Natural Language to MongoDB Queries: How I Built It

This post is being written. The outline and key topics are below — full content coming soon.

The problem

Non-technical users in a business should be able to ask questions about their data without knowing MongoDB query syntax. Questions like:

• "Show me all customers who haven't paid in the last 30 days"
• "Which product sold the most last quarter?"
• "List all orders above ₹50,000 from Mumbai"

And get real answers — not a dashboard they have to navigate.

The approach

I built a pipeline that:

1. Takes a plain English question as input
2. Sends it to Gemini with the MongoDB schema as context
3. Gets back a structured query (aggregation pipeline or find query)
4. Validates the query for safety before execution
5. Runs the query and formats the result in plain English

The stack

• LangChain — orchestration and prompt chaining
• Gemini — natural language to query conversion
• MongoDB — data store
• NestJS — API layer
• Schema introspection — dynamic schema passed to the model as context

Key topics

1. Schema-aware prompting (passing collection structure to the LLM)
2. Handling ambiguous queries (asking for clarification vs guessing)
3. Query validation before execution (preventing destructive queries)
4. Formatting results back in plain English
5. Handling aggregation pipelines vs simple find queries
6. Fallback when the LLM returns invalid MongoDB syntax

Coming soon

Full walkthrough with prompt templates, validation code, and real examples.