GigaChat Sber API Integration

GigaChat API Integration (Sber)

GigaChat is an LLM from Sberbank through SberCloud. Like YandexGPT — Russian jurisdiction for data processing, important for financial institutions, government structures and companies with data localization requirements under 152-FZ. GigaChat integrates with Sber ecosystem: SberDevices, SberCloud, GigaChain (LangChain fork).

Getting Access

# 1. Register on developers.sber.ru
# 2. Create project and get client_id / client_secret
# 3. OAuth 2.0 authorization to get access_token

import requests
import base64
import uuid

CLIENT_ID = "your-client-id"
CLIENT_SECRET = "your-client-secret"
SCOPE = "GIGACHAT_API_PERS"  # For individuals
# GIGACHAT_API_B2B for business
# GIGACHAT_API_CORP for corporate

def get_access_token() -> str:
    credentials = base64.b64encode(f"{CLIENT_ID}:{CLIENT_SECRET}".encode()).decode()

    response = requests.post(
        "https://ngw.devices.sberbank.ru:9443/api/v2/oauth",
        headers={
            "Authorization": f"Basic {credentials}",
            "RqUID": str(uuid.uuid4()),
            "Content-Type": "application/x-www-form-urlencoded",
        },
        data={"scope": SCOPE},
        verify=False,  # Sber self-signed certificate
    )
    return response.json()["access_token"]

Basic Requests

def gigachat_chat(prompt: str, access_token: str) -> str:
    response = requests.post(
        "https://gigachat.devices.sberbank.ru/api/v1/chat/completions",
        headers={"Authorization": f"Bearer {access_token}"},
        json={
            "model": "GigaChat",
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.1,
            "max_tokens": 1024,
        },
        verify=False,
    )
    return response.json()["choices"][0]["message"]["content"]

# With token caching
from datetime import datetime, timedelta

class GigaChatClient:
    def __init__(self, client_id: str, client_secret: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self._token = None
        self._token_expires = None

    def _ensure_token(self):
        if not self._token or datetime.now() >= self._token_expires:
            self._token = get_access_token()
            self._token_expires = datetime.now() + timedelta(minutes=25)  # Token lives 30 min

    def chat(self, messages: list[dict], model: str = "GigaChat") -> str:
        self._ensure_token()
        response = requests.post(
            "https://gigachat.devices.sberbank.ru/api/v1/chat/completions",
            headers={"Authorization": f"Bearer {self._token}"},
            json={"model": model, "messages": messages, "temperature": 0.1},
            verify=False,
        )
        return response.json()["choices"][0]["message"]["content"]

GigaChain — LangChain for GigaChat

from langchain_community.chat_models import GigaChat
from langchain_core.messages import HumanMessage, SystemMessage

chat = GigaChat(
    credentials="Base64(client_id:client_secret)",
    scope="GIGACHAT_API_PERS",
    model="GigaChat",
    verify_ssl_certs=False,
    streaming=False,
)

response = chat.invoke([
    SystemMessage(content="You are a financial consultant"),
    HumanMessage(content="Explain the key rate of the Central Bank of Russia"),
])
print(response.content)

GigaChat Models

Use Case

Bank contact center: automatic answers to customer questions about products. GigaChat chosen due to regulatory requirements for localization of bank customer data. Integration with internal CRM via REST API.

Timeline

• Access + basic integration: 2–3 days
• GigaChain/LangChain integration: 1 week
• Production with token refresh and retry: +2–3 days