Implement DeepSeek correctly in a real-time analytics copilot

Question

You have two weeks to ship DeepSeek-powered natural-language querying in a real-time analytics copilot: users ask "Why did checkout conversion drop in the last hour?" and the copilot writes SQL against a streaming analytics database, runs it, and explains the result. What implementation plan, interfaces, and validation steps would you use to avoid production surprises?

Accepted Answer

I would use DeepSeek-V3 (`deepseek-chat`) for the interactive path because it is fast and supports function calling and JSON output, and keep R1 only for optional deep-dive explanations since its reasoning adds latency. The model never touches the database directly: it calls a `run_query` tool, and our code parses the SQL, enforces read-only access, row limits, and timeouts, then returns results for the model to explain. Before launch I would build a golden set of questions with expected results, measure execution accuracy, and roll out behind a flag with cost and latency alerts.

Implement DeepSeek correctly in a real-time analytics copilot

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