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Prototype app enabling job description search using natural language description of a job seeker.

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Built with Cookiecutter

AI-Enabled Search Engine using LLM Embeddings, Django, and pgvector

A lot of people have asked us for ideas of how they can leverage Large Language Models (LLMs) for their business applications. A common example is to use the native language comprehension capabilities of LLMs to find matching content. This makes LLMs an excellent tool for search!

This repo demonstrates a prototype application that enables searching for job descriptions using an unstructured, English-language description of a job seeker.

Watch the Demo on Youtube

Video: How We're Building AI Search Engines using LLM Embeddings

Links & Resources

Setup

Docker

If this is your first time...

  1. Install Docker
  2. Run pipenv lock to generate a Pipfile.lock
  3. Run cd client && npm install so you have node_modules available outside of Docker
  4. Back in the root directory, run make build
  5. make run to start the app
  6. If the DB is new, run make create-test-data
    1. SuperUser admin@thinknimble.com with credentials from your .env
    2. User cypress@thinknimble.com with credentials from your .env is used by the Cypress tests
  7. View other available scripts/commands with make commands
  8. localhost:8080 to view the app.
  9. localhost:8000/staff/ to log into the Django admin
  10. localhost:8000/api/docs/ to view backend API endpoints available for frontend development

Backend

If not using Docker... See the backend README

Frontend

If not using Docker... See the frontend README

Testing & Linting Locally

  1. pipenv install --dev
  2. pipenv run pytest server
  3. pipenv run black server
  4. pipenv run isort server --diff (shows you what isort is expecting)
  5. npm run cypress

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Prototype app enabling job description search using natural language description of a job seeker.

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