This image outlines the application infrastructure architecture of the DataSageGen chatbot system, focusing on security and scalability. The various components are:
1. User Access: Users start by accessing the DataSageGen chatbot, through a web interface.
2. Identity Aware Proxy (IAP): As users attempt to access the DataSageGen chatbot, the IAP acts as a gatekeeper.
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Authorized access: IAP allows users with the right credentials (authenticated and authorized) to proceed.
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Unauthorized access: Users without proper credentials are blocked, ensuring that only legitimate users can interact with the chatbot.
HTTPS Cloud Load Balancing: Once past the IAP, the architecture employs HTTPS Cloud Load Balancing to manage incoming traffic. This provides global scalability and fast, 6 sec failover, meaning it can handle a high volume of requests and quickly reroute traffic if a part of the system fails.
3. Cloud Run: This is the final step, where the DataSageGen chatbot application is hosted. Cloud Run allows for application scalability and manages costs effectively, automatically scaling based on incoming requests and charging only for the compute resources used during request processing.
Overall, the DataSageGen chatbot application architecture emphasizes secure access control with IAP, robust traffic management with HTTPS Cloud Load Balancing, and efficient resource use and scalability with Cloud Run.
Conclusion
Knowledge-based chatbots are different. They don’t just spit out pre-programmed responses. Instead, they use large language models (LLMs) to analyze your entire knowledge base – FAQs, help articles, product catalogs – and generate natural, human-like responses that are contextually relevant. The benefits are immense:
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Aggregate your knowledge and capabilities in one place
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Get the right answers within the context, allowing teams to spend more time on their core activities
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Enjoy 24/7, multilingual support: No more waiting for agents — customers get instant help, regardless of the time of day or what language they speak.
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Scale your support without hiring: Automate repetitive queries, free up agents, and clear ticket backlogs.
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Future-proof your contact center: Handle unexpected surges in support volume without breaking a sweat.
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Get started quickly and easily: No technical expertise needed. Connect your knowledge base and you’re good to go.
But remember, to take advantage of generative AI to build a next-generation chatbot, you need to:
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Prep your knowledge base: Ensure information is up-to-date, consistent, and text-based.
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Set guardrails: Define what questions your bot can answer and what kind of responses it can give.
With these considerations, knowledge-based chatbots can revolutionize customer support, offering enhanced experiences, increased efficiency, and a future-proof solution for your business.
The field of generative AI is rapidly evolving, requiring ongoing adaptation and refinement of chatbot solutions.The DataSageGen architecture helps ensure that you get the most accurate and relevant information possible, saving you time and effort, while also being built with security and scalability in mind. It uses Identity Aware Proxy (IAP) to control access, HTTPS Cloud Load Balancing for efficient traffic management, and Cloud Run for cost-effective scalability. Whether you’re a data engineer, product manager, or simply curious about data and AI, DataSageGen is an invaluable tool for anyone looking to deepen their understanding and navigate this complex field with ease.
To learn more about BigQuery’s new RAG and vector search features, check out the documentation. Use this tutorial to apply Google’s best-in-class AI models to your data, deploy models and operationalize ML workflows without moving data from BigQuery. Check out this github repository to see how you can deploy such an application with your own corpus. You can also watch a demonstration on how to build an end-to-end data analytics and AI application directly from BigQuery while harnessing the potential of advanced models like Gemini.
