settings

agent/deep_assistant.py

@@ -13,10 +13,7 @@ from langgraph.checkpoint.memory import MemorySaver
 from utils.fastapi_utils import detect_provider
 
 from .guideline_middleware import GuidelineMiddleware
-
+from utils.settings import SUMMARIZATION_MAX_TOKENS
-MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 65536))
-MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
-SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000
 
 class LoggingCallbackHandler(BaseCallbackHandler):
     """自定义的 CallbackHandler，使用项目的 logger 来记录日志"""

embedding/embedding.py

@@ -8,6 +8,7 @@ import asyncio
 import hashlib
 import json
 import logging
+from utils.settings import FASTAPI_URL
 
 # Configure logger
 logger = logging.getLogger('app')
@@ -19,8 +20,7 @@ def encode_texts_via_api(texts, batch_size=32):
     
     try:
         # FastAPI 服务地址
-        fastapi_url = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
+        api_endpoint = f"{FASTAPI_URL}/api/v1/embedding/encode"
-        api_endpoint = f"{fastapi_url}/api/v1/embedding/encode"
         
         # 调用编码接口
         request_data = {

embedding/manager.py

@@ -13,6 +13,7 @@ import logging
 from typing import Dict, List, Optional, Any, Tuple
 from dataclasses import dataclass
 from collections import OrderedDict
+from utils.settings import SENTENCE_TRANSFORMER_MODEL
 import threading
 import psutil
 import numpy as np
@@ -126,6 +127,5 @@ def get_model_manager() -> GlobalModelManager:
     """获取模型管理器实例"""
     global _model_manager
     if _model_manager is None:
-        model_name = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")
+        _model_manager = GlobalModelManager(SENTENCE_TRANSFORMER_MODEL)
-        _model_manager = GlobalModelManager(model_name)
     return _model_manager

mcp/mcp_common.py

@@ -11,7 +11,6 @@ import asyncio
 from typing import Any, Dict, List, Optional, Union
 import re
 
-
 def get_allowed_directory():
     """获取允许访问的目录"""
     # 优先使用命令行参数传入的dataset_dir

mcp/rag_retrieve_server.py

@@ -26,8 +26,8 @@ from mcp_common import (
     load_tools_from_json,
     handle_mcp_streaming
 )
-
+BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
-backend_host = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
+MASTERKEY = os.getenv("MASTERKEY", "master")
 
 def rag_retrieve(query: str) -> Dict[str, Any]:
     """调用RAG检索API"""
@@ -36,7 +36,7 @@ def rag_retrieve(query: str) -> Dict[str, Any]:
         if len(sys.argv) > 1:
             bot_id = sys.argv[1]
 
-        url = f"{backend_host}/v1/rag_retrieve/{bot_id}"
+        url = f"{BACKEND_HOST}/v1/rag_retrieve/{bot_id}"
         if not url:
             return {
                 "content": [
@@ -48,7 +48,7 @@ def rag_retrieve(query: str) -> Dict[str, Any]:
             }
 
         # 获取masterkey并生成认证token
-        masterkey = os.getenv("MASTERKEY", "master")
+        masterkey = MASTERKEY
         token_input = f"{masterkey}:{bot_id}"
         auth_token = hashlib.md5(token_input.encode()).hexdigest()
         

mcp/semantic_search_server.py

@@ -25,6 +25,7 @@ from mcp_common import (
     create_tools_list_response,
     handle_mcp_streaming
 )
+from utils.settings import FASTAPI_URL
 
 import requests
 
@@ -32,7 +33,7 @@ import requests
 def encode_query_via_api(query: str, fastapi_url: str = None) -> np.ndarray:
     """通过API编码单个查询"""
     if not fastapi_url:
-        fastapi_url = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
+        fastapi_url = FASTAPI_URL
 
     api_endpoint = f"{fastapi_url}/api/v1/embedding/encode"
 

routes/chat.py

@@ -20,13 +20,14 @@ from utils.fastapi_utils import (
     create_stream_chunk
 )
 from langchain_core.messages import AIMessageChunk, HumanMessage, ToolMessage, AIMessage
+from utils.settings import MAX_OUTPUT_TOKENS, MAX_CACHED_AGENTS, SHARD_COUNT
 
 router = APIRouter()
 
