README.md

uni-api

English | Chinese

Introduction

For personal use, one/new-api is too complex with many commercial features that individuals don't need. If you don't want a complicated frontend interface and prefer support for more models, you can try uni-api. This is a project that unifies the management of large language model APIs, allowing you to call multiple backend services through a single unified API interface, converting them all to OpenAI format, and supporting load balancing. Currently supported backend services include: OpenAI, Anthropic, Gemini, Vertex, Cohere, Groq, Cloudflare, DeepBricks, OpenRouter, and more.

✨ Features

• No front-end, pure configuration file to configure API channels. You can run your own API station just by writing a file, and the documentation has a detailed configuration guide, beginner-friendly.
• Unified management of multiple backend services, supporting providers such as OpenAI, Deepseek, DeepBricks, OpenRouter, and other APIs in OpenAI format. Supports OpenAI Dalle-3 image generation.
• Simultaneously supports Anthropic, Gemini, Vertex AI, Cohere, Groq, Cloudflare. Vertex simultaneously supports Claude and Gemini API.
• Support OpenAI, Anthropic, Gemini, Vertex native tool use function calls.
• Support OpenAI, Anthropic, Gemini, Vertex native image recognition API.
• Support four types of load balancing.
  1. Supports channel-level weighted load balancing, allowing requests to be distributed according to different channel weights. It is not enabled by default and requires configuring channel weights.
  2. Support Vertex regional load balancing and high concurrency, which can increase Gemini and Claude concurrency by up to (number of APIs * number of regions) times. Automatically enabled without additional configuration.
  3. Except for Vertex region-level load balancing, all APIs support channel-level sequential load balancing, enhancing the immersive translation experience. It is not enabled by default and requires configuring SCHEDULING_ALGORITHM as round_robin.
  4. Support automatic API key-level round-robin load balancing for multiple API Keys in a single channel.
• Support automatic retry, when an API channel response fails, automatically retry the next API channel.
• Support fine-grained permission control. Support using wildcards to set specific models available for API key channels.
• Support rate limiting, you can set the maximum number of requests per minute as an integer, such as 2/min, 2 times per minute, 5/hour, 5 times per hour, 10/day, 10 times per day, 10/month, 10 times per month, 10/year, 10 times per year. Default is 60/min.
• Supports multiple standard OpenAI format interfaces: /v1/chat/completions, /v1/images/generations, /v1/audio/transcriptions, /v1/moderations, /v1/models.
• Support OpenAI moderation moral review, which can conduct moral reviews of user messages. If inappropriate messages are found, an error message will be returned. This reduces the risk of the backend API being banned by providers.

Usage method

To start uni-api, a configuration file must be used. There are two ways to start with a configuration file:

1. The first method is to use the CONFIG_URL environment variable to fill in the configuration file URL, which will be automatically downloaded when uni-api starts.
2. The second method is to mount a configuration file named api.yaml into the container.

Method 1: Mount the api.yaml configuration file to start uni-api

You must fill in the configuration file in advance to start uni-api, and you must use a configuration file named api.yaml to start uni-api, you can configure multiple models, each model can configure multiple backend services, and support load balancing. Below is an example of the minimum api.yaml configuration file that can be run:

providers:
  - provider: provider_name # Service provider name, such as openai, anthropic, gemini, openrouter, deepbricks, can be any name, required
    base_url: https://api.your.com/v1/chat/completions # Backend service API address, required
    api: sk-YgS6GTi0b4bEabc4C # Provider's API Key, required, automatically uses base_url and api to get all available models through the /v1/models endpoint.
  # Multiple providers can be configured here, each provider can configure multiple API Keys, and each API Key can configure multiple models.
api_keys:
  - api: sk-Pkj60Yf8JFWxfgRmXQFWyGtWUddGZnmi3KlvowmRWpWpQxx # API Key, user request uni-api requires API key, required
  # This API Key can use all models, that is, it can use all models in all channels set under providers, without needing to add available channels one by one.

