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Instead of only relying on the model’s internal knowledge (which can be outdated or incomplete), RAG allows the model to:

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

Why enable wiki_grounding?

First, install the SDK:

$
pip install -Uqq sarvamai

Then use the following Python code:

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from sarvamai import SarvamAI
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# Initialize the SarvamAI client with your API key
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client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
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# Example: Using wiki_grounding to improve factual accuracy
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response = client.chat.completions(
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    messages=[
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        {"role": "system", "content": "You are a knowledgeable history professor."},
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        {"role": "user", "content": "Who was the first woman to win a Nobel Prize?"}
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    ],
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    wiki_grounding=True,
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    reasoning_effort="high",
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    temperature=0.3
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)
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# Receive assistant's reply as output
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print(response.choices[0].message.content)