💡Quickstart Guide

To start using the context builder, let's build a simple application. In this example, we will:

• Add a data source in the data settings.

• Create and refine a Conversational Retrieval QA to answer questions.

• Debug the application, have a conversation with AI, and review the returned results.

Let's go step by step:

#Create a new Dataset

After creating a data source, you will see an interface with 2 main configurations:

Dataset name: Give your data source a name that will be used for future references.

Document loaders: We allow you to seamlessly import data from any source. Our data loaders perform the following operations:

• Load data from the source.

• Convert data into text or arrays.

• Split the data into smaller segments (with content overlapping).

• Return a list of data segments.

#Create a new application

Let's start by creating a new application in "my app." You will see an interface with 3 pieces of information:

• App Name: Give your application a name.

• Short description: Provide a brief description of this application's features.

• Image: Application icon or avatar.

#Explore the workflow interface

The "Add task" list provides two options:

Tool: Offers pre-packaged, ready-to-use large model tools of different types.

First, let's add a tool type called Conversational Retrieval QA. All configuration options will be displayed in the right drawer. Only two settings are required to complete the process.

• In the "prompt" module, enter the model prompt.

• In the "data" module, add the data source you uploaded earlier.

#Debugging the app

Now that our workflow is configured, click the "enter debug" button to access the debugging interface. You can try having a conversation with it, asking some questions related to the data source, and see how it responds.

#What's next?

Now that we have built an app ready for production, we can continue maintaining and improving it. Some next steps to try are:

1. Increase debugging: Test your app with multiple inputs to evaluate its intelligence on a larger scale.

2. Share: Share the app with users who need it and gather their feedback.

3. Fine-tuning: Use the data collected from users to fine-tune your custom app and optimize its performance for your specific tasks.