https://github.com/crunchydata/postgres-ai-tutorial
https://github.com/crunchydata/postgres-ai-tutorial
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/crunchydata/postgres-ai-tutorial
- Owner: CrunchyData
- Created: 2023-02-22T17:13:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-25T16:03:31.000Z (almost 3 years ago)
- Last Synced: 2025-04-01T10:35:34.698Z (about 1 year ago)
- Language: Python
- Size: 18.7 MB
- Stars: 58
- Watchers: 5
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Postgres + AI Tutorial
This code and data package accompanies a blog post written by the Crunchy Data team. Check it out at blog.crunchydata.com
## Contents
The data packet contains the following:
- ArmedForcesRecipes.xml - list of recipes
- parser.rb - a file for parsing the XML and loading it into a Postgres database
- classfier.rb - a file for that returns the OpenAI embeddings for the recipes
- recipe-tracker.sql - a SQL dump from a database ready to query
## Speedrun Walk Through
1. Load the data into a Postgres database:
```bash
bash> cat recipe-tracker.sql | psql recipe_tracker
```
2. Run the following query against your database:
```sql
SELECT
recipe_1.id,
recipe_1.name,
recipe_2.id,
recipe_2.name
FROM (SELECT * FROM recipes WHERE name = 'Dish, turkey, curry') recipe_1,
recipes AS recipe_2
WHERE recipe_1.id != recipe_2.id
ORDER BY recipe_1.embedding <=> recipe_2.embedding
LIMIT 10;
```
You’ll get the recommendations for the following similar meals:
```
id | name | id | name
-----+---------------------+-----+----------------------------------------
272 | Dish, turkey, curry | 271 | Dish, turkey & noodles, baked
272 | Dish, turkey, curry | 251 | Dish, pot pie, turkey
272 | Dish, turkey, curry | 675 | Soup, tomato
272 | Dish, turkey, curry | 659 | Soup, chicken rice
272 | Dish, turkey, curry | 672 | Soup, rice w/beef
272 | Dish, turkey, curry | 660 | Soup, chicken vegetable/Mulligatawny
272 | Dish, turkey, curry | 199 | Dish, chicken, a la king
272 | Dish, turkey, curry | 38 | Beef, simmered
272 | Dish, turkey, curry | 689 | Stuffing, savory bread
272 | Dish, turkey, curry | 686 | Stew, beef chunks, w/juices & veg, cnd
(10 rows)
```
## Typical Walk Through
1. Install the necessary Ruby gems: `bundle install`
2. Create a Postgres database, and run `create extension vector;`. If you need a Postgres database, checkout [Crunchy Bridge](https://crunchybridge.com/).
3. Sign up for OpenAI, and get your API token
4. Set `DATABASE_URL` to the Postgres connection string, and set `OPENAI_API_KEY` to the value from #3
4. Parse the `ArmedForcesRecipes.xml` file and load it into the database by running `ruby parser.rb`
7. Pull down the OpenAI embeddings for your recipes by running `ruby classfier.rb` (will take ~ 5 minutes due to rate limiting)
8. Query the database to find similar recipes:
```sql
SELECT
recipe_1.id,
recipe_1.name,
recipe_2.id,
recipe_2.name
FROM (SELECT * FROM recipes WHERE name = 'Dish, turkey, curry') recipe_1,
recipes AS recipe_2
WHERE recipe_1.id != recipe_2.id
ORDER BY recipe_1.embedding <=> recipe_2.embedding
LIMIT 10;
```
That is it. Now you have an AI powered recommendation engine.