Sarvam AI LLM

The Sarvam AI LLM provider enables your agent to use Sarvam AI's language models for text-based conversations and processing.

Installation

Install the Sarvam AI-enabled VideoSDK Agents package:

pip install "videosdk-plugins-sarvamai"

Importing

from videosdk.plugins.sarvamai import SarvamAILLM

note

When using Sarvam AI as the LLM option, the function tool calls and MCP tool will not work.

Example Usage

from videosdk.plugins.sarvamai import SarvamAILLM
from videosdk.agents import CascadingPipeline

# Initialize the Sarvam AI LLM model
llm = SarvamAILLM(
    model="sarvam-m",
    # When SARVAMAI_API_KEY is set in .env - DON'T pass api_key parameter
    api_key="your-sarvam-ai-api-key",
    temperature=0.7,
    tool_choice="auto",
    max_completion_tokens=1000
)

#  Add llm to cascading pipeline
pipeline = CascadingPipeline(llm=llm)

note

When using .env file for credentials, don't pass them as arguments to model instances or context objects. The SDK automatically reads environment variables, so omit api_key and other credential parameters from your code.

Configuration Options

• model: (str) The Sarvam AI model to use (default: "sarvam-m").
• api_key: (str) Your Sarvam AI API key. Can also be set via the SARVAMAI_API_KEY environment variable.
• temperature: (float) Sampling temperature for response randomness (default: 0.7).
• tool_choice: (ToolChoice) Tool selection mode (default: "auto").
• max_completion_tokens: (int) Maximum number of tokens in the completion response (optional).