Swiggy's Hermes V3: Conversational AI for Data Insights

Hermes V3: Dissecting the Architecture

Swiggy's Hermes V3 is a compelling example of applying GenAI to solve a practical business problem: making data accessible to non-technical employees. The move from a simple text-to-SQL interface to a conversational AI system with improved accuracy (from 54% to 93%) and multi-turn capabilities is a significant achievement. The incorporation of vector retrieval for prompt retrieval, conversational memory, and a ReAct-style agentic orchestration are innovative aspects. The explanation layer that surfaces the assumptions behind generated SQL queries is particularly noteworthy, fostering trust and transparency, a crucial element for AI adoption in enterprise environments. The integration with existing infrastructure like security and compliance is also commendable.

However, there are potential limitations. The article doesn't delve into the specifics of the training data or the fine-tuning process of the LLMs used, which are critical to the system's performance and generalization capabilities. The reliance on historical SQL queries for prompt retrieval could introduce biases present in the existing query patterns. The scalability of the system under increased user load and the complexity of more intricate queries are also areas that warrant further investigation. While the article mentions integrating with existing infrastructure, detailed discussion on monitoring and error handling is missing. Finally, the article's focus is on Swiggy's internal use case; it is not yet clear how easily this architecture could be adapted or if it is commercially viable.

Despite these points, the project is a valuable contribution to the field of conversational AI. It showcases a practical application of LLMs and vector databases in a real-world setting, providing valuable insights for other organizations looking to democratize data access. The emphasis on transparency and trust-building within the system is a key takeaway. The potential for this technology to improve internal workflows and reduce the reliance on specialized data experts is clear. A detailed analysis on the performance implications of the ReAct-style reasoning loop and the efficiency of the vector search would further strengthen the work.

Key Points

• Hermes V3 is a GenAI-powered text-to-SQL assistant that significantly improves SQL generation accuracy from 54% to 93% using few-shot learning and vector retrieval.
• The system incorporates conversational memory, enabling multi-turn queries, and employs a ReAct-style agentic orchestration for complex query decomposition.
• An explanation layer provides transparency by surfacing the assumptions behind generated SQL queries and assigning confidence scores, enhancing trust.
• The architecture integrates with existing security, compliance, and metadata infrastructure, ensuring data access adheres to internal governance policies.

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📖 Source: Swiggy Rolls Out Hermes V3: From Text-to-SQL to Conversational AI