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Materials for London-NY People Analytics Meetup July 2024

These materials allow you to test and build a simple Generative AI application from scratch using a Retrieval Augmented Generation (RAG) architecture. RAG architectures allow greater injection of useful information and context into the prompt sent to a language model.

RAG Architecture

The project converts a large set of comments from readers of the NY Times into vector embeddings and stores them in a local ChromaDB vector database. This is then used to provide context to a questions asked about the opinions of NY Times readers on specific issues.

Prerequisites

To fully complete this build you will need:

  1. A high specification computer, ideally with high RAM and high CPU, and preferably with GPU.
  2. Access to the OpenAI API
  3. ollama installed on your machines (see https://ollama.com)
  4. API access to Kaggle, with the credentials stored in the kaggle.json inside the root directory of this project
  5. All environment variables stored in a .env file in the root of this project.

Steps

  1. Use requirements.txt to set up your Python environment.
  2. Create the vector database and test it using the scripts located in chromadb_prep
  3. Test various versions of the RAG architecture with different language models using the notebooks in jupyter_test_rag_pipeline
  4. Launch the streamlit app using streamlit run app/app.py from project root in the terminal