How to improve factual accuracy with wiki_grounding

If wiki_grounding is enabled, the model uses a RAG (Retrieval-Augmented Generation) approach:

It retrieves relevant chunks from Wikipedia based on the user’s question.
It then uses those chunks to ground its answer — making the response more accurate and fact-based.

What is RAG?

RAG = Retrieval-Augmented Generation.

Instead of only relying on the model’s internal knowledge (which can be outdated or incomplete), RAG allows the model to:

Look up external data sources (in this case, Wikipedia)
Condition its response on those sources

Why enable `wiki_grounding`?

When to use	Benefit
Factual Q&A	Improves accuracy
Educational content	Provides verified explanations
”What is X?” or “Explain Y” type questions	Uses up-to-date info from Wikipedia
Historical or scientific queries	Reduces hallucination

1 # Install SarvamAI
2 !pip install -Uqq sarvamai
3 from sarvamai import SarvamAI

1 # Initialize the SarvamAI client with your API key
2 client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

1 # Example: Using wiki_grounding to improve factual accuracy
2 response = client.chat.completions(
3     messages=[
4         {"role": "system", "content": "You are a knowledgeable history professor."},
5         {"role": "user", "content": "Who was the first woman to win a Nobel Prize?"}
6     ],
7     wiki_grounding=True,
8     reasoning_effort="high",
9     temperature=0.3
10 )

1 # Receive assistant's reply as output.
2 print(response.choices[0].message.content)