計費

向內部團隊、外部客戶依其用量收費

🚨 必要條件

• 設定 Lago，用於依用量計費。我們建議遵循 他們的 Stripe 教學

步驟：

• 將 proxy 連接到 Lago
• 設定您要計費的 id（客戶、內部使用者、團隊）
• 開始！

快速開始

向內部團隊依其用量收費

1. 將 proxy 連接到 Lago

在您的 proxy config.yaml 中將 'lago' 設為 callback

model_list:
  - model_name: fake-openai-endpoint
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

litellm_settings:
  callbacks: ["lago"] # 👈 KEY CHANGE

general_settings:
  master_key: sk-1234

將您的 Lago 金鑰新增到環境變數

export LAGO_API_BASE="http://localhost:3000" # self-host - https://docs.getlago.com/guide/self-hosted/docker#run-the-app
export LAGO_API_KEY="3e29d607-de54-49aa-a019-ecf585729070" # Get key - https://docs.getlago.com/guide/self-hosted/docker#find-your-api-key
export LAGO_API_EVENT_CODE="openai_tokens" # name of lago billing code
export LAGO_API_CHARGE_BY="team_id" # 👈 Charges 'team_id' attached to proxy key

啟動 proxy

litellm --config /path/to/config.yaml

2. 為內部團隊建立 Key

curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{"team_id": "my-unique-id"}' # 👈 Internal Team's ID

回應物件：

{
  "key": "sk-tXL0wt5-lOOVK9sfY2UacA",
}

3. 開始計費！

Curl

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-tXL0wt5-lOOVK9sfY2UacA' \ # 👈 Team's Key
--data ' {
      "model": "fake-openai-endpoint",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
    }
'

OpenAI Python SDK

import openai
client = openai.OpenAI(
    api_key="sk-tXL0wt5-lOOVK9sfY2UacA", # 👈 Team's Key
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-4o", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

Langchain

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
import os 

os.environ["OPENAI_API_KEY"] = "sk-tXL0wt5-lOOVK9sfY2UacA" # 👈 Team's Key

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000",
    model = "gpt-4o",
    temperature=0.1,
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

在 Lago 上查看結果

進階 - Lago 記錄物件

這就是 LiteLLM 會記錄到 Lagos 的內容

{
    "event": {
      "transaction_id": "<generated_unique_id>",
      "external_customer_id": <selected_id>, # either 'end_user_id', 'user_id', or 'team_id'. Default 'end_user_id'. 
      "code": os.getenv("LAGO_API_EVENT_CODE"), 
      "properties": {
          "input_tokens": <number>,
          "output_tokens": <number>,
          "model": <string>,
          "response_cost": <number>, # 👈 LITELLM CALCULATED RESPONSE COST - https://github.com/BerriAI/litellm/blob/d43f75150a65f91f60dc2c0c9462ce3ffc713c1f/litellm/utils.py#L1473
      }
    }
}

進階 - 向客戶、內部使用者收費

適用於：

• 客戶（透過 /chat/completion 呼叫中的 'user' 參數傳入的 id）= 'end_user_id'
• 內部使用者（在 建立金鑰 時設定的 id）= 'user_id'
• 團隊（在 建立金鑰 時設定的 id）= 'team_id'

客戶計費

1. 將 'LAGO_API_CHARGE_BY' 設為 'end_user_id'

export LAGO_API_CHARGE_BY="end_user_id"

2. 測試看看！

Curl

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "gpt-4o",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
      "user": "my_customer_id" # 👈 whatever your customer id is
    }
'

OpenAI Python SDK

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-4o", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
], user="my_customer_id") # 👈 whatever your customer id is

print(response)

Langchain

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
import os 

os.environ["OPENAI_API_KEY"] = "anything"

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000",
    model = "gpt-4o",
    temperature=0.1,
    extra_body={
        "user": "my_customer_id"  # 👈 whatever your customer id is
    }
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)

內部使用者計費

1. 將 'LAGO_API_CHARGE_BY' 設為 'user_id'

export LAGO_API_CHARGE_BY="user_id"

2. 為該使用者建立一個 key

curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer <your-master-key>' \
--header 'Content-Type: application/json' \
--data-raw '{"user_id": "my-unique-id"}' # 👈 Internal User's id

回應物件：

{
  "key": "sk-tXL0wt5-lOOVK9sfY2UacA",
}

3. 使用該 Key 發出 API 請求

import openai
client = openai.OpenAI(
    api_key="sk-tXL0wt5-lOOVK9sfY2UacA", # 👈 Generated key
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-4o", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)