NatQuery
natquery is a Python package that enables users to interact with relational databases using natural language via a secure, performance-aware command-line interface along with database driven machine learning analytics.
natquery/
│
├── pyproject.toml
├── README.md
├── requirements.txt
│── src/
├ ├── natquery/
│ ├── __init__.py
│
│ # =========================
│ # 1. CLI LAYER
│ # =========================
│ ├── cli/
│ │ ├── __init__.py
│ │ ├── main.py # Entry point
│ │ ├── shell.py # Interactive REPL
│ │ ├── commands.py # CLI command registry
│
│ # =========================
│ # 2. CONFIG LAYER
│ # =========================
│ ├── config/
│ │ ├── __init__.py
│ │ ├── settings.py # API keys, DB config
│ │ ├── connection.py # PostgreSQL connection manager
│
│ # =========================
│ # 3. ORCHESTRATION LAYER
│ # =========================
│ ├── orchestration/
│ │ ├── __init__.py
│ │ ├── pipeline.py # End-to-end coordinator
│ │ ├── error_handler.py
│
│ # =========================
│ # 4. SCHEMA MODULE
│ # =========================
│ ├── schema/
│ │ ├── __init__.py
│ │ ├── extractor.py # Dynamic schema extraction
│ │ ├── formatter.py # Convert schema to prompt-friendly text
│
│ # =========================
│ # 5. PROMPT BUILDER
│ # =========================
│ ├── prompt/
│ │ ├── __init__.py
│ │ ├── builder.py # Structured prompt construction
│
│ # =========================
│ # 6. LLM CLIENT
│ # =========================
│ ├── llm/
│ │ ├── __init__.py
│ │ ├── client.py # LLM API calls
│ │ ├── self_corrector.py # Retry & correction mechanism
│
│ # =========================
│ # 7. SECURITY + VALIDATION
│ # =========================
│ ├── security/
│ │ ├── __init__.py
│ │ ├── validator.py # SELECT-only enforcement
│ │ ├── limiter.py # LIMIT enforcement
│
│ # =========================
│ # 8. EXECUTION ENGINE
│ # =========================
│ ├── execution/
│ │ ├── __init__.py
│ │ ├── engine.py # SQL execution
│ │ ├── explain.py # EXPLAIN / ANALYZE
│
│ # =========================
│ # 9. MACHINE LEARNING
│ # =========================
│ ├── ml/
│ │ ├── __init__.py
│ │ ├── preprocessing.py
│ │ ├── trainer.py
│ │ ├── inference.py
│ │
│ # =========================
│ # 10. PERFORMANCE + LOGGING
│ # =========================
│ ├── observability/
│ ├── __init__.py
│ ├── logger.py
│ ├── cost_analyzer.py
│ ├── index_recommender.py
│
│
├── database/
│ ├── schema.sql
│ ├── seed.py
│ ├── synthetic_data.py
│
├── tests/
│ ├── test_pipeline.py
│ ├── test_security.py
│ ├── test_ml.py
│
└── benchmarks/
├── nl_queries.json
Metadata
Release files for natquery 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| natquery-1.0.1.tar.gz | 20.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| natquery-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 45.7 kB
Release files / natquery-1.0.1.tar.gz
| Download URL | natquery-1.0.1.tar.gz |
|---|---|
| Size | 20.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a10cba24d6be80c453952d07b1e6d6d55fdf4cc179518cb1e44ce24fa5f7c64e
|
|
BLAKE2b-256 checksum How to use checksums |
3763311cf6c13e1ba0f153e93073a1a06b9a1eaf7b840ed50f5cdc7736cb6431
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Release files / natquery-1.0.1-py3-none-any.whl
| Download URL | natquery-1.0.1-py3-none-any.whl |
|---|---|
| Size | 25.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1cf68b068e46e73c1e2a8a3c64cc3b13c1c7dc3ada8650d1b68e3e391ac8f5f8
|
|
BLAKE2b-256 checksum How to use checksums |
ef34f91c430e44310d09e08e049f89756ae1fb6219064a3333a271068d3b3c0f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|