API-KEY 申请指导

• 登录 deepseek 开放平台，链接（https://platform.deepseek.com/sign_in）可通过【验证码登录】/ 【密码登录】 / 【使用微信扫码登录】三种方式登录

• 创建 API Keys。填入API-KEY名称后会生成一个API Key，这个API请务必保存好，因为无法通过管理界面再次查看

• 查看用量信息

当前deepseek平台充值功能已暂停，但是官方会赠送10 CNY的体验金额，如果一个帐号下体验金额用完后，可按上述操作换帐号重新申请API-KEY

首次调用API的Python示例

• 安装依赖

pip3 install openai

• Python源码

from openai import OpenAI

client = OpenAI(api_key="", base_url="https://api.deepseek.com")

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "帮我整理下deepseek的特点和优势是什么？"},
    ],
    stream=False
)

print(response.choices[0].message.content)

• 执行结果图示

• 说明：

1. 代码中 替换成自己的 API-KEY
2. 使用的是python 3.11.5 版本

deepseek 模型与价格

1. deepseek-chat 模型最新版本为 DeepSeek-V3【默认启用】
2. deepseek-reasoner 模型最新版本为 DeepSeek-R1【勾选深度思考后启用】
3. deepseek-reasoner 是deepseek思维链，会给出正式回答之前的思考过程。
4. 在调用API时默认最大输出长度为4K，可以使用max_tokens进行调整，以支持更长的输出

DeepSeek API调用常见错误码

Temperature 设置

deepseek 四种消息格式

1. System message

2. User message

3. Assistant message

4. Tool message

deepseek API 返回值说明

Python 调用对话API的三种方式

1. OpenAI SDK

• 源码示例

from openai import OpenAI

# for backward compatibility, you can still use `https://api.deepseek.com/v1` as `base_url`.
client = OpenAI(api_key="", base_url="https://api.deepseek.com")

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "帮我整理下deepseek的特点和优势是什么？"},
    ],
    max_tokens=1024,
    temperature=0.7,
    stream=False
)

print(response.choices[0].message.content)

• 结果截图

2. Requests

• 源码示例

import requests
import json

url = "https://api.deepseek.com/chat/completions"

payload = json.dumps({
    "messages": [
        {
            "content": "You are a helpful assistant",
            "role": "system"
        },
        {
            "content": "帮我整理下deepseek的特点和优势是什么？",
            "role": "user"
        }
    ],
    "model": "deepseek-chat",
    "frequency_penalty": 0,
    "max_tokens": 2048,
    "presence_penalty": 0,
    "response_format": {
        "type": "text"
    },
    "stop": None,
    "stream": False,
    "stream_options": None,
    "temperature": 1,
    "top_p": 1,
    "tools": None,
    "tool_choice": "none",
    "logprobs": False,
    "top_logprobs": None
})
headers = {
    'Content-Type': 'application/json',
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

• 结果截图

3. Httpclient

• 源码示例

import http.client
import json

conn = http.client.HTTPSConnection("api.deepseek.com")
payload = json.dumps({
    "messages": [
        {
            "content": "You are a helpful assistant",
            "role": "system"
        },
        {
            "content": "帮我整理下deepseek的特点和优势是什么？",
            "role": "user"
        }
    ],
    "model": "deepseek-chat",
    "frequency_penalty": 0,
    "max_tokens": 2048,
    "presence_penalty": 0,
    "response_format": {
        "type": "text"
    },
    "stop": None,
    "stream": False,
    "stream_options": None,
    "temperature": 1,
    "top_p": 1,
    "tools": None,
    "tool_choice": "none",
    "logprobs": False,
    "top_logprobs": None
})
headers = {
    'Content-Type': 'application/json',
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}
conn.request("POST", "/chat/completions", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

• 结果截图

Python 调用 FIM 补全 的三种示例

1. OpenAI SDK

• 源码示例

from openai import OpenAI

client = OpenAI(
    api_key="",
    base_url="https://api.deepseek.com/beta",
)
response = client.completions.create(
    model="deepseek-chat",
    prompt="def fib(a):",
    suffix="    return fib(a-1) + fib(a-2)",
    max_tokens=128)
print(response.choices[0].text)

• 结果截图

2. Requests

• 源码示例

import requests
import json

url = "https://api.deepseek.com/beta/completions"

payload = json.dumps({
    "model": "deepseek-chat",
    "prompt": "def fib(a):",
    "echo": False,
    "frequency_penalty": 0,
    "logprobs": 0,
    "max_tokens": 128,
    "presence_penalty": 0,
    "stop": None,
    "stream": False,
    "stream_options": None,
    "suffix": "    return fib(a-1) + fib(a-2)",
    "temperature": 1,
    "top_p": 1
})
headers = {
    'Content-Type': 'application/json',
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

• 结果截图

3. Httpclient

• 源码示例

import http.client
import json

conn = http.client.HTTPSConnection("api.deepseek.com")
payload = json.dumps({
    "model": "deepseek-chat",
    "prompt": "def fib(a):",
    "echo": False,
    "frequency_penalty": 0,
    "logprobs": 0,
    "max_tokens": 128,
    "presence_penalty": 0,
    "stop": None,
    "stream": False,
    "stream_options": None,
    "suffix": "    return fib(a-1) + fib(a-2)",
    "temperature": 1,
    "top_p": 1
})
headers = {
    'Content-Type': 'application/json',
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}
conn.request("POST", "/beta/completions", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

