id: 2001
name: GPT-4o
icon_uri: default_icon/openai_v2.png
icon_url: ""
description:
zh: gpt 模型简介
en: Multi-modal, 320ms, 88.7% MMLU, excels in education, customer support, health, and entertainment.
default_parameters:
- name: temperature
label:
zh: 生成随机性
en: Temperature
desc:
zh: '- **temperature**: 调高温度会使得模型的输出更多样性和创新性，反之，降低温度会使输出内容更加遵循指令要求但减少多样性。建议不要与“Top p”同时调整。'
en: '**Temperature**:\n\n- When you increase this value, the model outputs more diverse and innovative content; when you decrease it, the model outputs less diverse content that strictly follows the given instructions.\n- It is recommended not to adjust this value with \"Top p\" at the same time.'
type: float
min: "0"
max: "1"
default_val:
balance: "0.8"
creative: "1"
default_val: "1.0"
precise: "0.3"
precision: 1
options: []
style:
widget: slider
label:
zh: 生成多样性
en: Generation diversity
- name: max_tokens
label:
zh: 最大回复长度
en: Response max length
desc:
zh: 控制模型输出的Tokens 长度上限。通常 100 Tokens 约等于 150 个中文汉字。
en: You can specify the maximum length of the tokens output through this value. Typically, 100 tokens are approximately equal to 150 Chinese characters.
type: int
min: "1"
max: "4096"
default_val:
default_val: "4096"
options: []
style:
widget: slider
label:
zh: 输入及输出设置
en: Input and output settings
- name: top_p
label:
zh: Top P
en: Top P
desc:
zh: '- **Top p 为累计概率**: 模型在生成输出时会从概率最高的词汇开始选择，直到这些词汇的总概率累积达到Top p 值。这样可以限制模型只选择这些高概率的词汇，从而控制输出内容的多样性。建议不要与“生成随机性”同时调整。'
en: '**Top P**:\n\n- An alternative to sampling with temperature, where only tokens within the top p probability mass are considered. For example, 0.1 means only the top 10% probability mass tokens are considered.\n- We recommend altering this or temperature, but not both.'
type: float
min: "0"
max: "1"
default_val:
default_val: "0.7"
precision: 2
options: []
style:
widget: slider
label:
zh: 生成多样性
en: Generation diversity
- name: frequency_penalty
label:
zh: 重复语句惩罚
en: Frequency penalty
desc:
zh: '- **frequency penalty**: 当该值为正时，会阻止模型频繁使用相同的词汇和短语，从而增加输出内容的多样性。'
en: '**Frequency Penalty**: When positive, it discourages the model from repeating the same words and phrases, thereby increasing the diversity of the output.'
type: float
min: "-2"
max: "2"
default_val:
default_val: "0"
precision: 2
options: []
style:
widget: slider
label:
zh: 生成多样性
en: Generation diversity
- name: presence_penalty
label:
zh: 重复主题惩罚
en: Presence penalty
desc:
zh: '- **presence penalty**: 当该值为正时，会阻止模型频繁讨论相同的主题，从而增加输出内容的多样性'
en: '**Presence Penalty**: When positive, it prevents the model from discussing the same topics repeatedly, thereby increasing the diversity of the output.'
type: float
min: "-2"
max: "2"
default_val:
default_val: "0"
precision: 2
options: []
style:
widget: slider
label:
zh: 生成多样性
en: Generation diversity
- name: response_format
label:
zh: 输出格式
en: Response format
desc:
zh: '- **文本**: 使用普通文本格式回复\n- **Markdown**: 将引导模型使用Markdown格式输出回复\n- **JSON**: 将引导模型使用JSON格式输出'
en: '**Response Format**:\n\n- **Text**: Replies in plain text format\n- **Markdown**: Uses Markdown format for replies\n- **JSON**: Uses JSON format for replies'
type: int
min: ""
max: ""
default_val:
default_val: "0"
options:
- label: Text
value: "0"
- label: Markdown
value: "1"
- label: JSON
value: "2"
style:
widget: radio_buttons
label:
zh: 输入及输出设置
en: Input and output settings
meta:
name: gpt-4o protocol: openai
capability:
function_call: true
input_modal:
- text
- image
input_tokens: 128000
json_mode: false
max_tokens: 128000
output_modal:
- text
output_tokens: 16384
prefix_caching: false
reasoning: false
prefill_response: false
conn_config:
base_url: "https://one.ocoolai.com/v1"
api_key: "sk-你的秘钥"
timeout: 0s
model: "gpt-4o"
temperature: 0.7
frequency_penalty: 0
presence_penalty: 0
max_tokens: 4096
top_p: 1
top_k: 0
stop: []
openai: null
claude: null
ark: null
deepseek: null
qwen: null
gemini: null
custom: {}
status: 0
7. 编辑完成后保存文档，每个模型一个yaml文件，想要多个模型参考上述步骤重复添加
8. 回到项目目录，点击docker文件夹进入，点击.env.example文件，原地复制粘贴，在弹窗里将文件名重命名为.env ，然后点击确认
9. 双击.env 文件，找到#openai embedding 部分
* 将base URL改为`https://one.ocoolai.com/v1`
* embedding model改为`text-embedding-3-large`（或其他嵌入模型）
* API Key填成你自己的秘钥
* by azure改为`false`
* 向量维度（DIMS）填3072 -说明这个是模型支持的向量维度，每个模型的不一样，需要自行查询
* request dims小于向量维度即可 -这个是期望返回的向量维度，建议一致，都填3072
然后点击保存并关闭窗口
10. docker-compose.yaml文件需要说明一下，如果你的宝塔面板有安装过MySQL和Redis建议将端口映射改一下防止冲突，全新安装可以忽略，当然也建议修改一下，防止以后跟其他服务端口冲突。
* MySQL将`3306`:`3306`改为`3307或者其他差不多的数字`：`3306`
* Redis也一个道理，改成其他数字：6479
11. 然后保存，在docker文件夹内点击顶栏终端，执行`docker-compose up -d`
12. 等全部执行完成后，访问服务器IP地址:8888 ,输入一个邮箱地址和密码点击注册即可
[^1]: 模型名，自己方便辨认即可，建议跟站点一致
[^2]: 本站所有模型都填OpenAI，包括claude、deepseek等
[^3]: 需要替换为自己的秘钥
[^4]: 模型名称，必填，该名称属于ID，必须与站点请求模型名称一致，建议直接从模型广场复制
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://documentation.ocoolai.com/app_guides/coze.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.