Elevating Large Language Models to New Levels of Relevance and Accuracy
Implement Retrieval-Augmented Generation(RAG) with LangChain to Enhance Large Language Models(LLMs) to New Levels of Relevance and Factual Correctness
Discover RAG fundamentals with clarity and simplicity, free from technical intricacies here.
This blog post will walk you through how to implement Retrieval-Augmented Generation (RAG) using LangChain and Python.
RAG is a powerful technique that combines the strengths of large language models (LLMs) with external data sources to generate more comprehensive, context-aware, and accurate responses.
In this post, we will implement RAG to answer questions about a PDF file. We will start by reading and processing the PDF’s contents, chunking the large PDF content, and storing the embeddings in a vector database like FAISS or Pinecone. Finally, we will pose a question related to the PDF to the OpenAI LLM and receive a response that utilizes RAG to provide relevant and factual information.
