Post by Ritikesh Choube

So sometimes our use case is like, at the end, we want a one-word answer. Suppose you are working with an invoice processing system in which you have created a pipeline. First, it will go through the document and fetch the invoice amount, and in the next steps of the pipeline, it will process the payment of that particular amount. So in this, you can use regex, but to what extent? And also, regex is not an intelligent solution. So how to handle this intelligently? Here in RAG (Retrieval-Augmented Generation), there is a retriever method known as "Contextual Compression Retriever", which helps extract only the most relevant part of the document, ensuring accuracy and efficiency. Why Use Contextual Compression? Improved Relevance: Filters out noise and retains only the most important information. Reduced Token Costs: If used with LLMs, it decreases input size, reducing API costs. Better Performance: Helps downstream tasks (like answering questions) by feeding them cleaner, more focused data. To understand properly have a look into my medium article:- https://lnkd.in/dQwnReYF #llm #Ai #RAG #Data #Langchain