Guide on Constructing a RAG System with Qwen3
Mounish V, a Data Science Trainee with a passion for technology, innovation, Deep Learning, and Generative AI, has made a significant stride in the field with the development of a new Retrieval Augmented Generation (RAG) system based on the versatile Qwen3 models.
The Qwen3 RAG pipeline, built using the Qwen3's instruct, embedding, and reranker models, is designed to deliver superior performance. The script for building this pipeline uses the PYPDF2 library, FAISS, and processes data in chunks of size 800 with an overlap of 100.
At the heart of the Retriever in Retrieval Augmented Generation (RAG) lies the Qwen3-Embedding-0.6B model. This essential component is responsible for converting text to dense vector representations, enabling the system to effectively retrieve relevant documents.
The Qwen3-Reranker-0.6B model is employed to score each chunk against a query and order the list of documents or assign priority. The top-15 documents are retrieved based on similarity, which are then reranked to get the top-3. These top documents are passed to the instruct model to generate the final output from the RAG.
The article primarily focuses on the 'Qwen3-Instruct-2507' 4B variant, which boasts a context length of 256K, making it ideal for handling complex real-world tasks.
The Qwen family, developed by Alibaba Cloud a few months back, offers multilingual support for 119 languages and dialects, further broadening its applicability. The new models come in three sizes: 235B-A22B, 30B-A3B, and 4B, providing users with a range of options to suit their specific needs.
All Qwen3 models are open-source and are readily available on Hugging Face and Kaggle. The script for building a RAG using the Qwen3 models is available in the provided link to the repository. However, there is no publicly available information about the origin of Mounish V, the developer of this innovative RAG system.
For more detailed information about the documents retrieved, their similarity scores with the query, and the reranker scores, you can refer to the log file 'rag_retrieval_log.txt'. This file provides valuable insights into the workings of the Qwen3 RAG pipeline.
In conclusion, the Qwen3-Instruct-2507 4B model is a promising development in the field of RAG, offering multilingual support, a large context length, and open-source availability. Its potential applications in real-world tasks are vast, making it an exciting addition to the world of AI.