High-level abstractions provided by libraries such as llama-index and Langchain have made the development of Retrieval Augmented Generation (RAG) systems more straightforward. However, it is still essential for machine learning engineers to have a deep understanding of the underlying mechanics that enable these libraries in order to fully maximize their potential. In this guide, I will walk you through the process of building a RAG system from scratch and also show you how to create a containerized flask API. This practical walkthrough is based on real-life use cases to ensure that the knowledge you gain is not just theoretical but immediately actionable.
Use-case overview:
This implementation is designed to handle various types of documents. While the example uses small documents describing individual products with details like SKU, name, description, price, and dimensions, the approach is adaptable to different types of document libraries. Whether you are indexing books, extracting data from contracts, or working with any other set of documents, the system can be customized to meet specific requirements, enabling seamless integration and processing of diverse information.
Quick note:
This implementation focuses solely on text data. However, similar steps can be followed to convert images into embeddings using a multi-modal model like CLIP for indexing and querying purposes.
Outline of the modular framework:
1. Prepare the data
2. Chunking, indexing, and retrieval (core functionality)
3. LLM component
4. Build and deploy the API
The implementation consists of four main components that can be interchanged:
– Text data
– Embedding model
– LLM
– Vector store
Integrating these components into your project is highly flexible, allowing you to tailor them to your specific needs. For instance, starting with JSON data in this example, you can customize the embedding model, vector store, and LLM based on your project requirements. By following this approach, you can easily adapt to different needs without major changes to the core architecture.
The next step involves organizing the data into a structured format, converting each SKU into its own text file. While this example includes a limited number of files, in a real-world scenario, this process would be scaled up to handle millions of SKUs and descriptions. This data organization step is crucial for efficient processing and retrieval of information.
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