text = str(msg.get("content", "")).strip()
+        if not text:
+            continue
+        meta = {k: v for k, v in msg.items() if k not in ("role", "content")}
+        drafts.append(
+            ParsedItemDraft(
+                item_type="message",
+                role=_normalize_role(str(msg.get("role", "other"))),
+                content=text,
+                metadata=meta,
+            )
+        )
+    return drafts
+
+
+def _parse_fields(data: dict[str, Any]) -> list[ParsedItemDraft]:
+    drafts: list[ParsedItemDraft] = []
+    for key, value in data.items():
+        text = str(value).strip()
+        if text:
+            drafts.append(
+                ParsedItemDraft(
+                    item_type="profile_field",
+                    role="other",
+                    content=text,
+                    metadata={"field": key},
+                )
+            )
+    return drafts
+
+
+def _parse_paragraphs(text: str) -> list[ParsedItemDraft]:
+    drafts: list[ParsedItemDraft] = []
+    for chunk in text.split("\n\n"):
+        clean = chunk.strip()
+        if clean:
+            drafts.append(ParsedItemDraft(item_type="paragraph", role="other", content=clean))
+    return drafts
+
+
+def _normalize_role(role: str) -> str:
+    lowered = role.lower()
+    if lowered in ("user", "human"):
+        return "user"
+    if lowered in ("assistant", "ai", "bot"):
+        return "assistant"
+    if lowered == "system":
+        return "system"
+    return "other"