Unstructured documents to SQL(ite) tables

💥 Query your documents with SQL using the power of LLMs!

Small proof of concept using Instructor to extract structured data from documents and build a SQLite table with the results.

The meat of this is a Pydantic model to SQLite schema generator. It handles most Pydantic types but has not be extensively tested so you may encounter some edge cases. Feel free to open an issue if you do.

Installation

uv sync

Create a .env and add your OpenAI API key:

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Usage

For a practical example, let's say we want to analyze job postings at OpenAI so we can see who they're hiring or what we need to do to get a job there. The examples/openai_job_postings folder contains 85 job postings from the OpenAI careers page. Nothing fancy there, I just copy and pasted the job ad text.

Running the example will load each job posting and insert the result into the database. This will take a bit depending on how fast GPT-4o is today.

uv run src/docs_to_sql/main.py

The output will be a SQLite database named 'job_postings.db' with the extracted job postings in a table called job_postings.

Details

The basic steps are below. I'll skip some details to keep it readable, see src/docs_to_sql/main.py for the full code.

To use Instructor, we need to define a Pydantic model that represents the data we want to extract:

class JobPosting(BaseModel):
    minimumSalaryRange: Optional[int]
    maximumSalaryRange: Optional[int]
    jobTitle: str
    companyName: str
    department: Optional[str]
    description: Optional[str]
    requirements: list[str]
    responsibilities: list[str]
    location: Optional[str]
    datePosted: Optional[str]

Primitive types like str, int, float, bool and datetime are mapped to SQLite types. Nested data types like list and dict are mapped to JSON. The Optional type controls whether a NOT NULL constraint will be added to the column.

The basic fields above work but we can do a little better by providing some more specific ones for the LLM to extract. If we add a description it will show up in the JSON schema passed t othe LLM and provide further instructions. Let's add a requiredSkills field that will be extracted as a list of strings and a locationType field that's a string enum.

class JobPosting(BaseModel):
    # ...
    requiredSkills: list[str] = Field(
        description="Required skills for the job, mention as individual technologies, tools, languages, skills needed")
    locationType: Literal["onsite", "remote", "hybrid", "unknown"]

Then we can create a SQLite database and a table to store the data. The table structure will be generated from the Pydantic model:

import sqlite3
from docs_to_sql.sql import create_table

conn = sqlite3.connect("job_postings.db")
c = conn.cursor()

c.execute(create_table(JobPosting, "job_postings"))
conn.commit()

The extract function takes the Pydantic model, a system prompt for some context, and the content to extract the data from.

from docs_to_sql.extract import extract

job_posting = extract(JobPosting, "Analyze the job posting", f.read())
print(job_posting)

All we have to do now is insert the data into the database:

from docs_to_sql.sql import insert

insert(c, job_posting, "job_postings")
conn.commit()

Exploration

Now that the magical LLM has turned our unstructured data into a SQL table we can leverage our own magical SQL skills to use it.

We can do simple things like finding out which jobs pay the most:

SELECT jobTitle, maximumSalaryRange
FROM job_postings
ORDER BY maximumSalaryRange DESC
LIMIT 5;

Or any complicated thing we can dream up. For the nested data types we can use SQLite's JSON functions to manipulate them further. For example, which jobs require Python:

SELECT jobTitle
FROM job_postings, json_each(requiredSkills)
WHERE json_each.value LIKE '%python%';

Notes and credits

Many configs taken from python-boilerplate