 # 初始化全局助手管理器
 agent_manager = init_global_sharded_agent_manager(
-    max_cached_agents=int(os.getenv("MAX_CACHED_AGENTS", "50")),
+    max_cached_agents=MAX_CACHED_AGENTS,
-    shard_count=int(os.getenv("SHARD_COUNT", "16"))
+    shard_count=SHARD_COUNT
 )
 
 
@@ -148,7 +149,7 @@ async def enhanced_generate_stream_response(
                     config["configurable"] = {"thread_id": session_id}
                 if hasattr(agent, 'logging_handler'):
                     config["callbacks"] = [agent.logging_handler]
-                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=config):
+                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=config, max_tokens=MAX_OUTPUT_TOKENS):
                     new_content = ""
                     
                     if isinstance(msg, AIMessageChunk):
@@ -328,7 +329,7 @@ async def create_agent_and_generate_response(
         config["configurable"] = {"thread_id": session_id}
     if hasattr(agent, 'logging_handler'):
         config["callbacks"] = [agent.logging_handler]
-    agent_responses = await agent.ainvoke({"messages": final_messages}, config=config)
+    agent_responses = await agent.ainvoke({"messages": final_messages}, config=config, max_tokens=MAX_OUTPUT_TOKENS)
     append_messages = agent_responses["messages"][len(final_messages):]
     response_text = ""
     for msg in append_messages:

routes/system.py

@@ -20,6 +20,11 @@ except ImportError:
 from embedding import get_model_manager
 from pydantic import BaseModel
 import logging
+from utils.settings import (
+    MAX_CACHED_AGENTS, SHARD_COUNT, MAX_CONNECTIONS_PER_HOST, MAX_CONNECTIONS_TOTAL,
+    KEEPALIVE_TIMEOUT, CONNECT_TIMEOUT, TOTAL_TIMEOUT, FILE_CACHE_SIZE, FILE_CACHE_TTL,
+    TOKENIZERS_PARALLELISM
+)
 
 logger = logging.getLogger('app')
 
@@ -45,8 +50,8 @@ logger.info("正在初始化系统优化...")
 system_optimizer = setup_system_optimizations()
 
 # 全局助手管理器配置（使用优化后的配置）
-max_cached_agents = int(os.getenv("MAX_CACHED_AGENTS", "50"))  # 增加缓存大小
+max_cached_agents = MAX_CACHED_AGENTS  # 增加缓存大小
-shard_count = int(os.getenv("SHARD_COUNT", "16"))  # 分片数量
+shard_count = SHARD_COUNT  # 分片数量
 
 # 初始化优化的全局助手管理器
 agent_manager = init_global_sharded_agent_manager(
@@ -56,17 +61,17 @@ agent_manager = init_global_sharded_agent_manager(
 
 # 初始化连接池
 connection_pool = init_global_connection_pool(
-    max_connections_per_host=int(os.getenv("MAX_CONNECTIONS_PER_HOST", "100")),
+    max_connections_per_host=MAX_CONNECTIONS_PER_HOST,
-    max_connections_total=int(os.getenv("MAX_CONNECTIONS_TOTAL", "500")),
+    max_connections_total=MAX_CONNECTIONS_TOTAL,
-    keepalive_timeout=int(os.getenv("KEEPALIVE_TIMEOUT", "30")),
+    keepalive_timeout=KEEPALIVE_TIMEOUT,
-    connect_timeout=int(os.getenv("CONNECT_TIMEOUT", "10")),
+    connect_timeout=CONNECT_TIMEOUT,
-    total_timeout=int(os.getenv("TOTAL_TIMEOUT", "60"))
+    total_timeout=TOTAL_TIMEOUT
 )
 
