zhanghongyu_0626
3995cb44d8
- Added `auth.py` for mock login and JWT issuance. - Introduced `chat.py` for handling chat requests with role-based access control. - Enhanced `main.py` to include new routers and middleware for tracing. - Implemented input validation in `input_guard.py` to prevent SQL injection. - Created repositories for managing agent sessions and audit logs. - Added exception handling for authorization errors. - Updated settings to include JWT configuration. - Introduced tests for authentication and input validation.
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""对话接口：四 Agent 统一 chat 入口。"""
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from __future__ import annotations
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from typing import Annotated
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from fastapi import APIRouter, Depends, Request
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from app.gateway.auth_deps import get_auth_context
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from app.gateway.ownership import assert_customer_access, resolve_effective_customer_id
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from app.model.schemas import AuthContext, ChatRequest, ChatResponseData
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from app.repository.agent_repository import AgentSessionRepository
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from app.repository.audit_repository import AuditRepository
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from app.service.agent_service import run_chat
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from app.utils.input_guard import validate_user_message
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from app.utils.response import ok
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router = APIRouter(prefix="/api", tags=["chat"])
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@router.post("/chat")
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def chat(
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body: ChatRequest,
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request: Request,
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ctx: Annotated[AuthContext, Depends(get_auth_context)],
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):
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message = validate_user_message(body.message)
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customer_id = resolve_effective_customer_id(ctx, body.customer_id)
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if customer_id and ctx.agent_type in ("advisor", "analyst", "risk", "customer"):
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assert_customer_access(ctx, customer_id)
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session_repo = AgentSessionRepository()
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session_id = session_repo.ensure_session(ctx, body.session_id, customer_id)
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seq = session_repo.next_seq(session_id)
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session_repo.insert_message(
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session_id=session_id,
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trace_id=ctx.trace_id,
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seq_no=seq,
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role="user",
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content=message,
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)
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reply, has_disclaimer = run_chat(ctx, message, customer_id)
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session_repo.insert_message(
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session_id=session_id,
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trace_id=ctx.trace_id,
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seq_no=seq + 1,
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role="assistant",
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content=reply,
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has_disclaimer=has_disclaimer,
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)
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AuditRepository().insert(
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trace_id=ctx.trace_id,
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event_type="chat_completed",
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agent_type=ctx.agent_type,
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actor_id=ctx.sub,
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customer_id=customer_id,
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input_summary={"session_id": session_id, "message_len": len(message)},
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decision="success",
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)
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data = ChatResponseData(
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session_id=session_id,
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reply=reply,
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agent_type=ctx.agent_type,
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has_disclaimer=has_disclaimer,
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)
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return ok(data.model_dump(), ctx.trace_id)
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