From the course: Building Generative AI Apps to Talk to Your Data
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Building the Streamlit app - Snowflake Tutorial
From the course: Building Generative AI Apps to Talk to Your Data
Building the Streamlit app
- For a moment, imagine that I'm a senior manager that is not very happy with the basic Text2SQL workflow I have. The workflow doesn't deal well with joins and doesn't return very helpful searches of the structured data in my database. To fix this, I spent the time to build out a semantic model and a workflow that leverages agentic AI, and it now produces efficient, effective, and accurate returns. The heavy lifting is done, and let's go the last mile. The last step I need is to build a front end and deploy the workflow to users. This will let the users ask questions in natural language without the need for someone like me to craft a SQL query for them. No more interruptions and no more constant query-tweaking, real-time answers for everyone. More time for me to focus on high-value insights that can generate value. This is where I take the generative ability of AI and bridge the gap between builders and users. This is where all of my effort pays off. The foundation is built. It's time…
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Avoiding death by dashboard4m 51s
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Setting up the app4m 58s
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Understanding the semantic model8m 18s
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Working with the semantic model4m 37s
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Using Cortex Analyst5m 56s
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From text-to-SQL to TAG: Creating table-assisted generation3m 17s
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Expanding the scope of the semantic model12m 11s
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Building the Streamlit app11m 41s
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Congratulations!3m 6s
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