feat(i18n): add English/Chinese language toggle with full UI translation

.env.example

@@ -1,16 +1,47 @@
-# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
-# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
-# 注意消耗较大，可先进行小于40轮的模拟尝试
+# ===== LLM Provider =====
+# Supported values: auto | openai | anthropic | github-copilot | ollama | claude
+# 'auto' detects the provider from the model name and available env vars.
+LLM_PROVIDER=auto
+
+# ===== Option 1: Anthropic Claude (recommended for quality) =====
+# Get your key at: https://console.anthropic.com/settings/keys
+# LLM_API_KEY=sk-ant-api03-...
+# LLM_MODEL_NAME=claude-sonnet-4-20250514
+# Notes:
+#   - LLM_BASE_URL is not needed; the SDK uses the official endpoint automatically.
+#   - Model names starting with 'claude-' auto-select the anthropic provider.
+
+# ===== Option 2: Alibaba Bailian / Qwen (recommended for cost) =====
+# Get your key at: https://bailian.console.aliyun.com/
+# Note: high simulation rounds consume significant tokens — start with <40 rounds.
 LLM_API_KEY=your_api_key_here
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_MODEL_NAME=qwen-plus
 
-# ===== ZEP记忆图谱配置 =====
-# 每月免费额度即可支撑简单使用：https://app.getzep.com/
-ZEP_API_KEY=your_zep_api_key_here
+# ===== Option 3: GitHub Copilot (no separate API key needed) =====
+# Uses your existing Copilot subscription. Rate limits are lower than direct APIs.
+# LLM_PROVIDER=github-copilot
+# GITHUB_TOKEN=ghp_your_github_pat_here   # or use GH_TOKEN / COPILOT_GITHUB_TOKEN
+# LLM_MODEL_NAME=gpt-4o
+
+# ===== Option 4: Ollama (fully local, no API key) =====
+# Requires Ollama running locally: https://ollama.ai
+# LLM_PROVIDER=ollama
+# LLM_BASE_URL=http://localhost:11434/v1
+# LLM_MODEL_NAME=llama3.2
 
-# ===== 加速 LLM 配置（可选）=====
-# 注意如果不使用加速配置，env文件中就不要出现下面的配置项
-LLM_BOOST_API_KEY=your_api_key_here
-LLM_BOOST_BASE_URL=your_base_url_here
-LLM_BOOST_MODEL_NAME=your_model_name_here
+# ===== Option 5: Claude CLI subprocess =====
+# Uses the `claude` CLI — no API key needed beyond your Claude account login.
+# LLM_PROVIDER=claude
+# CLAUDE_CLI_PATH=claude
+# LLM_MODEL_NAME=claude-sonnet-4-20250514
+
+# ===== Optional: Boost LLM (faster/cheaper secondary model) =====
+# If unset, the primary LLM is used for all calls.
+# LLM_BOOST_API_KEY=your_api_key_here
+# LLM_BOOST_BASE_URL=your_base_url_here
+# LLM_BOOST_MODEL_NAME=your_model_name_here
+
+# ===== Zep memory graph =====
+# Free tier is sufficient for basic use: https://app.getzep.com/
+ZEP_API_KEY=your_zep_api_key_here

backend/app/__init__.py

@@ -1,12 +1,12 @@
 """
-MiroFish Backend - Flask应用工厂
+MiroFish Backend — Flask application factory.
 """
 
 import os
 import warnings
 
-# 抑制 multiprocessing resource_tracker 的警告（来自第三方库如 transformers）
-# 需要在所有其他导入之前设置
+# Suppress multiprocessing resource_tracker warnings from third-party libs (e.g. transformers).
+# Must be set before all other imports.
 warnings.filterwarnings("ignore", message=".*resource_tracker.*")
 
