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Generate chat completions using Sarvam AI's Sarvam-M model. Use when the user needs AI chat, text generation, question answering, or reasoning in Indian languages. Sarvam-M is a 24B parameter model with hybrid thinking, superior Indic language understanding, and OpenAI-compatible API. Free to use.
npx skill4agent add sarvamai/skills chatpip install sarvamai
# or
pip install openai # OpenAI-compatiblefrom sarvamai import SarvamAI
client = SarvamAI()
response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "भारत की राजधानी क्या है?"
}
]
)
print(response.choices[
0
].message.content)from openai import OpenAI
client = OpenAI(
api_key=os.environ[
"SARVAM_API_KEY"
],
base_url="https://api.sarvam.ai/v1"
)
response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "What is the capital of India?"
}
]
)
print(response.choices[
0
].message.content)response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "Solve: If a train travels 120km in 2 hours, what is its average speed?"
}
],
thinking=True # Enable reasoning
)
# Access thinking process
print("Thinking:", response.choices[
0
].message.thinking)
print("Answer:", response.choices[
0
].message.content)response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "system",
"content": "You are a helpful Hindi tutor. Always respond in Hindi with English transliteration in parentheses."
},
{
"role": "user",
"content": "How do I say 'Good morning'?"
}
]
)messages = [
{
"role": "system",
"content": "You are a knowledgeable assistant."
},
{
"role": "user",
"content": "Tell me about the Taj Mahal"
},
{
"role": "assistant",
"content": "The Taj Mahal is a white marble mausoleum..."
},
{
"role": "user",
"content": "Who built it and when?"
}
]
response = client.chat.completions.create(
model="sarvam-m",
messages=messages
)stream = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "Write a short poem about India"
}
],
stream=True
)
for chunk in stream:
if chunk.choices[
0
].delta.content:
print(chunk.choices[
0
].delta.content, end="", flush=True)# Creative (higher temperature)
response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "Write a creative story"
}
],
temperature=0.9
)
# Factual (lower temperature)
response = client.chat.completions.create(
model="sarvam-m",
messages=[
{
"role": "user",
"content": "What is 2+2?"
}
],
temperature=0.1
)import { SarvamAI
} from "sarvamai";
const client = new SarvamAI();
const response = await client.chat.completions.create({
model: "sarvam-m",
messages: [
{ role: "user", content: "भारत की राजधानी क्या है?"
}
]
});
console.log(response.choices[
0
].message.content);import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.SARVAM_API_KEY,
baseURL: "https://api.sarvam.ai/v1"
});
const response = await client.chat.completions.create({
model: "sarvam-m",
messages: [
{ role: "user", content: "Hello!"
}
]
});curl -X POST "https://api.sarvam.ai/v1/chat/completions" \
-H "api-subscription-key: $SARVAM_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "sarvam-m",
"messages": [
{
"role": "user",
"content": "What is the capital of India?"
}
]
}'| Parameter | Type | Required | Description |
|---|---|---|---|
| string | Yes | |
| array | Yes | Conversation history |
| float | No | 0.0-2.0 (default: 1.0) |
| int | No | Max response length |
| bool | No | Enable streaming |
| bool | No | Enable hybrid thinking |
| float | No | Nucleus sampling (0.0-1.0) |
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "sarvam-m",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "भारत की राजधानी नई दिल्ली है।"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 12,
"total_tokens": 27
}
}