langchain-sarvam

Overview

Integration details

Class	Package	Local	Serializable	JS support	Downloads	Version
ChatSarvam	langchain-sarvam	❌	beta	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
❌	❌	❌	❌	❌	❌	✅	✅	✅	❌

Integration package connecting Sarvam AI chat completions with LangChain.

Installation

with uv inside the package:

uv add langchain-sarvam

Setup

# Set the SARVAM API key
sarvam_Api_key = os.getenv("SARVAM_API_KEY")

Usage

Basic Usage

from langchain_sarvam import ChatSarvam

llm = ChatSarvam(model="sarvam-m", temperature=0.2, max_tokens=128)
resp = llm.invoke([("system", "You are helpful"), ("human", "Hello!")])
print(resp.content)

Batch Processing

from langchain_sarvam import ChatSarvam
from langchain_core.messages import HumanMessage

chat = ChatSarvam(model="sarvam-m")

# Batch processing - use list of message lists
messages = [
    [HumanMessage(content="Tell me a joke")],
    [HumanMessage(content="What's the weather like?")]
]

responses = chat.batch(messages)
for response in responses:
    print(response.content)

Using generate() Method

from langchain_sarvam import ChatSarvam
from langchain_core.messages import HumanMessage

chat = ChatSarvam(model="sarvam-m")

# generate() expects a list of message lists
inputs = [
    [HumanMessage(content="Tell me a joke with emojis only")],
    [HumanMessage(content="What's the weather like?")]
]

result = chat.generate(inputs)
for generation_list in result.generations:
    # generation_list is a list of ChatGeneration objects
    for generation in generation_list:
        print(generation.message.content)

Streaming

for chunk in ChatSarvam(model="sarvam-m", streaming=True).stream("Tell me a joke"):
    print(chunk.text, end="")