Optimizing vector database performance for real-time applications in retrieval-augmented generation
Martini, Ilaria (A.A. 2023/2024) Optimizing vector database performance for real-time applications in retrieval-augmented generation. Tesi di Laurea in Databases & big data, Luiss Guido Carli, relatore Blerina Sinaimeri, pp. 50. [Bachelor's Degree Thesis]
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Abstract/Index
Overview of Natural language processing (NLP). Introduction to Retrieval-augmented generation (RAG). Evolution of Large language models (LLMs). Importance of real-time data processing in RAG systems. Background. Retrieval-augmented generation (RAG). Large language models (LLMs). Vector databases. Embedding Techniques. Importance of embedding models. Optimization of real-time data processing in RAG systems. Motivation for optimization. Optimization methods. Examples of applications. Case study 1: real-time customer support system. Case study 2: scientific research data retrieval.
References
Bibliografia: pp. 48-50.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Databases & big data |
| Thesis Supervisor: | Sinaimeri, Blerina |
| Academic Year: | 2023/2024 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 18 Mar 2025 15:09 |
| Last Modified: | 18 Mar 2025 15:09 |
| URI: | https://tesi.luiss.it/id/eprint/41274 |
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