from config.logger import setup_logging
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from openai import OpenAI
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import json
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.model_name = config.get("model_name")
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self.base_url = config.get("base_url", "http://localhost:11434")
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# Initialize OpenAI client with Ollama base URL
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# 如果没有v1，增加v1
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if not self.base_url.endswith("/v1"):
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self.base_url = f"{self.base_url}/v1"
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self.client = OpenAI(
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base_url=self.base_url,
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api_key="ollama", # Ollama doesn't need an API key but OpenAI client requires one
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)
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# 检查是否是qwen3模型
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self.is_qwen3 = self.model_name and self.model_name.lower().startswith("qwen3")
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def response(self, session_id, dialogue, **kwargs):
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# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
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if self.is_qwen3:
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# 复制对话列表，避免修改原始对话
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dialogue_copy = dialogue.copy()
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# 找到最后一条用户消息
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for i in range(len(dialogue_copy) - 1, -1, -1):
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if dialogue_copy[i]["role"] == "user":
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# 在用户消息前添加/no_think指令
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dialogue_copy[i]["content"] = (
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"/no_think " + dialogue_copy[i]["content"]
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)
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logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
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break
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# 使用修改后的对话
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dialogue = dialogue_copy
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responses = self.client.chat.completions.create(
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model=self.model_name, messages=dialogue, stream=True
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)
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is_active = True
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# 用于处理跨chunk的标签
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buffer = ""
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try:
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for chunk in responses:
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try:
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delta = (
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chunk.choices[0].delta
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if getattr(chunk, "choices", None)
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else None
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)
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content = delta.content if hasattr(delta, "content") else ""
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if content:
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# 将内容添加到缓冲区
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buffer += content
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# 处理缓冲区中的标签
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while "<think>" in buffer and "</think>" in buffer:
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# 找到完整的<think></think>标签并移除
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pre = buffer.split("<think>", 1)[0]
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post = buffer.split("</think>", 1)[1]
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buffer = pre + post
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# 处理只有开始标签的情况
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if "<think>" in buffer:
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is_active = False
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buffer = buffer.split("<think>", 1)[0]
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# 处理只有结束标签的情况
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if "</think>" in buffer:
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is_active = True
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buffer = buffer.split("</think>", 1)[1]
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# 如果当前处于活动状态且缓冲区有内容，则输出
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if is_active and buffer:
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yield buffer
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buffer = "" # 清空缓冲区
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
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finally:
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responses.close()
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def response_with_functions(self, session_id, dialogue, functions=None):
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# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
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if self.is_qwen3:
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# 复制对话列表，避免修改原始对话
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dialogue_copy = dialogue.copy()
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# 找到最后一条用户消息
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for i in range(len(dialogue_copy) - 1, -1, -1):
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if dialogue_copy[i]["role"] == "user":
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# 在用户消息前添加/no_think指令
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dialogue_copy[i]["content"] = (
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"/no_think " + dialogue_copy[i]["content"]
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)
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logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
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break
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# 使用修改后的对话
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dialogue = dialogue_copy
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stream = self.client.chat.completions.create(
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model=self.model_name,
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messages=dialogue,
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stream=True,
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tools=functions,
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)
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is_active = True
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buffer = ""
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try:
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for chunk in stream:
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try:
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delta = (
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chunk.choices[0].delta
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if getattr(chunk, "choices", None)
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else None
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)
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content = delta.content if hasattr(delta, "content") else None
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tool_calls = (
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delta.tool_calls if hasattr(delta, "tool_calls") else None
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)
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# 如果是工具调用，直接传递
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if tool_calls:
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yield None, tool_calls
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continue
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# 处理文本内容
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if content:
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# 将内容添加到缓冲区
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buffer += content
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# 处理缓冲区中的标签
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while "<think>" in buffer and "</think>" in buffer:
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# 找到完整的<think></think>标签并移除
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pre = buffer.split("<think>", 1)[0]
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post = buffer.split("</think>", 1)[1]
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buffer = pre + post
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# 处理只有开始标签的情况
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if "<think>" in buffer:
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is_active = False
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buffer = buffer.split("<think>", 1)[0]
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# 处理只有结束标签的情况
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if "</think>" in buffer:
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is_active = True
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buffer = buffer.split("</think>", 1)[1]
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# 如果当前处于活动状态且缓冲区有内容，则输出
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if is_active and buffer:
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yield buffer, None
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buffer = "" # 清空缓冲区
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing function chunk: {e}")
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continue
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finally:
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stream.close()
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