OpenWebUI使用DeepSeek R1满血版，DeepSeek R1 API调用
https://www.dong-blog.fun/post/1935
API调用
登录这里：
https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint?config=%7B%7D
注册后，创建DeepSeek R1 API接入点：
接着Python就可以直接调用了：
import os
from openai import OpenAI
client = OpenAI(
api_key = "填写自己的key",
base_url = "https://ark.cn-beijing.volces.com/api/v3",
)
# Non-streaming:
print("----- standard request -----")
completion = client.chat.completions.create(
model = "ep-20250211175825-填写自己的模型名字", # your model endpoint ID
messages = [
{"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
{"role": "user", "content": "常见的十字花科植物有哪些？"},
],
)
print(completion.choices[0].message.content)
OpenWebUI使用
安装：
docker run -d -p 8888:8080 \
-v /root/ollama:/root/.ollama \
-v /root/openwebui-test:/app/backend/data \
--restart always -e HF_HUB_OFFLINE=1 \
ghcr.io/open-webui/open-webui:ollama
增加这个函数到OpenWebUI：
"""
title: DeepSeek R1
author: zgccrui
description: 在OpwenWebUI中显示DeepSeek R1模型的思维链 - 仅支持0.5.6及以上版本
version: 1.2.6
licence: MIT
"""
import json
import httpx
import re
from typing import AsyncGenerator, Callable, Awaitable
from pydantic import BaseModel, Field
import asyncio
class Pipe:
class Valves(BaseModel):
DEEPSEEK_API_BASE_URL: str = Field(
default="自己的baseurl",
description="Base Url",
)
DEEPSEEK_API_KEY: str = Field(
default="", description="用于身份验证的DeepSeek API密钥，可从控制台获取"
)
DEEPSEEK_API_MODEL: str = Field(
default="deepseek-reasoner",
description="API请求的模型名称，默认为 deepseek-reasoner ",
)
def __init__(self):
self.valves = self.Valves()
self.data_prefix = "data: "
self.thinking = -1 # -1:未开始 0:思考中 1:已回答
self.emitter = None
def pipes(self):
return [
{
"id": self.valves.DEEPSEEK_API_MODEL,
"name": self.valves.DEEPSEEK_API_MODEL,
}
]
async def pipe(
self, body: dict, __event_emitter__: Callable[[dict], Awaitable[None]] = None
) -> AsyncGenerator[str, None]:
"""主处理管道（已移除缓冲）"""
self.thinking = -1
self.emitter = __event_emitter__
# 验证配置
if not self.valves.DEEPSEEK_API_KEY:
yield json.dumps({"error": "未配置API密钥"}, ensure_ascii=False)
return
# 准备请求参数
headers = {
"Authorization": f"Bearer {self.valves.DEEPSEEK_API_KEY}",
"Content-Type": "application/json",
}
try:
# 模型ID提取
model_id = body["model"].split(".", 1)[-1]
payload = {**body, "model": model_id}
# 处理消息以防止连续的相同角色
messages = payload["messages"]
i = 0
while i < len(messages) - 1:
if messages[i]["role"] == messages[i + 1]["role"]:
# 插入具有替代角色的占位符消息
alternate_role = (
"assistant" if messages[i]["role"] == "user" else "user"
)
messages.insert(
i + 1,
{"role": alternate_role, "content": "[Unfinished thinking]"},
)
i += 1
# yield json.dumps(payload, ensure_ascii=False)
# 发起API请求
async with httpx.AsyncClient(http2=True) as client:
async with client.stream(
"POST",
f"{self.valves.DEEPSEEK_API_BASE_URL}/chat/completions",
json=payload,
headers=headers,
timeout=300,
) as response:
# 错误处理
if response.status_code != 200:
error = await response.aread()
yield self._format_error(response.status_code, error)
return
# 流式处理响应
async for line in response.aiter_lines():
if not line.startswith(self.data_prefix):
continue
# 截取 JSON 字符串
json_str = line[len(self.data_prefix) :]
try:
data = json.loads(json_str)
except json.JSONDecodeError as e:
# 格式化错误信息，这里传入错误类型和详细原因（包括出错内容和异常信息）
error_detail = f"解析失败 - 内容：{json_str}，原因：{e}"
yield self._format_error("JSONDecodeError", error_detail)
return
choice = data.get("choices", [{}])[0]
# 结束条件判断
if choice.get("finish_reason"):
return
# 状态机处理
state_output = await self._update_thinking_state(
choice.get("delta", {})
)
if state_output:
yield state_output # 直接发送状态标记
if state_output == "<think>":
yield "\n"
# 内容处理并立即发送
content = self._process_content(choice["delta"])
if content:
if content.startswith("<think>"):
match = re.match(r"^<think>", content)
if match:
content = re.sub(r"^<think>", "", content)
yield "<think>"
await asyncio.sleep(0.1)
yield "\n"
elif content.startswith("</think>"):
match = re.match(r"^</think>", content)
if match:
content = re.sub(r"^</think>", "", content)
yield "</think>"
await asyncio.sleep(0.1)
yield "\n"
yield content
except Exception as e:
yield self._format_exception(e)
async def _update_thinking_state(self, delta: dict) -> str:
"""更新思考状态机（简化版）"""
state_output = ""
# 状态转换：未开始 -> 思考中
if self.thinking == -1 and delta.get("reasoning_content"):
self.thinking = 0
state_output = "<think>"
# 状态转换：思考中 -> 已回答
elif (
self.thinking == 0
and not delta.get("reasoning_content")
and delta.get("content")
):
self.thinking = 1
state_output = "\n</think>\n\n"
return state_output
def _process_content(self, delta: dict) -> str:
"""直接返回处理后的内容"""
return delta.get("reasoning_content", "") or delta.get("content", "")
def _format_error(self, status_code: int, error: bytes) -> str:
"""错误格式化保持不变"""
try:
err_msg = json.loads(error).get("message", error.decode(errors="ignore"))[
:200
]
except:
err_msg = error.decode(errors="ignore")[:200]
return json.dumps(
{"error": f"HTTP {status_code}: {err_msg}"}, ensure_ascii=False
)
def _format_exception(self, e: Exception) -> str:
"""异常格式化保持不变"""
err_type = type(e).__name__
return json.dumps({"error": f"{err_type}: {str(e)}"}, ensure_ascii=False)
保存函数，配置函数参数：
使用
可以自己修改一个模型名称便于认识，然后这个函数就可以显示出思考过程了。
