The document also provides a detailed guide on how to install and use the RAG model. It includes instructions on setting up a Conda environment, installing necessary packages, and implementing the RAG model. It also provides a function to convert PDF to OCR and a guide on how to load a single PDF from a directory, chunk the text, and create a Vector Store Index. The document concludes with instructions on how to define a query engine and feed in a user query.
Key takeaways:
- The Personal Assistant tool leverages Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM) to revolutionize PDF document analysis tasks by combining machine learning with retrieval-based systems.
- RAG is a powerful technique in natural language processing that combines retrieval-based methods with generative models to produce more accurate and contextually relevant outputs.
- The RAG model consists of three main components: Indexer, Retriever, and Generator. The Indexer creates an index of the corpus, the Retriever retrieves relevant documents from the indexed corpus based on the input query, and the Generator produces a response based on the input query and the retrieved documents.
- The RAG model is trained in three stages: Indexer Training, Retriever Training, and Generator Training. During inference, the RAG model follows these steps: Indexing, Retrieval, and Generation.