Configuration of Chat Services
Rodel Agent supports a variety of mainstream AI chat services with different configurations. This document explains how to configure the supported AI services.
Please use the subsection index to navigate to the segment for the service you want to configure.
OpenAI
Documentation | https://platform.openai.com/docs/api-reference/chat/create |
---|---|
API Token | https://platform.openai.com/account/api-keys |
Configuring OpenAI is relatively simple. You just need to input your API Key in the Access Key
field of the settings.
Proxy and API Compatibility
Currently, the OpenAI interface data structure has become a de facto standard. Many AI services expose their interfaces by adopting similar address and data structures to OpenAI for easier usage. This is referred to as the OpenAI-compatible interface
.
If the AI service you are using is not listed in Rodel Agent's support list but uses an OpenAI-compatible interface, you can input the service's address in the Endpoint (API)
text box of the OpenAI settings block.
Another usage scenario involves using OpenAI services through a proxy server due to inaccessibility in some countries or regions, such as OpenAI API Proxy.
In this case, you still need to use your own API key, but instead of directly accessing the OpenAI server, you use a proxy service to avoid restrictions or bans.
You can input the service address in the Endpoint (API)
field to achieve API proxy functionality. For example, for the proxy service mentioned above, you need to input the address: https://api.openai-proxy.com/v1
.
WARNING
The OpenAI API Proxy mentioned above is provided for illustration purposes only. The developer does not take responsibility for its security. Users need to independently evaluate the reliability of the service and take responsibility for its usage.
TIP
For proxies, including a version number is usually necessary, such as v1
in https://api.openai-proxy.com/v1
. Because the API input field needs to be compatible with other API services, some services might not use a version number in the request path.
Azure OpenAI
Documentation | https://learn.microsoft.com/azure/ai-services/openai/overview |
---|---|
Azure Services | https://azure.microsoft.com/en-us/products/ai-services/openai-service |
Microsoft has a special relationship with OpenAI, akin to an authorized dealer, allowing you to create OpenAI resources on the Azure cloud service platform and deploy models such as GPT-3.5
and GPT-4o
as needed.
Though their models are consistent, the network request formats and required configuration items differ.
After deploying OpenAI resources, you can find the required keys (either one of the two keys) and endpoint in the resource page under Resource Management
-> Keys and Endpoints
.
Input the corresponding values in the application settings.
You might be curious about the API version; here is the documentation. Generally, it doesn't need to be changed.
Adding Custom Models
Compared to OpenAI, Azure OpenAI requires manual deployment of the needed models.
In the application, Azure OpenAI does not provide pre-configured models. Simply filling in the keys and endpoints will not make the configuration effective; we also need to create custom models.
In the Azure OpenAI Studio, you can deploy specific models from the model library.
Each model has its own Id, for instance, the Id for GPT-3.5 Turbo is gpt-35-turbo.
When deploying a model, Azure requires you to provide a Deployment Name, which is crucial. When interacting with the service through the API, the model identifier is not the model ID but the deployment name you provided during the model deployment.
It is recommended to use the same name as the model ID during deployment to avoid confusion. However, exceptions may occur, such as when you deploy multiple GPT-3.5 Turbo
models with different versions. In that case, your deployment names might be gpt-35-turbo-1106
or gpt-35-turbo-0613
. When creating a custom model in the application, you can do it as shown below:
Gemini
Documentation | https://ai.google.dev/gemini-api/docs |
---|---|
API Token | https://ai.google.dev/gemini-api/docs/api-key |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Anthropic
Documentation | https://docs.anthropic.com/zh-CN/docs/intro-to-claude |
---|---|
API Token | https://console.anthropic.com/account/keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Moonshot
Documentation | https://platform.moonshot.cn/docs/api/chat#api-说明 |
---|---|
API Token | https://platform.moonshot.cn/console/api-keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Zhipu AI
ChatGLM is from here.
Documentation | https://open.bigmodel.cn/dev/howuse/introduction |
---|---|
API Token | https://open.bigmodel.cn/usercenter/apikeys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
01.AI
Documentation | https://platform.lingyiwanwu.com/docs |
---|---|
API Token | https://platform.lingyiwanwu.com/apikeys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
DeepSeek
Documentation | https://platform.deepseek.com/api-docs/zh-cn |
---|---|
API Token | https://platform.deepseek.com/api_keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Qwen
Documentation | https://help.aliyun.com/en/dashscope/developer-reference/activate-dashscope-and-create-an-api-key |
---|---|
API Token | https://dashscope.console.aliyun.com/apiKey |
Tongyi Qianwen is Alibaba's public large language model, hosted on Alibaba Cloud's Dashscope platform, requiring you to register and activate the Aliyun Dashscope service.
