Grounding Knowledge with sivaGPT: An Agentic RAG System

Large pre-trained language models store extensive factual knowledge within their weights, but Sanskrit scriptures require strong grounding to prevent hallucinations. To address this, sivaGPT operates as an advanced agentic Retrieval Augmented Generation (RAG) system built with the Vercel AI SDK.

Key Architectural Capabilities

• Tool Use & Function Calling: Rather than blindly retrieving context for every prompt, sivaGPT intelligently determines when a query requires factual grounding. It autonomously invokes the search tool to retrieve target verses from siva.sh before formulating a response.
• Explicit Control: Users can manually instruct sivaGPT to trigger a search call if they want forced citation retrieval for ambiguous or broad prompts.
• Query History & Context Awareness: Maintains stateful conversational memory, allowing users to ask follow-up questions, request deeper commentary, or refine verse analyses iteratively.
• Source-Grounded Prompting via gpt-5.6-luna, When the search function is triggered, retrieved reference verses are dynamically injected into the system prompt of OpenAI’s gpt-5.6-luna, ensuring answers remain strictly anchored in digitized primary sources.

A Growing Corpus for Sanskrit Research

sivaGPT unifies the full digital ecosystem developed at siva.sh. By combining continuous scripture digitization with structured vector indexing, the agent delivers precise, natural-language answers backed by direct textual citations. As more Sanskrit literature is digitized and indexed, sivaGPT's retrieval accuracy and domain breadth will scale naturally alongside the platform.