opendbpylot

Ask your database questions in plain English and get back SQL, results, and charts.

opendbpylot turns a natural language question into SQL, runs it against your database, and returns the rows. It uses retrieval augmented generation (RAG): you train it on your schema and business rules, and it retrieves the relevant pieces so the model can write accurate SQL. Every generated query is validated against your real schema and repaired automatically before it runs.

Install

opendbpylot is published to three registries. Each one installs the same dbpylot command, which includes a setup wizard, a terminal chat, and an embedded web app. No Rust toolchain is needed for the Python or Node packages; they ship prebuilt binaries.

Once it is installed:

dbpylot init      # configure an LLM provider and a database
dbpylot           # chat with your database in the terminal
dbpylot serve     # open the web app in your browser
dbpylot ask "revenue by product category last quarter"
dbpylot doctor    # check your LLM and database connections
dbpylot status    # show the current configuration
dbpylot demo      # try it offline on a sample database

dbpylot init walks you through choosing a provider (OpenAI, Anthropic, or a local Ollama model), stores the API key in an encrypted vault, and connects a database. If you prefer a UI, dbpylot serve opens the same setup in the browser.

Features

• Natural language to SQL over your own database.
• Automatic query repair. Hallucinated columns and syntax errors are caught against your real schema and corrected before the query runs.
• Hybrid retrieval that combines keyword search (BM25) and vector similarity, so the right tables reach the prompt even on large schemas.
• Four databases: SQLite, PostgreSQL, MySQL, and DuckDB (query CSV and Parquet files directly).
• Three LLM providers: OpenAI, Anthropic, or a fully local Ollama model. API keys are stored encrypted, never in plain text.
• One binary for both the CLI and the web UI. The frontend is compiled in.
• Read only by default. Only SELECT and WITH queries run automatically; writes are blocked.
• Resilient in production: request timeouts, automatic retries on transient failures, and a prompt budget that keeps large schemas within the model's context window.

Web app

dbpylot serve starts a local web app at http://127.0.0.1:8080 with a chat interface that renders SQL, result tables, and Plotly charts. The whole UI is a Lit web component compiled into the binary, so there is nothing extra to deploy. From the sidebar you can:

• Configure the LLM and database in Settings, then connect with one click.
• Add documentation, table definitions, and example queries to train the model.
• Keep multiple conversations that persist across restarts.

On first run the app opens Settings and stays disconnected until you configure a provider and a database, so it never returns fake answers.

Use as a library

Besides the CLI, opendbpylot is a library in all three ecosystems.

Rust:

use opendbpylot::opendbpylot::OpenDbPylot;

let answer = bot.ask("How many orders shipped last week?").await?;
println!("{}", answer.sql);

Python:

import opendbpylot

bot = opendbpylot.OpenDbPylot()
result = bot.ask("orders per country")
print(result["sql"])

Node:

import { OpenDbPylot } from "opendbpylot";

const bot = new OpenDbPylot();
console.log(bot.ask("orders per country").sql);

See python/README.md and node/README.md for the full API.

How it works

question
  1. retrieve related schema, docs, and example queries   [vector store]
  2. assemble a prompt within a token budget              [prompt]
  3. generate SQL                                         [llm]
  4. validate against the schema, repair on failure      [schema + repair loop]
  5. run read only and return rows                       [sql runner]

You train the model once with three kinds of material:

bot.train_ddl("CREATE TABLE orders (id INTEGER, customer_id INTEGER, status TEXT, total REAL);").await?;
bot.train_documentation("Revenue excludes orders with status 'cancelled' or 'refunded'.").await?;
bot.train_question_sql("orders per status", "SELECT status, COUNT(*) FROM orders GROUP BY status;").await?;

Training material is embedded and stored in a vector store. At query time the most relevant tables, docs, and examples are retrieved and assembled into the prompt.

Architecture

Every layer is a Rust trait, so backends are interchangeable without touching the core.

Optional Cargo features: qdrant, keychain, fastembed, and duckdb. PostgreSQL and MySQL are on by default via the remote-db feature; use --no-default-features for SQLite only.

Project layout

opendbpylot/
  src/
    cli.rs           the dbpylot CLI (init, chat, ask, serve, doctor, status, demo)
    server.rs        axum web server with the embedded UI
    opendbpylot.rs   orchestrator: train, ask, and the repair loop
    app.rs           builds the engine from settings and the secret vault
    prompt.rs        assembles the prompt within a token budget
    schema.rs        pre execution schema validation
    retrieval.rs     hybrid BM25 and vector ranking
    sql.rs           SQL extraction and the read only gate
    llm/             LlmService: openai, anthropic, ollama, retry
    embedding/       EmbeddingService: local, openai, cache, fastembed
    vectorstore/     VectorStore: memory, file, qdrant
    sqlrunner/       SqlRunner: sqlite, postgres, mysql, duckdb
    conversation.rs  conversation history (memory and file)
    secret.rs        encrypted secret vault
    settings.rs      user settings
    bin/dbpylot.rs   thin entry point that calls cli::run()
  frontends/         TypeScript and Lit web component, compiled into the binary
  python/            Python bindings (pyo3 and maturin)
  node/              Node bindings (napi-rs)
  tests/             cross backend integration tests
  examples/          accuracy evaluation harness
  .github/workflows/ release automation for crates.io, PyPI, and npm

Development

cargo test                    # unit tests
cargo test --test backends    # cross backend integration tests
cargo run -- serve            # run the web app from source
cargo run --example eval      # accuracy benchmark (needs an OpenAI key)

Full documentation is in DOCUMENTATION.md.

License

Apache 2.0. See LICENSE.