Learn how to build an advanced "Chat with Local Files" application using Retrieval-Augmented Generation (RAG) with a Vector Database. This guide will walk you through the integration of Langchain, ChromaDB, embedding models and local machine learning models to create an intelligent, responsive chatbot capable of understanding and interacting with your data.

During this workshop, you'll setup langchain and chromadb, and build a FastAPI backend + a Streamlit frontend. You'll use embedding models from HuggingF ace and local models running on Ollama. Alternatively, you can use public LLMs with your own key.

We'll ingest a variety of files including PDF, text and DOCX, and discuss how to optimize the LLM prompts for RAG.

You will need the following libraries:
- Ollama (https://ollama.com) with a small model such as phi3 3B, gemma2 2B or access to an LLM via an API key
- An embedding model, such as sentence-transformers/all-MiniLM-L6-v2

To speed up access during the workflow, you can install the recommended libraries: `pip install -U sentence-transformers chromadb sentence-transformers streamlit langchain langchain-community langchain-ollama fastapi jupyterlab python-docx`

Estimated duration: 3 hours.