/generateContent

使用 LiteLLM 來呼叫 Google AI 的 generateContent 端點，以進行文字生成、多模態互動與串流回應。

概覽

功能	支援	備註
成本追蹤	✅
記錄	✅	適用於所有整合
終端使用者追蹤	✅
串流	✅
備援	✅	於受支援的模型之間
負載平衡	✅	於受支援的模型之間
中繼資料追蹤	✅	將 trace ID、metadata 傳遞給可觀測性回呼（例如 S3、Langfuse）

使用方式

---

LiteLLM Python SDK

基本使用

非串流範例

Basic Text Generation

from litellm.google_genai import agenerate_content
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Hello, can you tell me a short joke?")
    ],
    role="user",
)

response = await agenerate_content(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=100,
)
print(response)

串流範例

Streaming Text Generation

from litellm.google_genai import agenerate_content_stream
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Write a long story about space exploration")
    ],
    role="user",
)

response = await agenerate_content_stream(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=500,
)

async for chunk in response:
    print(chunk)

同步使用

同步非串流範例

Sync Text Generation

from litellm.google_genai import generate_content
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Hello, can you tell me a short joke?")
    ],
    role="user",
)

response = generate_content(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=100,
)
print(response)

同步串流範例

Sync Streaming Text Generation

from litellm.google_genai import generate_content_stream
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Write a long story about space exploration")
    ],
    role="user",
)

response = generate_content_stream(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=500,
)

for chunk in response:
    print(chunk)

LiteLLM Proxy Server

1. 設定 config.yaml

model_list:
    - model_name: gemini-flash
      litellm_params:
        model: gemini/gemini-2.0-flash
        api_key: os.environ/GEMINI_API_KEY

2. 啟動 proxy

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

3. 測試它！

Google GenAI SDK

Google GenAI SDK with LiteLLM Proxy

from google.genai import Client
import os

# Configure Google GenAI SDK to use LiteLLM proxy
os.environ["GOOGLE_GEMINI_BASE_URL"] = "http://localhost:4000"
os.environ["GEMINI_API_KEY"] = "sk-1234"

client = Client()

response = client.models.generate_content(
    model="gemini-flash",
    contents=[
        {
            "parts": [{"text": "Write a short story about AI"}],
            "role": "user"
        }
    ],
    config={"max_output_tokens": 100}
)

curl

生成內容

generateContent via LiteLLM Proxy

curl -L -X POST 'http://localhost:4000/v1beta/models/gemini-flash:generateContent' \
-H 'content-type: application/json' \
-H 'authorization: Bearer sk-1234' \
-d '{
  "contents": [
    {
      "parts": [
        {
          "text": "Write a short story about AI"
        }
      ],
      "role": "user"
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 100
  }
}'

串流生成內容

streamGenerateContent via LiteLLM Proxy

curl -L -X POST 'http://localhost:4000/v1beta/models/gemini-flash:streamGenerateContent' \
-H 'content-type: application/json' \
-H 'authorization: Bearer sk-1234' \
-d '{
  "contents": [
    {
      "parts": [
        {
          "text": "Write a long story about space exploration"
        }
      ],
      "role": "user"
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 500
  }
}'

原生請求欄位

generateContent 端點可直接替代 Google 的 Generative Language REST API，因此 Google 的 GenerateContentRequest 作為 generationConfig 同層級欄位所帶的頂層欄位，會原封不動轉送給 Google。這涵蓋 safetySettings、toolConfig、cachedContent 與 labels。請將它們直接以請求本文的頂層欄位送出，就像直接呼叫 Google 時一樣；不需要將它們包在 extra_body 中。若您同時傳入 extra_body，其中明確指定的值在衝突時會優先。

curl

Native top-level fields via LiteLLM Proxy

curl -L -X POST 'http://localhost:4000/v1beta/models/gemini-flash:generateContent' \
-H 'content-type: application/json' \
-H 'authorization: Bearer sk-1234' \
-d '{
  "contents": [
    {
      "parts": [{"text": "Say hi"}],
      "role": "user"
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 100
  },
  "safetySettings": [
    {
      "category": "HARM_CATEGORY_HATE_SPEECH",
      "threshold": "BLOCK_NONE"
    }
  ],
  "toolConfig": {
    "functionCallingConfig": {"mode": "AUTO"}
  }
}'

LiteLLM Python SDK

Native top-level fields via LiteLLM Python SDK

from litellm.google_genai import generate_content
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

response = generate_content(
    model="gemini/gemini-2.0-flash",
    contents=[{"role": "user", "parts": [{"text": "Say hi"}]}],
    safetySettings=[
        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}
    ],
    toolConfig={"functionCallingConfig": {"mode": "AUTO"}},
)
print(response)

相關內容

• 將 LiteLLM 與 gemini-cli 搭配使用