GigaChat

https://developers.sber.ru/docs/ru/gigachat/api/overview

GigaChat 是 Sber AI 的大型語言模型，也是俄羅斯領先的 LLM 提供者。

提示

我們支援所有 GigaChat 模型，只要在傳送 litellm 請求時將 model=gigachat/<any-model-on-gigachat> 作為前綴即可

注意

GigaChat API 使用自簽署 SSL 憑證。您必須在請求中傳遞 ssl_verify=False。

支援的功能

功能	支援
聊天完成	是
串流	是
非同步	是
函式呼叫 / 工具	是
結構化輸出（JSON Schema）	是（透過函式呼叫模擬）
圖片輸入	是（base64 和 URL）- 僅限 GigaChat-2-Max、GigaChat-2-Pro
嵌入	是

API 金鑰

GigaChat 使用 OAuth 驗證。請將您的憑證設定為環境變數：

import os

# Required: Set credentials (base64-encoded client_id:client_secret)
os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

# Optional: Set scope (default is GIGACHAT_API_PERS for personal use)
os.environ['GIGACHAT_SCOPE'] = "GIGACHAT_API_PERS"  # or GIGACHAT_API_B2B for business

您的憑證可在此取得：https://developers.sber.ru/studio/

範例用法

from litellm import completion
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

response = completion(
    model="gigachat/GigaChat-2-Max",
    messages=[
       {"role": "user", "content": "Hello from LiteLLM!"}
   ],
    ssl_verify=False,  # Required for GigaChat
)
print(response)

範例用法 - 串流

from litellm import completion
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

response = completion(
    model="gigachat/GigaChat-2-Max",
    messages=[
       {"role": "user", "content": "Hello from LiteLLM!"}
   ],
    stream=True,
    ssl_verify=False,  # Required for GigaChat
)

for chunk in response:
    print(chunk)

範例用法 - 函式呼叫

from litellm import completion
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get weather for a city",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }
    }
}]

response = completion(
    model="gigachat/GigaChat-2-Max",
    messages=[{"role": "user", "content": "What's the weather in Moscow?"}],
    tools=tools,
    ssl_verify=False,  # Required for GigaChat
)
print(response)

範例用法 - 結構化輸出

GigaChat 支援透過 JSON schema 的結構化輸出（透過函式呼叫模擬）：

from litellm import completion
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

response = completion(
    model="gigachat/GigaChat-2-Max",
    messages=[{"role": "user", "content": "Extract info: John is 30 years old"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "age": {"type": "integer"}
                }
            }
        }
    },
    ssl_verify=False,  # Required for GigaChat
)
print(response)  # Returns JSON: {"name": "John", "age": 30}

範例用法 - 圖片輸入

GigaChat 支援透過 base64 或 URL 的圖片輸入（僅限 GigaChat-2-Max 和 GigaChat-2-Pro）：

from litellm import completion
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

response = completion(
    model="gigachat/GigaChat-2-Max",  # Vision requires GigaChat-2-Max or GigaChat-2-Pro
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
        ]
    }],
    ssl_verify=False,  # Required for GigaChat
)
print(response)

範例用法 - Embeddings

from litellm import embedding
import os

os.environ['GIGACHAT_CREDENTIALS'] = "your-credentials-here"

response = embedding(
    model="gigachat/Embeddings",
    input=["Hello world", "How are you?"],
    ssl_verify=False,  # Required for GigaChat
)
print(response)

與 LiteLLM Proxy 搭配使用

1. 在 config.yaml 上設定 GigaChat 模型

model_list:
  - model_name: gigachat
    litellm_params:
      model: gigachat/GigaChat-2-Max
      api_key: "os.environ/GIGACHAT_CREDENTIALS"
      ssl_verify: false
  - model_name: gigachat-lite
    litellm_params:
      model: gigachat/GigaChat-2-Lite
      api_key: "os.environ/GIGACHAT_CREDENTIALS"
      ssl_verify: false
  - model_name: gigachat-embeddings
    litellm_params:
      model: gigachat/Embeddings
      api_key: "os.environ/GIGACHAT_CREDENTIALS"
      ssl_verify: false

2. 啟動 Proxy

litellm --config config.yaml

3. 測試

Curl 請求

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gigachat",
    "messages": [
        {
            "role": "user",
            "content": "Hello!"
        }
    ]
}'

OpenAI v1.0.0+

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

response = client.chat.completions.create(
    model="gigachat",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response)

支援的模型

聊天模型

模型名稱	Context 視窗	視覺	說明
gigachat/GigaChat-2-Lite	128K	否	快速、輕量級模型
gigachat/GigaChat-2-Pro	128K	是	具備視覺能力的專業模型
gigachat/GigaChat-2-Max	128K	是	最高能力模型

Embedding 模型

模型名稱	最大輸入	維度	說明
gigachat/Embeddings	512	1024	標準 embeddings
gigachat/Embeddings-2	512	1024	更新的 embeddings
gigachat/EmbeddingsGigaR	4096	2560	高維度 embeddings

備註

可用模型可能會因您的 API 存取層級（個人或企業）而有所不同。

限制

• 每個請求僅限一個函式呼叫（GigaChat API 限制）
• 每則訊息最多 1 張圖片，每次對話總共最多 10 張圖片
• GigaChat API 使用自簽署 SSL 憑證 - 需要 ssl_verify=False