tableQA

Tool for querying natural language on tabular data like csvs,excel sheet,etc.

Features

• Supports detection from multiple csvs
• Support FuzzyString implementation. i.e, incomplete csv values in query can be automatically detected and filled in the query.
• Open-Domain, No training required.
• Add manual schema for customized experience
• Auto-generate schemas in case schema not provided

Configuration:

installing from source:

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

cd tableqa

python setup.py install

Quickstart

Getting an SQL query from csv

from tableqa.agent import Agent
agent=Agent(data_dir) #specify the absolute path of the data directory.
print(agent.get_response("Your question here")) #returns an sql query

Do Sample query on sqlite database

from tableqa.database import Database
database=Database(data_dir) 
response=database.Query_Sqlite("Your question here")
print("Response ={}".format(response)) #returns the result of the sql query after feeding the csv to the database

Adding Manual schema

include the directory containing the schemas of the respective csvs, with the same filename. Refer "/cleaned_data" and "/schema" for examples.

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):

specify the data_dir and schema_dir with absolute path to cleaned_data and schema directories to test the dummy data.

SQL query

from tableqa.agent import Agent
agent=Agent(data_dir,schema_dir) 
print(agent.get_response("How many people died of stomach cancer in 2011")) 
#sql query: SELECT SUM(Death_Count) FROM cancer_death WHERE Cancer_site = "Stomach" AND Year = "2011" 

Database query

from tableqa.database import Database
database=Database(data_dir,schema_dir)
response=database.Query_Sqlite("how many people died of stomach cancer in 2011")
print("Response ={}".format(response)) #returns the result of the sql query after feeding the csv to the database
#Response =[(22,)]