 from flask import Flask, request
@@ -17,64 +17,62 @@
 
 
 def create_app(config_class=Config):
-    """Flask应用工厂函数"""
+    """Create and configure the Flask application."""
     app = Flask(__name__)
     app.config.from_object(config_class)
-    
-    # 设置JSON编码：确保中文直接显示（而不是 \uXXXX 格式）
-    # Flask >= 2.3 使用 app.json.ensure_ascii，旧版本使用 JSON_AS_ASCII 配置
+
+    # Ensure non-ASCII characters (e.g. Chinese) render as-is in JSON responses
+    # Flask >= 2.3 uses app.json.ensure_ascii; older versions use JSON_AS_ASCII config key
     if hasattr(app, 'json') and hasattr(app.json, 'ensure_ascii'):
         app.json.ensure_ascii = False
-
-    # 设置日志
+
     logger = setup_logger('mirofish')
-    
-    # 只在 reloader 子进程中打印启动信息（避免 debug 模式下打印两次）
+
+    # Only log startup info once — avoid duplicate output in debug/reloader mode
     is_reloader_process = os.environ.get('WERKZEUG_RUN_MAIN') == 'true'
     debug_mode = app.config.get('DEBUG', False)
     should_log_startup = not debug_mode or is_reloader_process
-    
+
     if should_log_startup:
         logger.info("=" * 50)
-        logger.info("MiroFish Backend 启动中...")
+        logger.info("MiroFish Backend starting...")
         logger.info("=" * 50)
-    
-    # 启用CORS
+
+    # Enable CORS for all API routes
     CORS(app, resources={r"/api/*": {"origins": "*"}})
-    
-    # 注册模拟进程清理函数（确保服务器关闭时终止所有模拟进程）
+
+    # Register simulation process cleanup on server shutdown
     from .services.simulation_runner import SimulationRunner
     SimulationRunner.register_cleanup()
     if should_log_startup:
-        logger.info("已注册模拟进程清理函数")
-    
-    # 请求日志中间件
+        logger.info("Simulation process cleanup registered")
+
+    # Request/response logging middleware
     @app.before_request
     def log_request():
-        logger = get_logger('mirofish.request')
-        logger.debug(f"请求: {request.method} {request.path}")
+        req_logger = get_logger('mirofish.request')
+        req_logger.debug(f"Request: {request.method} {request.path}")
         if request.content_type and 'json' in request.content_type:
-            logger.debug(f"请求体: {request.get_json(silent=True)}")
-    
+            req_logger.debug(f"Body: {request.get_json(silent=True)}")
+
     @app.after_request
     def log_response(response):
-        logger = get_logger('mirofish.request')
-        logger.debug(f"响应: {response.status_code}")
+        req_logger = get_logger('mirofish.request')
+        req_logger.debug(f"Response: {response.status_code}")
         return response
-    
-    # 注册蓝图
+
+    # Register blueprints
     from .api import graph_bp, simulation_bp, report_bp
     app.register_blueprint(graph_bp, url_prefix='/api/graph')
     app.register_blueprint(simulation_bp, url_prefix='/api/simulation')
     app.register_blueprint(report_bp, url_prefix='/api/report')
-    
-    # 健康检查
+
+    # Health check
     @app.route('/health')
     def health():
         return {'status': 'ok', 'service': 'MiroFish Backend'}
-    
+
     if should_log_startup:
-        logger.info("MiroFish Backend 启动完成")
-
-    return app
+        logger.info("MiroFish Backend ready")
 
+    return app

backend/app/api/__init__.py

@@ -1,5 +1,5 @@
 """
-API路由模块
+API route modules.
 """
 
 from flask import Blueprint

backend/app/config.py

@@ -1,75 +1,159 @@
 """
-配置管理
-统一从项目根目录的 .env 文件加载配置
+Configuration management.
+Loads settings from the .env file at the project root (MiroFish/.env).
 """
 
 import os
+import shutil
 from dotenv import load_dotenv
 
-# 加载项目根目录的 .env 文件
-# 路径: MiroFish/.env (相对于 backend/app/config.py)
-project_root_env = os.path.join(os.path.dirname(__file__), '../../.env')
+# Load .env from project root — path relative to backend/app/config.py
+_project_root_env = os.path.join(os.path.dirname(__file__), '../../.env')
 