 # 初始化文件缓存
 file_cache = init_global_file_cache(
-    cache_size=int(os.getenv("FILE_CACHE_SIZE", "1000")),
+    cache_size=FILE_CACHE_SIZE,
-    ttl=int(os.getenv("FILE_CACHE_TTL", "300"))
+    ttl=FILE_CACHE_TTL
 )
 
 logger.info("系统优化初始化完成")
@@ -191,11 +196,11 @@ async def get_system_config():
             "config": {
                 "max_cached_agents": max_cached_agents,
                 "shard_count": shard_count,
-                "tokenizer_parallelism": os.getenv("TOKENIZERS_PARALLELISM", "true"),
+                "tokenizer_parallelism": TOKENIZERS_PARALLELISM,
-                "max_connections_per_host": os.getenv("MAX_CONNECTIONS_PER_HOST", "100"),
+                "max_connections_per_host": str(MAX_CONNECTIONS_PER_HOST),
-                "max_connections_total": os.getenv("MAX_CONNECTIONS_TOTAL", "500"),
+                "max_connections_total": str(MAX_CONNECTIONS_TOTAL),
-                "file_cache_size": os.getenv("FILE_CACHE_SIZE", "1000"),
+                "file_cache_size": str(FILE_CACHE_SIZE),
-                "file_cache_ttl": os.getenv("FILE_CACHE_TTL", "300")
+                "file_cache_ttl": str(FILE_CACHE_TTL)
             }
         }
         

utils/fastapi_utils.py

@@ -10,6 +10,7 @@ from fastapi import HTTPException
 import logging
 from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 from langchain.chat_models import init_chat_model
+from utils.settings import MASTERKEY, BACKEND_HOST
 
 USER = "user"
 ASSISTANT = "assistant"
@@ -389,16 +390,14 @@ def extract_api_key_from_auth(authorization: Optional[str]) -> Optional[str]:
 
 def generate_v2_auth_token(bot_id: str) -> str:
     """生成v2接口的认证token"""
-    masterkey = os.getenv("MASTERKEY", "master")
+    token_input = f"{MASTERKEY}:{bot_id}"
-    token_input = f"{masterkey}:{bot_id}"
     return hashlib.md5(token_input.encode()).hexdigest()
 
 
 async def fetch_bot_config(bot_id: str) -> Dict[str, Any]:
     """获取机器人配置从后端API"""
     try:
-        backend_host = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
+        url = f"{BACKEND_HOST}/v1/agent_bot_config/{bot_id}"
-        url = f"{backend_host}/v1/agent_bot_config/{bot_id}"
 
         auth_token = generate_v2_auth_token(bot_id)
         headers = {

utils/settings.py

@@ -0,0 +1,35 @@
+import os
+
+# LLM Token Settings
+MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 65536))
+MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
+SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000
+
+# Agent and Shard Settings
+MAX_CACHED_AGENTS = int(os.getenv("MAX_CACHED_AGENTS", 50))
+SHARD_COUNT = int(os.getenv("SHARD_COUNT", 16))
+
+# Connection Settings
+MAX_CONNECTIONS_PER_HOST = int(os.getenv("MAX_CONNECTIONS_PER_HOST", 100))
+MAX_CONNECTIONS_TOTAL = int(os.getenv("MAX_CONNECTIONS_TOTAL", 500))
+KEEPALIVE_TIMEOUT = int(os.getenv("KEEPALIVE_TIMEOUT", 30))
+CONNECT_TIMEOUT = int(os.getenv("CONNECT_TIMEOUT", 10))
+TOTAL_TIMEOUT = int(os.getenv("TOTAL_TIMEOUT", 60))
+
+# File Cache Settings
+FILE_CACHE_SIZE = int(os.getenv("FILE_CACHE_SIZE", 1000))
+FILE_CACHE_TTL = int(os.getenv("FILE_CACHE_TTL", 300))
+
+# API Settings
+BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
+MASTERKEY = os.getenv("MASTERKEY", "master")
+FASTAPI_URL = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
+
+# Project Settings
+PROJECT_DATA_DIR = os.getenv("PROJECT_DATA_DIR", "./projects/data")
+
+# Tokenizer Settings
+TOKENIZERS_PARALLELISM = os.getenv("TOKENIZERS_PARALLELISM", "true")
+
+# Embedding Model Settings
+SENTENCE_TRANSFORMER_MODEL = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")