Explore Llama-index and NLP synergy in RAG applications. This repo showcases Python code for seamless vector store integration (Chroma-DB, Pinecone). Elevate your projects with LLM-powered NLP and efficient vector storage. Happy coding! 🚀 Explore the power of Llama-index in this series of tutorials focused on building Retrieval-Augmented Generation (RAG) applications. This repository contains Python code showcasing the implementation of different vector stores for efficient RAG application development.
Utilize Llama-index for advanced search and indexing capabilities. Learn how to integrate self-hosted vector storage with Chroma-DB. Explore cloud-based vector storage with Pinecone for scalable solutions. Enhance your RAG applications with versatile vector store functionalities.
main.py: Illustrates the core functionality of Llama-index and vector stores in the context of RAG applications. logic.py: Demonstrates persistence and loading of data using Chroma-DB for self-hosted solutions. init.py: Initialization file for the Python package. setup.py: Configuration file for packaging the code for distribution. Feel free to clone, fork, or contribute to this repository to elevate your RAG applications with Llama-index and versatile vector stores