Detailed advanced configuration of api.yaml:

providers:
  - provider: provider_name # Service provider name, such as openai, anthropic, gemini, openrouter, deepbricks, any name can be given, required
    base_url: https://api.your.com/v1/chat/completions # API address of the backend service, required
    api: sk-YgS6GTi0b4bEabc4C # Provider's API Key, required
    model: # Optional, if model is not configured, all available models will be automatically obtained via base_url and api through the /v1/models endpoint.
      - gpt-4o # Model name that can be used, required
      - claude-3-5-sonnet-20240620: claude-3-5-sonnet # Renamed model, claude-3-5-sonnet-20240620 is the provider's model name, claude-3-5-sonnet is the renamed name, you can use a simple name to replace the original complex name, optional
      - dall-e-3

  - provider: anthropic
    base_url: https://api.anthropic.com/v1/messages
    api: # Supports multiple API Keys, multiple keys automatically enable polling load balancing, at least one key, required
      - sk-ant-api03-bNnAOJyA-xQw_twAA
      - sk-ant-api02-bNnxxxx
    model:
      - claude-3-5-sonnet-20240620: claude-3-5-sonnet # Renamed model, claude-3-5-sonnet-20240620 is the provider's model name, claude-3-5-sonnet is the renamed name, you can use a simple name to replace the original complex name, optional
    tools: true # Whether to support tools, such as generating code, generating documents, etc., default is true, optional

  - provider: gemini
    base_url: https://generativelanguage.googleapis.com/v1beta # base_url supports v1beta/v1, only for Gemini models, required
    api: AIzaSyAN2k6IRdgw
    model:
      - gemini-1.5-pro
      - gemini-1.5-flash-exp-0827: gemini-1.5-flash # After renaming, the original model name gemini-1.5-flash-exp-0827 cannot be used, if you want to use the original name, you can add the original name in the model, just add the line below to use the original name
      - gemini-1.5-flash-exp-0827 # Add this line, both gemini-1.5-flash-exp-0827 and gemini-1.5-flash can be requested
    tools: true

  - provider: vertex
    project_id: gen-lang-client-xxxxxxxxxxxxxx # Description: Your Google Cloud project ID. Format: String, usually composed of lowercase letters, numbers, and hyphens. How to obtain: You can find your project ID in the project selector in Google Cloud Console.
    private_key: "-----BEGIN PRIVATE KEY-----\nxxxxx\n-----END PRIVATE" # Description: Private key of Google Cloud Vertex AI service account. Format: A JSON formatted string containing the private key information of the service account. How to obtain: Create a service account in Google Cloud Console, generate a JSON format key file, and then set its content as the value of this environment variable.
    client_email: xxxxxxxxxx@xxxxxxx.gserviceaccount.com # Description: Email address of Google Cloud Vertex AI service account. Format: Usually a string like "service-account-name@project-id.iam.gserviceaccount.com". How to obtain: Generated when creating a service account, can also be obtained by viewing the service account details in the "IAM and Admin" section of Google Cloud Console.
    model:
      - gemini-1.5-pro
      - gemini-1.5-flash
      - claude-3-5-sonnet@20240620: claude-3-5-sonnet
      - claude-3-opus@20240229: claude-3-opus
      - claude-3-sonnet@20240229: claude-3-sonnet
      - claude-3-haiku@20240307: claude-3-haiku
    tools: true
    notes: https://xxxxx.com/ # You can put the provider's website, notes, official documentation, optional

  - provider: cloudflare
    api: f42b3xxxxxxxxxxq4aoGAh # Cloudflare API Key, required
    cf_account_id: 8ec0xxxxxxxxxxxxe721 # Cloudflare Account ID, required
    model:
      - '@cf/meta/llama-3.1-8b-instruct': llama-3.1-8b # Renamed model, @cf/meta/llama-3.1-8b-instruct is the provider's original model name, the model name must be enclosed in quotes, otherwise yaml syntax error, llama-3.1-8b is the renamed name, you can use a simple name to replace the original complex name, optional
      - '@cf/meta/llama-3.1-8b-instruct' # The model name must be enclosed in quotes, otherwise yaml syntax error

  - provider: other-provider
    base_url: https://api.xxx.com/v1/messages
    api: sk-bNnAOJyA-xQw_twAA
    model:
      - causallm-35b-beta2ep-q6k: causallm-35b
      - anthropic/claude-3-5-sonnet
    tools: false
    engine: openrouter # Force to use a specific message format, currently supports gpt, claude, gemini, openrouter native format, optional

api_keys:
  - api: sk-KjjI60Yf0JFWxfgRmXqFWyGtWUd9GZnmi3KlvowmRWpWpQRo # API Key, users need an API key to use this service, required
    model: # The model that this API Key can use, required. Channel-level polling load balancing is enabled by default, each request model is requested in the order configured in the model. It is unrelated to the original channel order in providers. Therefore, you can set different request orders for each API key.
      - gpt-4o # Model name that can be used, can use the gpt-4o model provided by all providers
      - claude-3-5-sonnet # Model name that can be used, can use the claude-3-5-sonnet model provided by all providers
      - gemini/* # Model name that can be used, can only use all models provided by the provider named gemini, where gemini is the provider name, * represents all models
    role: admin

