Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/paulfitz/mlsql

inferring sql queries from plain-text questions about tables
https://github.com/paulfitz/mlsql
deep-learning natural-language sql wikisql
Last synced: 5 days ago
JSON representation
inferring sql queries from plain-text questions about tables
Host: GitHub
URL: https://github.com/paulfitz/mlsql
Owner: paulfitz
Created: 2019-05-26T18:45:42.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2023-06-12T21:28:24.000Z (over 1 year ago)
Last Synced: 2024-10-11T07:43:28.740Z (2 months ago)
Topics: deep-learning, natural-language, sql, wikisql
Language: Python
Homepage:
Size: 168 KB
Stars: 915
Watchers: 25
Forks: 67
Open Issues: 8
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project

README

        Infer SQL queries from plain-text questions and table headers.

Requirements:

 * install `docker`

 * install `curl` (or, if you're feeling brave, [asql](https://github.com/paulfitz/asql))

 * Make sure docker allows at least 3GB of RAM (see `Docker`>`Preferences`>`Advanced`

   or equivalent) for SQLova, or 5GB for IRNet or ValueNet.

I take pretrained models published along with academic papers, and do whatever it takes

to make them testable on fresh data (academic work often omits that, with code tied

to a particular benchmark dataset).  I spend days tracking down and patching obscure

data preprocessing steps so you don't have to.

![ValueNet example](https://user-images.githubusercontent.com/118367/89111827-75e5db80-d428-11ea-912a-e36a176bb56c.png)

So far I've packaged three models:

 * [SQLova](#sqlova).  Works on single tables.

 * [ValueNet](#valuenet).  Works on multiple tables, and

   makes an effort to predict parameters.

 * [IRNet](#irnet).  Works on multiple tables, but doesn't

   predict parameters.

In each case, I've mangled the original network somewhat, so if they interest you do follow up

with the original sources.

## SQLova

This wraps up a published pretrained model for SQLova (https://github.com/naver/sqlova/).

Fetch and start SQLova running as an api server on port 5050:

```

docker run --name sqlova -d -p 5050:5050 paulfitz/sqlova

```

Be patient, the image is about 4.2GB.  Once it is running, it'll take a few seconds

to load models and then you can start asking questions about CSV tables.  For example:

```

curl -F "[email protected]" -F "q=how long is throgs neck" localhost:5050

# {"answer":[1800],"params":["throgs neck"],"sql":"SELECT (length) FROM bridges WHERE bridge = ?"}

```

This is using the sample `bridges.csv` included in this repo.

| bridge | designer | length |

|---|---|---|

| Brooklyn | J. A. Roebling | 1595 |

| Manhattan | G. Lindenthal | 1470 |

| Williamsburg | L. L. Buck | 1600 |

| Queensborough | Palmer & Hornbostel | 1182 |

| Triborough | O. H. Ammann | 1380,383 |

| Bronx Whitestone | O. H. Ammann | 2300 |

| Throgs Neck | O. H. Ammann | 1800 |

| George Washington | O. H. Ammann | 3500 |

Here are some examples of the answers and sql inferred for plain-text questions about

this table:

| question | answer | sql |

|---|---|---|

| how long is throgs neck | 1800 | `SELECT (length) FROM bridges WHERE bridge = ? ['throgs neck']` |

| who designed the george washington | O. H. Ammann | `SELECT (designer) FROM bridges WHERE bridge = ? ['george washington']` |

| how many bridges are there | 8 | `SELECT count(bridge) FROM bridges` |

| how many bridges are designed by O. H. Ammann | 4 | `SELECT count(bridge) FROM bridges WHERE designer = ? ['O. H. Ammann']` |

| which bridge are longer than 2000 | Bronx Whitestone, George Washington | `SELECT (bridge) FROM bridges WHERE length > ? ['2000']` |

| how many bridges are longer than 2000 | 2 | `SELECT count(bridge) FROM bridges WHERE length > ? ['2000']` |

| what is the shortest length | 1182 | `SELECT min(length) FROM bridges` |

With the `players.csv` sample from WikiSQL:

| Player | No. | Nationality | Position | Years in Toronto | School/Club Team |

|---|---|---|---|---|---|

| Antonio Lang | 21 | United States | Guard-Forward | 1999-2000 | Duke |

| Voshon Lenard | 2 | United States | Guard | 2002-03 | Minnesota |

| Martin Lewis | 32, 44 | United States | Guard-Forward | 1996-97 | Butler CC (KS) |

| Brad Lohaus | 33 | United States | Forward-Center | 1996 | Iowa |

| Art Long | 42 | United States | Forward-Center | 2002-03 | Cincinnati |

| John Long | 25 | United States | Guard | 1996-97 | Detroit |

| Kyle Lowry | 3 | United States | Guard | 2012-present | Villanova |

| question | answer | sql |

|---|---|---|

| What number did the person playing for Duke wear? | 21 | `SELECT (No.) FROM players WHERE School/Club Team = ? ['duke']` |

| Who is the player that wears number 42? | Art Long  | `SELECT (Player) FROM players WHERE No. = ? ['42']` |

| What year did Brad Lohaus play? | 1996 | `SELECT (Years in Toronto) FROM players WHERE Player = ? ['brad lohaus']` |

| What country is Voshon Lenard from? | United States | `SELECT (Nationality) FROM players WHERE Player = ? ['voshon lenard']` |

