uni api
0.0.104
Bahasa Inggris | Cina
Untuk penggunaan pribadi, One/New-API terlalu rumit dengan banyak fitur komersial yang tidak dibutuhkan individu. Jika Anda tidak menginginkan antarmuka frontend yang rumit dan lebih suka dukungan untuk lebih banyak model, Anda dapat mencoba uni-API. Ini adalah proyek yang menyatukan pengelolaan API model bahasa besar, yang memungkinkan Anda untuk menghubungi beberapa layanan backend melalui satu antarmuka API terpadu tunggal, mengonversi semuanya menjadi format Openai, dan mendukung penyeimbangan beban. Layanan backend yang saat ini didukung meliputi: OpenAI, Anthropic, Gemini, Vertex, Cohere, Groq, Cloudflare, OpenRouter, dan banyak lagi.
SCHEDULING_ALGORITHM sebagai round_robin ./v1/chat/completions , /v1/images/generations , /v1/audio/transcriptions , /v1/moderations , /v1/models .Untuk memulai UNI-API, file konfigurasi harus digunakan. Ada dua cara untuk memulai dengan file konfigurasi:
CONFIG_URL untuk mengisi URL file konfigurasi, yang akan diunduh secara otomatis ketika uni-API dimulai.api.yaml ke dalam wadah.api.yaml untuk memulai UNIPI Anda harus mengisi file konfigurasi terlebih dahulu untuk memulai uni-api , dan Anda harus menggunakan file konfigurasi bernama api.yaml untuk memulai uni-api , Anda dapat mengonfigurasi beberapa model, setiap model dapat mengonfigurasi beberapa layanan backend, dan mendukung penyeimbangan beban. Di bawah ini adalah contoh file konfigurasi api.yaml minimum yang dapat dijalankan:
providers :
- provider : provider_name # Service provider name, such as openai, anthropic, gemini, openrouter, 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. Konfigurasi Lanjutan api.yaml yang terperinci:
providers :
- provider : provider_name # Service provider name, such as openai, anthropic, gemini, openrouter, 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
model : # Optional, if model is not configured, all available models will be automatically obtained through base_url and api via the /v1/models endpoint.
- gpt-4o # Usable model name, required
- claude-3-5-sonnet-20240620 : claude-3-5-sonnet # Rename 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 # Rename 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 model use, required
api : # Supports multiple API Keys, multiple keys automatically enable polling load balancing, at least one key, required
- AIzaSyAN2k6IRdgw123
- AIzaSyAN2k6IRdgw456
- AIzaSyAN2k6IRdgw789
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
preferences :
api_key_rate_limit : 15/min # Each API Key can request up to 15 times per minute, optional. The default is 999999/min. Supports multiple frequency constraints: 15/min,10/day
# api_key_rate_limit: # You can set different frequency limits for each model
# gemini-1.5-flash: 15/min,1500/day
# gemini-1.5-pro: 2/min,50/day
# default: 4/min # If the model does not set the frequency limit, use the frequency limit of default
api_key_cooldown_period : 60 # Each API Key will be cooled down for 60 seconds after encountering a 429 error. Optional, the default is 0 seconds. When set to 0, the cooling mechanism is not enabled. When there are multiple API keys, the cooling mechanism will take effect.
api_key_schedule_algorithm : round_robin # Set the request order of multiple API Keys, optional. The default is round_robin, and the optional values are: round_robin, random. It will take effect when there are multiple API keys. round_robin is polling load balancing, and random is random load balancing.
model_timeout : # Model timeout, in seconds, default 100 seconds, optional
gemini-1.5-pro : 10 # Model gemini-1.5-pro timeout is 10 seconds
gemini-1.5-flash : 10 # Model gemini-1.5-flash timeout is 10 seconds
default : 10 # Model does not have a timeout set, use the default timeout of 10 seconds, when requesting a model not in model_timeout, the timeout is also 10 seconds, if default is not set, uni-api will use the default timeout set by the environment variable TIMEOUT, the default timeout is 100 seconds
proxy : socks5://[username]:[password]@[ip]:[port] # Proxy address, optional. Supports socks5 and http proxies, default is not used.
