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