195 lines
7.4 KiB
Python
195 lines
7.4 KiB
Python
from django.core.management.base import BaseCommand, CommandError
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from django.db import transaction
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from django.utils import timezone
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from apps.agents.models import AgentDefinition, AgentStepBinding, AgentVersion
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from apps.modelhub.models import ModelDeployment
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from apps.skills.models import SkillDefinition, SkillVersion
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SKILLS = [
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{
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"code": "issue-structure",
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"name": "问题结构化",
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"description": "将技术支持问题整理为统一结构。",
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"prompt": (
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"请分析技术支持问题：{{ input.problem }}，环境：{{ input.environment }}。只输出 JSON。"
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),
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"input_schema": {
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"type": "object",
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"properties": {
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"problem": {"type": "string"},
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"environment": {"type": "string"},
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},
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"required": ["problem", "environment"],
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},
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"output_schema": {
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"type": "object",
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"properties": {
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"summary": {"type": "string"},
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"symptoms": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["summary", "symptoms"],
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},
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},
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{
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"code": "troubleshooting",
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"name": "排查建议",
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"description": "基于结构化问题生成排查步骤。",
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"prompt": (
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"根据问题摘要 {{ input.summary }} 和现象 {{ input.symptoms }} "
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"生成排查建议，只输出 JSON。"
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),
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"input_schema": {
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"type": "object",
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"properties": {
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"summary": {"type": "string"},
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"symptoms": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["summary", "symptoms"],
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},
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"output_schema": {
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"type": "object",
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"properties": {
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"likely_cause": {"type": "string"},
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"steps": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["likely_cause", "steps"],
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},
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},
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{
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"code": "customer-reply",
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"name": "客户回复草稿",
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"description": "将排查建议整理为客户可读回复。",
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"prompt": (
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"原因：{{ input.likely_cause }}；步骤：{{ input.steps }}。生成客户回复，只输出 JSON。"
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),
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"input_schema": {
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"type": "object",
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"properties": {
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"likely_cause": {"type": "string"},
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"steps": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["likely_cause", "steps"],
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},
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"output_schema": {
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"type": "object",
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"properties": {"reply": {"type": "string"}},
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"required": ["reply"],
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},
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},
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]
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class Command(BaseCommand):
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help = "Seed the technical support analysis Agent and its published Skills."
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def add_arguments(self, parser):
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parser.add_argument("--deployment-id", type=int)
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@transaction.atomic
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def handle(self, *args, **options):
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deployment_id = options.get("deployment_id")
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deployments = ModelDeployment.objects.filter(enabled=True, provider__enabled=True)
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deployment = (
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deployments.filter(id=deployment_id).first() if deployment_id else deployments.first()
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)
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if deployment is None:
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raise CommandError(
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"Configure an enabled model deployment before seeding the demo Agent."
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)
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versions = {}
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for spec in SKILLS:
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definition, _ = SkillDefinition.objects.get_or_create(
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code=spec["code"],
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defaults={"name": spec["name"], "description": spec["description"]},
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)
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version = definition.versions.filter(version=1).first()
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if version is None:
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version = SkillVersion.objects.create(
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definition=definition,
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version=1,
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status="published",
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prompt_template=spec["prompt"],
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input_schema=spec["input_schema"],
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output_schema=spec["output_schema"],
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example_input={},
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published_at=timezone.now(),
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)
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definition.active_version = version
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definition.save(update_fields=["active_version", "updated_at"])
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versions[spec["code"]] = version
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definition, _ = AgentDefinition.objects.get_or_create(
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code="support-analysis",
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defaults={
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"name": "技术支持分析 Agent",
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"description": "结构化问题、生成排查建议和客户回复草稿。",
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},
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)
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draft = definition.versions.filter(status="draft").first()
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if draft is None:
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next_version = (
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definition.versions.order_by("-version").values_list("version", flat=True).first()
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or 0
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) + 1
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draft = AgentVersion.objects.create(
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definition=definition,
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version=next_version,
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status="draft",
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model_deployment=deployment,
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system_prompt="你是企业技术支持助手。输出必须严格符合当前步骤要求的 JSON。",
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input_schema={
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"type": "object",
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"properties": {
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"problem": {"type": "string"},
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"environment": {"type": "string"},
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},
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"required": ["problem", "environment"],
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},
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example_input={
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"problem": "采集任务长时间没有更新",
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"environment": "生产环境",
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},
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)
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AgentStepBinding.objects.bulk_create(
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[
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AgentStepBinding(
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agent_version=draft,
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code="structure",
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order=1,
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skill_version=versions["issue-structure"],
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input_mapping={
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"problem": "$input.problem",
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"environment": "$input.environment",
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},
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),
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AgentStepBinding(
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agent_version=draft,
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code="troubleshooting",
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order=2,
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skill_version=versions["troubleshooting"],
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input_mapping={
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"summary": "$steps.structure.output.summary",
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"symptoms": "$steps.structure.output.symptoms",
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},
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),
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AgentStepBinding(
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agent_version=draft,
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code="reply",
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order=3,
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skill_version=versions["customer-reply"],
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input_mapping={
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"likely_cause": "$steps.troubleshooting.output.likely_cause",
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"steps": "$steps.troubleshooting.output.steps",
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},
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),
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]
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)
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self.stdout.write(
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self.style.SUCCESS(
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f"Seeded Agent {definition.code} draft v{draft.version} "
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f"with deployment {deployment.id}."
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)
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)
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