- 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 of the Google Cloud Console.
private_key : " -----BEGIN PRIVATE KEY----- n xxxxx n -----END PRIVATE " # Description: Private key for 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 formatted key file, and then set its content as the value of this environment variable.
client_email : [email protected] # Description: Email address of the Google Cloud Vertex AI service account. Format: Usually a string like "[email protected]". How to obtain: Generated when creating a service account, or you can view the service account details in the "IAM and Admin" section of the Google Cloud Console.
model :
- gemini-1.5-pro
- gemini-1.5-flash
- gemini-1.5-pro : gemini-1.5-pro-search # Only supports using the gemini-1.5-pro-search model to request uni-api when using the Vertex Gemini API, to automatically use the Google official search tool.
- 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 # Rename model, @cf/meta/llama-3.1-8b-instruct is the provider's original 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 ' # 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 the use of a specific message format, currently supports gpt, claude, gemini, openrouter native format, optional
api_keys :
- api : sk-KjjI60Yf0JFWxfgRmXqFWyGtWUd9GZnmi3KlvowmRWpWpQRo # API Key, required for users to use this service
model : # Models that can be used by this API Key, required. Default channel-level polling load balancing is enabled, and each request model is requested in sequence according to the model configuration. It is not related to the original channel order in providers. Therefore, you can set different request sequences for each API key.
- gpt-4o # Usable model name, can use all gpt-4o models provided by providers
- claude-3-5-sonnet # Usable model name, can use all claude-3-5-sonnet models provided by providers
- gemini/* # Usable model name, can only use all models provided by providers named gemini, where gemini is the provider name, * represents all models
role : admin
- api : sk-pkhf60Yf0JGyJxgRmXqFQyTgWUd9GZnmi3KlvowmRWpWqrhy
model :
- anthropic/claude-3-5-sonnet # Usable model name, can only use the claude-3-5-sonnet model provided by the provider named anthropic. Models with the same name from other providers cannot be used. This syntax 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 search for the claude-3-5-sonnet model under the channel named anthropic, but will take the entire anthropic/claude-3-5-sonnet as the model name. This syntax 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 moderation.
- sk-KjjI60Yd0JFWtxxxxxxxxxxxxxxwmRWpWpQRo/* # Support using other API keys as channels
preferences :
SCHEDULING_ALGORITHM : fixed_priority # When SCHEDULING_ALGORITHM is fixed_priority, use fixed priority scheduling, always execute the channel of the first model with a request. Default is enabled, SCHEDULING_ALGORITHM default value is fixed_priority. SCHEDULING_ALGORITHM optional values are: fixed_priority, round_robin, weighted_round_robin, lottery, random.
# When SCHEDULING_ALGORITHM is random, use random polling load balancing, randomly request the channel of the model with a request.
# When SCHEDULING_ALGORITHM is round_robin, use polling load balancing, request the channel of the model used by the user 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. Also supports setting a number, indicating the number of retries.
rate_limit : 15/min # Supports rate limiting, each API Key can request up to 15 times per minute, optional. The default is 999999/min. Supports multiple frequency constraints: 15/min,10/day
# rate_limit: # You can set different frequency limits for each model
# gemini-1.5-flash: 15/min,1500/day
# gemini-1.5-pro: 2/min,50/day
# default: 4/min # If the model does not set the frequency limit, use the frequency limit of default
ENABLE_MODERATION : true # Whether to enable message moderation, true for enable, false for disable, default is false, when enabled, it will moderate the user's message, 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 for all channels, and 10 requests will have 5 requests for the gcp1/* model, 2 requests for the gcp2/* model, and 3 requests for the gcp3/* model.
preferences :
SCHEDULING_ALGORITHM : weighted_round_robin # Only when SCHEDULING_ALGORITHM is weighted_round_robin and the above channel has weights, it will request according to the weighted order. Use weighted polling load balancing, request the channel of the model with a request according to the weight order. When SCHEDULING_ALGORITHM is lottery, use lottery polling load balancing, request the channel of the model with a request according to the weight randomly. Channels without weights automatically fall back to round_robin polling load balancing.
