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A Generative Al Engineer is helping a cinema extend its website's chat bot to be able to respond to questions about specific showtimes for movies currently playing at their local theater. They already have the location of the user provided by location services to their agent, and a Delta table which is continually updated with the latest showtime information by location. They want to implement this new capability In their RAG application.
Which option will do this with the least effort and in the most performant way?
The task is to extend a cinema chatbot to provide movie showtime information using a RAG application, leveraging user location and a continuously updated Delta table, with minimal effort and high performance. Let's evaluate the options.
Option A: Create a Feature Serving Endpoint from a FeatureSpec that references an online store synced from the Delta table. Query the Feature Serving Endpoint as part of the agent logic / tool implementation
Databricks Feature Serving provides low-latency access to real-time data from Delta tables via an online store. Syncing the Delta table to a Feature Serving Endpoint allows the chatbot to query showtimes efficiently, integrating seamlessly into the RAG agent's tool logic. This leverages Databricks' native infrastructure, minimizing effort and ensuring performance.
Databricks Reference: 'Feature Serving Endpoints provide real-time access to Delta table data with low latency, ideal for production systems' ('Databricks Feature Engineering Guide,' 2023).
Option B: Query the Delta table directly via a SQL query constructed from the user's input using a text-to-SQL LLM in the agent logic / tool
Using a text-to-SQL LLM to generate queries adds complexity (e.g., ensuring accurate SQL generation) and latency (LLM inference + SQL execution). While feasible, it's less performant and requires more effort than a pre-built serving solution.
Databricks Reference: 'Direct SQL queries are flexible but may introduce overhead in real-time applications' ('Building LLM Applications with Databricks').
Option C: Write the Delta table contents to a text column, then embed those texts using an embedding model and store these in the vector index. Look up the information based on the embedding as part of the agent logic / tool implementation
Converting structured Delta table data (e.g., showtimes) into text, embedding it, and using vector search is inefficient for structured lookups. It's effort-intensive (preprocessing, embedding) and less precise than direct queries, undermining performance.
Databricks Reference: 'Vector search excels for unstructured data, not structured tabular lookups' ('Databricks Vector Search Documentation').
Option D: Set up a task in Databricks Workflows to write the information in the Delta table periodically to an external database such as MySQL and query the information from there as part of the agent logic / tool implementation
Exporting to an external database (e.g., MySQL) adds setup effort (workflow, external DB management) and latency (periodic updates vs. real-time). It's less performant and more complex than using Databricks' native tools.
Databricks Reference: 'Avoid external systems when Delta tables provide real-time data natively' ('Databricks Workflows Guide').
Conclusion: Option A minimizes effort by using Databricks Feature Serving for real-time, low-latency access to the Delta table, ensuring high performance in a production-ready RAG chatbot.
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