tableQA

AI Tool for querying natural language on tabular data.

A tabular data can be:

Dataframes
CSV files

Features

Supports detection from multiple csvs (csvs can also be read from Amazon s3)
Supports FuzzyString implementation. i.e, incomplete column values in query can be automatically detected and filled in the query.
Supports Databases - SQLite, Postgresql, MySQL, Amazon RDS (Postgresql, MySQL).
Open-Domain, No training required.
Add manual schema for customized experience
Auto-generate schemas in case schema not provided
Data visualisations

Configuration:

install via pip:

pip install tableqa

installing from source:

git clone https://github.com/abhijithneilabraham/tableQA

cd tableqa

python setup.py install

Quickstart

Do sample query

from tableqa.agent import Agent
agent=Agent(df) #input your dataframe
response=agent.query_db("Your question here")
print(response)

Get an SQL query from the question

sql=agent.get_query("Your question here")  
print(sql) #returns an sql query

Adding Manual schema

Schema Format:

{
    "name": DATABASE NAME,
    "keywords":[DATABASE KEYWORDS],
    "columns":
    [
        {
        "name": COLUMN 1 NAME,
        "mapping":{
            CATEGORY 1: [CATEGORY 1 KEYWORDS],
            CATEGORY 2: [CATEGORY 2 KEYWORDS]
        }

        },
        {
        "name": COLUMN 2 NAME,
        "keywords": [COLUMN 2 KEYWORDS]
        },
        {
        "name": "COLUMN 3 NAME",
        "keywords": [COLUMN 3 KEYWORDS],
        "summable":"True"
        }
    ]
}

Mappings are for those columns whose values have only few distinct classes.
Include only the column names which need to have manual keywords or mappings.Rest will will be autogenerated.
summable is included for Numeric Type columns whose values are already count representations. Eg. Death Count,Cases etc. consists values which already represent a count.

Example (with manual schema):

Database query

Default Database - SQLite (File-based database, does not require creation of a separate connection.)

from tableqa.agent import Agent
agent=Agent(df,schema) #pass the dataframe and schema objects
response=agent.query_db("how many people died of stomach cancer in 2011")
print(response)
#Response =[(22,)]

To use PostgreSQL, you must have a postgresql server installed and running on your local. To download postgresql, visit the page.

from tableqa.agent import Agent
agent = Agent(df, schema_file, 'postgres', username='username', password='password', database='DBname', host='localhost', port=5432, aws_db=False)
response=agent.query_db("how many people died of stomach cancer in 2011")
print(response)
#Response =[(22,)]

To use MySQL, you must have a mysql server installed and running on your local. To download mysql, visit the page.

from tableqa.agent import Agent
agent = Agent(df, schema_file, 'mysql', username='username', password='password', database='DBname', host='localhost', port=5432, aws_db=False)
response=agent.query_db("how many people died of stomach cancer in 2011")
print(response)
#Response =[(22,)]

To use PostgreSQL or MySQL on Amazon RDS, you must create a database on Amazon RDS. The RDS must be in public subnet with security groups allowing connections from outside of AWS.

Refer to step 1 in the document to create a mysql db instance on Amazon RDS. Same steps can be followed for creating a PostgreSQL db instance by selecting PostgreSQL in the Engine tab. Obtain the username, password, database, endpoint, and port from your database connection details on Amazon RDS.

from tableqa.agent import Agent
agent = Agent(df, schema_file, 'postgres', username='Master username', password='Master password', database='DB name', host='Endpoint', port='Port', aws_db=True)
response=agent.query_db("how many people died of stomach cancer in 2011")
print(response)
#Response =[(22,)]

SQL query

sql=agent.get_query("How many people died of stomach cancer in 2011")
print(sql)
#sql query: SELECT SUM(Death_Count) FROM cancer_death WHERE Cancer_site = "Stomach" AND Year = "2011"

Multiple CSVs

Pass the absolute path of the directories containing the csvs and schemas respectively. Refer cleaned_data and schema for examples.

Example

Read CSV and Schema from local machine-

csv_path="/content/tableQA/tableqa/cleaned_data"
schema_path="/content/tableQA/tableqa/schema"
agent=Agent(csv_path,schema_path)

Read CSV and schema files from Amazon s3 -

Create a bucket on Amazon s3.
Upload objects to the bucket.
Create an IAM user and provide it access to read files from Amazon s3 storage.
Obtain the access key and secret access key for the user and pass it as an argument to the agent.

csv_path="s3://{bucket}/cleaned_data"
schema_path="s3://{bucket}/schema"
agent = Agent(csv_path, schema_path, aws_s3=True, access_key_id=access_key_id, secret_access_key=secret_access_key)

Join us

Join our workspace:Slack

rabeehrz/tableQA