AUTO_RETRY : true
preferences : # Global configuration
model_timeout : # Model timeout, in seconds, default 100 seconds, optional
gpt-4o : 10 # Model gpt-4o timeout is 10 seconds, gpt-4o is the model name, when requesting models like gpt-4o-2024-08-06, the timeout is also 10 seconds
claude-3-5-sonnet : 10 # Model claude-3-5-sonnet timeout is 10 seconds, when requesting models like claude-3-5-sonnet-20240620, the timeout is also 10 seconds
default : 10 # Model does not have a timeout set, use the default timeout of 10 seconds, when requesting a model not in model_timeout, the default timeout is 10 seconds, if default is not set, uni-api will use the default timeout set by the environment variable TIMEOUT, the default timeout is 100 seconds
o1-mini : 30 # Model o1-mini timeout is 30 seconds, when requesting models starting with o1-mini, the timeout is 30 seconds
o1-preview : 100 # Model o1-preview timeout is 100 seconds, when requesting models starting with o1-preview, the timeout is 100 seconds
cooldown_period : 300 # Channel cooldown time, in seconds, default 300 seconds, optional. When a model request fails, the channel will be automatically excluded and cooled down for a period of time, and will not request the channel again. After the cooldown time ends, the model will be automatically restored until the request fails again, and it will be cooled down again. When cooldown_period is set to 0, the cooling mechanism is not enabled.
error_triggers : # Error triggers, when the message returned by the model contains any of the strings in the error_triggers, the channel will return an error. Optional
- The bot's usage is covered by the developer
- process this request due to overload or policyPasang file konfigurasi dan mulai wadah Docker UNI-API:
docker run --user root -p 8001:8000 --name uni-api -dit
-v ./api.yaml:/home/api.yaml
yym68686/uni-api:latestCONFIG_URL Setelah menulis file konfigurasi sesuai dengan metode satu, unggah ke disk cloud, dapatkan tautan langsung file, dan kemudian gunakan variabel lingkungan CONFIG_URL untuk memulai wadah Docker uni-API:
docker run --user root -p 8001:8000 --name uni-api -dit
-e CONFIG_URL=http://file_url/api.yaml
yym68686/uni-api:latest Setelah mengklik tombol One-Click Deploy di atas, atur variabel lingkungan CONFIG_URL ke tautan langsung dari file konfigurasi, DISABLE_DATABASE ke true, dan kemudian klik Buat untuk membuat proyek. Setelah penyebaran, Anda perlu mengatur fungsi maksimum fungsi secara manual ke 60 detik di panel Proyek Vercel di bawah Pengaturan -> Fungsi, dan kemudian klik menu penyebaran dan klik Redeploy ke Redeploy, yang akan mengatur batas waktu hingga 60 detik. Jika Anda tidak menggunakan kembali, batas waktu default akan tetap pada 10 detik asli. Perhatikan bahwa Anda tidak boleh menghapus proyek Vercel dan membuatnya kembali; Sebagai gantinya, klik Redeploy di menu penyebaran dalam proyek Vercel yang saat ini digunakan untuk membuat fungsi modifikasi durasi maks berlaku.
Dalam rilis gudang, temukan versi terbaru dari file biner yang sesuai, misalnya, file bernama Uni-API-Linux-X86_64-0.0.99.pex. Unduh file biner di server dan jalankan:
wget https://github.com/yym68686/uni-api/releases/download/v0.0.99/uni-api-linux-x86_64-0.0.99.pex
chmod +x uni-api-linux-x86_64-0.0.99.pex
./uni-api-linux-x86_64-0.0.99.pexPertama, masuk ke panel, dalam layanan tambahan klik pada tab Jalankan aplikasi Anda sendiri untuk mengaktifkan opsi untuk menjalankan program Anda sendiri, lalu buka reservasi port panel untuk membuka port secara acak.
