sqlite-column-sentry 2025.12.21152531

A new package that helps developers ensure column safety in SQLite queries by analyzing and validating their SQL statements. The package takes a user's SQL query as text input and returns a structured

sqlite-column-sentry

sqlite-column-sentry is a lightweight Python package that helps developers validate SQLite queries for column‑related safety issues. It analyzes a user‑provided SQL statement and returns structured feedback about missing columns, incorrect data types, unsafe references, and more. The package leverages LLM7 (via langchain_llm7) and llmatch‑messages to provide clear, actionable suggestions, catching potential runtime errors before they happen.

---

Installation

pip install sqlite_column_sentry

---

Quick Start

from sqlite_column_sentry import sqlite_column_sentry

# Simple usage with the default LLM7 backend
sql = "SELECT name, age FROM users WHERE id = ?;"
issues = sqlite_column_sentry(user_input=sql)

print(issues)
# Example output: ['Column "age" may be NULLable but is used in a NOT NULL context']

Parameters

Parameter	Type	Description
user_input	str	The SQLite query you want to validate.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the package creates a default ChatLLM7 instance.
api_key	Optional[str]	API key for LLM7. If omitted, the function reads LLM7_API_KEY from the environment (or falls back to a placeholder).

---

Using a Custom LLM

You can plug any LangChain‑compatible LLM that follows the BaseChatModel interface.

OpenAI

from langchain_openai import ChatOpenAI
from sqlite_column_sentry import sqlite_column_sentry

llm = ChatOpenAI()
issues = sqlite_column_sentry(user_input="SELECT * FROM products;", llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from sqlite_column_sentry import sqlite_column_sentry

llm = ChatAnthropic()
issues = sqlite_column_sentry(user_input="INSERT INTO orders (id, amount) VALUES (1, 100);", llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from sqlite_column_sentry import sqlite_column_sentry

llm = ChatGoogleGenerativeAI()
issues = sqlite_column_sentry(user_input="UPDATE accounts SET balance = balance - 10 WHERE id = 5;", llm=llm)

---

LLM7 Default Configuration

• The package uses ChatLLM7 from the langchain_llm7 package by default.
• Free‑tier LLM7 rate limits are sufficient for most development workflows.
• To increase limits, provide your own API key:

  issues = sqlite_column_sentry(
      user_input="SELECT * FROM logs;",
      api_key="YOUR_LLM7_API_KEY"
  )
  

• Obtain a free API key by registering at https://token.llm7.io/.

---

Contributing

Contributions, bug reports, and feature requests are welcome! Please open an issue or submit a pull request on the GitHub repository.

---

License

This project is licensed under the MIT License.

---

Author

Eugene Evstafev
📧 Email: [e-mail]
🐙 GitHub: chigwell

---

Repository & Issues

• GitHub Repository: https://github.com/chigwell/sqlite-column-sentry
• Issue Tracker: https://github.com/chigwell/sqlite-column-sentry/issues