Files
microfish/backend/app/api/template.py
Kunthawat Greethong 8b84378fe1 feat: SaaS foundation for CrowdSight
Elevate MiroFish/CrowdSight from single-container dev to a SaaS foundation:

- Local memory backend (Zep-compatible): memory services/models, local graph
  builder + updater, AgentActivity seam, import-boundary isolation; Zep stays
  default, local is opt-in behind MEMORY_BACKEND. Semantic parity not yet proven.
- Durable product persistence: projects/simulations/reports schema (migration
  0007) + tenant/owner-scoped ProductRepository + dual-write + scoped_project
  read-first + ArtifactStore abstraction; durable JobQueue + worker.py.
- SaaS hardening: durable RateLimiter (wired to login), UsageService (LLM
  accounting), redacted AuditService, idempotency, CORS allowlist, safe API
  errors, single-use PasswordResetService + endpoints (covers invite-pending).
- Exactly 3 roles (super_admin/admin/user) with tenant authz policy.
- Admin UI: GET/POST/PATCH /api/admin/users + GET/PUT /api/admin/settings
  (super-admin only, encrypted/masked); AdminView.vue + SettingsView.vue with
  admin/super-admin route guards, th/en i18n.
- Production deploy topology: multi-stage Dockerfile (frontend build + gunicorn
  wsgi + nginx SPA-proxy + supervisord worker), backend/wsgi.py, gunicorn dep.

Backend 197 passed; frontend 10 tests + build green. ruff unavailable (gap).
No commit of credentials; secrets handled via env/.env.example.
Deferred: Zep semantic A/B parity, object storage cutover, mobile QA, EasyPanel
container build of deploy topology.
2026-08-31 13:05:21 +07:00

155 lines
4.9 KiB
Python

"""
Template Auto-Selection API
Analyze seed data and recommend the best simulation template
"""
import json
import os
from flask import Blueprint, request, jsonify
from ..utils.llm_client import LLMClient
from ..utils.locale import t, get_locale, get_language_instruction
from ..utils.logger import get_logger
from ..utils.api_errors import internal_error_payload
from ..security.auth import require_auth
from ..services.idempotency import idempotent
logger = get_logger('crowdsight.template')
template_bp = Blueprint('template', __name__)
# Load templates
_templates_path = os.path.join(os.path.dirname(__file__), '..', 'templates.json')
with open(_templates_path, 'r', encoding='utf-8') as f:
_templates_data = json.load(f)
_templates = _templates_data['templates']
@template_bp.route('/list', methods=['GET'])
@require_auth
def list_templates():
"""Return all available templates"""
locale = get_locale()
lang_key = f'prompt_{locale}' if locale in ('th', 'en', 'zh') else 'prompt_en'
result = []
for tmpl in _templates:
result.append({
'id': tmpl['id'],
'icon': tmpl['icon'],
'category': tmpl['category'],
'prompt': tmpl.get(lang_key, tmpl['prompt_en']),
'placeholders': tmpl['placeholders'],
'name': t(f'templates.{tmpl["id"]}'),
})
return jsonify({'success': True, 'templates': result})
@template_bp.route('/auto-select', methods=['POST'])
@require_auth
@idempotent
def auto_select_template():
"""
Analyze seed data and recommend the best template + pre-fill prompt.
Request JSON:
{
"text": "extracted text from uploaded documents",
"simulation_requirement": "optional user requirement"
}
Response JSON:
{
"success": true,
"template_id": "news_event",
"prompt": "pre-filled prompt with actual values",
"confidence": 0.85,
"reasoning": "why this template was selected"
}
"""
try:
data = request.get_json() or {}
text = data.get('text', '')[:5000] # Limit to 5000 chars
simulation_requirement = data.get('simulation_requirement', '')
if not text:
return jsonify({'success': False, 'error': 'No text provided'}), 400
locale = get_locale()
lang_instruction = get_language_instruction()
# Build template descriptions for the LLM
template_desc = []
lang_key = f'prompt_{locale}' if locale in ('th', 'en', 'zh') else 'prompt_en'
for tmpl in _templates:
template_desc.append(f"- {tmpl['id']}: {tmpl.get(lang_key, tmpl['prompt_en'])}")
templates_list = '\n'.join(template_desc)
system_prompt = f"""You are a smart assistant that analyzes document content and recommends the best simulation template.
Available templates:
{templates_list}
Your task:
1. Analyze the document content
2. Select the MOST appropriate template
3. Fill in the template placeholders with actual values from the document
4. Return the result in JSON format
{lang_instruction}"""
user_prompt = f"""Document content:
{text[:3000]}
{f'User requirement: {simulation_requirement}' if simulation_requirement else ''}
Return JSON:
{{
"template_id": "best_template_id",
"prompt": "the template with placeholders filled in with actual values from the document",
"confidence": 0.0-1.0,
"reasoning": "brief explanation of why this template was chosen"
}}"""
llm = LLMClient()
result = llm.chat_json(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.3
)
# Validate template_id
valid_ids = [t['id'] for t in _templates]
if result.get('template_id') not in valid_ids:
result['template_id'] = 'news_event' # Default fallback
return jsonify({
'success': True,
'template_id': result.get('template_id', 'news_event'),
'prompt': result.get('prompt', ''),
'confidence': result.get('confidence', 0.5),
'reasoning': result.get('reasoning', ''),
})
except Exception as e:
logger.error("Template auto-select failed: error_type=%s", type(e).__name__)
return jsonify(internal_error_payload(t)), 500
@template_bp.route('/<template_id>/filter-rules', methods=['GET'])
@require_auth
def get_filter_rules(template_id):
"""Return entity filter rules for a template (used by Step 1)"""
for tmpl in _templates:
if tmpl['id'] == template_id:
return jsonify({
'success': True,
'template_id': template_id,
'filter_rules': tmpl.get('entity_filter', {})
})
return jsonify({'success': False, 'error': 'Template not found'}), 404