The introduction of Retrieval-Augmented Generation (RAG) to Large Language Models (LLMs) has been a significant advancement, enabling these models to become more specific and tailored to individual needs. From an industry perspective, this innovation aligns perfectly with the demand for more customized AI solutions. However, adding new components like RAG to any system inevitably introduces new interactions and challenges, such as determining the best content retrieval methods and crafting effective prompts.
In today’s blog, we will explore how to combine LangChain’s RAG with DSPy to optimize these systems. So, let’s dive in.
Introduction
What Are RAGs?
RAGs (Retrieval-Augmented Generation) combine two core processes: Dense Vector Retrieval and In-Context Learning. Dense Vector Retrieval transforms a user’s query into a dense vector, which is then used to search a vector store for similar information. This retrieved data provides the model with the necessary context to generate more accurate and relevant responses. In-Context Learning then incorporates this context alongside the original query, structuring it in a prompt that guides the LLM in generating the final output.
What Is DSPy?
DSPy is a framework designed to treat LLMs as programmable modules, enabling automatic prompt optimization and system improvements. This framework can be integrated with RAG systems to enhance the entire generation process by better structuring and augmenting input prompts, ensuring that both the retrieval and generation phases work in harmony.
What Is RAGAs?
RAGAs (Retrieval-Augmented Generation Assessment) is a framework developed to evaluate the effectiveness of RAG systems. It focuses on metrics such as context relevancy, recall, and the accuracy of generated answers. Notably, RAGAs leverages LLMs for evaluation, reducing the need for extensive human-annotated data.
How to Combine RAG and DSPy?
To optimize RAG systems, integrating DSPy can streamline the process by enhancing how prompts are structured and how the retrieval and generation components interact. This combination ensures that the RAG system operates more effectively, delivering better contextual and relevant outputs.
Building Our RAG DSPy Pipeline
In this section, we will walk you through the process of building a RAG DSPy pipeline. By following this tutorial, you will learn how to integrate these systems and optimize your LLM pipelines effectively.
For the complete tutorial, check out the full guide here: Full Tutorial.