mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-07-21 06:07:13 +08:00
feat: support skills creator and gemini models
This commit is contained in:
saboteur7
@@ -8,6 +8,7 @@ Google gemini bot
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import json
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import time
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import requests
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from bot.bot import Bot
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import google.generativeai as genai
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from bot.session_manager import SessionManager
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@@ -118,82 +119,165 @@ class GoogleGeminiBot(Bot):
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def call_with_tools(self, messages, tools=None, stream=False, **kwargs):
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"""
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Call Gemini API with tool support for agent integration
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Call Gemini API with tool support using REST API (following official docs)
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Args:
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messages: List of messages
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tools: List of tool definitions (OpenAI format, will be converted to Gemini format)
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messages: List of messages (OpenAI format)
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tools: List of tool definitions (OpenAI/Claude format)
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stream: Whether to use streaming
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**kwargs: Additional parameters
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**kwargs: Additional parameters (system, max_tokens, temperature, etc.)
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Returns:
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Formatted response compatible with OpenAI format or generator for streaming
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"""
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try:
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# Configure Gemini
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genai.configure(api_key=self.api_key)
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model_name = kwargs.get("model", self.model)
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model_name = kwargs.get("model", self.model or "gemini-1.5-flash")
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# Extract system prompt from messages
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# Build REST API payload
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payload = {"contents": []}
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# Extract and set system instruction
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system_prompt = kwargs.get("system", "")
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gemini_messages = []
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if not system_prompt:
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for msg in messages:
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if msg.get("role") == "system":
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system_prompt = msg["content"]
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break
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for msg in messages:
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if msg.get("role") == "system":
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system_prompt = msg["content"]
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else:
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gemini_messages.append(msg)
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if system_prompt:
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payload["system_instruction"] = {
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"parts": [{"text": system_prompt}]
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}
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# Convert messages to Gemini format
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gemini_messages = self._convert_to_gemini_messages(gemini_messages)
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for msg in messages:
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role = msg.get("role")
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content = msg.get("content", "")
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if role == "system":
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continue
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# Convert role
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gemini_role = "user" if role in ["user", "tool"] else "model"
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# Handle different content formats
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parts = []
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if isinstance(content, str):
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# Simple text content
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parts.append({"text": content})
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elif isinstance(content, list):
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# List of content blocks (Claude format)
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for block in content:
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if not isinstance(block, dict):
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if isinstance(block, str):
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parts.append({"text": block})
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continue
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block_type = block.get("type")
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if block_type == "text":
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# Text block
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parts.append({"text": block.get("text", "")})
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elif block_type == "tool_result":
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# Convert Claude tool_result to Gemini functionResponse
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tool_use_id = block.get("tool_use_id")
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tool_content = block.get("content", "")
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# Try to parse tool content as JSON
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try:
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if isinstance(tool_content, str):
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tool_result_data = json.loads(tool_content)
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else:
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tool_result_data = tool_content
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except:
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tool_result_data = {"result": tool_content}
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# Find the tool name from previous messages
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# Look for the corresponding tool_call in model's message
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tool_name = None
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for prev_msg in reversed(messages):
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if prev_msg.get("role") == "assistant":
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prev_content = prev_msg.get("content", [])
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if isinstance(prev_content, list):
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for prev_block in prev_content:
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if isinstance(prev_block, dict) and prev_block.get("type") == "tool_use":
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if prev_block.get("id") == tool_use_id:
