Accelerating RAG: Enhancing Vector Search and Graph-Based Knowledge Extraction with Qwen 2.5, FAISS, and Parallel Processing
Preamble
As artificial intelligence (AI) continues to evolve, there’s an increasing demand for scalable and efficient systems capable of processing vast amounts of data, performing vector searches, and extracting meaningful knowledge. Such systems play a critical role in real-world AI applications such as recommendation engines, knowledge retrieval, and conversational agents. In this blog post, we’ll explore an enhanced system built on Qwen 2.5, GPU-accelerated FAISS for vector search, and graph-based knowledge extraction using IGraph.
In particular, we’ll focus on how these components are used together in a Retrieval-Augmented Generation (RAG) system, which leverages large language models (LLMs) to generate human-readable text based on retrieved vectors. We’ll also compare this improved system with its previous iteration to highlight the advancements in scalability, performance, and practical use cases.
