Azure OpenAI Proxy
Introduction
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Azure OpenAI Proxy is a proxy for Azure OpenAI API that can convert an OpenAI request to an Azure OpenAI request. It is designed to use as a backend for various open source ChatGPT web project. It also supports being used as a simple OpenAI API proxy to solve the problem of OpenAI API being restricted in some regions.
Highlights:
- 🌐 Supports proxying all Azure OpenAI APIs
- 🧠 Supports proxying all Azure OpenAI models and custom fine-tuned models
- 🗺️ Supports custom mapping between Azure deployment names and OpenAI models
- 🔄 Supports both reverse proxy and forward proxy usage
- 👍 Support mocking of OpenAI APIs that are not supported by Azure.
Supported APIs
The latest version of the Azure OpenAI service currently supports the following 3 APIs:
Path |
Status |
/v1/chat/completions |
✅ |
/v1/completions |
✅ |
/v1/embeddings |
✅ |
Other APIs not supported by Azure will be returned in a mock format (such as OPTIONS requests initiated by browsers). If you find your project need additional OpenAI-supported APIs, feel free to submit a PR.
Usage
1. Used as reverse proxy (i.e. an OpenAI API gateway)
Environment Variables
Parameters |
Description |
Default Value |
AZURE_OPENAI_PROXY_ADDRESS |
Service listening address |
0.0.0.0:8080 |
AZURE_OPENAI_PROXY_MODE |
Proxy mode, can be either "azure" or "openai". |
azure |
AZURE_OPENAI_ENDPOINT |
Azure OpenAI Endpoint, usually looks like https://{custom}.openai.azure.com. Required. |
|
AZURE_OPENAI_APIVERSION |
Azure OpenAI API version. Default is 2023-03-15-preview. |
2023-03-15-preview |
AZURE_OPENAI_MODEL_MAPPER |
A comma-separated list of model=deployment pairs. Maps model names to deployment names. For example, gpt-3.5-turbo=gpt-35-turbo , gpt-3.5-turbo-0301=gpt-35-turbo-0301 . If there is no match, the proxy will pass model as deployment name directly (in fact, most Azure model names are same with OpenAI). |
gpt-3.5-turbo=gpt-35-turbo
gpt-3.5-turbo-0301=gpt-35-turbo-0301 |
AZURE_OPENAI_TOKEN |
Azure OpenAI API Token. If this environment variable is set, the token in the request header will be ignored. |
"" |
Use in command line
curl https://{your-custom-domain}/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer {your azure api key}" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hello!"}]
}'
2. Used as forward proxy (i.e. an HTTP proxy)
When accessing Azure OpenAI API through HTTP, it can be used directly as a proxy, but this tool does not have built-in HTTPS support, so you need an HTTPS proxy such as Nginx to support accessing HTTPS version of OpenAI API.
Assuming that the proxy domain you configured is https://{your-domain}.com
, you can execute the following commands in the terminal to use the https proxy:
export https_proxy=https://{your-domain}.com
curl https://api.openai.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer {your azure api key}" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hello!"}]
}'
Or configure it as an HTTP proxy in other open source Web ChatGPT projects:
export HTTPS_PROXY=https://{your-domain}.com
Deploy
Deploying through Docker
docker pull ishadows/azure-openai-proxy:latest
docker run -d -p 8080:8080 --name=azure-openai-proxy \
--env AZURE_OPENAI_ENDPOINT={your azure endpoint} \
--env AZURE_OPENAI_MODEL_MAPPER={your custom model mapper ,like: gpt-3.5-turbo=gpt-35-turbo,gpt-3.5-turbo-0301=gpt-35-turbo-0301} \
ishadows/azure-openai-proxy:latest
Calling
curl https://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer {your azure api key}" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hello!"}]
}'
Model Mapping Mechanism
There are a series of rules for model mapping pre-defined in AZURE_OPENAI_MODEL_MAPPER
, and the default configuration basically satisfies the mapping of all Azure models. The rules include:
gpt-3.5-turbo
-> gpt-35-turbo
gpt-3.5-turbo-0301
-> gpt-35-turbo-0301
- A mapping mechanism that pass model name directly as fallback.
For custom fine-tuned models, the model name can be passed directly. For models with deployment names different from the model names, custom mapping relationships can be defined, such as:
Model Name |
Deployment Name |
gpt-3.5-turbo |
gpt-35-turbo-upgrade |
gpt-3.5-turbo-0301 |
gpt-35-turbo-0301-fine-tuned |
License
MIT
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