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tool_name = prev_block.get("name")
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break
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if tool_name:
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break
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# Gemini functionResponse format
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parts.append({
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"functionResponse": {
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"name": tool_name or "unknown",
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"response": tool_result_data
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}
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})
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elif "text" in block:
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# Generic text field
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parts.append({"text": block["text"]})
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if parts:
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payload["contents"].append({
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"role": gemini_role,
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"parts": parts
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})
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# Safety settings
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safety_settings = {
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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# Convert tools from OpenAI format to Gemini format if provided
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gemini_tools = None
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if tools:
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gemini_tools = self._convert_tools_to_gemini_format(tools)
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# Create model with system instruction if available
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model_kwargs = {"model_name": model_name}
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if system_prompt:
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model_kwargs["system_instruction"] = system_prompt
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model = genai.GenerativeModel(**model_kwargs)
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# Generate content
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generation_config = {}
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if kwargs.get("max_tokens"):
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generation_config["max_output_tokens"] = kwargs["max_tokens"]
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# Generation config
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gen_config = {}
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if kwargs.get("temperature") is not None:
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generation_config["temperature"] = kwargs["temperature"]
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gen_config["temperature"] = kwargs["temperature"]
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if kwargs.get("max_tokens"):
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gen_config["maxOutputTokens"] = kwargs["max_tokens"]
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if gen_config:
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payload["generationConfig"] = gen_config
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request_params = {
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"safety_settings": safety_settings
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# Convert tools to Gemini format (REST API style)
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if tools:
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gemini_tools = self._convert_tools_to_gemini_rest_format(tools)
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if gemini_tools:
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payload["tools"] = gemini_tools
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logger.info(f"[Gemini] Added {len(tools)} tools to request")
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# Make REST API call
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base_url = "https://generativelanguage.googleapis.com/v1beta"
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endpoint = f"{base_url}/models/{model_name}:generateContent"
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if stream:
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endpoint = f"{base_url}/models/{model_name}:streamGenerateContent?alt=sse"
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headers = {
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"x-goog-api-key": self.api_key,
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"Content-Type": "application/json"
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}
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if generation_config:
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request_params["generation_config"] = generation_config
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if gemini_tools:
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request_params["tools"] = gemini_tools
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logger.debug(f"[Gemini] REST API call: {endpoint}")
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response = requests.post(
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endpoint,
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headers=headers,
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json=payload,
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stream=stream,
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timeout=60
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)
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if stream:
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return self._handle_gemini_stream_response(model, gemini_messages, request_params, model_name)
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return self._handle_gemini_rest_stream_response(response, model_name)
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else:
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return self._handle_gemini_sync_response(model, gemini_messages, request_params, model_name)
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return self._handle_gemini_rest_sync_response(response, model_name)
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except Exception as e:
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logger.error(f"[Gemini] call_with_tools error: {e}")
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logger.error(f"[Gemini] call_with_tools error: {e}", exc_info=True)
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error_msg = str(e) # Capture error message before creating generator
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if stream:
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def error_generator():
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yield {
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"error": True,
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"message": str(e),
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"message": error_msg,
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"status_code": 500
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}
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return error_generator()
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@@ -204,6 +288,227 @@ class GoogleGeminiBot(Bot):
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"status_code": 500
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}
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def _convert_tools_to_gemini_rest_format(self, tools_list):
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"""
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Convert tools to Gemini REST API format
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Handles both OpenAI and Claude/Agent formats.
