While large language models (LLMs) have become increasingly potent over the last few years, use-cases have still not solidified. Besides text generation, one of the most promising use cases for LLMs is semantic search. Often, results are retrieved using embeddings generated by and embedding model, while then fulfilling a user’s query using a text-to-text LLM. This process is called retrieval augmented generation (RAG). In this application, models can be smaller, saving both R&D and operational cost. Search results are also more easily benchmarked, allowing for better quality control. Lastly, there is plenty of value in improving search results over traditional keyword search.
At Sopra Steria, we have been working on a smart search solution that can integrate in any IT-landscape, while providing state-of-the-art search results and RAG. The focus is on building a modular, extensible system, so that it can be adapted to serve customers with varying requirements. These requirements may range from running in a private cloud, to providing an explanation of model results.
This project stretched from verifying model and end-to-end system metrics on the R&D side, to operationalizing the system on both cloud and client infrastructure on the operations side.
In this talk, I will be discussing the architecture of our retrieval system, the design choices made, but also the challenges that arose while creating it. We will discuss the current state of the project and have a look at what components customers perceive as valuable. We will close highlighting the future direction of our search solution and where the opportunities lie for LLM-enabled search in general.
date 2024-12-17 21:29:49
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author UClvi7ix2CkjsdumUc_vkHrw
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