In today’s ever-evolving data landscape, recognizing entities across different sources is crucial, especially with the integration of large language models (LLMs) into modern data stacks. This article will guide you through using foundation models for entity matching in a streamlined way using dbt and Snowflake.

Overview of Foundation Models

This implementation transforms the idea of machine learning entity matching into a simple SQL workflow, leveraging the capabilities of GPT-3 without involving complex training procedures. By posing a question in natural language to GPT-3, such as “Are products A and B the same?”, the model returns a response that we interpret as a boolean for entity matching.

Setup Prerequisites

To successfully run this project, ensure you have the following:

• A Python environment ready
• Access to the OpenAI endpoint via an API key
• AWS access for deploying a Lambda function
• A Snowflake account linked to AWS
• A functional dbt-core setup on Snowflake

Step-by-Step Implementation

1. Setting Up Python

Create a virtual environment for the project dependencies:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. OpenAI API Key

Sign up for an API token through the OpenAI portal. Test if your API is functioning by running:

API_KEY=my_key python open_ai_playground.py

Replace “my_key” with your actual API token.

3. Setting Up Serverless

Confirm that the serverless CLI is installed and properly set up with your AWS credentials. Execute the following commands:

cd src/serverless
AWS_PROFILE=your_profile_name serverless deploy

4. Connecting to Snowflake

Establish a connection between Snowflake and AWS Lambda by creating a database schema and an external function. Run:

CREATE OR REPLACE database external_functions;
CREATE OR REPLACE schema external_functions.lambda;
CREATE OR REPLACE external function external_functions.lambda.resolution(x varchar, y varchar);

Type a test query to ensure the connection works:

SELECT external_functions.lambda.resolution('hello', 'world');

5. Configuring dbt

Point dbt to your Snowflake instance with the appropriate profile settings. Make sure the dbt role has permissions for the UDF registered in Snowflake. Use:

cd src/dbt
dbt seed

This command populates your schema with initial data for entity matching.

Running Entity Matching

After completing the above steps, you can run the entity matching process. Just navigate to the dbt folder and type:

cd src/dbt
dbt run

This will trigger the dbt DAG to pre-process the Walmart and Amazon product data, creating a table that indicates whether products match.

Troubleshooting Tips

If you encounter issues, consider the following:

• Check your API key and ensure you have the correct access to the OpenAI services.
• Verify that AWS Lambda is properly deployed in the correct region.
• Ensure that all required libraries are installed in your Python environment.
• Make sure that your Snowflake instance is correctly configured and that you have access to the required schemas.

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Conclusion

By following these steps, you can effectively implement entity matching using foundation models within a modern data stack. This approach not only simplifies the process but also enhances your data processing capabilities.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.