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.

Authentication

The Sarvam plugin requires a Sarvam API key.

Set SARVAM_API_KEY in your .env file.

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).

Additional Resources

The following resources provide more information about using Sarvam AI with VideoSDK Agents SDK.

• Python package: The videosdk-plugins-sarvamai package on PyPI.

• GitHub repo: View the source or contribute to the VideoSDK Sarvam AI LLM plugin.

• Sarvam docs: Sarvam's full docs site.