This publication demonstrates the implementation of a Retrieval-Augmented Generation (RAG) pipeline using LangChain and Pinecone. The goal is to enhance the ability of LLMs (Large Language Models) by integrating them with external document retrieval mechanisms. By retrieving relevant context from a vector database and passing it to the language model, the system generates more grounded and accurate responses.
Document Loading
Documents are loaded using PyMuPDF for PDF reading.
Text Splitting
Documents are split into manageable chunks using LangChain’s RecursiveCharacterTextSplitter.
Embedding
Each chunk is converted into vector embeddings using OpenAI's embedding model.
Vector Storage with Pinecone
These embeddings are stored in Pinecone, enabling semantic search over the content.
Retrieval and Generation
At query time, the system:
User Query: "What is LangChain and how is it used in RAG?"
RAG Output: LangChain is an open-source framework that enables developers to build applications using LLMs by chaining together components such as prompt templates, retrievers, and memory. In RAG, LangChain is used to load and split documents, convert them into embeddings, and retrieve context for LLM queries.
This project successfully implements a basic RAG system using LangChain and Pinecone. It showcases the power of combining vector-based search with language models, and how this synergy results in context-aware, high-quality answers for user queries.