Jika Anda tidak memiliki nama domain sendiri, buka situs web panel www dan hapus nama domain default yang disediakan. Kemudian buat domain baru dengan domain menjadi yang baru saja Anda hapus. Setelah mengklik pengaturan lanjutan, atur jenis situs web ke domain proxy, dan port proxy harus menunjuk ke port yang baru saja Anda buka. Jangan pilih Gunakan https.
Login SSH ke server SERV00, jalankan perintah berikut:
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.txtCtrl+BD untuk keluar dari Tmux, tunggu beberapa jam untuk diselesaikan instalasi, dan setelah instalasi selesai, jalankan perintah berikut:
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.pyGunakan Ctrl+BD untuk keluar dari Tmux, memungkinkan program berjalan di latar belakang. Pada titik ini, Anda dapat menggunakan UNI-API di klien obrolan lain. skrip tes keriting:
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"}]} 'Dokumen referensi:
https://docs.serv00.com/python/
https://linux.do/t/topic/201181
https://linux.do/t/topic/218738
Mulai wadahnya
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:latestAtau jika Anda ingin menggunakan Docker Compose, berikut adalah contoh Docker-compose.yml:
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 folderConfig_url adalah URL dari file konfigurasi jarak jauh yang dapat diunduh secara otomatis. Misalnya, jika Anda tidak nyaman memodifikasi file konfigurasi pada platform tertentu, Anda dapat mengunggah file konfigurasi ke layanan hosting dan memberikan tautan langsung ke uni-API untuk diunduh, yang merupakan config_url. Jika Anda menggunakan file konfigurasi yang dipasang lokal, tidak perlu mengatur config_url. Config_url digunakan saat tidak nyaman untuk memasang file konfigurasi.
Jalankan Docker Compose Container di latar belakang
docker-compose pull
docker-compose up -dDocker 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:latestGambar restart docker satu klik
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-apiTes keriting yang tenang
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} 'Kemasan Pex Linux:
VERSION= $( cat VERSION )
pex -D . -r requirements.txt
-c uvicorn
--inject-args ' main:app --host 0.0.0.0 --port 8000 '
--platform linux_x86_64-cp-3.10.12-cp310
--interpreter-constraint ' ==3.10.* '
--no-strip-pex-env
-o uni-api-linux-x86_64- ${VERSION} .pexKemasan MacOS:
VERSION= $( cat VERSION )
pex -r requirements.txt
-c uvicorn
--inject-args ' main:app --host 0.0.0.0 --port 8000 '
-o uni-api-macos-arm64- ${VERSION} .pexKami berterima kasih kepada sponsor berikut atas dukungan mereka:
Jika Anda ingin mendukung proyek kami, Anda dapat mensponsori kami dengan cara berikut:
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Terima kasih atas dukungan Anda!
Error processing request or performing moral check: 404: No matching model found selalu muncul?Pengaturan enable_moderation ke false akan memperbaiki masalah ini. Ketika enable_moderation benar, API harus dapat menggunakan model teks-tertutup teks, dan jika Anda belum menyediakan teks-moderasi-tertutup dalam pengaturan model penyedia, kesalahan akan terjadi yang menunjukkan bahwa model tidak dapat ditemukan.
Langsung atur urutan saluran di API_Keys. Tidak ada pengaturan lain yang diperlukan. Contoh file konfigurasi:
providers :
- provider : ai1
base_url : https://xxx/v1/chat/completions
api : sk-xxx
- provider : ai2
base_url : https://xxx/v1/chat/completions
api : sk-xxx
api_keys :
- api : sk-1234
model :
- ai2/*
- ai1/*Dengan cara ini, minta AI2 terlebih dahulu, dan jika gagal, minta AI1.
