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Databricks

LiteLLM 支援 Databricks 上的所有模型

提示

我們支援所有 Databricks 模型；在傳送 litellm 請求時，只要將 model=databricks/<any-model-on-databricks> 設為前綴即可

驗證​

LiteLLM 依偏好順序支援 Databricks 的多種驗證方法：

使用 Service Principal 憑證的 OAuth 機器對機器驗證，是依 Databricks Partner 要求，正式環境部署的建議方法。

import os
from litellm import completion

# Set OAuth credentials (Service Principal)
os.environ["DATABRICKS_CLIENT_ID"] = "your-service-principal-application-id"
os.environ["DATABRICKS_CLIENT_SECRET"] = "your-service-principal-secret"
os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints"

response = completion(
    model="databricks/databricks-dbrx-instruct",
    messages=[{"role": "user", "content": "Hello!"}],
)

個人存取權杖（PAT）​

支援 PAT 驗證，用於開發與測試情境。

import os
from litellm import completion

os.environ["DATABRICKS_API_KEY"] = "dapi..."  # Your Personal Access Token
os.environ["DATABRICKS_API_BASE"] = "https://adb-xxx.azuredatabricks.net/serving-endpoints"

response = completion(
    model="databricks/databricks-dbrx-instruct",
    messages=[{"role": "user", "content": "Hello!"}],
)

Databricks SDK 驗證（自動）​

如果未提供任何憑證，LiteLLM 會使用 Databricks SDK 進行自動驗證。這支援您環境中設定的 OAuth、Azure AD 及其他統一驗證方法。

from litellm import completion

# No environment variables needed - uses Databricks SDK unified auth
# Requires: uv add databricks-sdk
response = completion(
    model="databricks/databricks-dbrx-instruct",
    messages=[{"role": "user", "content": "Hello!"}],
)

用於合作夥伴歸因的自訂 User-Agent​

如果您正在以 LiteLLM 為基礎建立一個與 Databricks 整合的產品，您可以傳遞自己的合作夥伴識別碼，以便在 Databricks telemetry 中正確歸因。

合作夥伴名稱會以前綴形式加到 LiteLLM user agent 之前：

# Via parameter
response = completion(
    model="databricks/databricks-dbrx-instruct",
    messages=[{"role": "user", "content": "Hello!"}],
    user_agent="mycompany/1.0.0",
)
# Resulting User-Agent: mycompany_litellm/1.79.1

# Via environment variable
os.environ["DATABRICKS_USER_AGENT"] = "mycompany/1.0.0"
# Resulting User-Agent: mycompany_litellm/1.79.1
輸入產生的 User-Agent
（無）litellm/1.79.1
mycompany/1.0.0mycompany_litellm/1.79.1
partner_product/2.5.0partner_product_litellm/1.79.1
acmeacme_litellm/1.79.1

注意： 來自您自訂 user agent 的版本會被忽略；LiteLLM 一律使用自己的版本。

安全性​

LiteLLM 會自動從所有除錯記錄中遮罩敏感資訊（權杖、密鑰、API 金鑰），以防止憑證外洩。這包括：

  • Authorization 標頭
  • API 金鑰與權杖
  • 用戶端密鑰
  • 個人存取權杖（PATs）

使用方式​

環境變數​

import os 
os.environ["DATABRICKS_API_KEY"] = ""
os.environ["DATABRICKS_API_BASE"] = ""

範例呼叫​

from litellm import completion
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks base url" # e.g.: https://adb-3064715882934586.6.azuredatabricks.net/serving-endpoints

# Databricks dbrx-instruct call
response = completion(
    model="databricks/databricks-dbrx-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

傳遞額外參數 - max_tokens、temperature​

請參閱所有 litellm.completion 支援的參數 此處

# !uv add litellm
from litellm import completion
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks api base"

# databricks dbrx call
response = completion(
    model="databricks/databricks-dbrx-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)

代理

  model_list:
    - model_name: llama-3
      litellm_params:
        model: databricks/databricks-meta-llama-3-70b-instruct
        api_key: os.environ/DATABRICKS_API_KEY
        max_tokens: 20
        temperature: 0.5

使用方式 - Thinking / reasoning_content​

LiteLLM 會將 OpenAI 的 reasoning_effort 轉換為 Anthropic 的 thinking 參數。程式碼

reasoning_effortthinking
"low""budget_tokens": 1024
"medium""budget_tokens": 2048
"high""budget_tokens": 4096

已知限制：

  • 支援將 thinking 區塊回傳給 Claude Issue
from litellm import completion
import os

# set ENV variables (can also be passed in to .completion() - e.g. `api_base`, `api_key`)
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks base url"

resp = completion(
    model="databricks/databricks-claude-3-7-sonnet",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    reasoning_effort="low",
)

