Building RAG Pipeline with Qwen 2: Step-by-Step Guide
In this video, Mosleh Mahamud demonstrates how to build a Retrieval-Augmented Generation (RAG) pipeline using the Qwen 2 model from Alibaba Cloud. Qwen 2 is a large language model that excels in various benchmarks, supports multiple languages, and is open-source. The video highlights the steps to set up a RAG pipeline, including installing necessary packages, preparing data, creating a vector database, and building the RAG pipeline itself.
Mosleh starts by introducing Qwen 2, noting its superior performance compared to other models like LLaMA 3 and its support for multiple languages. He then outlines the process of building a RAG pipeline, emphasizing the simplicity and efficiency of the approach.
The video is structured as follows:
1. Installing necessary packages.
2. Preparing data using a dataset from LeBron James’ game logs.
3. Converting the data into a vector database using Hugging Face embeddings and LangChain.
4. Building the RAG pipeline by defining the model, tokenizer, and pipeline for text generation.
5. Demonstrating the use of the RAG pipeline with example queries.
Mosleh also provides code snippets and explains each step in detail, ensuring viewers can follow along and implement the pipeline themselves. He concludes by showing how to use the RAG pipeline to retrieve and generate answers based on the input data.
Key points covered in the video:
– Introduction to Qwen 2 and its advantages.
– Steps to set up a RAG pipeline.
– Installing necessary packages and preparing data.
– Converting data into a vector database.
– Building and using the RAG pipeline with Hugging Face models.
– Demonstrating the pipeline with example queries.
Mosleh encourages viewers to subscribe to his channel for more content on machine learning and AI, and to join his Discord community for further discussions.
Overall, the video provides a comprehensive guide to building a RAG pipeline using Qwen 2, highlighting its capabilities and practical implementation steps.