Build a Voice Assistant with Alan AI and Cloud Firestore

Build a Voice Assistant with Alan AI and Cloud Firestore

Table of Contents

1. Introduction
2. How Alan AI Works with Google Firestore
3. Querying Data from the Database
4. Improvements in Querying Data
5. Getting Product Information
6. Using Dynamic Slots for Product Names
7. Improving Dynamic Slots with Labels
8. Writing Data to the Database
9. Authenticated Requests with Cloud Firestore
10. Conclusion

Introduction

In this article, we will explore how Alan AI can work with Google Firestore, a popular database provided by Google Cloud. We will dive into querying data from the database, retrieving product information, and writing data to the database using Alan AI's voice system. Additionally, we will discuss the use of dynamic slots and authenticated requests with Cloud Firestore.

How Alan AI Works with Google Firestore

Google Firestore is a cloud-hosted, NoSQL database offered by Google Cloud. Unlike traditional databases, Firestore stores data as a collection of documents, making it more flexible and scalable. Alan AI seamlessly integrates with Firestore, allowing developers to interact with the database through voice commands.

Querying Data from the Database

To query data from the Firestore database, we need to send a POST request to an endpoint and include the "run query" parameter. We construct a structured query object that specifies the collection to fetch data from and any desired filters. By using the REST API, we can easily retrieve a list of products above or below a specific price.

Improvements in Querying Data

To enhance the user experience, we can make some improvements to the querying process. We can customize the voice assistant to name the prices of products in a currency format. Additionally, we can modify the function and intent to accept different conditions such as equal, less than, or greater than. This allows users to choose the threshold they want to apply to their queries.

Getting Product Information

Users often want to know more details about specific products. To cater to this need, we can create an intent that captures the user's request to learn about a product. By using dynamic slots, we can retrieve the names of products from the database and adapt to any changes or additions made to the product list. The voice assistant can then retrieve the Product Description from Firestore and provide it to the user.

Using Dynamic Slots for Product Names

Dynamic slots allow us to retrieve data from the Firestore database without maintaining a separate product list in the voice script. We create a function to retrieve the names of products and store them in a list. This list is then used as a dynamic slot, allowing the voice assistant to recognize and adapt to different product names provided by the user.

Improving Dynamic Slots with Labels

To handle variations in user input and ensure accurate retrieval of product information, we can add labels to the dynamic slots. Labels contain the exact values stored in the database, while the value field captures user input. By passing the label to our function instead of the value, we can accurately match product names, even if they are slightly different from what is stored in the database.

Writing Data to the Database

The voice system can also enable users to interact with the database by writing data to it. For example, we may want users to leave comments about the app and store them in a "comments" collection. By creating a function that sends a POST request to Firestore with the comment data, users can easily provide feedback or suggestions through voice commands.

Authenticated Requests with Cloud Firestore

To ensure data security, we can restrict the ability to write data to the database to authorized users only. By obtaining an authentication token for the user in the app and passing it to the voice script using Alan's auth data object, we can authenticate requests. This allows us to include the token as a header parameter in the POST request, ensuring that only authorized users can leave comments or modify data in the database.

Conclusion

Alan AI provides a developer-friendly platform for building dynamic and adaptable dialogues with any data source. By leveraging the power of Google Firestore and Alan AI's voice system, developers can create voice-controlled applications that interact with the database in real-time. This seamless integration opens up new possibilities for intuitive and convenient user interactions.

Highlights

• Using Alan AI with Google Firestore to create voice-controlled applications
• Querying data from Firestore using REST API
• Customizing voice assistant to handle different conditions in queries
• Retrieving product information using dynamic slots
• Adapting dynamic slots to changes in the Firestore database
• Enhancing dynamic slots with labels for improved accuracy
• Allowing users to write data to the database using voice commands
• Implementing authenticated requests for secure data modification
• Enjoying the benefits of a developer-friendly platform with Alan AI

FAQ

Q: Can Alan AI work with other databases apart from Firestore?

A: Yes, Alan AI is compatible with various data sources and can work with different databases, APIs, and platforms.

Q: How does Alan AI handle changes in the database?

A: Alan AI's dynamic slots adapt to changes in the database by retrieving real-time data, eliminating the need to update the voice script manually.

Q: Can users leave comments using voice commands without any additional authentication?

A: By default, Alan AI allows users to leave comments without authentication. However, for enhanced security, authenticated requests can be implemented to restrict access to authorized users only.

Q: Can Alan AI handle multiple queries simultaneously?

A: Yes, Alan AI is designed to handle multiple queries simultaneously, providing a seamless and efficient user experience.

Q: Can the voice assistant understand different languages and accents?

A: Alan AI utilizes advanced natural language processing capabilities and can understand various languages and accents, making it accessible to a global audience.

Resources:

• Google Firestore
• Alan AI Documentation
• Authenticated Requests with Firestore