mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-03-19 21:38:18 +08:00
feat: support skills creator and gemini models
This commit is contained in:
saboteur7
@@ -5,6 +5,7 @@ Agent Bridge - Integrates Agent system with existing COW bridge
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from typing import Optional, List
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from agent.protocol import Agent, LLMModel, LLMRequest
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from bot.openai_compatible_bot import OpenAICompatibleBot
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from bridge.bridge import Bridge
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from bridge.context import Context
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from bridge.reply import Reply, ReplyType
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@@ -12,11 +13,51 @@ from common import const
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from common.log import logger
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def add_openai_compatible_support(bot_instance):
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"""
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Dynamically add OpenAI-compatible tool calling support to a bot instance.
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This allows any bot to gain tool calling capability without modifying its code,
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as long as it uses OpenAI-compatible API format.
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"""
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if hasattr(bot_instance, 'call_with_tools'):
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# Bot already has tool calling support
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return bot_instance
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# Create a temporary mixin class that combines the bot with OpenAI compatibility
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class EnhancedBot(bot_instance.__class__, OpenAICompatibleBot):
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"""Dynamically enhanced bot with OpenAI-compatible tool calling"""
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def get_api_config(self):
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"""
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Infer API config from common configuration patterns.
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Most OpenAI-compatible bots use similar configuration.
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"""
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from config import conf
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return {
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'api_key': conf().get("open_ai_api_key"),
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'api_base': conf().get("open_ai_api_base"),
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'model': conf().get("model", "gpt-3.5-turbo"),
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'default_temperature': conf().get("temperature", 0.9),
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'default_top_p': conf().get("top_p", 1.0),
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'default_frequency_penalty': conf().get("frequency_penalty", 0.0),
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'default_presence_penalty': conf().get("presence_penalty", 0.0),
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}
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# Change the bot's class to the enhanced version
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bot_instance.__class__ = EnhancedBot
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logger.info(
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f"[AgentBridge] Enhanced {bot_instance.__class__.__bases__[0].__name__} with OpenAI-compatible tool calling")
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return bot_instance
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class AgentLLMModel(LLMModel):
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"""
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LLM Model adapter that uses COW's existing bot infrastructure
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"""
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def __init__(self, bridge: Bridge, bot_type: str = "chat"):
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# Get model name directly from config
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from config import conf
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@@ -28,9 +69,11 @@ class AgentLLMModel(LLMModel):
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@property
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def bot(self):
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"""Lazy load the bot"""
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"""Lazy load the bot and enhance it with tool calling if needed"""
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if self._bot is None:
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self._bot = self.bridge.get_bot(self.bot_type)
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# Automatically add tool calling support if not present
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self._bot = add_openai_compatible_support(self._bot)
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return self._bot
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def call(self, request: LLMRequest):
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@@ -157,9 +200,23 @@ class AgentBridge:
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model=model,
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tools=tools,
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max_steps=kwargs.get("max_steps", 15),
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output_mode=kwargs.get("output_mode", "logger")
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output_mode=kwargs.get("output_mode", "logger"),
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workspace_dir=kwargs.get("workspace_dir"), # Pass workspace for skills loading
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enable_skills=kwargs.get("enable_skills", True), # Enable skills by default
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memory_manager=kwargs.get("memory_manager"), # Pass memory manager
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max_context_tokens=kwargs.get("max_context_tokens"),
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context_reserve_tokens=kwargs.get("context_reserve_tokens")
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)
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# Log skill loading details
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if self.agent.skill_manager:
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logger.info(f"[AgentBridge] SkillManager initialized:")
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logger.info(f"[AgentBridge] - Managed dir: {self.agent.skill_manager.managed_skills_dir}")
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logger.info(f"[AgentBridge] - Workspace dir: {self.agent.skill_manager.workspace_dir}")
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logger.info(f"[AgentBridge] - Total skills: {len(self.agent.skill_manager.skills)}")
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for skill_name in self.agent.skill_manager.skills.keys():
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logger.info(f"[AgentBridge] * {skill_name}")
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return self.agent
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def get_agent(self) -> Optional[Agent]:
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@@ -169,24 +226,28 @@ class AgentBridge:
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return self.agent
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def _init_default_agent(self):
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"""Initialize default super agent with config and memory"""
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"""Initialize default super agent with new prompt system"""
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from config import conf
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import os
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# Get base system prompt from config
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base_prompt = conf().get("character_desc", "你是一个AI助手")
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# Setup memory if enabled
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# Get workspace from config
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workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
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# Initialize workspace and create template files
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from agent.prompt import ensure_workspace, load_context_files, PromptBuilder
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workspace_files = ensure_workspace(workspace_root, create_templates=True)
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logger.info(f"[AgentBridge] Workspace initialized at: {workspace_root}")
