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embeddings-search-demo

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

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# 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](http://img.youtube.com/vi/ZCPUmC37HLU/0.jpg)](http://www.youtube.com/watch?v=ZCPUmC37HLU "How We're Building AI Search Engines using LLM Embeddings")

## Links & Resources

- https://www.sbert.net/ - Sentence Transformers package for Python
- https://github.com/pgvector/pgvector - Vector database plugin for PostgreSQL
- https://huggingface.co/spaces/mteb/leaderboard - The "Massive Text Embedding" leaderboard from HuggingFace. Look at this to find models you can use to generate embeddings.
- https://www.djangoproject.com/

## Setup

### Docker

If this is your first time...

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

### Backend

If not using Docker...
See the [backend README](server/README.md)

### Frontend

If not using Docker...
See the [frontend README](client/README.md)

## Testing & Linting Locally

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

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