Sahil Bansal

Congmin Min, Rhea Mathew, Joyce Pan et al.

We propose a scalable and cost-efficient framework for deploying Graph-based Retrieval Augmented Generation (GraphRAG) in enterprise environments. While GraphRAG has shown promise for multi-hop reasoning and structured retrieval, its adoption has been limited by the high computational cost of constructing knowledge graphs using large language models (LLMs) and the latency of graph-based retrieval. To address these challenges, we introduce two core innovations: (1) a dependency-based knowledge graph construction pipeline that leverages industrial-grade NLP libraries to extract entities and relations from unstructured text completely eliminating reliance on LLMs; and (2) a lightweight graph retrieval strategy that combines hybrid query node identification with efficient one-hop traversal for high-recall, low-latency subgraph extraction. We evaluate our framework on two SAP datasets focused on legacy code migration and demonstrate strong empirical performance. Our system achieves up to 15% and 4.35% improvements over traditional RAG baselines based on LLM-as-Judge and RAGAS metrics, respectively. Moreover, our dependency-based construction approach attains 94% of the performance of LLM-generated knowledge graphs (61.87% vs. 65.83%) while significantly reducing cost and improving scalability. These results validate the feasibility of deploying GraphRAG systems in real-world, large-scale enterprise applications without incurring prohibitive resource requirements paving the way for practical, explainable, and domain-adaptable retrieval-augmented reasoning.

Gaurav Verma, Vishwa Vinay, Sahil Bansal et al.

Interactive search sessions often contain multiple queries, where the user submits a reformulated version of the previous query in response to the original results. We aim to enhance the query recommendation experience for a commercial image search engine. Our proposed methodology incorporates current state-of-the-art practices from relevant literature -- the use of generation-based sequence-to-sequence models that capture session context, and a multitask architecture that simultaneously optimizes the ranking of results. We extend this setup by driving the learning of such a model with captions of clicked images as the target, instead of using the subsequent query within the session. Since these captions tend to be linguistically richer, the reformulation mechanism can be seen as assistance to construct more descriptive queries. In addition, via the use of a pairwise loss for the secondary ranking task, we show that the generated reformulations are more diverse.