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Returns: [{"functionDeclarations": [...]}]
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"""
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function_declarations = []
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for tool in tools_list:
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# Extract name, description, and parameters based on format
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if tool.get("type") == "function":
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# OpenAI format: {"type": "function", "function": {...}}
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func = tool.get("function", {})
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name = func.get("name")
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description = func.get("description", "")
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parameters = func.get("parameters", {})
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else:
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# Claude/Agent format: {"name": "...", "description": "...", "input_schema": {...}}
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name = tool.get("name")
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description = tool.get("description", "")
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parameters = tool.get("input_schema", {})
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if not name:
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logger.warning(f"[Gemini] Skipping tool without name: {tool}")
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continue
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logger.debug(f"[Gemini] Converting tool: {name}")
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function_declarations.append({
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"name": name,
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"description": description,
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"parameters": parameters
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})
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# All functionDeclarations must be in a single tools object (per Gemini REST API spec)
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return [{
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"functionDeclarations": function_declarations
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}] if function_declarations else []
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def _handle_gemini_rest_sync_response(self, response, model_name):
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"""Handle Gemini REST API sync response and convert to OpenAI format"""
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try:
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if response.status_code != 200:
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error_text = response.text
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logger.error(f"[Gemini] API error ({response.status_code}): {error_text}")
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return {
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"error": True,
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"message": f"Gemini API error: {error_text}",
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"status_code": response.status_code
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}
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data = response.json()
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logger.debug(f"[Gemini] Response received")
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# Extract from Gemini response format
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candidates = data.get("candidates", [])
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if not candidates:
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logger.warning("[Gemini] No candidates in response")
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return {
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"error": True,
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"message": "No candidates in response",
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"status_code": 500
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}
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candidate = candidates[0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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# Extract text and function calls
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text_content = ""
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tool_calls = []
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for part in parts:
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# Check for text
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if "text" in part:
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text_content += part["text"]
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# Check for functionCall (per REST API docs)
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if "functionCall" in part:
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fc = part["functionCall"]
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logger.info(f"[Gemini] Function call detected: {fc.get('name')}")
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tool_calls.append({
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"id": f"call_{int(time.time() * 1000000)}",
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"type": "function",
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"function": {
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"name": fc.get("name"),
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"arguments": json.dumps(fc.get("args", {}))
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}
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})
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logger.info(f"[Gemini] Response: text={len(text_content)} chars, tool_calls={len(tool_calls)}")
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# Build OpenAI format response
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message_dict = {
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"role": "assistant",
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"content": text_content or None
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}
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if tool_calls:
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message_dict["tool_calls"] = tool_calls
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return {
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"id": f"chatcmpl-{time.time()}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"message": message_dict,
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"finish_reason": "tool_calls" if tool_calls else "stop"
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}],
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"usage": data.get("usageMetadata", {})
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}
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except Exception as e:
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logger.error(f"[Gemini] sync response error: {e}")
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return {
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"error": True,
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"message": str(e),
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"status_code": 500
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}
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def _handle_gemini_rest_stream_response(self, response, model_name):
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"""Handle Gemini REST API stream response"""
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try:
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all_tool_calls = []
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has_sent_tool_calls = False
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for line in response.iter_lines():
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if not line:
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continue
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line = line.decode('utf-8')
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# Skip SSE prefixes
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if line.startswith('data: '):
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line = line[6:]
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if not line or line == '[DONE]':
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continue
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try:
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chunk_data = json.loads(line)
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candidates = chunk_data.get("candidates", [])
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if not candidates:
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continue
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candidate = candidates[0]
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content = candidate.get("content", {})
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parts = content.get("parts", [])
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# Stream text content
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for part in parts:
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if "text" in part and part["text"]:
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yield {
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"id": f"chatcmpl-{time.time()}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {"content": part["text"]},
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"finish_reason": None
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}]
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}
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# Collect function calls
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if "functionCall" in part:
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fc = part["functionCall"]
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all_tool_calls.append({
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"index": len(all_tool_calls), # Add index to differentiate multiple tool calls
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"id": f"call_{int(time.time() * 1000000)}_{len(all_tool_calls)}",
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"type": "function",
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"function": {
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"name": fc.get("name"),
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"arguments": json.dumps(fc.get("args", {}))
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}
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})
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except json.JSONDecodeError:
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continue
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# Send tool calls if any were collected
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if all_tool_calls and not has_sent_tool_calls:
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logger.info(f"[Gemini] Stream detected {len(all_tool_calls)} tool calls")
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yield {
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"id": f"chatcmpl-{time.time()}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {"tool_calls": all_tool_calls},
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"finish_reason": None
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}]
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}
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has_sent_tool_calls = True
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# Final chunk
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yield {
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"id": f"chatcmpl-{time.time()}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": "tool_calls" if all_tool_calls else "stop"
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}]
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}
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except Exception as e:
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logger.error(f"[Gemini] stream response error: {e}", exc_info=True)
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error_msg = str(e)
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yield {
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"error": True,
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"message": error_msg,
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"status_code": 500
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}
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def _convert_tools_to_gemini_format(self, openai_tools):
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"""Convert OpenAI tool format to Gemini function declarations"""
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import google.generativeai as genai
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