Query Processing in RAG

Query processing in Retrieval Augmented Generation (RAG) is about understanding and using the questions people ask. It's a key step in getting good answers from RAG systems. When someone asks a question, RAG needs to figure out what they mean and find the right info to answer it.

The first step in query processing is to clean up the question. This means fixing spelling mistakes, removing extra words, and making sure the question is clear. It's like tidying up a messy room so you can find what you need.

Textual transformation

Next, RAG turns the question into a format it can use. This often means turning the words into vectors, just like we do with documents. These vectors help RAG understand what the question is about.

RAG then uses these vectors to search for relevant info in its vector store. This is where semantic search comes in. Semantic search looks for meaning, not just exact words. It helps RAG find info that's related to the question, even if it uses different words.

Sometimes, RAG might change the question a bit to get better results. It might add words or rephrase the question. This is called query expansion. It helps RAG find more relevant info.

RAG also needs to decide how much info to retrieve. If it gets too little, it might miss important details. If it gets too much, it might get confused or take too long to answer. Finding the right balance is important.

Contextual understanding

Query processing also connects to large context windows. With larger windows, RAG can look at more info at once when trying to answer a question. This can help it understand complex questions better.

Some advanced RAG systems use AI to improve query processing. They might learn from past questions to get better at understanding new ones. They might also use context from earlier in the conversation to understand questions better.

In the end, query processing is about bridging the gap between how humans ask questions and how computers understand them. It's a crucial part of making RAG systems that can answer questions accurately and helpfully.