-if os.path.exists(project_root_env):
-    load_dotenv(project_root_env, override=True)
+if os.path.exists(_project_root_env):
+    load_dotenv(_project_root_env, override=True)
 else:
-    # 如果根目录没有 .env，尝试加载环境变量（用于生产环境）
+    # No .env at root — fall back to environment variables (production mode)
     load_dotenv(override=True)
 
 
 class Config:
-    """Flask配置类"""
-    
-    # Flask配置
+    """Flask configuration class."""
+
+    # ── Flask ────────────────────────────────────────────────────────────────
     SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key')
     DEBUG = os.environ.get('FLASK_DEBUG', 'True').lower() == 'true'
-    
-    # JSON配置 - 禁用ASCII转义，让中文直接显示（而不是 \uXXXX 格式）
+
+    # Disable ASCII escaping so non-ASCII characters display correctly in JSON
     JSON_AS_ASCII = False
-
-    # LLM配置（统一使用OpenAI格式）
+
+    # ── LLM provider selection ────────────────────────────────────────────────
+    # Supported values:
+    #   auto (default) — infer from LLM_MODEL_NAME and available env vars
+    #   openai         — OpenAI SDK (works with any OpenAI-compatible API)
+    #   anthropic      — Anthropic Claude SDK (direct API, requires LLM_API_KEY)
+    #   github-copilot — GitHub Copilot via token exchange (requires GITHUB_TOKEN)
+    #   ollama         — Local Ollama via OpenAI-compatible endpoint
+    #   claude         — Claude CLI subprocess (requires `claude` CLI installed)
+    LLM_PROVIDER = os.environ.get('LLM_PROVIDER', 'auto')
+
+    # ── LLM credentials & model ──────────────────────────────────────────────
     LLM_API_KEY = os.environ.get('LLM_API_KEY')
     LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
     LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
-
-    # Zep配置
+
+    # Optional: faster/cheaper secondary LLM for non-critical calls
+    LLM_BOOST_API_KEY = os.environ.get('LLM_BOOST_API_KEY')
+    LLM_BOOST_BASE_URL = os.environ.get('LLM_BOOST_BASE_URL')
+    LLM_BOOST_MODEL_NAME = os.environ.get('LLM_BOOST_MODEL_NAME')
+
+    # ── Provider-specific settings ───────────────────────────────────────────
+    # Claude CLI: path to the `claude` executable (used when LLM_PROVIDER=claude)
+    CLAUDE_CLI_PATH = os.environ.get('CLAUDE_CLI_PATH', 'claude')
+
+    # GitHub Copilot: token env vars (checked in priority order)
+    # COPILOT_GITHUB_TOKEN > GH_TOKEN > GITHUB_TOKEN
+    GITHUB_TOKEN = (
+        os.environ.get('COPILOT_GITHUB_TOKEN')
+        or os.environ.get('GH_TOKEN')
+        or os.environ.get('GITHUB_TOKEN')
+    )
+
+    @classmethod
+    def get_resolved_provider(cls) -> str:
+        """
+        Resolve the effective LLM provider.
+
+        In 'auto' mode the provider is inferred:
+          1. Model name starts with 'claude-'  → anthropic
+          2. GitHub token present, no API key  → github-copilot
+          3. Otherwise                         → openai
+        """
+        provider = (cls.LLM_PROVIDER or 'auto').lower()
+        if provider != 'auto':
+            return provider
+
+        model = (cls.LLM_MODEL_NAME or '').lower()
+        if model.startswith('claude-'):
+            return 'anthropic'
+        if cls.GITHUB_TOKEN and not cls.LLM_API_KEY:
+            return 'github-copilot'
+        return 'openai'
+
+    # ── Zep memory graph ─────────────────────────────────────────────────────
     ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
-    
-    # 文件上传配置
-    MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50MB
+
+    # ── File uploads ─────────────────────────────────────────────────────────
+    MAX_CONTENT_LENGTH = 50 * 1024 * 1024  # 50 MB
     UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), '../uploads')
