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Implement a Copilot

Using your own data?

Incorporating your own data into the solution accelerator requires adapting the existing architecture to align with your specific data structures. Here are some recommendations:

1. Implement Design Patterns and LangChain in Your Solution To effectively integrate AI capabilities, you need to incorporate design patterns that facilitate seamless interaction between your data and AI models. Utilizing LangChain can help in constructing these patterns, enabling efficient data processing and AI orchestration.

2. Customize the chat_functions.py file The chat_functions.py file serves as a bridge between the user inputs and AI responses. To tailor this to your data:

  • Understand the Existing Structure: Review the current implementation to comprehend how data flows and functions are structured.
  • Map Your Data: Identify how your data schema aligns with the existing functions.
  • Modify Functions: Adjust or rewrite functions to query and process your data appropriately, ensuring that the AI services can accurately interpret and respond based on your dataset.

In this section, you will add an AI copilot to the Woodgrove Bank Contract Management application using Python, the GenAI capabilities of Azure Database for PostgreSQL - Flexible Server, and the Azure AI extension. Using the AI-validated data, the copilot will use RAG to provide insights and answer questions about vendor contract performance and invoicing accuracy, serving as an intelligent assistant for Woodgrove Banks users. Here's what you will accomplish:

  • Explore the API codebase
  • Review the RAG design
  • Leverage LangChain Orchestration
  • Implement and test the Chat endpoint
  • Refine the copilot prompt using standard prompt engineering techniques
  • Add and test the Copilot Chat UI component

Following these steps will transform your application into a powerful AI-enhanced platform capable of executing advanced generative AI tasks and providing deeper insights from your data.

What are copilots?

Copilots are advanced AI assistants designed to augment human capabilities and improve productivity by providing intelligent, context-aware support, automating repetitive tasks, and enhancing decision-making processes. For instance, the Woodgrove Bank copilot will assist in data analysis, helping users identify patterns and trends in financial datasets.

Why use Python?

Python's simplicity and readability make it a popular programming language for AI and machine learning projects. Its extensive libraries and frameworks, such as LangChain, FastAPI, and many others, provide robust tools for developing sophisticated copilots. Python's versatility allows developers to iterate and experiment quickly, making it a top choice for building AI applications.