kaiyuan/chatgpt-on-wechat
1
0
Fork 0
mirror of https://github.com/zhayujie/chatgpt-on-wechat.git synced 2026-06-02 00:57:41 +08:00
Code Issues Packages Projects Releases Wiki Activity

feat: optimize agent configuration and memory

Browse Source
This commit is contained in:
zhayujie
2026-02-02 11:48:53 +08:00
parent a8d5309c90
commit 46fa07e4a9
36 changed files with 1245 additions and 355 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
bridge/agent_bridge.py
View File

@@ -230,12 +230,7 @@ class AgentBridge:
# Log skill loading details
if agent.skill_manager:
logger.info(f"[AgentBridge] SkillManager initialized:")
logger.info(f"[AgentBridge] - Managed dir: {agent.skill_manager.managed_skills_dir}")
logger.info(f"[AgentBridge] - Workspace dir: {agent.skill_manager.workspace_dir}")
logger.info(f"[AgentBridge] - Total skills: {len(agent.skill_manager.skills)}")
for skill_name in agent.skill_manager.skills.keys():
logger.info(f"[AgentBridge] * {skill_name}")
logger.debug(f"[AgentBridge] SkillManager initialized with {len(agent.skill_manager.skills)} skills")
return agent
@@ -469,14 +464,19 @@ class AgentBridge:
logger.info("[AgentBridge] System prompt built successfully")
# Get cost control parameters from config
max_steps = conf().get("agent_max_steps", 20)
max_context_tokens = conf().get("agent_max_context_tokens", 50000)
# Create agent with configured tools and workspace
agent = self.create_agent(
system_prompt=system_prompt,
tools=tools,
max_steps=50,
max_steps=max_steps,
output_mode="logger",
workspace_dir=workspace_root, # Pass workspace to agent for skills loading
enable_skills=True # Enable skills auto-loading
enable_skills=True, # Enable skills auto-loading
max_context_tokens=max_context_tokens
)
# Attach memory manager to agent if available
@@ -507,7 +507,7 @@ class AgentBridge:
try:
from dotenv import load_dotenv
load_dotenv(env_file, override=True)
logger.info(f"[AgentBridge] Loaded environment variables from {env_file} for session {session_id}")
logger.debug(f"[AgentBridge] Loaded environment variables from {env_file} for session {session_id}")
except ImportError:
logger.warning(f"[AgentBridge] python-dotenv not installed, skipping .env file loading for session {session_id}")
except Exception as e:
@@ -543,12 +543,12 @@ class AgentBridge:
api_key=openai_api_key,
api_base=openai_api_base or "https://api.openai.com/v1"
)
logger.info(f"[AgentBridge] OpenAI embedding initialized for session {session_id}")
logger.debug(f"[AgentBridge] OpenAI embedding initialized for session {session_id}")
except Exception as embed_error:
logger.warning(f"[AgentBridge] OpenAI embedding failed for session {session_id}: {embed_error}")
logger.info(f"[AgentBridge] Using keyword-only search for session {session_id}")
else:
logger.info(f"[AgentBridge] No OpenAI API key, using keyword-only search for session {session_id}")
logger.debug(f"[AgentBridge] No OpenAI API key, using keyword-only search for session {session_id}")
# 创建 memory config
memory_config = MemoryConfig(workspace_root=workspace_root)
@@ -564,15 +564,15 @@ class AgentBridge:
if loop.is_running():
# 如果事件循环正在运行，创建任务
asyncio.create_task(memory_manager.sync())
logger.info(f"[AgentBridge] Memory sync scheduled for session {session_id}")
logger.debug(f"[AgentBridge] Memory sync scheduled for session {session_id}")
else:
# 如果没有运行的循环，直接执行
loop.run_until_complete(memory_manager.sync())
logger.info(f"[AgentBridge] Memory synced successfully for session {session_id}")
logger.debug(f"[AgentBridge] Memory synced successfully for session {session_id}")
except RuntimeError:
# 没有事件循环，创建新的
asyncio.run(memory_manager.sync())
logger.info(f"[AgentBridge] Memory synced successfully for session {session_id}")
logger.debug(f"[AgentBridge] Memory synced successfully for session {session_id}")
except Exception as sync_error:
logger.warning(f"[AgentBridge] Memory sync failed for session {session_id}: {sync_error}")
@@ -619,7 +619,7 @@ class AgentBridge:
from agent.tools.scheduler.integration import init_scheduler
if init_scheduler(self):
self.scheduler_initialized = True
logger.info(f"[AgentBridge] Scheduler service initialized for session {session_id}")
logger.debug(f"[AgentBridge] Scheduler service initialized for session {session_id}")
except Exception as e:
logger.warning(f"[AgentBridge] Failed to initialize scheduler for session {session_id}: {e}")
@@ -651,7 +651,7 @@ class AgentBridge:
try:
from agent.skills import SkillManager
skill_manager = SkillManager(workspace_dir=workspace_root)
logger.info(f"[AgentBridge] Initialized SkillManager with {len(skill_manager.skills)} skills for session {session_id}")
logger.debug(f"[AgentBridge] Initialized SkillManager with {len(skill_manager.skills)} skills for session {session_id}")
except Exception as e:
logger.warning(f"[AgentBridge] Failed to initialize SkillManager for session {session_id}: {e}")
@@ -716,15 +716,20 @@ class AgentBridge:
if is_first:
mark_conversation_started(workspace_root)
# Get cost control parameters from config
max_steps = conf().get("agent_max_steps", 20)
max_context_tokens = conf().get("agent_max_context_tokens", 50000)
# Create agent for this session
agent = self.create_agent(
system_prompt=system_prompt,
tools=tools,
max_steps=50,
max_steps=max_steps,
output_mode="logger",
workspace_dir=workspace_root,
skill_manager=skill_manager,
enable_skills=True
enable_skills=True,
max_context_tokens=max_context_tokens
)
if memory_manager:
@@ -893,16 +898,16 @@ class AgentBridge:
for config_key, env_key in key_mapping.items():
# Skip if already in .env file
if env_key in existing_env_vars:
logger.debug(f"[AgentBridge] Skipping {env_key} - already in .env")
continue
# Get value from config.json
value = conf().get(config_key, "")
if value and value.strip(): # Only migrate non-empty values
keys_to_migrate[env_key] = value.strip()
logger.debug(f"[AgentBridge] Will migrate {env_key} from config.json")
else:
logger.debug(f"[AgentBridge] Skipping {env_key} - no value in config.json")
# Log summary if there are keys to skip
if existing_env_vars:
logger.debug(f"[AgentBridge] {len(existing_env_vars)} env vars already in .env")
# Write new keys to .env file
if keys_to_migrate: