rrrrrzy / rag-demo

Read this blog post for context: Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs

📦 Setup

With Conda, Mamba, or Micromamba

conda env create -f environment.yaml

Without Conda

pip install -r requirements.txt

🔀🔁 Data collection workflows

Chunk Markdown files in GitHub repositories

Run the repository extraction and Markdown chunking flow. Looks in /flows/config/repo_params.py to specify new configurations; be mindful of repository licensing!

python flows/markdown_chunker.py run

Post-process the results

Post-process the results of the MarkdownChunker flow.

python flows/data_table_processor.py run

Analyze what just happened

Let's inspect the results of these workflows in a notebook.

If you are in a Conda environment, you need to install the kernel like so before opening the notebooks:

pip install ipykernel
python -m ipykernel install --user --name rag-demo --display-name "RAG demo"

Open notebooks/analyze_chunks.ipynb to use the Metaflow Client API to explore the results of the flow runs:

jupyter notebook notebooks/analyze_chunks.ipynb

🦙🧠 RAG time

LlamaIndex

Now that we have indexed a bunch of source objects, let's index them and use them to modify an LLM context window.

You can either use a Llama 2 model, or OpenAI APIs. Letting LlamaIndex use Llama 2 runs locally, so it doesn't require an API key but takes a lot longer. Using OpenAI APIs is faster and cheap. You can create/find your API key here.

Open notebooks/llama_index_exploration.ipynb

jupyter notebook notebooks/llama_index_exploration.ipynb

▶️ Run a demo in Streamlit

🔑 Get an OpenAI API Key

Go here, copy your key value, and set the following environment variable:

export OPENAI_API_KEY=<YOUR KEY>

🤖 Run the app

streamlit run chat_app.py

Setup for indexing workflows

There are two indexing workflows in the /flows folder, one indexes into Pinecone for a VectorDB and another that uses the open-source LanceDB.

🔑 Get a Pinecone API Key

Go here, create a Pinecone account if you have to, copy your API key, and set the following environment variable:

export PINECONE_API_KEY=<YOUR KEY>

Set the following environment variable too:

export GCP_ENVIRONMENT=us-central1-gcp

▶️ Run the flows

python flows/pinecone_index.py run
python flows/lancedb_index.py run