commit d6d92fd966ce6aef3480b3976adacbd4c1625bdc Author: Codex Agent Date: Wed Oct 22 19:59:48 2025 +0800 chore: initial commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f869654 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +.env +.venv/ +__pycache__/ +*.pyc +*.pyo +*.pyd +*.log +.DS_Store +Thumbs.db +.env.* diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..3923e85 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,44 @@ +# Repository Guidelines + +## Project Structure & Module Organization +- Root scripts: smart_cors_middleware.py (Flask CORS add-on), export_risk_json.py (PostgreSQL export). +- Data/outputs: risk_tables_export.json (generated by export script). +- Docs: PRD.md. +- Python 3.10+ is required (uses PEP 604 unions like str | None). + +## Build, Test, and Development Commands +- Create venv (Windows): + ~~~powershell + python -m venv .venv; .venv\Scripts\activate; pip install Flask pg8000 black ruff pytest + ~~~ +- Run DB export (writes risk_tables_export.json): + ~~~bash + python export_risk_json.py + ~~~ +- Verify CORS middleware in your Flask app (diagnosis endpoint): + ~~~bash + curl -i http://localhost:5000/api/cors-diagnosis + ~~~ +- Lint/format (optional tools): ruff . and black . +- Tests (when added): pytest -q + +## Coding Style & Naming Conventions +- Python: 4-space indents, UTF-8 files, snake_case for functions/vars, SCREAMING_SNAKE_CASE for constants. +- Prefer type hints; keep functions small and side-effect free. +- Formatting: black (line length 100). Linting: ruff (default rules). +- Filenames: modules like smart_cors_middleware.py; tests as test_*.py under tests/. + +## Testing Guidelines +- Framework: pytest with Flask test client for middleware behavior. +- Target cases: origin matching (wildcard, exact, subdomains), preflight handling, X-CORS-Decision header, NGINX_CORS_MODE behavior. +- Coverage: prioritize core branches in _origin_matches, preflight (OPTIONS), and after_request logic. + +## Commit & Pull Request Guidelines +- No Git history found here; use Conventional Commits (e.g., feat: add CORS diagnosis endpoint). +- PRs should include: purpose, concise summary, screenshots or curl examples for HTTP changes, and any config/env notes. +- Link related issues; keep PRs focused and under ~300 changed lines when possible. + +## Security & Configuration Tips +- Do NOT hardcode secrets. Move DB credentials in export_risk_json.py to env vars and load via os.getenv() or a .env file. +- CORS env vars supported by middleware: ALLOWED_ORIGINS, CORS_STRICT, CORS_DEBUG, NGINX_CORS_MODE, CORS_MAX_AGE, CORS_EXPOSE_HEADERS. +- Validate inputs from the DB export; avoid writing outside the repo. diff --git a/API.md b/API.md new file mode 100644 index 0000000..6af6a00 --- /dev/null +++ b/API.md @@ -0,0 +1,86 @@ +# LawRisk 检索接口文档 + +- Base URL: https://YOUR_HOST +- 路径: /fs-ai-asistant/api/workflow/lawrisk +- 方法: POST（推荐）, GET（便捷调试） +- 鉴权: 无（如需可在网关/反向代理层添加） +- CORS: 已启用（复用 smart_cors_middleware.py） + +## 请求格式 +- Content-Type: application/x-www-form-urlencoded +- 表单字段 + - query | q | text (string，必填): 用户输入的中文问题 + - mode (string，可选): llm(默认) 或 embed + - debug (boolean-like，可选): 1/true/yes/on 视为开启调试 + - top (int，可选): 调试时返回候选数量（默认 5） + +提示: GET 模式下同名参数通过查询串传递；POST 模式优先解析 x-www-form-urlencoded，若未提供则回退 JSON(application/json)。 + +## 响应 +- 成功 (200) + - risk_subject: 数组，每项包含 + - id (string) + - name (string) + - permit_ids (string[]) + - score (number，可选，仅在 debug=1 且 embed 模式或回退时出现) + - debug (object，可选，debug=1 时返回) + - model (string): 使用模型（如 qwen-plus-latest） + - num_subjects (number): 参与检索的主题数量 + - selected_ids (string[], 仅 llm 模式): LLM 选择的主题 ID 列表 + - thresholds (object, 仅 embed 模式): 相似度阈值 + - top_candidates (array, 仅 embed 模式): 前 N 候选及分数 + - allow_empty (boolean): LLM 允许返回空结果 +- 失败 + - 400: { "error": "query is required" } + - 500: { "error": "<错误信息>" } + +## 示例 +POST（推荐，LLM 模式 + 调试） + +curl -s -X POST "http://www.chinaweal.com.cn:8090/fs-ai-asistant/api/workflow/lawrisk" -H "Content-Type: application/x-www-form-urlencoded" -d "query=我要办一家电影院&mode=llm&debug=1&top=5" + +示例响应（命中） + +{ + "risk_subject": [ + {"id":"384a...05e7","name":"开办电影院","permit_ids":["04bf...","509b...","..."]} + ], + "debug": { + "model": "qwen-plus-latest", + "num_subjects": 123, + "selected_ids": ["384a...05e7"], + "allow_empty": true + } +} + +示例响应（无匹配，允许空） + +{ + "risk_subject": [], + "debug": { + "model": "qwen-plus-latest", + "num_subjects": 123, + "selected_ids": [], + "allow_empty": true + } +} + +GET 便捷调试 + +curl -s "http://www.chinaweal.com.cn:8090T/fs-ai-asistant/api/workflow/lawrisk?query=%E6%88%91%E8%A6%81%E5%8A%9E%E4%B8%80%E5%AE%B6%E7%94%B5%E5%BD%B1%E9%99%A2&mode=llm&debug=1&top=5" + +前端调用示例（fetch） + +fetch("http://www.chinaweal.com.cn:8090/fs-ai-asistant/api/workflow/lawrisk", { + method: "POST", + headers: {"Content-Type": "application/x-www-form-urlencoded"}, + body: new URLSearchParams({ query: "我要办一家电影院", mode: "llm", debug: "1", top: "5" }) +}).then(r => r.json()).then(console.log) + +## 模式说明 +- llm（默认）：将主题清单（id 与名称）传给 Qwen（qwen-plus-latest），由 LLM 选择最相关的一个或多个主题 ID；若判断无匹配，返回空数组。 +- embed（可选）：基于向量相似度检索；阈值可通过环境变量配置（LAWRISK_RETURN_IF_GE、LAWRISK_FALLBACK_GT）。 + +## 兼容与跨域 +- 服务端已启用 CORS，可在 .env 中配置：ALLOWED_ORIGINS、CORS_STRICT、CORS_DEBUG、NGINX_CORS_MODE 等。 +- 如需鉴权（例如加 Token），建议在网关或反代层统一处理。 diff --git a/DB_GUIDE.md b/DB_GUIDE.md new file mode 100644 index 0000000..309558d --- /dev/null +++ b/DB_GUIDE.md @@ -0,0 +1,73 @@ +# Database Schema & Query Guide + +## Overview +The `licensing_risks` PostgreSQL database stores municipal licensing risk prompts parsed from Excel workbooks. Each record links regions, themes, permits, and risk narratives so downstream systems can query compliance obligations quickly. + +## Tables +| Table | Purpose | Key Columns | +| --- | --- | --- | +| `regions` | Administrative areas (市级、禅城区等) | `id` (PK), `name` (unique) | +| `business_scopes` | Scoped经营范围条目 | `id` (PK), `description` | +| `region_scopes` | Region-to-scope mapping | `region_id` → `regions.id`, `scope_id` → `business_scopes.id` | +| `themes` | “一照通行”主题事项 | `id` (PK), `name` | +| `region_themes` | Region-to-theme mapping | `region_id`, `theme_id` | +| `permits` | 许可（备案）事项 | `id` (PK), `name` | +| `region_theme_permits` | Region + theme + permit linkage | `region_id`, `theme_id`, `permit_id` | +| `risks` | 风险提示主体信息 | `id` (PK), `risk_content`, `legal_basis`, `document_no`, `summary` | +| `region_permit_risks` | Region + permit + risk linkage | `region_id`, `permit_id`, `risk_id` | + +All primary keys are integer sequences; unique indexes and `ON CONFLICT DO NOTHING` logic make repeated imports idempotent. Foreign keys should be enforced in the target schema to prevent orphan rows. + +## Query Cheatsheet +### 列出所有主题事项（总表） +```sql +SELECT t.id, + t.name AS theme_name, + r.name AS region_name +FROM themes t +JOIN region_themes rt ON rt.theme_id = t.id +JOIN regions r ON r.id = rt.region_id +ORDER BY r.name, t.name; +``` + +### 根据主题事项获取许可事项列表 +Replace `%主题关键词%` with the desired theme name or keyword. +```sql +SELECT DISTINCT