QueryQuest

QueryQuest is a Python CLI assistant that converts natural language requests into SQL queries and executes them against Excel workbooks.

What It Can Do

• Read workbook context (sheet names, schema summaries, sample rows)
• Generate SQL from natural language prompts using an LLM
• Preview SQL statements in table form before execution
• Execute SELECT, UPDATE, INSERT, and DELETE via DuckDB
• Join across sheets/workbooks in SELECT reads (workbook__sheet tables)
• Confirm before writing DML changes back to Excel files
• Enforce a hard SQL sandbox (no file-read functions, no admin/DDL, no comments or multi-statements)
• Handle model responses with extra prose around JSON payloads

Architecture

The natural-language-to-SQL logic lives in an async, prompt-free QueryEngine (core/) driven by an approval Policy. The CLI supplies an interactive policy; a future fully-agentic website can drive the same engine with AutoApprovePolicy.

Supported Providers

Configured via OpenAI-compatible chat APIs:

• Gemini
• Groq
• NVIDIA NIM
• Ollama (local)

Project Structure

src/queryquest/
	app.py               # Package entrypoint
	cli.py               # CLI parsing and normalization
	config.py            # Provider and app configuration
	setup_flow.py        # Interactive provider/model/key setup
	state.py             # Persisted setup and Excel context cache
	logger.py            # JSON event logging
	chat_session.py      # Interactive CLI adapter over the engine
	excel/context.py     # Excel discovery and context building
	core/
		engine.py          # Async, prompt-free QueryEngine orchestrator
		models.py          # EngineConfig / EngineResult dataclasses
		policy.py          # Approval policies (interactive / auto / deny)
		llm.py             # Async provider access (AsyncOpenAI)
	sql/
		handoff.py         # JSON extraction from model output
		validation.py      # SQL sandbox (allowlist, blocklist, table check)
		rewrite.py         # Identifier rewriting (JOIN-aware)
		registry.py        # Excel -> DuckDB registration
		execution.py       # Statement execution + DELETE/UPDATE previews
		writeback.py       # DML write-back to workbooks
		executor.py        # Legacy interactive facade (compatibility)
		preview.py         # Rich table rendering

tests/
	test_cli.py
	test_core_engine.py
	test_policy.py
	test_sql_executor.py
	test_sql_handoff.py
	test_sql_preview.py
	test_sql_rewrite_join.py
	test_sql_validation.py
	test_state.py

Requirements

• Python 3.12+
• uv recommended for environment/dependency management

Setup

From the repository root:

1. Create a virtual environment (if needed):

uv venv

2. Install dependencies and package entrypoints:

uv sync

Alternative with pip (works well on Windows):

pip install -e .

3. Run the CLI:

qq

Usage

Setup provider/model credentials:

qq --setup

Run interactive mode:

qq

Run with one prompt:

qq -p "show the top 10 highest list prices"

Show help:

qq -h

Web (Chainlit prototype)

A Chainlit chat UI that drives the same async QueryEngine. Configure a provider first with qq --setup, then launch:

uv run chainlit run chainlit_app.py

Open http://localhost:8000. How it works:

• Attach your data — the app works only on files you attach via the 📎 icon (.xlsx/.xls). They are copied into a per-session temp directory, so your originals are never modified. Attachments accumulate across messages.
• Execution mode (picker below the composer):
  • Human-in-the-loop — confirm before statements run, preview the rows a DML statement will change as a table, then confirm again before saving.
  • Fully agentic — runs and saves automatically; the SQL sandbox is the only safety boundary.
• Results render as interactive Dataframe elements; SELECT JOINs across attached sheets are supported.
• Download — after an UPDATE/DELETE is saved, you get a link to download the edited workbook.

Deploy to Hugging Face Spaces

The repo is ready to run as a Docker Space (the YAML header in this README sets sdk: docker, app_port: 7860). No server-side API key is stored — each visitor brings their own:

1. Create a new Space → Docker → blank, then push this repo (or duplicate it).
2. The included Dockerfile builds the app and serves it on port 7860.
3. On first message, the app asks each user to pick a provider and paste their own API key (kept only for that session). No shared key, so no cost to you.

Locally the Docker image runs the same way:

docker build -t queryquest . && docker run -p 7860:7860 queryquest

Testing

Run tests from project root:

uv run python -m unittest discover -s tests -v

Data Expectations

• Default workbook directory: excel_files/
• QueryQuest registers every sheet as a SQL table using workbook__sheet naming.
• Example: inventory.xlsx sheet Archive Data becomes table inventory__Archive_Data.
• DML (INSERT/UPDATE/DELETE) writes back to the exact sheet targeted by the SQL statement.

Notes

• Logs are appended to logs.txt.
• Provider/model/api key setup is stored in .provider.json.
• The SQL extractor is robust to mixed responses (plain text + fenced JSON + embedded JSON object).