• 结果截图

Python 调用获取模型API的三种方式

1. OpenAI

• 源码示例

from openai import OpenAI

# for backward compatibility, you can still use `https://api.deepseek.com/v1` as `base_url`.
client = OpenAI(api_key="", base_url="https://api.deepseek.com")
print(client.models.list())

• 结果截图

2. Requests

• 源码示例

import requests

url = "https://api.deepseek.com/models"

payload = {}
headers = {
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}

response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

• 结果截图

3. Httpclient

• 源码示例

import http.client

conn = http.client.HTTPSConnection("api.deepseek.com")
payload = ''
headers = {
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}
conn.request("GET", "/models", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

• 结果截图

Python 调用查询帐户余额的两种方式

1. Requests

• 源码示例

import requests

url = "https://api.deepseek.com/user/balance"

payload = {}
headers = {
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}

response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

• 结果截图

2. Httpclient

• 源码示例

import http.client

conn = http.client.HTTPSConnection("api.deepseek.com")
payload = ''
headers = {
    'Accept': 'application/json',
    'Authorization': 'Bearer '
}
conn.request("GET", "/user/balance", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

• 结果截图

推理模型的流式和非流式示例

• deepseek-reasoner 是DeepSeek 推出的推理模型。在输出最终结果之前，模型会先输出一段思维链内容，以提升最终答案的准确性。在使用deepseek-reasoner时，需要先将openai升级到最新

pip install -U openai

• 如果在输入的messages序列中，传入了reasoning_content, API会返回 400 错误， 因此需要删除 reasoning_content字段
• Python 非流式调用示例
• 源码示例

from openai import OpenAI

client = OpenAI(api_key="", base_url="https://api.deepseek.com")

# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages
)

reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
print(f"Round1 content: {content}")

# Round 2
messages.append({'role': 'assistant', 'content': content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages
)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
print(f"Round2 content: {content}")
# ...

• 结果载图

• Python 流式调用示例
• 源码示例

from openai import OpenAI

client = OpenAI(api_key="", base_url="https://api.deepseek.com")

# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages,
    stream=True
)

reasoning_content = ""
content = ""

for chunk in response:
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content
    elif chunk.choices[0].delta.content:

        content += chunk.choices[0].delta.content
    else:
        pass
print(f'round1 content: {content}')

# Round 2
messages.append({"role": "assistant", "content": content})
messages.append({'role': 'user', 'content': "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=messages,
    stream=True
)
reasoning_content = ""
content = ""

for chunk in response:
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content
    elif chunk.choices[0].delta.content:

        content += chunk.choices[0].delta.content
    else:
        pass
print(f'round2 content: {content}')

• 结果截图

Python 调用多轮对话示例

• Deepseek的 chat/completions API是一个无状态API， 即服务端不记录用户请求的上下文，用户每次请求时，需将之前所有对话历史拼接好后，传递给对话API
• 源码示例

from openai import OpenAI

client = OpenAI(api_key="", base_url="https://api.deepseek.com")

# Round 1
messages = [{"role": "user", "content": "What's the highest mountain in the world?"}]
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

messages.append(response.choices[0].message)
print(f"Messages Round 1: {messages}")

# Round 2
messages.append({"role": "user", "content": "What is the second?"})
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

messages.append(response.choices[0].message)
print(f"Messages Round 2: {messages}")

• 结果截图

• 在第一轮请求时，传北给API的messages为

[
    {"role": "user", "content": "What's the highest mountain in the world?"}
]

• 在第二轮请求时，需要第一轮模型的输出添加到messages末尾，还要将新的提问添加到messages末尾，最终传递给API的messages为

[
    {"role": "user", "content": "What's the highest mountain in the world?"},
    {"role": "assistant", "content": "The highest mountain in the world is Mount Everest."},
    {"role": "user", "content": "What is the second?"}
]

Json 格式输出

注意事项

• 设置 response_format 参数为 {'type': 'json_object'}
• 用户传入的 system 或 user prompt 中必须含有 json 字样，并给出希望模型输出的 JSON 格式的样例，以指导模型来输出合法 JSON
• 需要合理设置 max_tokens 参数，防止 JSON 字符串被中途截断
• 在使用 JSON Output 功能时，API 有概率会返回空的 content。我们正在积极优化该问题，您可以尝试修改 prompt 以缓解此类问题

• 源码示例

import json
from openai import OpenAI

client = OpenAI(
    api_key="",
    base_url="https://api.deepseek.com",
)

system_prompt = """
The user will provide some exam text. Please parse the "question" and "answer" and output them in JSON format. 

EXAMPLE INPUT: 
Which is the highest mountain in the world? Mount Everest.

EXAMPLE JSON OUTPUT:
{
    "question": "Which is the highest mountain in the world?",
    "answer": "Mount Everest"
}
"""

user_prompt = "Which is the longest river in the world? The Nile River."

messages = [{"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}]

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages,
    response_format={
        'type': 'json_object'
    }
)

print(json.loads(response.choices[0].message.content))

• 结果截图

自定义方法调用

当前版本的Function Calling功能不稳定，暂时先不做演示