--- title: "GigaChat" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/gigachat source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:24:48.779Z license: CC-BY-4.0 attribution: "GigaChat — Claudary (https://claudary.paisolsolutions.com/skills/gigachat)" --- # GigaChat import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # GigaChat https://developers.sber.ru/docs/ru/gigachat/api/overview GigaChat is Sber AI's large language model, Russia's leading LLM provider. :::tip **We support ALL GigaChat models, just set `model=gigachat/` as a prefix when sending litellm requests** ::: :::warning GigaChat API uses self-signed SSL certificates. You must pass `ssl_verify=False` in your requests. ::: ## Supported Features | Feature | Supported | |---------|-----------| | Chat Completion | Yes | | Streaming | Yes | | Async | Yes | | Function Calling / Tools | Yes | | Structured Output (JSON Schema) | Yes (via function call emulation) | | Image Input | Yes (base64 and URL) - GigaChat-2-Max, GigaChat-2-Pro only | | Embeddings | Yes | ## API Key GigaChat uses OAuth authentication. Set your credentials as environment variables: ```python 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 ``` Get your credentials at: https://developers.sber.ru/studio/ ## Sample Usage ```python 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) ``` ## Sample Usage - Streaming ```python 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) ``` ## Sample Usage - Function Calling ```python 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) ``` ## Sample Usage - Structured Output GigaChat supports structured output via JSON schema (emulated through function calling): ```python 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} ``` ## Sample Usage - Image Input GigaChat supports image input via base64 or URL (GigaChat-2-Max and GigaChat-2-Pro only): ```python 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) ``` ## Sample Usage - Embeddings ```python 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) ``` ## Usage with LiteLLM Proxy ### 1. Set GigaChat Models on config.yaml ```yaml 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. Start Proxy ```bash litellm --config config.yaml ``` ### 3. Test it ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "gigachat", "messages": [ { "role": "user", "content": "Hello!" } ] }' ``` ```python 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) ``` ## Supported Models ### Chat Models | Model Name | Context Window | Vision | Description | |------------|----------------|--------|-------------| | gigachat/GigaChat-2-Lite | 128K | No | Fast, lightweight model | | gigachat/GigaChat-2-Pro | 128K | Yes | Professional model with vision | | gigachat/GigaChat-2-Max | 128K | Yes | Maximum capability model | ### Embedding Models | Model Name | Max Input | Dimensions | Description | |------------|-----------|------------|-------------| | gigachat/Embeddings | 512 | 1024 | Standard embeddings | | gigachat/Embeddings-2 | 512 | 1024 | Updated embeddings | | gigachat/EmbeddingsGigaR | 4096 | 2560 | High-dimensional embeddings | :::note Available models may vary depending on your API access level (personal or business). ::: ## Limitations - Only one function call per request (GigaChat API limitation) - Maximum 1 image per message, 10 images total per conversation - GigaChat API uses self-signed SSL certificates - `ssl_verify=False` is required --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/gigachat) · https://claudary.paisolsolutions.com