feat(models): 组织、用户、角色、客户、会话与消息模型
按组织边界定义数据关系，补齐仓储层与通用工具，迁移脚本可重复执行。
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"""
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Copyright 2024-2026 ChatterMate
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from typing import List, Optional
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from pydantic import BaseModel, Field
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class TriageResult(BaseModel):
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"""Structured output of the AI triage pass over a new ticket."""
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# Rewritten, clearer title. The original is preserved on the ticket.
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title: str = Field(max_length=500)
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priority: str = Field(description="urgent, high, medium or low")
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severity: Optional[int] = Field(default=None, ge=1, le=3)
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# Short intent classification, e.g. "billing_issue", "bug_report",
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# "feature_request", "account_access", "outage".
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intent: str = Field(max_length=100)
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# 1-3 sentence internal summary of the issue.
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summary: str = Field(max_length=2000)
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# ["payments", "ledger"] — up to two short lowercase topic tags.
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tags: List[str] = Field(default_factory=list, max_length=3)
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confidence: float = Field(ge=0.0, le=1.0)
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class HypothesisSpec(BaseModel):
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"""One proposed root-cause hypothesis, before testing."""
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title: str = Field(max_length=300)
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rationale: str = Field(max_length=2000)
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class HypothesisPlan(BaseModel):
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"""Output of the hypothesis-generation step (H1..Hn, most likely first)."""
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hypotheses: List[HypothesisSpec] = Field(min_length=1, max_length=5)
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class HypothesisVerdict(BaseModel):
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"""Outcome of testing a single hypothesis with bounded tool calls."""
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status: str = Field(description="validated, invalidated or inconclusive")
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confidence: float = Field(ge=0.0, le=1.0)
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# What the evidence showed, citing the queries/results that decided it.
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conclusion: str = Field(max_length=4000)
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class RCATimelineEntry(BaseModel):
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time: str = Field(max_length=100)
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event: str = Field(max_length=500)
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class RCAResult(BaseModel):
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"""Structured root-cause analysis synthesized from the tested hypotheses
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and captured evidence."""
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summary: str = Field(max_length=4000)
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impact: Optional[str] = Field(default=None, max_length=4000)
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timeline: List[RCATimelineEntry] = Field(default_factory=list, max_length=20)
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investigation_log: Optional[str] = Field(default=None, max_length=6000)
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contributing_factors: List[str] = Field(default_factory=list, max_length=10)
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# Must cite hypotheses inline, e.g. "[H1 · 0.92]".
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conclusion: str = Field(max_length=6000)
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remediation: Optional[str] = Field(default=None, max_length=4000)
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prevention: Optional[str] = Field(default=None, max_length=4000)
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# Plain-language section a human reviews and sends to the customer.
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customer_summary: str = Field(max_length=4000)
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confidence: float = Field(ge=0.0, le=1.0)
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TRIAGE_JSON_TOOL_SCHEMA = {
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"type": "object",
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"properties": {
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"title": {"type": "string", "description": "Clear, specific rewritten title."},
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"priority": {"type": "string", "enum": ["urgent", "high", "medium", "low"]},
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"severity": {"type": "integer", "minimum": 1, "maximum": 3},
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"intent": {"type": "string", "description": "Short intent slug, e.g. bug_report."},
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"summary": {"type": "string", "description": "1-3 sentence internal summary."},
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"tags": {"type": "array", "items": {"type": "string"}, "description": "Up to two short lowercase topic tags."},
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"confidence": {"type": "number", "minimum": 0, "maximum": 1},
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},
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"required": ["title", "priority", "intent", "summary", "confidence"],
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"additionalProperties": False,
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}
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HYPOTHESES_JSON_TOOL_SCHEMA = {
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"type": "object",
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"properties": {
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"hypotheses": {
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"type": "array",
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"minItems": 1,
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"maxItems": 5,
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string", "description": "Short testable hypothesis."},
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"rationale": {"type": "string", "description": "Why this is suspected and what evidence would confirm it."},
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},
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"required": ["title", "rationale"],
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"additionalProperties": False,
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},
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},
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},
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"required": ["hypotheses"],
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"additionalProperties": False,
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}
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VERDICT_JSON_TOOL_SCHEMA = {
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"type": "object",
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"properties": {
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"status": {"type": "string", "enum": ["validated", "invalidated", "inconclusive"]},
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"confidence": {"type": "number", "minimum": 0, "maximum": 1},
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"conclusion": {"type": "string", "description": "What the evidence showed, citing queries/results."},
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},
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"required": ["status", "confidence", "conclusion"],
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"additionalProperties": False,
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}
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RCA_JSON_TOOL_SCHEMA = {
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"type": "object",
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"properties": {
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"summary": {"type": "string"},
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"impact": {"type": "string"},
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"timeline": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {"time": {"type": "string"}, "event": {"type": "string"}},
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"required": ["time", "event"],
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"additionalProperties": False,
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},
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},
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"investigation_log": {"type": "string"},
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"contributing_factors": {"type": "array", "items": {"type": "string"}},
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"conclusion": {"type": "string", "description": "Cites hypotheses inline, e.g. [H1 · 0.92]."},
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"remediation": {"type": "string"},
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"prevention": {"type": "string"},
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"customer_summary": {"type": "string", "description": "Plain language for the customer."},
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"confidence": {"type": "number", "minimum": 0, "maximum": 1},
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},
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"required": ["summary", "conclusion", "customer_summary", "confidence"],
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"additionalProperties": False,
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}
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