chromadb tutorial for RAG and LMM performance improvement

In this tutorial, you’ll learn how to build a Retrieval-Augmented Generation (RAG)-powered Large Language Model (LLM) chat application using ChromaDB. ChromaDB is an AI-native, open-source embedding database known for efficiently handling large data sets. Here are the key steps:
Set up the Project Environment:
Create a new directory for your project and set up a virtual environment.
Install the necessary Python packages using pip install -r requirements.txt.
Load and Process Documents:
The LLM application handles various document formats (PDF, DOCX, TXT) using LangChain loaders.
This ensures external data accessibility and efficient data processing.
Implement RAG with Chroma and Llama 2:
Use Chroma to improve the quality of the Llama 2 model.
Integrate ChromaDB into your workflow for seamless retrieval and generation.