LLM Bootcamp - Module 13 - Build A Multi-Agent LLM Application
In the final day of the LLM Bootcamp, learners will apply all the knowledge gained during the course to build a fully functional LLM application. The goal is to combine various concepts from the bootcamp, such as agent-based systems, LLM architectures, and cloud deployment, to create a real-world application. In this guide, we will outline the steps for building one of the following projects:
Basic Chatbot: A simple conversational agent designed to answer general queries.
Chatbot Agent: An advanced agent that integrates with your data to provide tailored responses.
Chat with Your Data: An application that allows users to upload documents (e.g., PDFs) and interact with the content through queries.
1. Project Overview and Goals
By the end of this bootcamp, you will have built a fully operational LLM application deployed on a public cloud platform (e.g., Streamlit). The goal is not only to create a working application but also to understand the deployment process, continuous integration and continuous deployment (CI/CD), and how to manage cloud resources.
2. Project Choices
Each learner will select one of the following project options, depending on their preference and desired complexity:
2.1. Basic Chatbot
A Basic Chatbot is a conversational agent designed to handle general queries without the need for specific domain knowledge or integration with external data sources. This is an ideal project for beginners who want to understand the basics of LLMs and chatbot creation.
Key Features:
Simple question-answering capability.
Can handle common queries such as greetings, weather, and simple facts.
No integration with external data or documents.
2.2. Chatbot Agent
A Chatbot Agent is an advanced version of the basic chatbot. It integrates with your specific data sources, allowing it to provide more tailored responses based on the user’s input. This project allows you to experiment with data integration, API calls, and more complex interaction logic.
Key Features:
Integration with an external knowledge base or API (e.g., knowledge graphs, industry-specific data).
Use of retrieval-augmented generation (RAG) to fetch and incorporate real-time information into the conversation.
Tailored responses that consider the context of user inputs and pre-defined data.
2.3. Chat with Your Data
This project allows users to upload documents (e.g., PDFs) and interact with them through queries. The goal is to integrate document retrieval, semantic search, and LLM-powered question answering to allow users to query the contents of documents directly.
Key Features:
Uploading documents in formats such as PDFs, Word docs, etc.
Extracting text from documents and processing it using LLMs.
Allowing users to query documents and receive contextually relevant responses.
Incorporating text chunking, embedding models, and retrieval systems.
3. Key Components for the Project
3.1. Comprehensive Datasets
Learners will have access to a wide range of datasets across different industries, including:
Legal: Contracts, case law, legal dictionaries.
Healthcare: Medical journals, research papers, drug information.
Finance: Stock reports, market analysis, financial statements.
Customer Support: Knowledge base articles, product manuals, FAQ databases.
These datasets will be used to populate your project’s data, enhancing the application’s functionality and helping to tailor responses to specific user needs.
Key Takeaways:
The datasets provide diverse data sources to enrich your LLM application’s responses.
You will have the freedom to integrate and query these datasets based on your project’s requirements.
3.2. Step-by-Step Implementation Guides
Each learner will receive detailed, step-by-step guides that cover:
Project Setup: Setting up the development environment and required libraries (e.g., LangChain, OpenAI).
Data Integration: How to load and preprocess your datasets.
Model Integration: How to interface with LLMs using Model I/O components and integrate various APIs or tools.
Application Flow: Structuring your application using chains, agents, and memory.
Deployment: Detailed instructions on deploying your project to Streamlit, including setting up the CI/CD pipeline.
Key Takeaways:
Implementation guides help you throughout the process, from development to deployment.
3.3. Ready-to-Use Code Templates
To streamline the development process, learners will receive:
Code templates in Data Science Dojo’s sandbox environment.
These templates provide starting points for core functionalities like chatbot responses, document uploading, and data retrieval.
Key Takeaways:
Code templates make it easy to jump-start your project and focus on building out unique features.
Templates can be customized to fit the specific needs of your project.
3.4. Cloud-Based Resources
OpenAI API Key: Learners will get access to an OpenAI key to interact with GPT models.
Streamlit Deployment: You’ll deploy your LLM application on Streamlit, a cloud-based platform, for real-time usage.
Continuous Integration/Deployment (CI/CD): A CI/CD pipeline will be set up for effortless application updates and maintenance. This pipeline ensures that code changes are automatically tested and deployed.
Key Takeaways:
Cloud-based resources facilitate a seamless development and deployment experience.
CI/CD ensures that your application can be easily updated and maintained.
4. Building the Application
Here’s a high-level breakdown of how to approach building your chosen application:
4.1. Setup
Environment: Install necessary libraries (e.g., OpenAI, LangChain, Streamlit).
API Integration: Set up OpenAI API access and configure any additional APIs (e.g., for document retrieval).
Data Import: Load your chosen dataset(s) and preprocess them for use within the application.
4.2. Design the Application Flow
Create a Chain: Use LangChain to create a chain that will manage the workflow (e.g., querying the user, retrieving data, generating responses).
Define Memory: Implement memory to retain context across multiple interactions (useful for Chatbot Agent or Chat with Your Data).
Agent Integration: For more complex tasks, implement agents that can use external tools to gather information or act on queries.
4.3. Implement User Interface (UI)
Chat Interface: Use Streamlit to create an interactive web-based chat interface, allowing users to input queries and receive responses.
Document Upload: For the "Chat with Your Data" project, implement file upload functionality so users can upload documents for querying.
4.4. Testing
Functional Testing: Test the application to ensure that it handles basic and advanced queries as expected.
User Testing: Conduct user testing to ensure the application is intuitive and useful.
4.5. Deployment
Deploy the application on Streamlit, making sure it’s accessible through a public URL.
Set up CI/CD: Ensure that your code is automatically tested and deployed as changes are made.
Key Takeaways:
Step-by-step implementation allows you to incrementally build and deploy your project.
User testing and feedback ensure that your LLM application meets real-world needs.
5. Conclusion
At the end of the LLM Bootcamp, you will have:
A fully operational LLM application deployed on Streamlit.
Hands-on experience integrating LLMs with external data and building multi-agent systems.
Knowledge and skills to deploy and scale applications using cloud platforms and CI/CD pipelines.
Practical experience in building applications for real-world tasks like chatbots, document querying, and knowledge integration.
This project will solidify your understanding of the concepts covered throughout the bootcamp and provide you with a portfolio-ready application to showcase your skills.