Files
breakpilot-compliance/backend-compliance/compliance/api/llm_audit_routes.py
Benjamin Admin e6201d5239 feat: Anti-Fake-Evidence System (Phase 1-4b)
Implement full evidence integrity pipeline to prevent compliance theater:
- Confidence levels (E0-E4), truth status tracking, assertion engine
- Four-Eyes approval workflow, audit trail, reject endpoint
- Evidence distribution dashboard, LLM audit routes
- Traceability matrix (backend endpoint + Compliance Hub UI tab)
- Anti-fake badges, control status machine, normative patterns
- 2 migrations, 4 test suites, MkDocs documentation

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-23 17:15:45 +01:00

163 lines
5.1 KiB
Python

"""
FastAPI routes for LLM Generation Audit Trail.
Endpoints:
- POST /llm-audit: Record an LLM generation event
- GET /llm-audit: List audit records with filters
"""
import logging
import uuid as uuid_module
from datetime import datetime
from typing import Optional
from fastapi import APIRouter, Depends, Query
from pydantic import BaseModel
from sqlalchemy.orm import Session
from classroom_engine.database import get_db
from ..db.models import LLMGenerationAuditDB
logger = logging.getLogger(__name__)
router = APIRouter(tags=["compliance-llm-audit"])
# ============================================================================
# Schemas
# ============================================================================
class LLMAuditCreate(BaseModel):
entity_type: str
entity_id: Optional[str] = None
generation_mode: str
truth_status: str = "generated"
may_be_used_as_evidence: bool = False
llm_model: Optional[str] = None
llm_provider: Optional[str] = None
prompt_hash: Optional[str] = None
input_summary: Optional[str] = None
output_summary: Optional[str] = None
metadata: Optional[dict] = None
tenant_id: Optional[str] = None
class LLMAuditResponse(BaseModel):
id: str
tenant_id: Optional[str] = None
entity_type: str
entity_id: Optional[str] = None
generation_mode: str
truth_status: str
may_be_used_as_evidence: bool
llm_model: Optional[str] = None
llm_provider: Optional[str] = None
prompt_hash: Optional[str] = None
input_summary: Optional[str] = None
output_summary: Optional[str] = None
metadata: Optional[dict] = None
created_at: datetime
class Config:
from_attributes = True
# ============================================================================
# Routes
# ============================================================================
@router.post("/llm-audit", response_model=LLMAuditResponse)
async def create_llm_audit(
data: LLMAuditCreate,
db: Session = Depends(get_db),
):
"""Record an LLM generation event for audit trail."""
from ..db.models import EvidenceTruthStatusEnum
# Validate truth_status
try:
truth_enum = EvidenceTruthStatusEnum(data.truth_status)
except ValueError:
truth_enum = EvidenceTruthStatusEnum.GENERATED
record = LLMGenerationAuditDB(
id=str(uuid_module.uuid4()),
tenant_id=data.tenant_id,
entity_type=data.entity_type,
entity_id=data.entity_id,
generation_mode=data.generation_mode,
truth_status=truth_enum,
may_be_used_as_evidence=data.may_be_used_as_evidence,
llm_model=data.llm_model,
llm_provider=data.llm_provider,
prompt_hash=data.prompt_hash,
input_summary=data.input_summary[:500] if data.input_summary else None,
output_summary=data.output_summary[:500] if data.output_summary else None,
extra_metadata=data.metadata or {},
)
db.add(record)
db.commit()
db.refresh(record)
return LLMAuditResponse(
id=record.id,
tenant_id=record.tenant_id,
entity_type=record.entity_type,
entity_id=record.entity_id,
generation_mode=record.generation_mode,
truth_status=record.truth_status.value if record.truth_status else "generated",
may_be_used_as_evidence=record.may_be_used_as_evidence,
llm_model=record.llm_model,
llm_provider=record.llm_provider,
prompt_hash=record.prompt_hash,
input_summary=record.input_summary,
output_summary=record.output_summary,
metadata=record.extra_metadata,
created_at=record.created_at,
)
@router.get("/llm-audit")
async def list_llm_audit(
entity_type: Optional[str] = Query(None),
entity_id: Optional[str] = Query(None),
page: int = Query(1, ge=1),
limit: int = Query(50, ge=1, le=200),
db: Session = Depends(get_db),
):
"""List LLM generation audit records with optional filters."""
query = db.query(LLMGenerationAuditDB)
if entity_type:
query = query.filter(LLMGenerationAuditDB.entity_type == entity_type)
if entity_id:
query = query.filter(LLMGenerationAuditDB.entity_id == entity_id)
total = query.count()
offset = (page - 1) * limit
records = query.order_by(LLMGenerationAuditDB.created_at.desc()).offset(offset).limit(limit).all()
return {
"records": [
LLMAuditResponse(
id=r.id,
tenant_id=r.tenant_id,
entity_type=r.entity_type,
entity_id=r.entity_id,
generation_mode=r.generation_mode,
truth_status=r.truth_status.value if r.truth_status else "generated",
may_be_used_as_evidence=r.may_be_used_as_evidence,
llm_model=r.llm_model,
llm_provider=r.llm_provider,
prompt_hash=r.prompt_hash,
input_summary=r.input_summary,
output_summary=r.output_summary,
metadata=r.extra_metadata,
created_at=r.created_at,
)
for r in records
],
"total": total,
"page": page,
"limit": limit,
}