  - api: sk-pkhf60Yf0JGyJxgRmXqFQyTgWUd9GZnmi3KlvowmRWpWqrhy
    model:
      - anthropic/claude-3-5-sonnet # Model name that can be used, can only use the claude-3-5-sonnet model provided by the provider named anthropic. The claude-3-5-sonnet model from other providers cannot be used. This way of writing will not match the model named anthropic/claude-3-5-sonnet provided by other-provider.
      - <anthropic/claude-3-5-sonnet> # By adding angle brackets on both sides of the model name, it will not look for the claude-3-5-sonnet model under the channel named anthropic, but will use the entire anthropic/claude-3-5-sonnet as the model name. This way of writing can match the model named anthropic/claude-3-5-sonnet provided by other-provider. But it will not match the claude-3-5-sonnet model under anthropic.
      - openai-test/text-moderation-latest # When message moderation is enabled, the text-moderation-latest model under the channel named openai-test can be used for moral review.
    preferences:
      SCHEDULING_ALGORITHM: fixed_priority # When SCHEDULING_ALGORITHM is fixed_priority, use fixed priority scheduling, always execute the first channel with a request. Enabled by default, the default value of SCHEDULING_ALGORITHM is fixed_priority. Optional values for SCHEDULING_ALGORITHM are: fixed_priority, round_robin, weighted_round_robin, lottery, random.
      # When SCHEDULING_ALGORITHM is random, use random polling load balancing, randomly request the channel with the requested model.
      # When SCHEDULING_ALGORITHM is round_robin, use polling load balancing, request the channel of the user's model in order.
      AUTO_RETRY: true # Whether to automatically retry, automatically retry the next provider, true for automatic retry, false for no automatic retry, default is true
      RATE_LIMIT: 2/min # Supports rate limiting, maximum number of requests per minute, can be set to an integer, such as 2/min, 2 times per minute, 5/hour, 5 times per hour, 10/day, 10 times per day, 10/month, 10 times per month, 10/year, 10 times per year. Default is 60/min, optional
      ENABLE_MODERATION: true # Whether to enable message moderation, true for enable, false for disable, default is false, when enabled, messages will be morally reviewed, if inappropriate messages are found, an error message will be returned.

  # Channel-level weighted load balancing configuration example
  - api: sk-KjjI60Yd0JFWtxxxxxxxxxxxxxxwmRWpWpQRo
    model:
      - gcp1/*: 5 # The number after the colon is the weight, weight only supports positive integers.
      - gcp2/*: 3 # The size of the number represents the weight, the larger the number, the greater the probability of the request.
      - gcp3/*: 2 # In this example, there are a total of 10 weights across all channels, and 5 out of 10 requests will request the gcp1/* model, 2 requests will request the gcp2/* model, and 3 requests will request the gcp3/* model.

    preferences:
      SCHEDULING_ALGORITHM: weighted_round_robin # Only when SCHEDULING_ALGORITHM is weighted_round_robin and the channels above have weights, requests will be made in the weighted order. Use weighted polling load balancing, request the channel of the model with the request in weight order. When SCHEDULING_ALGORITHM is lottery, use lottery polling load balancing, randomly request the channel of the model with the request according to weight. Channels without weights automatically fall back to round_robin polling load balancing.
      AUTO_RETRY: true

Mount the configuration file and start the uni-api docker container:

docker run --user root -p 8001:8000 --name uni-api -dit \
-v ./api.yaml:/home/api.yaml \
yym68686/uni-api:latest

Method two: Start uni-api using the CONFIG_URL environment variable

After writing the configuration file according to method one, upload it to the cloud disk, get the file's direct link, and then use the CONFIG_URL environment variable to start the uni-api docker container:

docker run --user root -p 8001:8000 --name uni-api -dit \
-e CONFIG_URL=http://file_url/api.yaml \
yym68686/uni-api:latest

Environment variable

• CONFIG_URL: The download address of the configuration file, which can be a local file or a remote file, optional
• TIMEOUT: Request timeout, default is 100 seconds. The timeout can control the time needed to switch to the next channel when one channel does not respond. Optional
• DISABLE_DATABASE: Whether to disable the database, default is false, optional

Vercel remote deployment

After clicking the one-click deployment button, set the environment variable CONFIG_URL to the direct link of the configuration file, and set DISABLE_DATABASE to true, then click Create to create the project.

