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Project Walkthrough: askFSDL

Project by Charles Frye. Published May 9, 2023.

View the project repository.

Interact with the bot on our Discord.

Chapter Summaries

SWE Tooling: make, precommit, etc

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  • Walked everyone through the code base for the Discord bot they interacted with
  • Sourced question-answering over a corpus of information using Vector storage for retrieval
  • GitHub repo available for this project, but may not be able to execute the code without accounts on all services
  • Makefile created for easier project management, setting up environment and authentication, and running setup commands
  • Incorporated software tools like pre-commit checks, black for Python auto-formatting, and rust-powered formatter
  • Shell check tool useful for catching issues in bash scripts

Data Cleaning

Chapter 1 Cover Image

  • Initial approach of scraping data and chunking into smaller pieces did not yield good results
  • Improved results by spending time understanding the data and preserving the structure during processing
  • Extracting textual information from other sources like images and YouTube videos can enhance the usefulness of language models
  • Sometimes simple solutions to specific data sources and problems can greatly improve the quality of results
  • The unglamorous work of getting to know the data and writing code to manage it properly can result in big dividends for language model applications

Infrastructure: Modal

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  • Discussed the ETL component of extracting, transforming, and loading data from various sources
  • Discussed using Python packages for data transformation and addressing dependency issues with tools like pre-commit
  • Explained the benefits of the modal component in creating lightweight virtual containers for different tasks
  • Modal containers are fast and efficient, aiding in quick development cycles and allowing for containerization without the pains of traditional Docker images
  • Modal also allows for the creation of serverless applications with auto-scaling and resource management
  • Debugging and local development can be done through the interactive mode by connecting to a container running on modal
  • showModal provides an interface for tracking application activity, utilization, and resource allocation, making it a versatile tool for various projects

Frontend: Gradio & Discord

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  • Introduced Gradio user interface, allowing users to create interfaces in pure Python
  • Gradio UI is flexible, supported by Hugging Face, and rapidly adopting machine learning features
  • Examples of Gradio UI use include Alpaca, Flamingo, and Dolly mini
  • Gradio UI is easy to set up, portable, flexible, and comes with an API with OpenAPI spec
  • Discord bot integrated with Python library; alternative library is also available
  • Gradio UI is built on FastAPI for asynchronous Python web service
  • Application mainly runs on the model's infrastructure in containers, serving traffic as needed

Embeddings & ETL

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  • Used OpenAI's ada002 model to generate embeddings, which are much cheaper than generation endpoints
  • Currently using a vector index for data storage, but considering adding additional types of search
  • Discussed processing PDFs in a previous lecture, mentioned using local code to extract URLs and using a map function with controlled concurrency
  • Retrieval results are put into the zero-shot problem using an F-string template in LangChain's prompt template
  • Compared LangChain to Hugging Face Transformers Library as a framework and mentioned that their code is often simple, but valuable for its interface and compatibility with other tools

Monitoring & Improvement: Gantry

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  • Top three challenges in bringing the spot to the next level: improving retrieval, improving the quality of model outputs, and identifying a solid user base.
  • Using tools like Datadog, Sentry, Honeycomb, and Gantry for handling web services, logging, and monitoring model behavior.
  • The same principle of tracing and monitoring applies to both ML-powered apps and LLM-powered apps.
  • Gantry provides a useful service for tracking and enriching logged data, including toxicity checks and other natural language-based or numerical analyses.
  • Using language models to check on the performance and outputs of other language models.
  • Contributing to the development of the tool as a teaching and learning application is open and encouraged.

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