Deepseek

https://deepseek.com/

我們支援所有 Deepseek 模型，送出 completion 請求時只要將 deepseek/ 作為前綴即可

API 金鑰

# env variable
os.environ['DEEPSEEK_API_KEY']

範例用法

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
response = completion(
    model="deepseek/deepseek-chat", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

範例用法 - 串流

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
response = completion(
    model="deepseek/deepseek-chat", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

支援的模型 - 支援所有 Deepseek 模型！

我們支援所有 Deepseek 模型，送出 completion 請求時只要將 deepseek/ 作為前綴即可

模型名稱	函式呼叫
deepseek-chat	completion(model="deepseek/deepseek-chat", messages)
deepseek-coder	completion(model="deepseek/deepseek-coder", messages)

推理模型

模型名稱	函式呼叫
deepseek-reasoner	completion(model="deepseek/deepseek-reasoner", messages)

思考 / 推理模式

使用 thinking 或 reasoning_effort 參數，為 DeepSeek reasoner 模型啟用思考模式：

thinking 參數

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""

resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    thinking={"type": "enabled"},
)
print(resp.choices[0].message.reasoning_content)  # Model's reasoning
print(resp.choices[0].message.content)  # Final answer

reasoning_effort 參數

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""

resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    reasoning_effort="medium",  # low, medium, high all map to thinking enabled
)
print(resp.choices[0].message.reasoning_content)  # Model's reasoning
print(resp.choices[0].message.content)  # Final answer

備註

DeepSeek 只支援 {"type": "enabled"} - 不像 Anthropic，它不支援 budget_tokens。任何不是 "none" 的 reasoning_effort 值都會啟用思考模式。

基本用法

SDK

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "Tell me a joke."}],
)

print(
    resp.choices[0].message.reasoning_content
)

PROXY

1. 設定 config.yaml

model_list:
  - model_name: deepseek-reasoner
    litellm_params:
        model: deepseek/deepseek-reasoner
        api_key: os.environ/DEEPSEEK_API_KEY

2. 執行 proxy

python litellm/proxy/main.py

3. 測試！

curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Hi, how are you ?"
          }
        ]
      }
    ]
}'