Serv00 remote deployment

First, log in to the panel, in Additional services click on the tab Run your own applications to enable the option to run your own programs, then go to the panel Port reservation to randomly open a port.

If you don't have your own domain name, go to the panel WWW websites and delete the default domain name provided. Then create a new domain with the Domain being the one you just deleted. After clicking Advanced settings, set the Website type to Proxy domain, and the Proxy port should point to the port you just opened. Do not select Use HTTPS.

ssh login to the serv00 server, execute the following command:

git clone --depth 1 -b main --quiet https://github.com/yym68686/uni-api.git
cd uni-api
python -m venv uni-api
tmux new -s uni-api
source uni-api/bin/activate
export CFLAGS="-I/usr/local/include"
export CXXFLAGS="-I/usr/local/include"
export CC=gcc
export CXX=g++
export MAX_CONCURRENCY=1
export CPUCOUNT=1
export MAKEFLAGS="-j1"
CMAKE_BUILD_PARALLEL_LEVEL=1 cpuset -l 0 pip install -vv -r requirements.txt
cpuset -l 0 pip install -r -vv requirements.txt

ctrl+b d to exit tmux, wait a few hours for the installation to complete, and after the installation is complete, execute the following command:

tmux attach -t uni-api
source uni-api/bin/activate
export CONFIG_URL=http://file_url/api.yaml
export DISABLE_DATABASE=true
# Modify the port, xxx is the port, modify it yourself, corresponding to the port opened in the panel Port reservation
sed -i '' 's/port=8000/port=xxx/' main.py
sed -i '' 's/reload=True/reload=False/' main.py
python main.py

Use ctrl+b d to exit tmux, allowing the program to run in the background. At this point, you can use uni-api in other chat clients. curl test script:

curl -X POST https://xxx.serv00.net/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-xxx' \
-d '{"model": "gpt-4o","messages": [{"role": "user","content": "Hello"}]}'

Reference document:

https://docs.serv00.com/Python/

https://linux.do/t/topic/201181

https://linux.do/t/topic/218738

Docker local deployment

Start the container

docker run --user root -p 8001:8000 --name uni-api -dit \
-e CONFIG_URL=http://file_url/api.yaml \ # If the local configuration file has already been mounted, there is no need to set CONFIG_URL
-v ./api.yaml:/home/api.yaml \ # If CONFIG_URL is already set, there is no need to mount the configuration file
-v ./uniapi_db:/home/data \ # If you do not want to save statistical data, there is no need to mount this folder
yym68686/uni-api:latest

Or if you want to use Docker Compose, here is a docker-compose.yml example:

services:
  uni-api:
    container_name: uni-api
    image: yym68686/uni-api:latest
    environment:
      - CONFIG_URL=http://file_url/api.yaml # If a local configuration file is already mounted, there is no need to set CONFIG_URL
    ports:
      - 8001:8000
    volumes:
      - ./api.yaml:/home/api.yaml # If CONFIG_URL is already set, there is no need to mount the configuration file
      - ./uniapi_db:/home/data # If you do not want to save statistical data, there is no need to mount this folder

CONFIG_URL is the URL of the remote configuration file that can be automatically downloaded. For example, if you are not comfortable modifying the configuration file on a certain platform, you can upload the configuration file to a hosting service and provide a direct link to uni-api to download, which is the CONFIG_URL. If you are using a local mounted configuration file, there is no need to set CONFIG_URL. CONFIG_URL is used when it is not convenient to mount the configuration file.

Run Docker Compose container in the background

docker-compose pull
docker-compose up -d

Docker build

docker build --no-cache -t uni-api:latest -f Dockerfile --platform linux/amd64 .
docker tag uni-api:latest yym68686/uni-api:latest
docker push yym68686/uni-api:latest

One-Click Restart Docker Image

set -eu
docker pull yym68686/uni-api:latest
docker rm -f uni-api
docker run --user root -p 8001:8000 -dit --name uni-api \
-e CONFIG_URL=http://file_url/api.yaml \
-v ./api.yaml:/home/api.yaml \
-v ./uniapi_db:/home/data \
yym68686/uni-api:latest
docker logs -f uni-api

RESTful curl test

curl -X POST http://127.0.0.1:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${API}" \
-d '{"model": "gpt-4o","messages": [{"role": "user", "content": "Hello"}],"stream": true}'

⭐ Star History