How to Build an ETL App for Cohere Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Cohere-connected applications and pipelines for extracting, transforming, and loading Cohere data. This article shows how to connect to Cohere with the CData Python Connector and use petl and pandas to extract, transform, and load Cohere data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Cohere data in Python. When you issue complex SQL queries from Cohere, the driver pushes supported SQL operations, like filters and aggregations, directly to Cohere and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Cohere Data

Connecting to Cohere data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Start by setting the Profile connection property to the location of the Cohere Profile on disk (e.g. C:\profiles\Cohere.apip). Next, set the ProfileSettings connection property to the connection string for Cohere (see below).

Cohere API Profile Settings

Obtain your API key from your Cohere account at dashboard.cohere.com by navigating to the API Keys section.

After installing the CData Cohere Connector, follow the procedure below to install the other required modules and start accessing Cohere through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Cohere Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData Cohere Connector to create a connection for working with Cohere data.

cnxn = mod.connect("Profile=C:\profiles\Cohere.apip;ProfileSettings='APIKey=your_api_key';")

Create a SQL Statement to Query Cohere

Use SQL to create a statement for querying Cohere. In this article, we read data from the ChatMessages entity.

sql = "SELECT Id, ConversationId FROM ChatMessages WHERE ConversationId = 'abc123'"

Extract, Transform, and Load the Cohere Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Cohere data. In this example, we extract Cohere data, sort the data by the ConversationId column, and load the data into a CSV file.

Loading Cohere Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ConversationId')

etl.tocsv(table2,'chatmessages_data.csv')

With the CData API Driver for Python, you can work with Cohere data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Cohere data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\Cohere.apip;ProfileSettings='APIKey=your_api_key';")

sql = "SELECT Id, ConversationId FROM ChatMessages WHERE ConversationId = 'abc123'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ConversationId')

etl.tocsv(table2,'chatmessages_data.csv')