Build Intelligent Chat Applications with Berry AI

Build Intelligent Chat Applications with Berry AI

Table of Contents

1. Introduction
2. What is Berry AI?
3. Connecting to a Data Source
4. Setting up the Database Connection
5. Selecting a Column for Semantic Search
6. Choosing the Search Strategy
7. Selecting the Intent
8. Building the Chat GPT Instance
9. Interacting with the Embedded Data
10. Using the API Endpoint
11. Adding a New Row to the Database
12. Refreshing the Application
13. Conclusion

📚 Introduction

In this article, we will explore the capabilities of Berry AI and how it can be used to build chat applications using GPT-3 or GPT-4. Berry AI allows you to connect a data source and embed that data, enabling you to ask questions and interact with the data using your preferred GPT model. We will walk through a demo of connecting GPT-3/gpt-4 to a Postgres database and demonstrate how to use the embedded data for semantic search and question-answering.

📚 What is Berry AI?

Berry AI is a powerful tool that enables developers to build applications that utilize chat with GPT-3 or GPT-4 models. It provides a seamless way to connect to a data source and embed the data for easy interaction. With Berry AI, you can harness the capabilities of GPT models to process and retrieve information from your data with great accuracy.

📚 Connecting to a Data Source

To begin using Berry AI, you need to establish a connection to your desired data source. This can be achieved by specifying the hostname, port, username, and password for the database. Once the connection is established, you can proceed to select the table you want to use for your instance.

📚 Setting up the Database Connection

After connecting to the database, you can view the tables available in your database. Choose the Relevant table for your instance. In this demo, we will be using the "ramp FAQ" table, which contains frequently asked questions. Select the table and proceed to the next step.

📚 Selecting a Column for Semantic Search

Next, you need to choose a specific column from the selected table to perform semantic search over. In this case, we will choose the "raw data" column to conduct our semantic search. By selecting this column, we will be able to ask questions and search for relevant information within the data.

📚 Choosing the Search Strategy

Once the column is selected, you can now decide on the search strategy. Berry AI provides options such as summarization, question-answering, or using raw text for the search process. For the purpose of this demo, we will choose the "summarize" strategy to generate concise summaries based on the raw data.

📚 Selecting the Intent

Since we are working with FAQ data, we can choose between question-answering or document thread as the intent for our instance. Depending on the type of data you are working with, you can select the appropriate intent to achieve the desired results.

📚 Building the Chat GPT Instance

Once all the settings are configured, you can proceed to build your chat GPT instance. This will create a web app that allows you to interact with the embedded data using GPT-3 or GPT-4. Click on the "build" button to generate the web app for your instance.

📚 Interacting with the Embedded Data

With the web app generated, you can now start asking questions and interacting with the embedded data. Enter a question in the chat interface and observe the response generated by the GPT model. The response will be based on the embedded data from the selected column.

📚 Using the API Endpoint

In addition to using the web app, Berry AI also provides an API endpoint for direct integration with your application. This allows you to make API calls and retrieve responses without relying on the web app interface. You can seamlessly incorporate the API endpoint into your application's workflow.

📚 Adding a New Row to the Database

To expand the capabilities of your instance, you can add new rows to the database. By adding more data, you enrich the training and embedding process, enabling your GPT model to provide more accurate responses. Simply add a new row with relevant information and save it in the database.

📚 Refreshing the Application

After adding new data, you need to refresh the application to ensure the changes are reflected in the embedding pipeline. Click on the refresh button in the web app interface to update the embedded data. Once refreshed, you can ask questions based on the new embedding and observe the improved responses.

📚 Conclusion

In conclusion, Berry AI offers a powerful solution for building chat applications that interact with data using GPT-3 or GPT-4 models. By connecting to a data source, embedding the data, and utilizing the capabilities of GPT models, developers can create intelligent chat systems that provide accurate and informative responses. With the ability to add new data and continuously improve the embedded data, Berry AI empowers developers to build advanced chat applications with ease.

FAQ

Q: Can I use Berry AI with any database? A: Berry AI is compatible with various database systems, including Postgres, MySQL, and more. It provides flexibility in connecting to different data sources.

Q: How accurate are the responses generated by the GPT model? A: The accuracy of the responses depends on the quality and relevance of the embedded data. Adding more data and refining the embedding process can lead to improved accuracy.

Q: Is Berry AI suitable for real-time applications? A: Yes, Berry AI can be used in real-time applications. With the provided API endpoint, you can seamlessly integrate chat capabilities into your application's workflow.

Q: Can I use Berry AI for document search? A: Yes, Berry AI supports document search capabilities. By selecting the appropriate intent and configuring the search strategy, you can perform document-based queries.

Q: What is the difference between GPT-3 and GPT-4? A: GPT-3 and GPT-4 are different versions of the GPT model developed by OpenAI. GPT-4 offers enhanced capabilities and improved performance compared to GPT-3.