p.id, + p.name AS permit_name, + r.name AS region_name +FROM permits p +JOIN region_theme_permits rtp + ON rtp.permit_id = p.id +JOIN region_themes rt + ON rt.region_id = rtp.region_id + AND rt.theme_id = rtp.theme_id +JOIN themes t ON t.id = rt.theme_id +JOIN regions r ON r.id = rt.region_id +WHERE t.name ILIKE '%主题关键词%' +ORDER BY r.name, permit_name; +``` + +### 根据许可事项检索风险条目 +Substitute `'具体许可名称'` with the permit you care about. +```sql +SELECT r.name AS region_name, + p.name AS permit_name, + rk.risk_content, + rk.legal_basis, + rk.document_no, + rk.summary +FROM region_permit_risks rpr +JOIN regions r ON r.id = rpr.region_id +JOIN permits p ON p.id = rpr.permit_id +JOIN risks rk ON rk.id = rpr.risk_id +WHERE p.name = '具体许可名称' +ORDER BY r.name, rk.risk_content; +``` +For fuzzy lookups, switch to `WHERE p.name ILIKE '%关键词%'`. + +## Execution Tips +- Connect via `psql -h 172.24.240.1 -U postgres -d licensing_risks`. +- Export query results with `\copy (SELECT …) TO '/tmp/export.csv' WITH CSV HEADER;`. +- Run queries after imports commit; the loaders already wrap operations in transactions. + diff --git a/PRD.md b/PRD.md new file mode 100644 index 0000000..7b5d633 --- /dev/null +++ b/PRD.md @@ -0,0 +1,72 @@ +我需要你帮我构建一个检索系统，用户会输入问题（中文），期望匹配到对应的事项，输出事项ID 事项名称 许可事项列表，例如： + +用户输入：我要办一家电影院 + +输出： + +  "risk\_subject": \[ + +  { + +  "id": "384aeb24a23e913268aad33354f705e7", + +  "name": "开办电影院", + +  "permit\_ids": \[ + +  "04bfa019634ca1aa0b9f7c783fd85dce", + +  "509b2872fc7c38c08f252a2b426fd49f", + +  "54a79077-bd72-4ea9-8bb1-35afc69e2973", + +  "709b4718d72229311066e529650b8abf", + +  "8d49de002f24d37fcf3663574723e693", + +  "8f7c8c613adfbd815a78c1e60ec4330e", + +  "a0572119839422e1d11ee8801d6c58b7", + +  "fa2f3e05c92297be096b63e25d30bfbe" + +  ] + +  }] + +我希望你能用embedding模型来处理 + +首先先把事项名称从risk\_tables\_export.json 中提取出来，然后建立一个fs\_law\_risk数据库,建立表law\_sub用来存放事项向量，以json文件中的ID为主键，保存名称和向量到数据库 + +再建立一个表，名为law\_sub\_per,保存主题事项与许可事项的映射关系，需要有主题事项id，许可事项id列表 + +设置embedding相似度阈值0.5，大于阈值以上的事项全部返回 + +如果检索结果都小于0.5，但大于0.4，返回第一个 + +暴露接口/fs-ai-asistant/api/workflow/lawrisk + +跨域问题处理请复用：smart\_cors\_middleware.py，你可以把这个文件移动到合适的目录 + +* 你可以使用的postgreSQL： + +  - IP :8.138.196.105 + +  - port:5432 + +  - user:postgres + +  - password:difyai123456 + +* API 以及doc参考 + +我们应该只需要用同步接口 + +  - 通用文本向量同步接口API详情：https://help.aliyun.com/zh/model-studio/text-embedding-synchronous-api?spm=a2c4g.11186623.help-menu-2400256.d\_2\_7\_0.693e48233phHX8 + +  - 通用文本批处理接口API详情：https://help.aliyun.com/zh/model-studio/text-embedding-batch-api?spm=a2c4g.11186623.help-menu-2400256.d\_2\_7\_1.59233560WBHuRz + +  - API key：sk-288824ef003e4e02bb963b8b3024b06a + + + diff --git a/app.py b/app.py new file mode 100644 index 0000000..d57c378 --- /dev/null +++ b/app.py @@ -0,0 +1,134 @@ +from __future__ import annotations + +import os +from flask import Flask, jsonify, request + +from env_loader import load_env +from smart_cors_middleware import init_smart_cors +import time +from concurrent.futures import ThreadPoolExecutor +from lawrisk_service import ( + ensure_database, + ensure_schema, + search_subjects, + search_subjects_llm, + shortlist_subjects, + suggest_questions_from_subjects, + suggest_questions_embed, +) + + +def create_app() -> Flask: + # Load .env before creating app to make CORS/env configs available + load_env() + # Ensure DB and schema exist before serving + try: + ensure_database() + ensure_schema() + except Exception: + # Do not block app start; errors will surface on first request + pass + app = Flask(__name__) + # Enable CORS using existing middleware + init_smart_cors(app) + + @app.route("/fs-ai-asistant/api/workflow/lawrisk", methods=["POST", "GET"]) + def lawrisk_search(): + if request.method == "GET": + query = request.args.get("query") or request.args.get("q") or request.args.get("text") + debug_flag = request.args.get("debug") in {"1", "true", "yes", "on"} + top_k = request.args.get("top") + try: + top_k_int = int(top_k) if top_k else 5 + except Exception: + top_k_int = 5 + mode = (request.args.get("mode") or "llm").lower() + else: + # Prefer x-www-form-urlencoded; fallback to JSON if provided + if request.is_json: + payload = request.get_json(silent=True) or {} + else: + payload = request.form.to_dict(flat=True) if request.form else {} + + query = payload.get("query") or payload.get("q") or payload.get("text") + debug_flag = str(payload.get("debug", "")).strip().lower() in {"1", "true", "yes", "on"} + try: + top_k_int = int(payload.get("top", 5)) + except Exception: + top_k_int = 5 + mode = str(payload.get("mode", "llm")).lower() + + if not query or not isinstance(query, str): + return jsonify({"error": "query is required"}), 400 + try: + t0 = time.time() + with ThreadPoolExecutor(max_workers=3) as ex: + fut_ret = ex.submit( + search_subjects if mode == "embed" else search_subjects_llm, + query, + debug_flag, + top_k_int, + ) + # Use embedding-based question suggestion (falls back internally if not available) + fut_qs = ex.submit(suggest_questions_embed, query, max(1, top_k_int)) + + result = fut_ret.result() + rec_questions = fut_qs.result() or [] + + # If debug requested, still log to backend for visibility + if debug_flag and isinstance(result, dict) and "debug" in result: + dbg = result["debug"] + model = dbg.get("model") or "embed" + app.logger.info("[LAWRISK-DEBUG] mode=%s", model) + + # Extract risk_subject and optional debug + risk_subject = [] + dbg = {} + if isinstance(result, dict): + risk_subject = result.get("risk_subject", []) + if debug_flag: + dbg = result.get("debug", {}) + + found = bool(risk_subject) + llm_resp = "" if found else "抱歉，无法检索到相关答案" + exec_time = int((time.time() - t0) * 1000) + + # rec_questions 已由 embedding 建议生成（内部包含兜底） + + data = { + "llmRespond": llm_resp, + "lawRisk": "", + "questionExtend": rec_questions, + "conversationId": "", + "messageId": "", + "roundNumber": 0, + "conversationInfo": {}, + "knowledgeSources": [], + "totalKnowledgeSources": 0, + "executionTime": exec_time, + "workflowStatus": "ok" if found else "no_match", + "executionSteps": [], + "costStatistics": {}, + "workflowTrackingId": "", + # extra fields requested + "risk_subject": risk_subject, + "debug": dbg if debug_flag else {}, + } + resp = {"success": True, "message": "OK", "data": data} + return jsonify(resp) + except Exception as e: + app.logger.exception("lawrisk_search error") + return jsonify({"success": False, "message": str(e), "data": {}}), 500 + + # Basic health check + @app.get("/healthz") + def healthz(): + return jsonify({"status": "ok"}) + + return app + + +if __name__ == "__main__": + port = int(os.getenv("PORT", "8000")) + app = create_app() + app.run(host="0.0.0.0", port=port) diff --git a/env_loader.py b/env_loader.py new file mode 100644 index 0000000..08c1069 --- /dev/null +++ b/env_loader.py @@ -0,0 +1,36 @@ +"""Minimal .env loader to populate os.environ from a .env file. + +Supports lines of the form KEY=VALUE, optional quotes, and comments (# ...). +By default, does not override existing environment variables. +""" +from __future__ import annotations + +import os +from typing import Optional + + +def load_env(path: str = ".env", override: bool = False) -> None: + if not os.path.exists(path): + return + try: + with open(path, "r", encoding="utf-8") as f: + for raw in f: + line = raw.strip() + if not line or line.startswith("#"): + continue + if "=" not in line: + continue + key, val = line.split("=", 1) + key = key.strip() + val = val.strip() + # Strip quotes if wrapped + if (val.startswith("\"") and val.endswith("\"")) or ( + val.startswith("'") and val.endswith("'") + ): + val = val[1:-1] + if key and (override or key not in os.environ): + os.environ[key] = val + except Exception: + # Fail silently; callers still can rely on existing env + return + diff --git a/export_risk_json.py b/export_risk_json.py new file mode 100644 index 0000000..6ebb4b9 --- /dev/null +++ b/export_risk_json.py @@ -0,0 +1,75 @@ +import json +import os +import sys +import pg8000 +from env_loader import load_env + +# Read DB config from environment; provide sensible defaults +CONFIG = { + 'host': os.getenv('PG_HOST', '8.138.196.105'), + 'port': int(os.getenv('PG_PORT', '5432')), + 'database': os.getenv('PG_DATABASE', 'fs_law_risk'), + 'user': os.getenv('PG_USER', 'postgres'), + 'password': os.getenv('PG_PASSWORD', 'difyai123456'), +} + +# Export file path can be overridden via env +OUTPUT = os.getenv('RISK_EXPORT_OUTPUT', 'risk_tables_export.json') + +SQLS = { + 'risk_subject': "SELECT sub_id AS id, sub_name AS name FROM public.risk_subject ORDER BY sub_name;", + 'risk_permit': "SELECT per_id AS id, per_name AS name FROM public.risk_permit ORDER BY per_name;", + 'risk_sub_per': "SELECT sub_id, per_id FROM public.risk_sub_per;", +} + +def fetch_all(cursor): + cols = [d[0] for d in cursor.description] + return [dict(zip(cols, row)) for row in cursor.fetchall()] + +def main(): + load_env() + try: + conn = pg8000.connect(**CONFIG) + except Exception as e: + print('DB connect error:', e, file=sys.stderr) + sys.exit(2) + + result = {} + try: + with conn.cursor() as cur: + cur.execute(SQLS['risk_subject']) + subjects = fetch_all(cur) + cur.execute(SQLS['risk_permit']) + permits = fetch_all(cur) + cur.execute(SQLS['risk_sub_per']) + rels = fetch_all(cur) + conn.commit() + finally: + conn.close() + + # Build mapping: subject -> list of permit_ids + sub_to_permit_ids = {} + for r in rels: + sub_id = r['sub_id'] + per_id = r['per_id'] + sub_to_permit_ids.setdefault(sub_id, set()).add(per_id) + + # Subjects with aggregated permit_ids + subjects_out = [] + for s in subjects: + subjects_out.append({ + 'id': s['id'], + 'name': s['name'], + 'permit_ids': sorted(list(sub_to_permit_ids.get(s['id'], []))) + }) + + # Final JSON: keep full permit catalog (id+name), and subjects contain aggregated permit_ids + result['risk_subject'] = subjects_out + result['risk_permit'] = permits + + with open(OUTPUT, 'w', encoding='utf-8') as f: + json.dump(result, f, ensure_ascii=False, indent=2) + print('Exported to', OUTPUT) + +if __name__ == '__main__': + main() diff --git a/ingest_lawrisk.py b/ingest_lawrisk.py new file mode 100644 index 0000000..76f182f --- /dev/null +++ b/ingest_lawrisk.py @@ -0,0 +1,100 @@ +"""Ingest subjects and permit mappings into PostgreSQL. + +Reads risk_tables_export.json from repo root, embeds each subject name using +Aliyun DashScope embeddings (OpenAI-compatible), and stores into: + - law_sub(id TEXT PK, name TEXT, vector JSONB) + - law_sub_per(subject_id TEXT PK, permit_ids JSONB) + +Usage: + python ingest_lawrisk.py + +Ensure env var DASHSCOPE_API_KEY is set. +Optionally set PG_* vars (PG_HOST, PG_PORT, PG_USER, PG_PASSWORD, PG_DATABASE). +""" +from __future__ import annotations + +import json +import os +from typing import List + +from env_loader import load_env +from lawrisk_service import ( + ensure_database, + ensure_schema, + EmbeddingClient, + upsert_subjects, + upsert_subject_permits, + upsert_permits, +) + +REPO_JSON = os.getenv("LAWRISK_JSON", "risk_tables_export.json") + + +def main() -> None: + # Load .env first so service reads correct env + load_env() + ensure_database() + ensure_schema() + + with open(REPO_JSON, "r", encoding="utf-8") as f: + data = json.load(f) + + subjects = data.get("risk_subject", []) + permits = data.get("risk_permit", []) + # Prepare embeddings in small batches to avoid large payloads + client = EmbeddingClient() + + batched_rows = [] + BATCH = 32 + names: List[str] = [] + metas = [] # (id, name) + for s in subjects: + sid = s.get("id") + name = s.get("name") + if not sid or not name: + continue + names.append(name) + metas.append((sid, name)) + if len(names) >= BATCH: + vecs = client.embed_texts(names) + for (sid, name), vec in zip(metas, vecs): + batched_rows.append((sid, name, vec)) + names.clear() + metas.clear() + + if names: + vecs = client.embed_texts(names) + for (sid, name), vec in zip(metas, vecs): + batched_rows.append((sid, name, vec)) + + upsert_subjects(batched_rows) + + # Build subject->permit_ids mapping + per_rows = [] + for s in subjects: + sid = s.get("id") + pids = s.get("permit_ids", []) + if sid and isinstance(pids, list): + # ensure strings + per_rows.append((sid, [str(x) for x in pids])) + + if per_rows: + upsert_subject_permits(per_rows) + + # Upsert permit catalog (id -> name) + per_catalog = [] + for p in permits: + pid = p.get("id") + pname = p.get("name") + if pid and pname: + per_catalog.append((pid, pname)) + if per_catalog: + upsert_permits(per_catalog) + + print( + f"Ingested {len(batched_rows)} subjects, {len(per_rows)} subject-permit mappings, and {len(per_catalog)} permits into PostgreSQL." + ) + + +if __name__ == "__main__": + main() diff --git a/lawrisk_service.py b/lawrisk_service.py new file mode 100644 index 0000000..e4315f7 --- /dev/null +++ b/lawrisk_service.py @@ -0,0 +1,568 @@ +""" +LawRisk embedding retrieval service. + +Responsibilities: +- DB connection helpers (PostgreSQL via pg8000) +- Schema management (fs_law_risk.law_sub, fs_law_risk.law_sub_per) +- Embedding client (Aliyun DashScope OpenAI-compatible embeddings API) +- Chat client for LLM-based selection (Qwen via OpenAI-compatible /chat/completions) +- Search logic: embedding cosine or LLM subject selection + +Env vars used: +- PG_HOST, PG_PORT, PG_USER, PG_PASSWORD (PostgreSQL credentials) +- PG_DATABASE (defaults to fs_law_risk) +- PG_ADMIN_DB (defaults to postgres; used for CREATE DATABASE) +- DASHSCOPE_API_KEY (embedding API key) +- DASHSCOPE_BASE_URL (defaults to https://dashscope.aliyuncs.com/compatible-mode/v1) +- DASHSCOPE_EMBED_MODEL (defaults to text-embedding-v4) +- DASHSCOPE_EMBED_DIM (defaults to 1024) +- DASHSCOPE_CHAT_MODEL (defaults to qwen-plus-latest) +""" +from __future__ import annotations + +import json +import math +import os +import ssl +import urllib.request +import urllib.error +from typing import Any, Dict, Iterable, List, Optional, Tuple + +import pg8000.dbapi as pg + + +DEFAULT_DB = "fs_law_risk" +DEFAULT_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" +EMBED_MODEL = os.getenv("DASHSCOPE_EMBED_MODEL", "text-embedding-v4") +EMBED_DIM = int(os.getenv("DASHSCOPE_EMBED_DIM", "1024")) +EMBED_MAX_BATCH = max(1, int(os.getenv("DASHSCOPE_MAX_BATCH", "10"))) # DashScope limit <=10 +CHAT_MODEL = os.getenv("DASHSCOPE_CHAT_MODEL", "qwen-plus-latest") + +# Similarity thresholds (env configurable) +RETURN_IF_GE = float(os.getenv("LAWRISK_RETURN_IF_GE", "0.7")) +FALLBACK_GT = float(os.getenv("LAWRISK_FALLBACK_GT", "0.4")) +# Similarity thresholds (env configurable) +RETURN_IF_GE = float(os.getenv("LAWRISK_RETURN_IF_GE", "0.7")) +FALLBACK_GT = float(os.getenv("LAWRISK_FALLBACK_GT", "0.4")) + + +def _pg_conn(database: Optional[str] = None, autocommit: bool = False) -> pg.Connection: + host = os.getenv("PG_HOST", "8.138.196.105") + port = int(os.getenv("PG_PORT", "5432")) + user = os.getenv("PG_USER", "postgres") + password = os.getenv("PG_PASSWORD", "difyai123456") + dbname = database or os.getenv("PG_DATABASE", DEFAULT_DB) + conn = pg.connect(host=host, port=port, user=user, password=password, database=dbname) + conn.autocommit = autocommit + return conn + + +def ensure_database(dbname: str = DEFAULT_DB) -> None: + # Create database if not exists by connecting to postgres + admin_db = os.getenv("PG_ADMIN_DB", "postgres") + with _pg_conn(database=admin_db, autocommit=True) as c: + cur = c.cursor() + cur.execute("SELECT 1 FROM pg_database WHERE datname=%s", (dbname,)) + if cur.fetchone() is None: + cur.execute(f"CREATE DATABASE {dbname}") + + +def ensure_schema() -> None: + with _pg_conn() as c: + cur = c.cursor() + # Store vectors and permit ids as JSONB for portability + cur.execute( + """ + CREATE TABLE IF NOT EXISTS law_sub ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL, + vector JSONB NOT NULL + ) + """ + ) + cur.execute( + """ + CREATE TABLE IF NOT EXISTS law_sub_per ( + subject_id TEXT PRIMARY KEY, + permit_ids JSONB NOT NULL + ) + """ + ) + cur.execute( + """ + CREATE TABLE IF NOT EXISTS law_permit ( + id TEXT PRIMARY KEY, + name TEXT NOT NULL + ) + """ + ) + c.commit() + + +class EmbeddingClient: + def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None): + self.api_key = api_key or os.getenv("DASHSCOPE_API_KEY") + self.base_url = base_url or os.getenv("DASHSCOPE_BASE_URL", DEFAULT_BASE_URL) + if not self.api_key: + raise RuntimeError("DASHSCOPE_API_KEY is not set") + + def embed_texts(self, texts: List[str]) -> List[List[float]]: + # sanitize inputs + clean_inputs = [str(t) for t in texts if isinstance(t, str) and str(t).strip()] + if not clean_inputs: + raise ValueError("No valid input texts for embeddings") + + # chunk by provider batch limit and concatenate results to preserve order + out: List[List[float]] = [] + for i in range(0, len(clean_inputs), EMBED_MAX_BATCH): + chunk = clean_inputs[i : i + EMBED_MAX_BATCH] + out.extend(self._embed_batch(chunk)) + if len(out) != len(clean_inputs): + raise RuntimeError( + f"Embedding API returned unexpected result count: got {len(out)}, want {len(clean_inputs)}" + ) + return out + + def _embed_batch(self, texts: List[str]) -> List[List[float]]: + url = self.base_url.rstrip("/") + "/embeddings" + body = { + "model": EMBED_MODEL, + "input": texts, + "dimensions": EMBED_DIM, + "encoding_format": "float", + } + data = json.dumps(body).encode("utf-8") + req = urllib.request.Request(url, data=data, method="POST") + req.add_header("Authorization", f"Bearer {self.api_key}") + req.add_header("Content-Type", "application/json") + ctx = ssl.create_default_context() + try: + with urllib.request.urlopen(req, context=ctx, timeout=30) as resp: + raw = resp.read().decode("utf-8", errors="replace") + except urllib.error.HTTPError as e: + err_body = e.read().decode("utf-8", errors="replace") if hasattr(e, 'read') else "" + raise RuntimeError( + f"Embedding API error {e.code}: {err_body or e.reason} | sent={json.dumps(body, ensure_ascii=False)[:500]}" + ) from e + payload = json.loads(raw) + out: List[List[float]] = [] + for item in payload.get("data", []): + emb = item.get("embedding") + if isinstance(emb, list): + out.append([float(x) for x in emb]) + return out + + def embed_one(self, text: str) -> List[float]: + return self.embed_texts([text])[0] + + +class ChatClient: + def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None): + self.api_key = api_key or os.getenv("DASHSCOPE_API_KEY") + self.base_url = base_url or os.getenv("DASHSCOPE_BASE_URL", DEFAULT_BASE_URL) + if not self.api_key: + raise RuntimeError("DASHSCOPE_API_KEY is not set") + + def chat(self, messages: List[Dict[str, str]], model: Optional[str] = None, temperature: float = 0.2) -> str: + url = self.base_url.rstrip("/") + "/chat/completions" + body = { + "model": model or CHAT_MODEL, + "messages": messages, + "temperature": temperature, + } + data = json.dumps(body, ensure_ascii=False).encode("utf-8") + req = urllib.request.Request(url, data=data, method="POST") + req.add_header("Authorization", f"Bearer {self.api_key}") + req.add_header("Content-Type", "application/json") + ctx = ssl.create_default_context() + try: + with urllib.request.urlopen(req, context=ctx, timeout=60) as resp: + raw = resp.read().decode("utf-8", errors="replace") + except urllib.error.HTTPError as e: + err_body = e.read().decode("utf-8", errors="replace") if hasattr(e, 'read') else "" + raise RuntimeError( + f"Chat API error {e.code}: {err_body or e.reason}" + ) from e + payload = json.loads(raw) + choices = payload.get("choices", []) + if not choices: + raise RuntimeError("Chat API returned no choices") + msg = choices[0].get("message", {}) + content = msg.get("content", "") + return str(content) + + +def generate_question_suggestions(query: str, max_q: int = 5) -> List[str]: + """Legacy LLM-based suggestion generator (kept for reference). Not used by default.""" + try: + chat = ChatClient() + content = chat.chat([ + {"role": "system", "content": "你是政务事项问答助手。请输出与主题事项高度相关的精简推荐问题，仅输出 JSON 数组。"}, + {"role": "user", "content": f"请针对: {query} 给出不超过 {max_q} 条中文推荐问题，仅输出 JSON 数组。"}, + ], model=CHAT_MODEL, temperature=0.3) + txt = content.strip() + start = txt.find("[") + end = txt.rfind("]") + arr = json.loads(txt[start : end + 1] if start != -1 and end != -1 and end > start else txt) + out: List[str] = [] + if isinstance(arr, list): + for x in arr: + if isinstance(x, str) and x.strip(): + out.append(x.strip()) + return out[:max_q] + except Exception: + return [] + + +def _normalize_text(s: str) -> str: + return "".join(ch for ch in str(s) if ch.isalnum()) + + +def shortlist_subjects(query: str, k: int = 5) -> List[Tuple[str, str]]: + """Return up to k subjects with highest lexical overlap to query. + + Simple char-level overlap score to keep it deterministic and fast. + """ + q = set(_normalize_text(query)) + if not q: + q = set(query) + with _pg_conn() as c: + cur = c.cursor() + cur.execute("SELECT id, name FROM law_sub") + subs = [(str(sid), str(name)) for sid, name in cur.fetchall()] + scored: List[Tuple[float, Tuple[str, str]]] = [] + for sid, name in subs: + n = set(_normalize_text(name)) or set(name) + inter = len(q & n) + denom = max(1, len(n)) + score = inter / denom + if inter > 0: + scored.append((score, (sid, name))) + scored.sort(key=lambda x: x[0], reverse=True) + return [row for _s, row in scored[:k]] + + +def suggest_questions_from_subjects(subject_names: List[str], max_q: int = 5) -> List[str]: + """Return subject names directly (no extra wording).""" + out: List[str] = [] + for nm in subject_names: + nm = (nm or "").strip() + if nm and nm not in out: + out.append(nm) + if len(out) >= max_q: + break + return out + + +def suggest_questions_embed(query: str, max_q: int = 5) -> List[str]: + """Use embeddings to pick top-N subject names (no added text).""" + try: + client = EmbeddingClient() + qvec = client.embed_one(query) + except Exception: + # Embedding not available; fallback to lexical shortlist + subs = shortlist_subjects(query, max(1, max_q)) + return [name for _sid, name in subs][:max_q] + + # Load subjects with vectors + with _pg_conn() as c: + cur = c.cursor() + cur.execute("SELECT id, name, vector FROM law_sub") + subjects: List[Tuple[str, str, List[float]]] = [] + for sid, name, vec_json in cur.fetchall(): + if isinstance(vec_json, str): + try: + vec = json.loads(vec_json) + except Exception: + vec = [] + else: + vec = vec_json + if isinstance(vec, list) and vec: + subjects.append((str(sid), str(name), [float(x) for x in vec])) + + if not subjects: + subs = shortlist_subjects(query, max(1, max_q)) + return [name for _sid, name in subs][:max_q] + + scored: List[Tuple[float, Tuple[str, str]]] = [] + for sid, name, vec in subjects: + s = _cosine(qvec, vec) + scored.append((s, (sid, name))) + scored.sort(key=lambda x: x[0], reverse=True) + + # Take top subjects (more than max_q to allow templating to fill up to max_q) + top_subjects = [nm for _score, (_sid, nm) in scored[: max_q]] + return top_subjects + +def _cosine(a: List[float], b: List[float]) -> float: + if not a or not b or len(a) != len(b): + return 0.0 + dot = 0.0 + na = 0.0 + nb = 0.0 + for x, y in zip(a, b): + dot += x * y + na += x * x + nb += y * y + if na == 0.0 or nb == 0.0: + return 0.0 + return dot / math.sqrt(na * nb) + + +def upsert_subjects( + rows: Iterable[Tuple[str, str, List[float]]] +) -> None: + """Upsert subjects into law_sub.""" + with _pg_conn() as c: + cur = c.cursor() + for sid, name, vec in rows: + cur.execute( + """ + INSERT INTO law_sub (id, name, vector) + VALUES (%s, %s, %s::jsonb) + ON CONFLICT (id) DO UPDATE SET name=EXCLUDED.name, vector=EXCLUDED.vector + """, + (sid, name, json.dumps(vec)), + ) + c.commit() + + +def upsert_subject_permits(rows: Iterable[Tuple[str, List[str]]]) -> None: + with _pg_conn() as c: + cur = c.cursor() + for sid, permit_ids in rows: + cur.execute( + """ + INSERT INTO law_sub_per (subject_id, permit_ids) + VALUES (%s, %s::jsonb) + ON CONFLICT (subject_id) DO UPDATE SET permit_ids=EXCLUDED.permit_ids + """, + (sid, json.dumps(permit_ids)), + ) + c.commit() + + +def upsert_permits(rows: Iterable[Tuple[str, str]]) -> None: + """Upsert permit catalog into law_permit (id -> name).""" + with _pg_conn() as c: + cur = c.cursor() + for pid, name in rows: + cur.execute( + """ + INSERT INTO law_permit (id, name) + VALUES (%s, %s) + ON CONFLICT (id) DO UPDATE SET name=EXCLUDED.name + """, + (pid, name), + ) + c.commit() + + +def search_subjects(query: str, return_debug: bool = False, top_k_debug: int = 5) -> Dict[str, Any]: + """Search by embedding similarity, return JSON object compliant with PRD. + + Thresholds: + - return all with score >= 0.5 + - if none >= 0.5 but max > 0.4, return the single best one + """ + client = EmbeddingClient() + qvec = client.embed_one(query) + + # load all subjects + with _pg_conn() as c: + cur = c.cursor() + cur.execute("SELECT id, name, vector FROM law_sub") + subs: List[Tuple[str, str, List[float]]] = [] + for sid, name, vec_json in cur.fetchall(): + # vec_json may come back as Python list or JSON string depending on driver version + if isinstance(vec_json, str): + try: + vec = json.loads(vec_json) + except Exception: + vec = [] + else: + vec = vec_json + subs.append((str(sid), str(name), list(vec) if isinstance(vec, list) else [])) + + # Build permit lookup + cur.execute("SELECT subject_id, permit_ids FROM law_sub_per") + per_map: Dict[str, List[str]] = {} + for sid, pids in cur.fetchall(): + # pids may be list or JSON string + if isinstance(pids, str): + try: + p_list = json.loads(pids) + except Exception: + p_list = [] + else: + p_list = list(pids) if isinstance(pids, list) else [] + per_map[str(sid)] = [str(x) for x in p_list] + + scored: List[Tuple[float, Tuple[str, str, List[float]]]] = [] + for row in subs: + score = _cosine(qvec, row[2]) + scored.append((score, row)) + scored.sort(key=lambda x: x[0], reverse=True) + + # Build permit name lookup + permit_name: Dict[str, str] = {} + try: + with _pg_conn() as c2: + cur2 = c2.cursor() + cur2.execute("SELECT id, name FROM law_permit") + for pid, pname in cur2.fetchall(): + permit_name[str(pid)] = str(pname) + except Exception: + # If table missing or query fails, leave map empty; upstream should seed via ingest + permit_name = {} + + results: List[Dict[str, Any]] = [] + for score, (sid, name, _vec) in scored: + if score >= RETURN_IF_GE: + item = { + "id": sid, + "name": name, + # Build permit map: name -> id + "permit": {permit_name.get(pid, ""): pid for pid in per_map.get(sid, []) if permit_name.get(pid)}, + } + if return_debug: + item["score"] = round(float(score), 6) + results.append(item) + + if not results and scored and scored[0][0] > FALLBACK_GT: + sid, name, _ = scored[0][1] + best_score = scored[0][0] + item = { + "id": sid, + "name": name, + "permit": {permit_name.get(pid, ""): pid for pid in per_map.get(sid, []) if permit_name.get(pid)}, + } + if return_debug: + item["score"] = round(float(best_score), 6) + results = [item] + + out: Dict[str, Any] = {"risk_subject": results} + if return_debug: + decision = ( + "returned_ge_threshold" if results + else "returned_top_fallback" if (scored and scored[0][0] > FALLBACK_GT) + else "no_match_below_fallback" + ) + top_list = [] + for s, (sid, name, _v) in scored[: max(0, top_k_debug) or 5]: + top_list.append({"id": sid, "name": name, "score": round(float(s), 6)}) + out["debug"] = { + "query": query, + "qvec_dim": len(qvec), + "thresholds": {"return_if_ge": RETURN_IF_GE, "fallback_gt": FALLBACK_GT}, + "num_subjects": len(subs), + "max_score": round(float(scored[0][0]), 6) if scored else 0.0, + "top_candidates": top_list, + "decision": decision, + } + return out + + +def search_subjects_llm(query: str, return_debug: bool = False, top_k_debug: int = 5) -> Dict[str, Any]: + """Use LLM to pick one or more subject IDs from the catalog by instruction. + + Steps: + - Load subject id+name list from DB + - Ask LLM (Qwen) to select at least one subject id from the list that best matches the user query + - Map selected ids to full entries (name+permit_ids) and return + """ + # Load catalog + with _pg_conn() as c: + cur = c.cursor() + cur.execute("SELECT id, name FROM law_sub") + subjects = [(str(sid), str(name)) for sid, name in cur.fetchall()] + cur.execute("SELECT subject_id, permit_ids FROM law_sub_per") + per_map: Dict[str, List[str]] = {} + for sid, pids in cur.fetchall(): + if isinstance(pids, str): + try: + p_list = json.loads(pids) + except Exception: + p_list = [] + else: + p_list = list(pids) if isinstance(pids, list) else [] + per_map[str(sid)] = [str(x) for x in p_list] + + # Build concise subject list block: id | name per line + # Keep within reasonable token limits; if too long, truncate and rely on LLM suggestion quality. + lines = [f"{sid}\t{name}" for sid, name in subjects] + subjects_block = "\n".join(lines) + + system_msg = ( + "你是政务事项检索助手。