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# Setup memory system
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memory_manager = None
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memory_tools = []
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try:
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# Try to initialize memory system
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from agent.memory import MemoryManager, MemoryConfig
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from agent.tools import MemorySearchTool, MemoryGetTool
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# Create memory config directly with sensible defaults
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workspace_root = os.path.expanduser("~/cow")
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memory_config = MemoryConfig(
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workspace_root=workspace_root,
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embedding_provider="local", # Use local embedding (no API key needed)
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@@ -202,35 +263,24 @@ class AgentBridge:
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MemoryGetTool(memory_manager)
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]
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# Build memory guidance and add to system prompt
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memory_guidance = memory_manager.build_memory_guidance(
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lang="zh",
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include_context=True
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)
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system_prompt = base_prompt + "\n\n" + memory_guidance
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logger.info(f"[AgentBridge] Memory system initialized")
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logger.info(f"[AgentBridge] Workspace: {memory_config.get_workspace()}")
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except Exception as e:
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logger.warning(f"[AgentBridge] Memory system not available: {e}")
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logger.info("[AgentBridge] Continuing without memory features")
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system_prompt = base_prompt
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import traceback
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traceback.print_exc()
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logger.info("[AgentBridge] Initializing super agent")
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# Configure file tools to work in the correct workspace
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file_config = {"cwd": workspace_root} if memory_manager else {}
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# Use ToolManager to dynamically load all available tools
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from agent.tools import ToolManager
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tool_manager = ToolManager()
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tool_manager.load_tools()
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# Create tool instances for all available tools
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tools = []
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file_config = {
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"cwd": workspace_root,
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"memory_manager": memory_manager
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} if memory_manager else {"cwd": workspace_root}
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for tool_name in tool_manager.tool_classes.keys():
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try:
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tool = tool_manager.create_tool(tool_name)
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@@ -238,30 +288,61 @@ class AgentBridge:
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# Apply workspace config to file operation tools
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if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']:
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tool.config = file_config
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tool.cwd = file_config.get("cwd", tool.cwd if hasattr(tool, 'cwd') else None)
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if 'memory_manager' in file_config:
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tool.memory_manager = file_config['memory_manager']
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tools.append(tool)
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logger.debug(f"[AgentBridge] Loaded tool: {tool_name}")
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except Exception as e:
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logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
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# Add memory tools
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if memory_tools:
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tools.extend(memory_tools)
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logger.info(f"[AgentBridge] Added {len(memory_tools)} memory tools")
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logger.info(f"[AgentBridge] Loaded {len(tools)} tools: {[t.name for t in tools]}")
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# Create agent with configured tools
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# Load context files (SOUL.md, USER.md, etc.)
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context_files = load_context_files(workspace_root)
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logger.info(f"[AgentBridge] Loaded {len(context_files)} context files: {[f.path for f in context_files]}")
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# Build system prompt using new prompt builder
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prompt_builder = PromptBuilder(
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workspace_dir=workspace_root,
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language="zh"
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)
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# Get runtime info
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runtime_info = {
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"model": conf().get("model", "unknown"),
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"workspace": workspace_root,
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"channel": "web" # TODO: get from actual channel, default to "web" to hide if not specified
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}
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system_prompt = prompt_builder.build(
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tools=tools,
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context_files=context_files,
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memory_manager=memory_manager,
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runtime_info=runtime_info
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)
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logger.info("[AgentBridge] System prompt built successfully")
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# Create agent with configured tools and workspace
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agent = self.create_agent(
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system_prompt=system_prompt,
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tools=tools,
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max_steps=50,
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output_mode="logger"
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output_mode="logger",
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workspace_dir=workspace_root, # Pass workspace to agent for skills loading
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enable_skills=True # Enable skills auto-loading
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)
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# Attach memory manager to agent if available
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if memory_manager:
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agent.memory_manager = memory_manager
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# Add memory tools if available
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if memory_tools:
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for tool in memory_tools:
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agent.add_tool(tool)
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logger.info(f"[AgentBridge] Added {len(memory_tools)} memory tools")
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logger.info(f"[AgentBridge] Memory manager attached to agent")
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def agent_reply(self, query: str, context: Context = None,
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on_event=None, clear_history: bool = False) -> Reply:
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