Skip to main content

Tool for querying natural language on tabular data

Project description

tableQA

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

Build Status

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:

install via pip:

pip install tableqa

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_query("Your question here")) #returns an sql query

Do Sample query on database

response=agent.query_db("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):

SQL query
from tableqa.agent import Agent
agent=Agent(data_dir,schema_dir) 
print(agent.get_query("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
response=agent.query_db("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,)]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tableqa-0.0.5.tar.gz (5.3 MB view hashes)

Uploaded source

Built Distribution

tableqa-0.0.5-py3-none-any.whl (5.3 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page