# πŸ‡ΊπŸ‡Έ Glancer β€” Ask your Rails database questions in plain language > Source: > Published: 2026-05-27 17:11:10+00:00 A Rails engine that allows you to query the database using natural language, with RAG and LLMs. No need to write any code. Every Rails project reaches a point where someone on the team (product, support, management) needs data, but no one has time to write a quick query for them. You know the drill: a Slack message, a GitHub issue titled "how many users signed up last week?", or a CSV request that ends up in someone's backlog. So, I think about **Glancer**. Glancer is a Ruby on Rails engine that mounts a full chat interface at `/glancer` inside your app. You type a question in plain language, it retrieves the relevant schema context, generates a `SELECT` query (or an ActiveRecord expression), validates and executes it safely, then returns the result with a human-readable explanation. ``` "How many orders were placed in the last 30 days, grouped by status?" β†’ SELECT executed, results shown, answer written in plain language. ``` It is inspired by [Blazer](https://github.com/ankane/blazer), but instead of writing SQL yourself, you just ask. Add it to your `Gemfile` : ``` gem "glancer" ``` Run the generator: ``` rails generate glancer:install rails db:migrate rails glancer:index:all ``` Then visit `/glancer` and start asking questions. The generator creates an initializer where you configure your LLM provider. A minimal setup with Gemini looks like this: ``` Glancer.configure do |config| config.llm_provider = :gemini config.llm_model = "gemini-2.0-flash" config.gemini_api_key = ENV["GEMINI_API_KEY"] config.schema_permission = true end ``` OpenAI and OpenRouter are also supported. It runs on top of [RubyLLM](https://rubyllm.com/), so any model it supports works here too. You can assign different models per role: a smarter model for query generation, a cheaper one for writing the response. A few things worth knowing: **Safety is not optional.** Every query runs inside a transaction that always rolls back. The keyword blocklist rejects `DELETE` , `UPDATE` , `INSERT` , `DROP` , and friends before they even touch the executor. You can also point it at a read-only replica. **No extra infrastructure.** Embeddings live in your existing database as a JSON column. No Pinecone, no Weaviate, nothing to set up. **It understands your domain.** You can drop a Markdown file at `config/glancer/llm_context.glancer.md` and teach Glancer your business rules β€” what `status` values mean, how revenue is calculated, which columns to ignore: ``` - `orders.status` values: "pending" | "paid" | "shipped" | "refunded" - Monthly revenue = SUM(orders.total) WHERE status = "paid" - When asked about "churn", use the `churned_at` column on `subscriptions` ``` **The UI has some nice touches.** Results render in a table with one-click CSV export. Charts are auto-generated where they make sense. Queries run in a background thread, so the main app thread stays free. You can type `@table_name` to pin a specific table to your question, edit the generated SQL inline and re-run it, and even dictate questions via microphone. There is also a schema viewer at `/glancer/db-schema` . **Blazer integration.** If you already have Blazer installed, Glancer shows a button to open the generated SQL there directly. This started as a study project. I had never published a gem before and wanted to understand the full process, from engine internals to gemspec to CI. I shared it with a few colleagues and they actually found it useful, so I decided to put it out there for the community. It is version 1.0.0. If you try it, I would love to hear what you think, about the code or the idea itself. Issues and pull requests are open. If you find it useful, a star on the repo goes a long way in helping more people discover it. **GitHub:** [https://github.com/ErnaneJ/glancer](https://github.com/ErnaneJ/glancer) **RubyGems:** [https://rubygems.org/gems/glancer](https://rubygems.org/gems/glancer)