Building a RAG Pipeline with llama.cpp in Python

A new guide details how to create a retrieval augmented generation pipeline using llama.cpp in Python. This framework allows efficient inference of large language models on local devices, particularly those using CPUs. The article outlines the necessary package installations and the process of setting up a local large language model.

To establish a retrieval augmented generation system, users should first download a relatively smaller model, specifically the Llama 2 7B quantized model from Hugging Face. The guide emphasizes the importance of creating a document base to read various formats, including .doc and .txt, and generating a default sample text document.

The article explains the need to process documents by splitting them into chunks, enhancing retrieval accuracy. A vector store is then built to contain embeddings of text documents, utilizing Chroma, a lightweight and open-source vector database.

Following the setup, llama.cpp is used to initialize the language model with specified settings. The guide also introduces a RAG prompt template, which structures the input for the language model during inference.

Finally, the article showcases how to implement a function that runs the RAG pipeline to respond to user inquiries, demonstrating the practical application of the created pipeline.