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Implementing a DIY Retrieval-Enhanced Generation (RERG) app: Basics and Step-by-Step Creation

Large language models such as OpenAI's GPT-4 are currently gaining attention due to the implementation of Retrieval Augmented Generation (RAG), a feature that allows these models to utilize and capitalize on their own data. This article aims to explain the basic concept of RAG and provide a...

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Creating a Retrieval Augmented Generation (RAG) application from the ground up: A step-by-step...
Creating a Retrieval Augmented Generation (RAG) application from the ground up: A step-by-step tutorial for novices

Implementing a DIY Retrieval-Enhanced Generation (RERG) app: Basics and Step-by-Step Creation

In this article, we'll walk you through the process of creating a Retrieval Augmented Generation (RAG) application from scratch, without relying on libraries or external services. This technique allows large language models to use and leverage their own data, offering benefits such as helping the model avoid hallucinations, manually referring to sources of truth, and leveraging data not trained on by the language model.

Step-by-Step Process

  1. Collect and Clean Your Dataset
  2. Gather relevant data from trusted sources suitable for your application's domain (e.g., documents, databases, archives).
  3. Remove duplicates, irrelevant and outdated entries, normalize formats, and address inconsistencies to ensure high-quality input that improves retrieval and generation accuracy.
  4. Prepare the Data for Retrieval
  5. Divide large documents into smaller, manageable pieces (chunks or passages) to allow fine-grained retrieval.
  6. Implement or train a method to convert text chunks into fixed-size numerical vectors representing their semantic content.
  7. Build a Vector Store / Retrieval Index
  8. Design a data structure to store chunk embeddings efficiently.
  9. Create a function to compare query embeddings with stored embeddings using distance metrics.
  10. Retrieve top-k relevant chunks for each user query.
  11. Develop an Embedding Model for Queries
  12. Convert user queries into vector representations using a similar mechanism.
  13. Prompt Augmentation
  14. Combine retrieved information with the user query to augment the model’s context with relevant external knowledge.
  15. Build or Train a Generation Model
  16. Implement a language model from scratch or customize a basic model to consume the augmented prompt and generate text.
  17. Testing and Validation
  18. Test the retrieval system separately for relevance and accuracy.
  19. Test the combined RAG pipeline end-to-end, checking if the generated output aligns with the augmented knowledge and user queries.
  20. Data Updating and Maintenance
  21. Implement procedures to update your knowledge base and embeddings periodically to keep the retrieval information current.

This entire process requires you to implement foundational components usually provided by libraries, including text vectorization methods, similarity search algorithms, and language generation architectures. Building a robust RAG system from scratch is feasible but demands substantial expertise in natural language processing, machine learning, and software engineering.

Potential Areas for Improvement

  • Increasing the number of documents
  • Improving the depth/size of documents
  • Feeding multiple documents to the LLM
  • Chunking documents
  • Changing the document storage tool
  • Altering the similarity measure
  • Pre-processing the documents and user input
  • Changing the LLM
  • Modifying the prompt
  • Implementing a circuit breaker for harmful output
  • Exploring vector stores and embeddings.

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