Dashscope itself has its own API interface and data structure, but conveniently, it also provides an OpenAI-compatible interface, which is what Rodel Agent uses directly here:
https://dashscope.aliyuncs.com/compatible-mode/v1
Therefore, it supports limited models. Refer to this document for specifics: OpenAI Interface Compatibility
ERNIE (Yiyan)
Documentation | https://cloud.baidu.com/doc/WENXINWORKSHOP/s/flfmc9do2 |
---|---|
API Token | https://console.bce.baidu.com/qianfan/ais/console/applicationConsole/application |
Wenxin Yiyan is a large language model provided by Baidu, hosted on Baidu Cloud's Qianfan platform. It has its own hosting steps, and different models may require separate service activations, as detailed in its documentation.
After creating an application on the Qianfan platform, fill in the API Key
in the API Key
field and the Secret Key
in the Secret Key
field.
Custom Models
Qianfan platform supports many common large language models. If the model you intend to use is not in the predefined list, confirm the model ID on the Qianfan platform's model list and create a custom model in the application.
For example, if you want to use the Meta-Llama-3-8B
model, follow these steps:
On the Qianfan platform's online services console, find
Meta-Llama-3-8B
. The service address suggests the model ID isllama_3_8b
.Create a custom model based on the model ID:
Tencent HunYuan
Documentation | https://cloud.tencent.com/document/product/1729/105701 |
---|---|
API Token | https://console.cloud.tencent.com/cam/capi |
HunYuan models are hosted on Tencent Cloud. You need to create a key in the API Key Management.
Note that due to security restrictions, the Secret Key
is only visible when creating the key.
iFLYTEK Spark
Documentation | https://www.xfyun.cn/doc/spark/Web.html |
---|---|
API Token | https://console.xfyun.cn/services/bm35 |
iFLYTEK Spark is a large language model launched by iFLYTEK. Before using this model family, you need to register an application on the iFLYTEK Open Platform and activate the corresponding model services.
Unactivated models cannot be used.
After registration, you can find the Service Interface Authentication Information
on the model page of iFLYTEK Spark
.
ByteDance Doubao
Documentation | https://www.volcengine.com/docs/82379/1263482 |
---|---|
API Token | https://console.volcengine.com/ark/region:ark+cn-beijing/apiKey |
The Doubao model is a large language model launched by ByteDance. Before using this model, you need to register an account on VolcEngine and then create a reasoning access point on the model reasoning page on Volcano方舟.
You need to manually deploy the model access point, and VolcEngine does not provide a preset reasoning access point.
After that, you need to create an API Key in API Key Management, and fill it in the settings of the application.
Custom Models
Please refer to Create a Reasoning Access Point to deploy the model first.
Then click Create Custom Model
in the Doubao setting section, paste the reasoning access point ID (ep-xxxxxxx) into the model ID, and give a descriptive name to the custom model. In this way, a custom model is created, and you can start chatting with this model.
Silicon Flow
Documentation | https://openrouter.ai/docs/quick-start |
---|---|
API Token | https://docs.siliconflow.cn/docs/4-api调用 |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
OpenRouter
Documentation | https://openrouter.ai/docs/quick-start |
---|---|
API Token | https://openrouter.ai/keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Together AI
Documentation | https://docs.together.ai/docs/quickstart |
---|---|
API Token | https://api.together.xyz/settings/api-keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Groq
Documentation | https://console.groq.com/docs/quickstart |
---|---|
API Token | https://console.groq.com/keys |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Perplexity
Documentation | https://docs.perplexity.ai/docs/getting-started |
---|---|
API Token | https://www.perplexity.ai/settings/api |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Mistral AI
Documentation | https://docs.mistral.ai/ |
---|---|
API Token | https://console.mistral.ai/api-keys/ |
Similar to OpenAI, after obtaining the access key, fill it in the Access Key
field.
Ollama
Documentation | https://github.com/ollama/ollama/blob/main/README.md#quickstart |
---|---|
Service Address | Default is http://localhost:11434/v1 |
API Token | Default is ollama |
Ollama is a popular local model hosting service. Its usage itself is extensive enough to warrant a lengthy document, which is not covered here. Please refer to their documentation.
Rodel Agent supports accessing Ollama via API. Ollama provides an OpenAI-compatible interface, allowing simple integration with Rodel Agent.
Since it runs locally, the application does not include any initial models. You need to create custom models based on the models already pulled to your local environment.
Creating Custom Models
Assuming you just installed Ollama and plan to run the Qwen 2
(Qwen 2) large model, follow these steps:
Pull the model to your local environment using Ollama (using qwen2 0.5b as an example)
shellollama pull qwen2:0.5b
Confirm the model has been downloaded locally by executing:
shellollama list
It should return content similar to the following:
shellNAME ID SIZE MODIFIED qwen2:0.5b 6f48b936a09f 352 MB 36 seconds ago
Create a custom model in the application based on the model name: