How to integrate Mistral AI with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData API Driver for JDBC, Airflow can work with live Mistral AI data. This article describes how to connect to and query Mistral AI data from an Apache Airflow instance and store the results in a CSV file.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Mistral AI data. When you issue complex SQL queries to Mistral AI, the driver pushes supported SQL operations, like filters and aggregations, directly to Mistral AI and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Mistral AI data using native data types.

Configuring the Connection to Mistral AI

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Mistral AI JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.api.jar

Fill in the connection properties and copy the connection string to the clipboard.

The MistralAI API uses API key authentication.

Using API Key Authentication

Your MistralAI API Key is required to create a connection to MistralAI. API Keys can be obtained from your MistralAI account at console.mistral.ai by navigating to the API Keys section. Once you have obtained the API key, set it in the ProfileSettings connection property.

Example Connection string

Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;

👁 Using the built-in connection string designer to generate a JDBC URL (mistral ai is shown.)

To host the JDBC driver in clustered environments or in the cloud, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

The following are essential properties needed for our JDBC connection.

Establishing a JDBC Connection within Airflow

1. Log into your Apache Airflow instance.
2. On the navbar of your Airflow instance, hover over Admin and then click Connections. 👁 Clicking connections
   
3. Next, click the + sign on the following screen to create a new connection.
4. In the Add Connection form, fill out the required connection properties:
   • Connection Id: Name the connection, i.e.: api_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:api:RTK=5246...;Profile=C:\profiles\MistralAI.apip;ProfileSettings='APIKey=my_api_key;';AuthScheme=APIKey;)
   • Driver Class: cdata.jdbc.api.APIDriver
   • Driver Path: PATH/TO/cdata.jdbc.api.jar
   👁 Add JDBC connection form
   
5. Test your new connection by clicking the Test button at the bottom of the form.
6. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections: 👁 New connection added
   

Creating a DAG

A DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow. Our workflow is to simply run a SQL query against Mistral AI data and store the results in a CSV file.

1. To get started, in the Home directory, there should be an "airflow" folder. Within there, we can create a new directory and title it "dags". In here, we store Python files that convert into Airflow DAGs shown on the UI.
2. Next, create a new Python file and title it mistral ai_hook.py. Insert the following code inside of this new file:

   	import time
   	from datetime import datetime
   	from airflow.decorators import dag, task
   	from airflow.providers.jdbc.hooks.jdbc import JdbcHook
   	import pandas as pd
   
   	# Declare Dag
   	@dag(dag_id="mistral ai_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=['load_csv'])
   	
   	# Define Dag Function
   	def extract_and_load():
   	# Define tasks
   		@task()
   		def jdbc_extract():
   			try:
   				hook = JdbcHook(jdbc_conn_id="jdbc")
   				sql = """ select * from Account """
   				df = hook.get_pandas_df(sql)
   				df.to_csv("/{some_file_path}/{name_of_csv}.csv",header=False, index=False, quoting=1)
   				# print(df.head())
   				print(df)
   				tbl_dict = df.to_dict('dict')
   				return tbl_dict
   			except Exception as e:
   				print("Data extract error: " + str(e))
    
   		jdbc_extract()
    
   	sf_extract_and_load = extract_and_load()
   

3. Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "mistral ai_hook". 👁 New DAG added
   
4. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e. play) button to run the DAG. This executes the SQL query in our mistral ai_hook.py file and export the results as a CSV to whichever file path we designated in our code. 👁 Run the DAG
   
5. After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv. 👁 CSV created
   
6. Open the CSV file to see that your Mistral AI data is now available for use in CSV format thanks to Apache Airflow. 👁 CSV file with Mistral AI data.
   

More Information & Free Trial

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Mistral AI data in Apache Airflow. Reach out to our Support Team if you have any questions.