     ALLOWED_EXTENSIONS = {'pdf', 'md', 'txt', 'markdown'}
-    
-    # 文本处理配置
-    DEFAULT_CHUNK_SIZE = 500  # 默认切块大小
-    DEFAULT_CHUNK_OVERLAP = 50  # 默认重叠大小
-    
-    # OASIS模拟配置
+
+    # ── Text chunking ────────────────────────────────────────────────────────
+    DEFAULT_CHUNK_SIZE = 500
+    DEFAULT_CHUNK_OVERLAP = 50
+
+    # ── OASIS simulation ─────────────────────────────────────────────────────
     OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
-    OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations')
-
-    # OASIS平台可用动作配置
+    OASIS_SIMULATION_DATA_DIR = os.path.join(
+        os.path.dirname(__file__), '../uploads/simulations'
+    )
+
     OASIS_TWITTER_ACTIONS = [
-        'CREATE_POST', 'LIKE_POST', 'REPOST', 'FOLLOW', 'DO_NOTHING', 'QUOTE_POST'
+        'CREATE_POST', 'LIKE_POST', 'REPOST', 'FOLLOW', 'DO_NOTHING', 'QUOTE_POST',
     ]
     OASIS_REDDIT_ACTIONS = [
         'LIKE_POST', 'DISLIKE_POST', 'CREATE_POST', 'CREATE_COMMENT',
         'LIKE_COMMENT', 'DISLIKE_COMMENT', 'SEARCH_POSTS', 'SEARCH_USER',
-        'TREND', 'REFRESH', 'DO_NOTHING', 'FOLLOW', 'MUTE'
+        'TREND', 'REFRESH', 'DO_NOTHING', 'FOLLOW', 'MUTE',
     ]
-    
-    # Report Agent配置
+
+    # ── Report Agent ─────────────────────────────────────────────────────────
     REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5'))
-    REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
+    REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(
+        os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2')
+    )
     REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
-    
+
     @classmethod
     def validate(cls):
-        """验证必要配置"""
+        """Validate required configuration and return a list of error strings."""
+        import logging
+        log = logging.getLogger('mirofish.config')
         errors = []
-        if not cls.LLM_API_KEY:
-            errors.append("LLM_API_KEY 未配置")
+        provider = cls.get_resolved_provider()
+
+        if provider == 'anthropic':
+            if not cls.LLM_API_KEY:
+                errors.append(
+                    "LLM_API_KEY is required for the anthropic provider. "
+                    "Get a key at https://console.anthropic.com/settings/keys"
+                )
+        elif provider == 'openai':
+            if not cls.LLM_API_KEY:
+                errors.append("LLM_API_KEY is not configured (required for openai provider)")
+        elif provider == 'github-copilot':
+            if not cls.GITHUB_TOKEN:
+                errors.append(
+                    "github-copilot provider requires a GitHub token. "
+                    "Set COPILOT_GITHUB_TOKEN, GH_TOKEN, or GITHUB_TOKEN in .env."
+                )
+        elif provider == 'ollama':
+            if cls.LLM_BASE_URL and '11434' not in cls.LLM_BASE_URL:
+                log.warning(
+                    "LLM_BASE_URL (%s) doesn't include port 11434; "
+                    "make sure Ollama is reachable at that address.",
+                    cls.LLM_BASE_URL,
+                )
+        elif provider == 'claude':
+            if shutil.which(cls.CLAUDE_CLI_PATH) is None:
+                errors.append(
+                    f"Claude CLI not found at '{cls.CLAUDE_CLI_PATH}'. "
+                    "Install it (https://claude.ai/download) or set CLAUDE_CLI_PATH in .env."
+                )
+
         if not cls.ZEP_API_KEY:
-            errors.append("ZEP_API_KEY 未配置")
+            errors.append(
+                "ZEP_API_KEY is not configured. "
+                "Get a free key at https://app.getzep.com/"
+            )
         return errors
-