根据用户的中文查询，从给定的主题事项清单中选择最相关的主题事项。" + "只允许从清单中选择，不能编造。若没有足够相关的主题，请返回空数组 []." + "始终以 JSON 数组返回所选主题事项的 id 列表，例如: [\"id1\", \"id2\"]." + ) + user_msg = ( + f"用户问题: {query}\n\n" + f"主题事项清单(格式: id名称):\n{subjects_block}\n\n" + "请仅输出 JSON 数组 (仅数组本身)。若无匹配请输出 []." + ) + + chat = ChatClient() + content = chat.chat([ + {"role": "system", "content": system_msg}, + {"role": "user", "content": user_msg}, + ], model=CHAT_MODEL, temperature=0.2) + + # Try parsing as JSON array of strings; robustly extract if wrapped text exists + selected_ids: List[str] = [] + try: + txt = content.strip() + start = txt.find("[") + end = txt.rfind("]") + if start != -1 and end != -1 and end > start: + arr = json.loads(txt[start : end + 1]) + else: + arr = json.loads(txt) + if isinstance(arr, list): + for x in arr: + if isinstance(x, str): + selected_ids.append(x) + elif isinstance(x, dict) and "id" in x and isinstance(x["id"], str): + selected_ids.append(x["id"]) + except Exception: + selected_ids = [] + + # Deduplicate and keep only ids that exist + id_set = {sid for sid, _ in subjects} + chosen = [] + for sid in selected_ids: + if sid in id_set and sid not in chosen: + chosen.append(sid) + # Allow empty result when nothing is relevant + + # Load permit names + permit_name: Dict[str, str] = {} + try: + with _pg_conn() as c2: + cur2 = c2.cursor() + cur2.execute("SELECT id, name FROM law_permit") + for pid, pname in cur2.fetchall(): + permit_name[str(pid)] = str(pname) + except Exception: + permit_name = {} + + results = [] + name_map = {sid: name for sid, name in subjects} + for sid in chosen: + results.append({ + "id": sid, + "name": name_map.get(sid, ""), + "permit": {permit_name.get(pid, ""): pid for pid in per_map.get(sid, []) if permit_name.get(pid)}, + }) + + out: Dict[str, Any] = {"risk_subject": results} + if return_debug: + out["debug"] = { + "model": CHAT_MODEL, + "num_subjects": len(subjects), + "selected_ids": chosen, + "allow_empty": True, + } + return out diff --git a/risk_tables_export.json b/risk_tables_export.json new file mode 100644 index 0000000..61d1330 --- /dev/null +++ b/risk_tables_export.json @@ -0,0 +1,1130 @@ +{ + "risk_subject": [ + { + "id": "edfbb171-9a45-4c57-b8e3-34adfd78ed85", + "name": "报关单位备案（进出口货物收发货人、报关企业）", + "permit_ids": [ + "29254acf77f93913a96e577bf1c7894d", + "6a357c0aaa56458d6da64d90c08ffaaa", + "b40fc5ef86fdb6f3a8456a691a762696" + ] + }, + { + "id": "992da9ff-e4ad-4298-9989-9e2266891cf4", + "name": "出口食品", + "permit_ids": [ + "6a357c0aaa56458d6da64d90c08ffaaa" + ] + }, + { + "id": "4c18f6576e842d5087796807f005e8bd", + "name": "开办包装装潢印刷企业（内资）", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "ba00f4d2e916d969c6ee0ac1babbd728", + "c78f43f131ad63bd21822ddc2e742850" + ] + }, + { + "id": "bd8201633df470633bdbb944d8f7916f", + "name": "开办便利店", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "3c4b5347a4476d891214a34811a5d0db", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "709b4718d72229311066e529650b8abf", + "71655107646f3e1cf09e9be42f8f431e", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "9b8c389a0d49508c78a7345ec4fb02f2" + ] + }, + { + "id": "70df6bc48c7b1f191610d82381edd95a", + "name": "开办病媒生物预防控制有偿服务企业", + "permit_ids": [] + }, + { + "id": "0e69c1812b01592ad3db2c6d6a631ff0", + "name": "开办超市", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "3c4b5347a4476d891214a34811a5d0db", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "709b4718d72229311066e529650b8abf", + "71655107646f3e1cf09e9be42f8f431e", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "9b8c389a0d49508c78a7345ec4fb02f2", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "e81115c82dcdf917eba748a660758989", + "name": "开办出版零售企业（非书店）", + "permit_ids": [ + "01601e30790dd10206bd5d33a86b483d", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "f1f4a486b80c9efc7854c4712869e291", + "name": "开办出版物批发企业", + "permit_ids": [ + "0f63bc97bd44ae34074a8eb80a7e6d0d", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "384aeb24a23e913268aad33354f705e7", + "name": "开办电影院", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "a0572119839422e1d11ee8801d6c58b7", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "092d25a8340919207b539f7d4183f64c", + "name": "开办动物隔离、无害化处理企业", + "permit_ids": [ + "88761ee3fdac02c5d29508adc6e54e05", + "8d49de002f24d37fcf3663574723e693" + ] + }, + { + "id": "27c67f5642734bec43068f298c7cd3fa", + "name": "开办动物诊所", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "9cd43d6cd00db09f4c0dcb73eccdb6af" + ] + }, + { + "id": "b558ec9cb9512fd41047cd0a3988c3c8", + "name": "开办二类医疗器械销售", + "permit_ids": [ + "709b4718d72229311066e529650b8abf", + "71655107646f3e1cf09e9be42f8f431e", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "8a46ec7f67e50098e5973550a46d22b2", + "name": "开办饭店", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "16e96bf1-fb4f-42bf-98e9-5307f154b513", + "709b4718d72229311066e529650b8abf", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "9b8c389a0d49508c78a7345ec4fb02f2" + ] + }, + { + "id": "961299e3879ca93101c26df1804006ea", + "name": "开办房地产经纪机构", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "ce3d1528c441a4638a69c8731eb8f780" + ] + }, + { + "id": "92151a5bf64c0738b4bd22c6115dab78", + "name": "开办高端装备制造企业", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "c78f43f131ad63bd21822ddc2e742850" + ] + }, + { + "id": "51b70ec786a87928da0d232e2d46e34b", + "name": "开办歌舞娱乐场所", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "16e96bf1-fb4f-42bf-98e9-5307f154b513", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "baaf120e02ad1265f2ab0a59e82bb4ee", + "f770843f4a910bae9767e81956961713", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "ba4e253fd02bbd9aefe1104c5fffa8cf", + "name": "开办公路旅客班车运输企业", + "permit_ids": [] + }, + { + "id": "b94f7d20dc9dbe1e56e1eac26fbdfc93", + "name": "开办公路旅客包车运输企业", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "fdf74943b5b3c46ae828a48913fde8af" + ] + }, + { + "id": "6133e22af6e29d5f0c2b8470744c3da4", + "name": "开办烘焙坊面包房", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "787d394080c907900958001726f65146", + "name": "开办货运公司", + "permit_ids": [ + "33280c3ebdd72bec6760bfbfe4a7cb29", + "8d49de002f24d37fcf3663574723e693", + "8dea1f04ecbe80d1b01e5eade81f0453" + ] + }, + { + "id": "beb115b8821d589ca599a0edf9938c18", + "name": "开办健身房", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "6a6ca32900a7959129c8080d304b5a8b", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "8e64f11b3a5297dfb97566b23cebc89b", + "name": "开办进出口贸易企业", + "permit_ids": [ + "42717417fb691ccdb988f966fb3dcc0b", + "8d49de002f24d37fcf3663574723e693" + ] + }, + { + "id": "779ff0e115988ac273ef69c13046f3a4", + "name": "开办酒吧", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "709b4718d72229311066e529650b8abf", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "9b8c389a0d49508c78a7345ec4fb02f2" + ] + }, + { + "id": "c49061441e3ed6a4e12fe0b41a5702aa", + "name": "开办咖啡馆茶馆", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "709b4718d72229311066e529650b8abf", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "9b8c389a0d49508c78a7345ec4fb02f2" + ] + }, + { + "id": "0e2c8fbf14fdd295eabc04fb3f09094e", + "name": "开办卡丁车游乐场", + "permit_ids": [ + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "86386c8aec80a19e3f2079754906ad8f", + "name": "开办林产品采集企业", + "permit_ids": [] + }, + { + "id": "c761acf3521b1b90cf91b442fb0b46db", + "name": "开办林木育种和育苗企业", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "id7h4ad89993ba572db7b818ec85ce5e" + ] + }, + { + "id": "681e706bc77c4f43f89e9bd86155e688", + "name": "开办轮滑馆", + "permit_ids": [ + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "3b62fb82ac19bfe90d3e1e3434597eb4", + "name": "开办旅馆", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "6a6ca32900a7959129c8080d304b5a8b", + "709b4718d72229311066e529650b8abf", + "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "993e8ee7c5bb6cca27d6e4b50417c396", + "9b8c389a0d49508c78a7345ec4fb02f2", + "baaf120e02ad1265f2ab0a59e82bb4ee", + "c78f43f131ad63bd21822ddc2e742850", + "f770843f4a910bae9767e81956961713", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "6b881e4207ab57c51a2e43a4fc303051", + "name": "开办旅行社", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693" + ] + }, + { + "id": "30e073eb2b1e992f56fc07e60a674f76", + "name": "开办旅行社分社", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693" + ] + }, + { + "id": "e1fd1ae615a7cc7f94668fffcab0301c", + "name": "开办美容美发店", + "permit_ids": [ + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "5731f5eee8259e5c10d0a1d1868e00ba", + "name": "开办美术馆", + "permit_ids": [ + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e", + "fa2f3e05c92297be096b63e25d30bfbe" + ] + }, + { + "id": "5fbec896221e5564d5211713ddfa19f5", + "name": "开办母婴用品", + "permit_ids": [ + "04bfa019634ca1aa0b9f7c783fd85dce", + "509b2872fc7c38c08f252a2b426fd49f", + "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "709b4718d72229311066e529650b8abf", + "71655107646f3e1cf09e9be42f8f431e", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "c333eb7a24dc69776b798ee884dd0990", + "name": "开办农副食品加工企业", + "permit_ids": [ + "2b12555dfcb8d4c00d32d9170f705800", + "8d49de002f24d37fcf3663574723e693", + "c78f43f131ad63bd21822ddc2e742850" + ] + }, + { + "id": "4ebcaa9d210921673268396b87b5e4fb", + "name": "开办农药店", + "permit_ids": [ + "8d49de002f24d37fcf3663574723e693", + "f4b54f92ac348d2e082c17e5e3968865" + ] + }, + { + "id": "81a4d5990bbb56d735c339fcfedd8759", + "name": "开办攀岩馆", + "permit_ids": [ + "6a6ca32900a7959129c8080d304b5a8b", + "709b4718d72229311066e529650b8abf", + "8d49de002f24d37fcf3663574723e693", + "8f7c8c613adfbd815a78c1e60ec4330e" + ] + }, + { + "id": "0123dd7c1174065f506ea18ef11cd05e", + "name": "开办其他印刷品印刷企业（内资）", + "permit_ids": [ + "40650656b4dc2bc58ce7b75b6a76cf7e", + "8d49de002f24d37fcf3663574723e693", + "c78f43f131ad63bd21822ddc2e742850" + ] + }, + { + "id": "3f2a212f68f45cb4bc0a6bb1e217099f", + "name": "开办潜水馆", + "permit_ids": [ + 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"f25650cef89c3c66aab0229ff9909dd1", + "name": "生鲜乳收购站许可" + }, + { + "id": "dc57e3f6c54a62d7e3cf0fc1b33e7cd8", + "name": "生鲜乳准运证明核发" + }, + { + "id": "16e96bf1-fb4f-42bf-98e9-5307f154b513", + "name": "食品经营许可-餐饮服务" + }, + { + "id": "54a79077-bd72-4ea9-8bb1-35afc69e2973", + "name": "食品经营许可-食品销售" + }, + { + "id": "2b12555dfcb8d4c00d32d9170f705800", + "name": "食品生产许可" + }, + { + "id": "0e774a1157608ff22d3be62c2441d027", + "name": "食品小作坊登记" + }, + { + "id": "966f4c5389c093b06c0003b6f4bd9ea7", + "name": "食用菌菌种生产经营许可证核发" + }, + { + "id": "9cf4a6d006a16ade6bae428cf89f4864", + "name": "兽药经营许可证核发（非生物制品类）" + }, + { + "id": "a34b0d8ab2378a222ab940106a45c2f1", + "name": "水产苗种场（不含原种场）的水产苗种生产许可证核发" + }, + { + "id": "429bd2f7fc00d2d928f1bf3a22a9e5dd", + "name": "水产苗种进出口审批" + }, + { + "id": "45eddd7ff9d0b85143a8ca2fda1d39eb", + "name": "水产原种场的水产苗种生产许可证核发" + }, + { + "id": "787ed631e137dbc03a6f2f8fa38c2bd2", + "name": "水路运输辅助业登记备案" + }, + { + "id": "71203b16487e96e8ee2f073d5df764ac", + "name": "水域滩涂养殖证核发" + }, + { + "id": "571426f8-2099-4745-a623-73fab88be54c", + "name": "特种设备使用登记" + }, + { + "id": "7c89a34348412d9327ff25f2fe7381ac", + "name": "托育机构备案" + }, + { + "id": "a071ebc11e97bfc2c39749f82517cc7d", + "name": "外商投资农作物新品种选育和种子生产经营审批" + }, + { + "id": "c7c7f51ca840c7cfb6ef344276bf914d", + "name": "危险废物收集经营许可证核发" + }, + { + "id": "01620f6e29d4e282a2b7854795446541", + "name": "危险化学品建设项目安全条件审查、安全设施设计审查" + }, + { + "id": "c3cf098fa5a36d55bac68657be8f1958", + "name": "危险化学品经营许可证核发" + }, + { + "id": "f885c920425f99ea3f8d5617d26a4819", + "name": "校车使用许可" + }, + { + "id": "44ce24e29a9d7fcc93ea1cd5a6cf4826", + "name": "校车使用许可" + }, + { + "id": "a7db266d402b10a24003e9bcee68fbc3", + "name": "学前教育机构" + }, + { + "id": "0b3c9ebffc32717af7eab0018e8aada3", + "name": "巡游出租汽车车辆运营证核发" + }, + { + "id": "6f256f454d5e1aaf48ec8e5a4fd55a8f", + "name": "巡游出租汽车经营许可" + }, + { + "id": "8a130133-b3c4-44c6-ad80-c1c201da15d8", + "name": "烟草专卖零售许可（电子烟）" + }, + { + "id": "9b8c389a0d49508c78a7345ec4fb02f2", + "name": "烟草专卖零售许可证" + }, + { + "id": "dae383b05d9a99c7bc157271c61f0655", + "name": "烟花爆竹经营许可证核发" + }, + { + "id": "or54d56026631uq5gb3c60dc5ca98sgd", + "name": "养老机构备案" + }, + { + "id": "3c4b5347a4476d891214a34811a5d0db", + "name": "药品经营许可证核准（零售）" + }, + { + "id": "aad5a9f345bb87d7f0fd6cef6fee9fdf", + "name": "医疗废物经营许可证核发" + }, + { + "id": "7c75fb4bf0e537d4d395523633bfc025", + "name": "医疗机构（不含诊所）执业登记" + }, + { + "id": "4e7e34a18c75ba6c7ac85f5eab87a233", + "name": "医疗机构（三级医院、三级妇幼保健院、急救中心、急救站、临床检验中心、中外合资合作医疗机构、港澳台独资医疗机构）设置审批" + }, + { + "id": "e171d96e91cf63bda6c1ac78e876e001", + "name": "饮用水供水单位卫生许可" + }, + { + "id": "f770843f4a910bae9767e81956961713", + "name": "游艺娱乐场所从事娱乐场所经营活动审批" + }, + { + "id": "7837ac6c5c0c3aae87652f56da53fe19", + "name": "诊所备案" + }, + { + "id": "wo9dd56026631uq5gb3c60dc5ca22b4b", + "name": "中介机构从事代理记账业务审批" + }, + { + "id": "f295e6c8619a5e99f2b545386217ed50", + "name": "中医诊所备案" + }, + { + "id": "a99ba07a5cd4e84a7e4d614aa8823e59", + "name": "种畜禽生产经营许可" + }, + { + "id": "aa70e0352a81f456cdceba85efb090af", + "name": "重要水产苗种进出口审批" + }, + { + "id": "21feb3ae0987ed9cfec1204aad2979ed", + "name": "转基因棉花种子生产经营许可证核发（初审）" + }, + { + "id": "b6b1e9f37c3a70aef25dc78d7ab7bf77", + "name": "转基因农作物种子生产经营许可证核发" + }, + { + "id": "baeb999d7a7ed56803b25af6c1d5a0cc", + "name": "转基因水产苗种生产经营许可证核发" + } + ] +} \ No newline at end of file diff --git a/smart_cors_middleware.py b/smart_cors_middleware.py new file mode 100644 index 