預期回應

ModelResponse(
    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
    created=1740470510,
    model='claude-3-7-sonnet-20250219',
    object='chat.completion',
    system_fingerprint=None,
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="The capital of France is Paris.",
                role='assistant',
                tool_calls=None,
                function_call=None,
                provider_specific_fields={
                    'citations': None,
                    'thinking_blocks': [
                        {
                            'type': 'thinking',
                            'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                            'signature': 'EuYBCkQYAiJAy6...'
                        }
                    ]
                }
            ),
            thinking_blocks=[
                {
                    'type': 'thinking',
                    'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                    'signature': 'EuYBCkQYAiJAy6AGB...'
                }
            ],
            reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
        )
    ],
    usage=Usage(
        completion_tokens=68,
        prompt_tokens=42,
        total_tokens=110,
        completion_tokens_details=None,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=None,
            cached_tokens=0,
            text_tokens=None,
            image_tokens=None
        ),
        cache_creation_input_tokens=0,
        cache_read_input_tokens=0
    )
)

引用​

透過 Databricks 提供的 Anthropic 模型可以回傳引用中繼資料。LiteLLM 會透過 response.choices[0].message.provider_specific_fields["citations"] 提供這些資料。

傳遞 thinking 給 Anthropic 模型​

您也可以將 thinking 參數傳遞給 Anthropic 模型。

您也可以將 thinking 參數傳遞給 Anthropic 模型。

from litellm import completion
import os

# set ENV variables (can also be passed in to .completion() - e.g. `api_base`, `api_key`)
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks base url"

response = litellm.completion(
  model="databricks/databricks-claude-3-7-sonnet",
  messages=[{"role": "user", "content": "What is the capital of France?"}],
  thinking={"type": "enabled", "budget_tokens": 1024},
)

支援的 Databricks Chat Completion 模型​

提示

我們支援所有 Databricks 模型；在傳送 litellm 請求時，只要將 model=databricks/<any-model-on-databricks> 設為前綴即可

模型名稱指令
databricks/databricks-claude-3-7-sonnetcompletion(model='databricks/databricks/databricks-claude-3-7-sonnet', messages=messages)
databricks-meta-llama-3-1-70b-instructcompletion(model='databricks/databricks-meta-llama-3-1-70b-instruct', messages=messages)
databricks-meta-llama-3-1-405b-instructcompletion(model='databricks/databricks-meta-llama-3-1-405b-instruct', messages=messages)
databricks-dbrx-instructcompletion(model='databricks/databricks-dbrx-instruct', messages=messages)
databricks-meta-llama-3-70b-instructcompletion(model='databricks/databricks-meta-llama-3-70b-instruct', messages=messages)
databricks-llama-2-70b-chatcompletion(model='databricks/databricks-llama-2-70b-chat', messages=messages)
databricks-mixtral-8x7b-instructcompletion(model='databricks/databricks-mixtral-8x7b-instruct', messages=messages)
databricks-mpt-30b-instructcompletion(model='databricks/databricks-mpt-30b-instruct', messages=messages)
databricks-mpt-7b-instructcompletion(model='databricks/databricks-mpt-7b-instruct', messages=messages)

嵌入模型​

傳遞 Databricks 特定參數 - 'instruction'​

對於嵌入模型，databricks 允許您傳入額外參數 'instruction'. 完整規格

# !uv add litellm
from litellm import embedding
import os
## set ENV variables
os.environ["DATABRICKS_API_KEY"] = "databricks key"
os.environ["DATABRICKS_API_BASE"] = "databricks url"

# Databricks bge-large-en call
response = litellm.embedding(
      model="databricks/databricks-bge-large-en",
      input=["good morning from litellm"],
      instruction="Represent this sentence for searching relevant passages:",
  )

代理

  model_list:
    - model_name: bge-large
      litellm_params:
        model: databricks/databricks-bge-large-en
        api_key: os.environ/DATABRICKS_API_KEY
        api_base: os.environ/DATABRICKS_API_BASE
        instruction: "Represent this sentence for searching relevant passages:"

支援的 Databricks 嵌入模型​

提示

我們支援所有 Databricks 模型；在傳送 litellm 請求時，只要將 model=databricks/<any-model-on-databricks> 設為前綴即可

模型名稱指令
databricks-bge-large-enembedding(model='databricks/databricks-bge-large-en', messages=messages)
databricks-gte-large-enembedding(model='databricks/databricks-gte-large-en', messages=messages)
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