A set of instructional materials, code samples and Python scripts featuring LLMs (GPT etc) through interfaces like llamaindex, langchain, Chroma (Chromadb), Pinecone etc. Mainly used to store reference code for my LangChain tutorials on YouTube.

Learn LangChain from my YouTube channel (~8 hours of LLM hands-on building tutorials); Each lesson is accompanied by the corresponding code in this repo and is designed to be self-contained -- while still focused on some key concepts in LLM (large language model) development and tooling.

Feel free to pick and choose your starting point based on your learning goals:

The full lesson playlist can be found here.

1. Clone this repo
2. Install requirements: pip install -r requirements.txt
3. Some sample data are provided to you in the news foldeer, but you can use your own data by replacing the content (or adding to it) with your own text files.
4. Create a .env file which contains your OpenAI API key. You can get one from here. HUGGINGFACEHUB_API_TOKEN and PINECONE_API_KEY are optional, but they are used in some of the lessons.
   • Lesson 10 uses Cohere and Stability AI, both of which offers a free tier (no credit card required). You can add the respective keys as COHERE_API_KEY and STABILITY_API_KEY in the .env file.

The .env file should look like this:

OPENAI_API_KEY=your_api_key_here

# optionals (not required for most of the series)
HUGGINGFACEHUB_API_TOKEN=your_api_token_here
PINECONE_API_KEY=your_api_key_here

HuggingFace and Pinecone are optional but is recommended if you want to use the Inference API and explore those models outside of the OpenAI ecosystem. This is demonstrated in Part 3 of the tutorial series. 5. Run the examples in any order you want. For example, python 6_team.py will run the website Q&A example, which uses GPT-3 to answer questions about a company and the team of people working at Supertype.ai. Watch the corresponding video to follow along each of the examples.

💡 Thanks to the work of @VanillaMacchiato, this project is updated as of 2023-06-30 to use the latest version of LlamaIndex (0.6.31) and LangChain (0.0.209). Installing the dependencies should be as simple as pip install -r requirements.txt. If you encounter any issues, please let me know.

If you're watching the LLM video tutorials, they may have very minor differences (typically 1-2 lines of code that needs to be changed) from the code in this repo since these videos have been released with the respective versions at the time of recording (LlamaIndex 0.5.7 and LangChain 0.0.157). Please refer to the code in this repo for the latest version of the code.

I will try to keep this repo up to date with the latest version of the libraries, but if you encounter any issues, please: (1) raise a discussion through Issues or (2) volunteer a PR to update the code.

I run a mentorship program under Supertype Fellowship. The program is self-paced and free, with a community of other learners and practitioners around the world (English-speaking). You can optionally book a 1-on-1 session with my team of mentors to help you through video tutoring and code reviews.

MIT © Supertype 2023

In a big data era increasingly defined by the velocity and volume of information, businesses are turning to streaming analytics to make sense of their data in real-time. Streaming data pipelines are designed for ingesting, processing, and analyzing data as it arrives from different sources, affording businesses the opportunity to act on the most up-to-date information available.

In this article, we’ll explore the benefits of streaming analytics and how to build an end-to-end streaming data pipeline using Apache Kafka, Apache Spark, Cassandra, MySQL, Streamlit and Docker.