0000000..f7f01b9 --- /dev/null +++ b/smart_cors_middleware.py @@ -0,0 +1,231 @@ +import os +from typing import Iterable, Tuple + +from flask import Flask, request, Response, jsonify +from datetime import datetime + + +def _get_bool(name: str, default: bool = False) -> bool: + v = os.getenv(name) + if v is None: + return default + return str(v).strip().lower() in {"1", "true", "yes", "on"} + + +def _parse_allowed(origins_raw: str | None) -> list[str]: + if not origins_raw: + return ["*"] + parts = [p.strip() for p in origins_raw.split(",") if p.strip()] + return parts or ["*"] + + +def _origin_matches(origin: str, allowed: Iterable[str], strict: bool) -> tuple[bool, str | None]: + # strict=True: only exact match items that are not wildcards + # strict=False: allow simple suffix matches: .example.com or *.example.com + for pat in allowed: + if pat == "*": + if strict: + # strict mode ignores wildcard + continue + return True, "*" + if origin == pat: + return True, pat + if not strict: + if pat.startswith("*."): + suf = pat[1:] # .example.com + if origin.endswith(suf): + return True, pat + if pat.startswith("."): + if origin.endswith(pat): + return True, pat + return False, None + + +def init_smart_cors(app: Flask) -> None: + """Attach Smart CORS handlers to a Flask app. + + Env vars: + - ALLOWED_ORIGINS: comma-separated, e.g. https://a.com,https://b.com or * + - CORS_STRICT: true/false (default false) + - CORS_DEBUG: true/false + - NGINX_CORS_MODE: true/false (when Nginx sets Allow-Origin; app only supplements others) + - CORS_MAX_AGE: seconds for preflight caching (default 86400) + - CORS_EXPOSE_HEADERS: override exposed headers list + """ + + allowed = _parse_allowed(os.getenv("ALLOWED_ORIGINS")) + + + # 添加前端实际使用的域名 + frontend_origins = [ + "http://chinaweal.com.cn:8090", + "http://www.chinaweal.com.cn:8090", + "https://chinaweal.com.cn", + "https://www.chinaweal.com.cn", + "http://172.22.80.130:8000", # 从日志中看到的referer + ] + + # 添加默认的本地开发源 + default_origins = [ + "http://localhost:3000", + "http://localhost:8000", + "http://localhost:8081", + "http://127.0.0.1:3000", + "http://127.0.0.1:8000", + "http://127.0.0.1:8081" + ] + + # 合并并去重 + all_origins = list(set(allowed + frontend_origins + default_origins)) + strict = _get_bool("CORS_STRICT", False) + debug = _get_bool("CORS_DEBUG", False) + nginx_mode = _get_bool("NGINX_CORS_MODE", False) + max_age = os.getenv("CORS_MAX_AGE", "86400") + expose_headers = os.getenv( + "CORS_EXPOSE_HEADERS", + "Content-Length, Content-Type, Authorization, X-Request-Id", + ) + + def _is_proxy_request() -> bool: + hdrs = request.headers + return any(h in hdrs for h in ("X-Forwarded-For", "X-Forwarded-Proto", "X-Real-IP")) + + def _log(level: str, msg: str, **fields): + if not debug and level == "DEBUG": + return + logger = app.logger + kv = " ".join(f"{k}={v}" for k, v in fields.items()) if fields else "" + logger.log( + 20 if level == "INFO" else 10 if level == "DEBUG" else 30, + f"[CORS] {msg} {kv}".rstrip(), + ) + + @app.before_request + def _handle_preflight(): # type: ignore + if request.method != "OPTIONS": + return None + origin = request.headers.get("Origin", "") + acrm = request.headers.get("Access-Control-Request-Method", "") + acrh = request.headers.get("Access-Control-Request-Headers", "") + + allowed_ok, matched = _origin_matches(origin, all_origins, strict) if origin else (False, None) + + # Build a minimal preflight response + resp = Response(status=204) + + # When Nginx handles Allow-Origin, do not override it + if not nginx_mode: + if allowed_ok: + if matched == "*": + # If behind proxy (likely Nginx will add AO), skip to avoid duplicates + if not _is_proxy_request(): + resp.headers["Access-Control-Allow-Origin"] = "*" + else: + _log("DEBUG", "proxy-skip-allow-origin(preflight)") + else: + resp.headers["Access-Control-Allow-Origin"] = origin + resp.headers["Access-Control-Allow-Credentials"] = "true" + resp.headers.add("Vary", "Origin") + # Always supplement the rest + resp.headers["Access-Control-Allow-Methods"] = acrm or "GET, POST, OPTIONS" + if acrh: + resp.headers["Access-Control-Allow-Headers"] = acrh + else: + resp.headers["Access-Control-Allow-Headers"] = ( + "Content-Type, Authorization, X-Requested-With, X-Request-Id" + ) + resp.headers["Access-Control-Max-Age"] = max_age + resp.headers["Access-Control-Expose-Headers"] = expose_headers + + decision = "deny" + if not origin: + decision = "no-origin" + elif allowed_ok: + decision = "allow-*" if matched == "*" else "allow-origin" + resp.headers["X-CORS-Decision"] = f"preflight; {decision}; nginx_mode={nginx_mode}" + + _log( + "INFO", + "preflight", + path=request.path, + origin=origin, + request_method=acrm or "", + request_headers=acrh or "", + allowed=allowed_ok, + matched=matched or "", + nginx_mode=nginx_mode, + decision=decision, + ) + return resp + + @app.after_request + def _add_cors_headers(response: Response): # type: ignore + origin = request.headers.get("Origin", "") + if not origin: + return response + + allowed_ok, matched = _origin_matches(origin, all_origins, strict) + + # When Nginx sets Allow-Origin, we only supplement others to avoid duplicates + if not nginx_mode: + if allowed_ok: + if matched == "*": + # Do not set credentials when wildcard is used; if proxied, skip to avoid dup + if not _is_proxy_request(): + response.headers.setdefault("Access-Control-Allow-Origin", "*") + else: + _log("DEBUG", "proxy-skip-allow-origin(response)") + else: + response.headers.setdefault("Access-Control-Allow-Origin", origin) + response.headers.setdefault("Access-Control-Allow-Credentials", "true") + # Ensure caches vary by Origin when dynamic + vary_val = response.headers.get("Vary") + if not vary_val: + response.headers["Vary"] = "Origin" + elif "Origin" not in vary_val: + response.headers["Vary"] = f"{vary_val}, Origin" + + # Expose common headers (safe to always include) + response.headers.setdefault("Access-Control-Expose-Headers", expose_headers) + + # Add decision header and log + decision = "deny" + if allowed_ok: + decision = "allow-*" if matched == "*" else "allow-origin" + response.headers.setdefault( + "X-CORS-Decision", + f"response; {decision}; nginx_mode={nginx_mode}", + ) + _log( + "INFO", + "response", + path=request.path, + method=request.method, + origin=origin, + allowed=allowed_ok, + matched=matched or "", + nginx_mode=nginx_mode, + decision=decision, + ) + return response + + # Diagnosis endpoint to aid verification and debugging + @app.get("/api/cors-diagnosis") + def cors_diagnosis(): # type: ignore + origin = request.headers.get("Origin", "") + ok, matched = _origin_matches(origin, all_origins, strict) if origin else (False, None) + data = { + "nginx_mode": nginx_mode, + "detected_proxy": _is_proxy_request(), + "allowed_origins": all_origins, + "strict": strict, + "request_origin": origin, + "matched_rule": matched, + "origin_allowed": ok, + "notes": ( + "Nginx handles Allow-Origin; app supplements other headers" + if nginx_mode + else "App sets CORS headers end-to-end" + ), + } + return jsonify(data)