Semua algoritma penjadwalan perlu diaktifkan dengan mengatur api_keys. (API) .preferences.scheduling_algorithm dalam file konfigurasi ke salah satu nilai: fixed_priority, weighted_round_robin, lotre, acak, round_robin.
Fixed_priority: Penjadwalan prioritas tetap. Semua permintaan selalu dijalankan oleh saluran model yang pertama kali memiliki permintaan pengguna. Dalam hal kesalahan, itu akan beralih ke saluran berikutnya. Ini adalah algoritma penjadwalan default.
Weighted_round_robin: Balancing beban round-robin tertimbang, meminta saluran dengan model yang diminta pengguna sesuai dengan pesanan berat yang ditetapkan dalam file konfigurasi API_Keys. (API) .model.
Lotere: Draw round-robin load balancing, minta secara acak Saluran model dengan permintaan pengguna sesuai dengan set bobot dalam file konfigurasi API_Keys. (API) .model.
Round_robin: Penyeimbangan beban round-robin, meminta saluran yang memiliki model yang diminta oleh pengguna sesuai dengan urutan konfigurasi dalam file konfigurasi API_Keys. (API) .model. Anda dapat memeriksa pertanyaan sebelumnya tentang cara mengatur prioritas saluran.
Kecuali untuk beberapa saluran khusus yang ditunjukkan dalam konfigurasi lanjutan, semua penyedia format OpenAI perlu mengisi base_url sepenuhnya, yang berarti base_url harus diakhiri dengan/v1/obrolan/penyelesaian. Jika Anda menggunakan model GitHub, base_url harus diisi sebagai https://models.inference.ai.azure.com/chat/completions, bukan URL Azure.
Pengaturan batas waktu level saluran memiliki prioritas yang lebih tinggi daripada pengaturan batas waktu model global. Urutan prioritas adalah: Pengaturan batas waktu model level saluran> Pengaturan batas waktu default level saluran> Pengaturan batas waktu model global> Pengaturan batas waktu default global> Timeout variabel lingkungan.
Dengan menyesuaikan waktu batas waktu model, Anda dapat menghindari kesalahan beberapa saluran waktu. Jika Anda menghadapi kesalahan {'error': '500', 'details': 'fetch_response_stream Read Response Timeout'} , silakan cobalah untuk meningkatkan waktu batas waktu model.
Jika Anda ingin menetapkan batas frekuensi yang sama untuk empat model Gemini-1..5-pro-latest, Gemini-1.5-Pro, Gemini-1.5-Pro-001, Gemini-1.5-Pro-002 secara bersamaan, Anda dapat mengaturnya seperti ini:
api_key_rate_limit :
gemini-1.5-pro : 1000/minIni akan cocok dengan semua model yang berisi string Gemini-1.5-Pro. Batas frekuensi untuk empat model ini, Gemini-1.5-pro-latest, Gemini-1.5-Pro, Gemini-1.5-Pro-001, Gemini-1.5-Pro-002, semuanya akan ditetapkan ke 1000/mnt. Logika untuk mengkonfigurasi bidang API_KEY_RATE_LIMIT adalah sebagai berikut, berikut adalah file konfigurasi sampel:
api_key_rate_limit :
gemini-1.5-pro : 1000/min
gemini-1.5-pro-002 : 500/minPada saat ini, jika ada permintaan menggunakan model Gemini-1..5-Pro-002.
Pertama, UNI-API akan berusaha untuk mencocokkan model di API_KEY_RATE_LIMIT. Jika batas tarif untuk Gemini-1..5-Pro-002 ditetapkan, maka batas tarif untuk Gemini-1.5-Pro-002 adalah 500/menit. If the requested model at this time is not gemini-1.5-pro-002, but gemini-1.5-pro-latest, since the api_key_rate_limit does not have a rate limit set for gemini-1.5-pro-latest, it will look for any model with the same prefix as gemini-1.5-pro-latest that has been set, thus the rate limit for gemini-1.5-pro-latest will be set to 1000/mnt.
