In today's digital age, leveraging artificial intelligence to streamline complex tasks has become increasingly popular. One such application is the development of an AI-powered research assistant.
How to build
Steps
- Splitting the Query into Related Sub-Queries: The first step involves dissecting the main query into smaller, related sub-queries. This breakdown is crucial as it allows the assistant to tackle the query in a more focused and detailed manner, ensuring that all aspects of the main question are adequately addressed. We use a "model chat" action to do this.
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Using a "Google Search" Action: Once the query is segmented, the assistant employs a "Google Search" action. This function enables the AI to scour the internet for web pages related to each of the sub-queries. It's akin to conducting a manual search on Google, but here, the AI performs this task, saving time and effort.
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Crawling Web Page Content: After identifying relevant web pages, the assistant proceeds to crawl these pages. We use another assistant agent to do this for us. Crawling refers to the process of systematically browsing the web to gather information from various web pages. Through this method, the assistant extracts crucial data and content linked to the sub-queries.
- Summarizing and Answering: With the data from crawled web pages at hand, the assistant utilizes another model-chat action to synthesize and summarize the information. Here,we use GPT-3.5, which is known for its advanced language processing capabilities, analyzes the content, extracts key points, and generates a concise summary that addresses the original query.
- Delivering Comprehensive Responses: The culmination of this process is a comprehensive response that encapsulates the essence of the gathered information. The assistant, through GPT-3.5, not only provides a summary but also answers the original question, and provide source of information for you.
Conclusion
The beauty of using an AI platform for this task lies in its simplicity and efficiency. It eliminates the need for intricate coding and leverages the power of AI actions like "Google Search" and advanced language models like GPT-3.5. This approach significantly reduces the workload and time required for research, making it an invaluable tool for students, researchers, and professionals seeking quick and thorough answers to complex queries.
In summary, an AI-powered research assistant developed on ReByte represents a significant leap in automating and enhancing research tasks. It combines the simplicity of using pre-built AI actions with the sophistication of state-of-the-art language models to deliver accurate, detailed, and relevant information, revolutionizing the way we conduct research in the digital era.
Demos
Here, we've already built the app and agents. For more details, click the below links!