DemoGPT
Welcome to the documentation for DemoGPT. This application uses OpenAI's GPT-3.5-turbo model to automate the process of creating Large Language Model (LLM) based applications. It refines and generates Python code, constructs LangChain pipelines, and offers an interactive Streamlit interface for users to work with.
Core Functionalities of DemoGPT
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Automatic LangChain Pipelines: DemoGPT creates automatic LangChain pipelines that enable the effortless creation of LLM-based applications. It takes advantage of LangChain, a framework designed for developing applications powered by language models.
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Interactive Demo Generation: DemoGPT empowers developers to create engaging and interactive product demos. It generates applications that showcase key features, providing a hands-on experience with simulated interactions and real-time visualizations.
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Version Control and Management: DemoGPT simplifies version control, enabling seamless management and generation of multiple product versions. It tracks changes to ensure structured version management and allows developers to showcase different iterations and enhancements to stakeholders. (Note: This feature is planned for future development)
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Automated Pipeline Generation: DemoGPT automates pipeline generation, saving developers time and effort. It intelligently constructs data processing pipelines based on specific requirements, streamlining the development process and eliminating the need for manual construction.
Modules in DemoGPT
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Model: The
LogicModel
andStreamlitModel
classes interact with the OpenAI API and generate Python code, respectively. Explore how these two models work together to deliver the core functionalities of DemoGPT. -
Prompts: Learn how we use
ChatPromptTemplate
instances to guide the GPT-3.5-turbo model for code generation, testing, and refining in a structured manner. -
App: Discover the main Streamlit application that integrates all the components and serves as the user interface for our code generator.
Please navigate through the links to learn more about each module.