Some questions about [iris.csv](https://en.wikipedia.org/wiki/Iris_flower_data_set):

| question | answer | sql |

|---|---|---|

| what is the average petal width for virginica | 2.026 | `SELECT avg(Petal.Width) FROM iris WHERE Species = ? ['virginica']` |

| what is the longest sepal for versicolor | 7.0 | `SELECT max(Sepal.Length) FROM iris WHERE Species = ? ['versicolor']` |

| how many setosa rows are there | 50 | `SELECT count(col0) FROM iris WHERE Species = ? ['setosa']` |

There are plenty of types of questions this model cannot answer (and that aren't covered

in the dataset it is trained on, or in the sql it is permitted to generate).

## ValueNet

This wraps up a published pretrained model for ValueNet (https://github.com/brunnurs/valuenet).

Fetch and start ValueNet running as an api server on port 5050:

```

docker run --name valuenet -d -p 5050:5050 paulfitz/valuenet

```

You can then ask questions of individual csv files as before, or several csv files

(just repeat `-F "[email protected]"`) or a simple sqlite db with tables related by foreign keys.

In this last case, the model can answer using joins.

```

curl -F "[email protected]" -F "q=who is the CEO of Omni Cooperative" localhost:5050

# {"answer":[["Dracula"]], "sql":"SELECT T1.name FROM people AS T1 JOIN organizations AS T2 \

#   ON T1.id = T2.ceo_id WHERE T2.company = 'Omni Cooperative'"}

curl -F "[email protected]" -F "q=how many designers are there?" localhost:5050

# {"answer":[[5]],"sql":"SELECT DISTINCT count(DISTINCT T1.designer) FROM bridges AS T1"}

curl -F "[email protected]" -F "[email protected]" -F "q=how many designers are there?" localhost:5050

# same answer

curl -F "[email protected]" -F "[email protected]" -F "q=what is the name of the airport with the highest latitude?" localhost:5050

# {"answer":[["Disraeli Inlet Water Aerodrome"]],

#  "sql":"SELECT T1.name FROM airports AS T1 ORDER BY T1.latitude_deg DESC LIMIT 1"}

```

I've includes material to convert user tables into the form needed to query them.  Don't

judge the network by its quality here, go do a deep dive with the original - I've deviated

from the original in important respects, including how named entity recognition is done.

I've written up [some experiments with ValueNet](https://paulfitz.github.io/2020/08/01/translate-english-to-sql-progress-updates.html).

## IRNet

This wraps up a published pretrained model for IRNet (https://github.com/microsoft/IRNet).

Upstream released a better model after I packaged this, so don't judge the model by playing

with it here.

Fetch and start IRNet running as an api server on port 5050:

```

docker run --name irnet -d -p 5050:5050 -v $PWD/cache:/cache paulfitz/irnet

```

Be super patient! Especially on the first run, when a few large models need to

be downloaded and unpacked.

You can then ask questions of individual csv files as before, or several csv files

(just repeat `-F "[email protected]"`) or a simple sqlite db with tables related by foreign keys.

In this last case, the model can answer using joins.

```

curl -F "[email protected]" -F "q=what city is The Firm headquartered in?" localhost:5050

# Answer: SELECT T1.city FROM locations AS T1 JOIN organizations AS T2 WHERE T2.company = 1

curl -F "[email protected]" -F "q=who is the CEO of Omni Cooperative" localhost:5050

# Answer: SELECT T1.name FROM people AS T1 JOIN organizations AS T2 WHERE T2.company = 1

curl -F "[email protected]" -F "q=what company has Dracula as CEO" localhost:5050

# Answer: SELECT T1.company FROM organizations AS T1 JOIN people AS T2 WHERE T2.name = 1

```

(Note there's no value prediction, so e.g. the where clauses are `= 1` rather than something

more useful).

## Postman users

Curl can be replaced by Postman for those who like that.  Here's a working set-up:

![Postman version](https://user-images.githubusercontent.com/118367/73127529-b05d5000-3f8f-11ea-8499-b58273ca1961.png)

## Other models

I hope to track research in the area and substitute in models as they become available:

 * [Spider leaderboard](https://yale-lily.github.io/spider)

 * [WikiSQL leaderboard](https://github.com/salesforce/WikiSQL#leaderboard)

 * [SparC leaderboard](https://yale-lily.github.io/sparc)

 

 * [RAT-SQL](https://github.com/Microsoft/rat-sql)

## Live demoes

 * [Photon](https://naturalsql.com/) by the SalesForce group is a good live demo of

   text-to-SQL.