AI Content Search (RAG) with Docs Agent | Build with Google AI

Introduction

In this article, we’ll delve into how you can create an AI-powered conversational search solution tailored for your content. Searching for information is one of the most prevalent applications for artificial intelligence language models. The model's capacity to provide detailed answers to specific queries enhances user experience, even if the responses aren't always spot-on. But is it feasible to establish such an experience for your own content and users? The answer is a resounding yes.

Overview of Docs Agent

Docs Agent is an open-source project developed by one of Google’s technical writing teams. This tool interacts with AI language models, such as the one powering Google Bard, and utilizes an innovative method to generate custom prompts to address developer inquiries in a conversational manner.

Demonstration

For this demonstration, we will use Flutter developer documentation, which contains an abundance of useful information. However, let’s say you have a specific question: How can Flutter display long lists when pulling data from a database?

Using the Doc Agent’s web interface, you can pose a question directly to the AI model inherent to your content. Typing in your question and clicking the ask button initiates a brief waiting period while the model processes your inquiry. Upon completion, the tool offers a concise and informative answer.

In our example regarding Flutter and long list displays, the AI suggests using the ListView Builder constructor, which aligns well with the need in our question. The response is enhanced with context, explaining how it derived the answer and providing relevant documentation links for further exploration.

Behind the Scenes

You may wonder how the team managed to pull this off without training an AI language model specifically on Flutter content. The truth is, they implemented a simpler technique. Instead of training or fine-tuning the AI model, they utilized a distinctive method to provide additional context to the model.

When users pose questions, the system converts chunks of text—such as headings and paragraphs—into numerical representations. This data is stored in a vector database. When a question is asked, the system also converts it into a numerical form and searches against the stored data. It identifies the closest context and returns relevant information, complete with related links for further verification.

Customization for Your Content

If you wish to adapt the Docs Agent project for your content, the first task is populating a vector database. This involves breaking your content down into smaller, plain-text chunks—ideally in Markdown format, which supports important formatting like headings. Initially, the team divided content randomly by character count, but found that segmenting by headings yielded better results, offering a more meaningful context for the AI model.

You do not need to train your AI model to implement this solution, making it accessible for a wider range of users. Their goal was to explore how to engage with AI technology in a manner that’s more inclusive and accessible.

Getting Started

To initiate your own project, follow these steps:

1. Clone the Project: Begin by cloning the Docs Agent code repository.
2. Set Up Environment: Complete the installation phase and obtain a Google Palm API key, which should be set as an environment variable.
3. Content Preparation: Gather and prepare your content in Markdown format, including structured headings.
4. Process and Store Content: Use scripts to convert your content into plain text chunks and populate the vector database with embeddings generated via the Google Palm API.
5. Launch the Chatbot: Finally, run the chatbot launch script to start interacting with your AI-powered search interface.

Conclusion

Utilizing AI technology for content search can significantly enhance user interaction. It allows users to ask specific questions and receive tailored responses, emulating human-like interactions. However, it’s vital to implement checks within the system to verify answers, ensuring users can double-check the information provided.

The Docs Agent project exemplifies how a group of curious technical writers has leveraged AI technology to improve documentation and assist users in finding quick answers. For further details, links to the code repository and the tutorial are available in the description.

We encourage you to experiment with this project and look forward to seeing your unique implementations of AI-powered search for your content.

Keywords

• AI-powered conversational search
• Docs Agent
• Open-source project
• Google Bard
• Vector database
• Text embeddings
• AI language models
• Flutter documentation

FAQ

Q: What is Docs Agent?
A: Docs Agent is an open-source project that creates an AI-powered conversational search solution, allowing users to ask questions about specific content and receive accurate answers.

Q: Do I need to train an AI model to use Docs Agent?
A: No, you do not need to train or fine-tune an AI model. The system uses numerical representations of text stored in a vector database to generate responses.

Q: How can I implement this for my content?
A: You can start by preparing your content in Markdown format, then processing it into a vector database to enable the AI to answer questions accurately.

Q: What are text embeddings?
A: Text embeddings are numeric representations of text content that allow the AI system to understand the meaning of the text for matching questions and relevant content.