Data Source Connect

Replace the demo unicorns data with user's own data source. Supports two modes:

1. •Database URL - Connect to an existing PostgreSQL database
2. •CSV File - Import a CSV file and create a new table

Mode 1: Database URL

When user provides a PostgreSQL connection URL:

Step 1: Update Environment

Update .env file:

POSTGRES_URL="<user-provided-connection-string>"

Step 2: Discover Schema

Connect to the database and retrieve schema information:

SELECT table_name, column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_schema = 'public'
ORDER BY table_name, ordinal_position;

Step 3: Update Actions

Edit app/actions.ts - update the schema in these locations:

1. •

   generateQuery function (~line 18): Replace the unicorns schema in the system prompt with the new table schema. Include:

   • •Table name and column definitions
   • •Data type hints (e.g., "valuation is in billions")
   • •Available enum values for categorical fields
   • •Query patterns specific to the data
2. •

   explainQuery function (~line 115): Replace the unicorns schema in the system prompt.

3. •

   runGenerateSQLQuery function (~line 93): Update the table name in error handling.

Step 4: Generate Sample Prompts

Create 3-5 sample natural language queries for the new data to help users understand what they can ask.

Mode 2: CSV File

When user provides a CSV file:

Step 1: Analyze CSV Structure

Read the CSV file and analyze:

• •Column names (header row)
• •Data types (infer from sample values)
• •Null/empty value handling requirements

Step 2: Generate Schema

Create SQL schema based on CSV structure. Type inference rules:

• •Numbers with decimals -> DECIMAL(10, 2)
• •Integers -> INTEGER
• •Dates (various formats) -> DATE
• •Boolean values -> BOOLEAN
• •Everything else -> VARCHAR(255) or TEXT for long content

Step 3: Update seed.ts

Edit lib/seed.ts:

1. •Update CREATE TABLE statement with new schema
2. •Update CSV parsing logic for new column names
3. •Add type conversion for each column
4. •Handle missing/null values with appropriate defaults or skip logic

Example seed.ts structure:

export async function seed() {
  await pool.query(`
    CREATE TABLE IF NOT EXISTS <table_name> (
      id SERIAL PRIMARY KEY,
      <column1> <type1>,
      <column2> <type2>,
      ...
    );
  `);

  // Parse CSV
  const results: any[] = [];
  const csvFilePath = path.join(process.cwd(), '<filename>.csv');

  await new Promise((resolve, reject) => {
    fs.createReadStream(csvFilePath)
      .pipe(csv())
      .on('data', (data) => results.push(data))
      .on('end', resolve)
      .on('error', reject);
  });

  // Insert with null handling
  for (const row of results) {
    // Skip rows with critical missing data or use defaults
    const value1 = row['ColumnName'] || null;

    await pool.query(
      `INSERT INTO <table_name> (...) VALUES (...) ON CONFLICT DO NOTHING`,
      [value1, ...]
    );
  }
}

Step 4: Update Actions

Same as Database URL Mode Step 3 - update schema references in app/actions.ts.

Step 5: Run Seed

pnpm run seed

Step 6: Generate Sample Prompts

Create 3-5 sample natural language queries for the new data to help users understand what they can ask. Examples should cover:

• •Basic filtering (e.g., "Show all records where X > Y")
• •Aggregations (e.g., "What is the total/average X by Y?")
• •Time-based queries if applicable (e.g., "Show trends over time")
• •Top/bottom queries (e.g., "Top 10 by X")

Critical Files to Update

Schema Prompt Template

When updating the schema in app/actions.ts, use this template:

system: `You are a SQL (postgres) and data visualization expert. Your job is to help the user write a SQL query to retrieve the data they need. The table schema is as follows:

<table_name> (
  id SERIAL PRIMARY KEY,
  <column_name> <data_type> <constraints>,
  ...
);

Only retrieval queries are allowed.

[Add data-specific hints here, e.g.:]
- Use ILIKE for case-insensitive text search
- <field_name> values include: value1, value2, value3
- <numeric_field> is in <units> (e.g., thousands, millions)

EVERY QUERY SHOULD RETURN QUANTITATIVE DATA THAT CAN BE PLOTTED ON A CHART!
`,

Null Value Handling

For CSV imports with missing data:

• •Skip row: When critical identifier is missing
• •Default value: Use sensible defaults (0 for numbers, 'Unknown' for strings)
• •Allow null: For truly optional fields