Convert Typst to SQL

Why Convert Typst to SQL?

Converting Typst documents to SQL enables researchers and data scientists to extract structured data from modern typesetting documents and load it into relational databases. Typst's clean table syntax using the #table() function makes it particularly well-suited for data extraction, as tables are defined with explicit column counts and cell contents that map naturally to SQL schemas.

Typst's built-in scripting language allows authors to embed computational data directly within documents using #let bindings and expressions. When converting to SQL, these programmatic data structures can be automatically mapped to database tables, preserving the relationship between variables and their values in a queryable format.

The conversion process transforms Typst tables into CREATE TABLE statements and INSERT operations, while document metadata such as titles, authors, and dates become records in metadata tables. Typst's structured content model, with its clear separation of markup and data, makes automated extraction more reliable than parsing traditional LaTeX documents.

This workflow is valuable for research teams building literature databases, systematic review projects, and institutional repositories. By converting Typst documents to SQL, the structured data within academic papers becomes queryable, enabling cross-study comparisons, automated meta-analyses, and integration with existing data management systems.

Key Benefits of Converting Typst to SQL:

• Clean Extraction: Typst's structured table syntax maps directly to SQL schemas
• Scripted Data: Extract computed values from Typst's #let bindings
• Meta-Analysis: Aggregate results from multiple publications into databases
• Queryable Content: Run SQL queries across your document collection
• Reproducibility: Store research data in a standardized, versionable format
• Automation: Build data pipelines from Typst source to database ingestion
• Archival: Preserve research data in a universally accessible format

Practical Examples

Example 1: Extracting Experiment Results

Input Typst file (results.typ):

= Classification Results

#table(
  columns: 4,
  [*Model*],  [*Precision*], [*Recall*], [*F1*],
  [BERT],     [94.2],        [93.8],     [94.0],
  [GPT-2],    [91.5],        [90.3],     [90.9],
  [XLNet],    [95.1],        [94.7],     [94.9],
)

Output SQL file (results.sql):

-- Classification Results
CREATE TABLE classification_results (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  model VARCHAR(50) NOT NULL,
  precision_score DECIMAL(5,2),
  recall_score DECIMAL(5,2),
  f1_score DECIMAL(5,2)
);

INSERT INTO classification_results
  (model, precision_score, recall_score, f1_score)
VALUES
  ('BERT', 94.2, 93.8, 94.0),
  ('GPT-2', 91.5, 90.3, 90.9),
  ('XLNet', 95.1, 94.7, 94.9);

Example 2: Document Metadata Extraction

Input Typst file (paper.typ):

#set document(
  title: "Advances in Quantum Computing",
  author: ("Alice Chen", "Bob Williams"),
  date: datetime(year: 2026, month: 1, day: 15),
)

= Advances in Quantum Computing

== Abstract
We survey recent developments in quantum
error correction and fault-tolerant computing.

Output SQL file (paper.sql):

-- Paper metadata
CREATE TABLE IF NOT EXISTS papers (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  title TEXT NOT NULL,
  authors TEXT,
  pub_date DATE,
  abstract TEXT
);

INSERT INTO papers (title, authors, pub_date, abstract)
VALUES (
  'Advances in Quantum Computing',
  'Alice Chen, Bob Williams',
  '2026-01-15',
  'We survey recent developments in quantum
   error correction and fault-tolerant computing.'
);

Example 3: Benchmark Data with Variables

Input Typst file (benchmarks.typ):

== Benchmark Results

#let datasets = (
  (name: "MNIST", samples: 70000, features: 784),
  (name: "CIFAR-10", samples: 60000, features: 3072),
  (name: "ImageNet", samples: 1281167, features: 150528),
)

#table(
  columns: 3,
  [Dataset], [Samples], [Features],
  ..datasets.map(d =>
    (d.name, str(d.samples), str(d.features))
  ).flatten()
)

Output SQL file (benchmarks.sql):

-- Benchmark Results
CREATE TABLE benchmarks (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  dataset VARCHAR(100) NOT NULL,
  samples INTEGER,
  features INTEGER
);

INSERT INTO benchmarks
  (dataset, samples, features)
VALUES
  ('MNIST', 70000, 784),
  ('CIFAR-10', 60000, 3072),
  ('ImageNet', 1281167, 150528);

Frequently Asked Questions (FAQ)

Q: What is Typst?

A: Typst is a modern typesetting system created in 2023 as a faster and more user-friendly alternative to LaTeX. It uses a clean markup syntax with = for headings, *bold*, _italic_, and a built-in scripting language with #set, #let, and #if commands. Typst compiles incrementally and is written in Rust for exceptional performance.

Q: Which Typst content gets converted to SQL?

A: The conversion primarily targets structured data: Typst #table() functions become CREATE TABLE and INSERT statements, document metadata from #set document() becomes metadata records, and #let variable bindings with structured data are extracted. Narrative text content is stored as TEXT fields in appropriate columns.

Q: Which SQL dialect is used in the output?

A: The output uses standard ANSI SQL compatible with most database engines. It avoids vendor-specific extensions to ensure maximum portability. The generated SQL works with SQLite, MySQL, PostgreSQL, and other ANSI-compliant databases. Minor dialect adjustments like auto-increment syntax may be needed for specific engines.

Q: How are Typst math expressions stored in SQL?

A: Mathematical expressions from Typst's $ ... $ syntax are stored as text strings. For example, $ E = m c^2 $ becomes a VARCHAR or TEXT value containing the equation text. This preserves the mathematical content for downstream rendering by MathJax, KaTeX, or other math rendering libraries.

Q: Can I import the SQL output directly into my database?

A: Yes. The generated SQL file can be executed directly using command-line tools like `mysql < file.sql`, `psql -f file.sql`, or `sqlite3 db.sqlite3 < file.sql`. You can also import it through GUI tools like DBeaver, pgAdmin, or MySQL Workbench.

Q: How does Typst's scripting help with SQL conversion?

A: Typst's scripting language using #let bindings and structured data (arrays, dictionaries) provides clearly defined data structures that map naturally to SQL tables. Unlike LaTeX where data is embedded in markup, Typst's programmatic data definitions are easier to parse and convert to SQL schemas accurately.

Q: Are Typst's #table() functions well supported?

A: Yes. Typst's #table() function with explicit column counts and cell contents maps directly to SQL table structures. Column headers become SQL column names, and data rows become INSERT statements. Complex tables with spanning cells are flattened into regular rectangular data as required by relational databases.

Q: What about Typst documents without tables?

A: Documents without tabular data are converted with a focus on metadata and text content. Section titles, paragraphs, and list items are stored in structured tables with fields for section hierarchy, content type, and text. This creates a searchable database of your document collection that supports queries across multiple converted papers.