Crafting an AI Chat Assistant: Step-by-Step Guide

Crafting an AI Chat Assistant: Step-by-Step Guide

Table of Contents

• Introduction
• Setting Up the Environment
  • Installing Dependencies
  • Importing Libraries and Datasets
• Managing Test Cases
  • Uploading Test Cases to Brain Trust
• Creating a Chroma Database
• Defining a Pipeline and Prompt
• Evaluating the AI App
  • Implementing Evaluation Functions
  • Running Experiments
• Analyzing Results
  • Digging into Test Cases and Results
• Improving the Pipeline
  • Introducing Pipeline B
  • testing and Comparing Results
• Conclusion
• FAQs

Introduction

🤖 Building an AI Chat assistant that accesses user information is an exciting endeavor. Today's Tutorial focuses on developing an AI assistant capable of leveraging a vector database for user data retrieval and utilizing Brain Trust for evaluation and approval.

Setting Up the Environment

Installing Dependencies

To begin, we'll set environment variables for OpenAI and Brain Trust. These variables are obtained from respective dashboards. Next, we'll install essential dependencies, including the Branches Python library and auto-evals library, facilitating model-grade evaluations.

Importing Libraries and Datasets

Importing libraries and datasets is crucial. For this tutorial, we'll include various test cases and a database of user facts, generated by a language model. These resources will aid in training and testing our assistant.

Managing Test Cases

Uploading Test Cases to Brain Trust

Efficient management of test cases is pivotal. We'll upload our test cases to a Brain Trust dataset, streamlining Team Collaboration and experiment tracking.

Creating a Chroma Database

Now, let's delve into the creation of a Chroma database—a vector DB designed to store user details and facts securely.

Defining a Pipeline and Prompt

Our next step involves defining a pipeline and prompt capable of extracting Relevant facts from the vector database based on user input or queries. This ensures our AI assistant provides accurate responses.

Evaluating the AI App

Implementing Evaluation Functions

Brain Trust offers evaluation functions to streamline app assessment. By defining a data function and task, we can automatically evaluate our app's performance across various inputs.

Running Experiments

Executing experiments enables us to gauge the effectiveness of our app. By analyzing scores and outputs, we gain insights into its functionality.

Analyzing Results

Digging into Test Cases and Results

Through the Brain Trust UI, we can delve into test cases and results, identifying areas of improvement and ensuring optimal performance.

Improving the Pipeline

Introducing Pipeline B

We'll develop a new pipeline (Pipeline B) and prompt designed to enhance response accuracy. By incorporating multiple facts and avoiding apologies, we aim to refine our AI assistant's performance.

Testing and Comparing Results

Conducting experiments allows us to compare results between the new and old pipelines, validating improvements and ensuring enhanced functionality.

Conclusion

That concludes our tutorial on building an AI chat assistant. Explore our documentation for further examples, and don't hesitate to share feedback with us via Discord or email. Thank you for joining us on this journey!

FAQs

Q: How can I access the documentation for additional examples?

A: You can find our comprehensive documentation on our website, which includes a range of examples and use cases for building AI chat assistants.

Q: Is Brain Trust suitable for evaluating other types of AI applications?

A: Yes, Brain Trust offers versatile evaluation functions that can be applied to various AI applications beyond chat assistants. Feel free to explore its capabilities for your specific use case.

Q: Can I integrate additional databases with the Chroma database for more extensive user data retrieval?

A: Absolutely! The Chroma database supports integration with external databases, allowing for seamless access to a wealth of user information for enhanced AI interactions.