How To Build LangChain RAG With DSPy?
The addition of RAG to LLMs was an excellent idea. It helped the LLMs to become more specific and individualized. From the industry point of view, this is exactly what we wanted. But as we all know, adding new components to any system leads to more interactions and its own sets of problems. Adding RAG to LLMs leads to several problems such as how to retrieve the best content, what type of prompt to write, and many more.
In today’s blog, we are going to combine the LangChain RAG with DSPy. So, without further ado, let’s begin.
Introduction
- What Are RAGs?
- What Is DSPy?
- What Is RAGAs? Evaluating LLM Pipelines
- How To Combine RAG and DSPy?
- Building our RAG DSPy Pipeline
What Are RAGs?
Simply put, RAG is the technique to put additional context for our LLMs to generate better and more specific responses. LLMs are trained on the publicly available data, they are really intelligent systems independently, yet they can’t answer specific questions, because they lack the context to answer those queries. With RAG, we provide the necessary context for them to answer our queries correctly.
