This blog explains how to improve Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) using an innovative Python library called LlamaIndex. The author first shows the necessary Python libraries and their related installation commands.
The next step is to set up the knowledge base, which involves defining various parameters for the embedding model, chunk size, and chunk overlap. The blog uses the Bge-small-en-v1.5 embedding model from BAAI, noting that other model options are available on the text embedding leaderboard. The author then imports a set of documents (in PDF format), chunk them into smaller pieces, and discards non-relevant sections to form a list of cleaned documents. These chunks are then saved in a VectorStoreIndex for future use.
The author then details how to set up the retriever, a tool to extract the most similar chunks to a user query. It returns the top three most similar chunks to a user query when initialized with LlamaIndex’s VectorIndexRetriever(). A query engine is also built, which retrieves relevant chunks based on the user’s queries.
For actual use, a question (“What is fat-tailedness?”) is passed to the query engine to determine which chunks are relevant. The result is a context-relevant response object that lists out the text, metadata, and indexes of relevant chunks.
The author also outlines how to download the fine-tuned model from the Hugging Face hub to improve model performance. To do this, another prompt template is designed that includes context from the RAG system. The related Python code to accomplish this is also discussed.
The author concludes by noting that Retrieval-Augmented Generation (RAG) makes the explanation process more accurate, particularly when incorporating context. This approach, therefore, significantly improves an LLM system’s effectiveness by providing domain-specific knowledge.
While the blog primarily focuses on the implementation of RAG using LlamaIndex and the associated result, the author also promises to further explore how semantic search and classification tasks can benefit from text embeddings in future articles.
