AIRabbitTransforming JSON with AI: Dynamic Processing vs. Filter Generation
Transforming JSON data from one format to another is a common challenge in today's data-driven world. Whether you're integrating APIs, migrating data between systems, or processing real-time data streams, you need a reliable and efficient way to reshape JSON structures. Manually writing code for each individual transformation is not only time-consuming, but also error-prone.
So what's the best way to tackle this problem? There are two main approaches:
Using dynamic AI on every element: This method involves applying AI to transform each JSON entry individually. While it can handle complex transformations, it's often slow and can lead to inconsistent results due to variations in AI interpretation.
Use AI to generate a filter (A to B transformation):Instead of transforming each element individually, use AI to create a transformation filter that can be applied to the entire dataset. This approach is faster and ensures consistency across all data inputs. However, it is only suitable if you are not generating new knowledge and are using existing knowledge in your JSON data.
For the first approach, there are many tools that use AI to process individual JSON items. However, they may not be efficient for large datasets due to the processing time required for each entry.
For the second approach, you can transform JSON on the fly using tools such as the ChatWithJSON Playground. This tool allows you to quickly create transformation filters and apply them to your data, streamlining the process.
How does it work?
You provide your JSON data and specify the desired transformation using natural language. The AI generates a transformation filter, often using JQ under the hood, that you can apply to your data.
Note: ChatWithJSON is in beta and does not yet support tall jq operations. Please note that it only generates the JQ and not the data based on existing data, it does not generate new data like summarises of columns etc. For the latter you may want to consider other tools like jsongpt.
Let's explore an example:
Example: Transforming JSON data with a generated filter
Scenario: You have a JSON dataset from an API and need to transform it into a specific format required by your application.
Your command: "Transform the data so that it has the fields 'id', 'name', 'email', and 'dept', where 'name' is a combination of 'firstName' and 'lastName'".
Explanation: The AI generates a filter that combines
firstNameandlastNameinto a singlenamefield and extracts the other required fields.
Transformation filter generated:
.employees[] | {id: .empId, name: (.firstName + " " + .lastName), email: .contact.email, dept: .department}
Output JSON:
[
{
"id": "E001",
"name": "John Doe",
"email": "john.doe@example.com",
"department": "Sales"
},
{
"id": "E002",
"name": "Jane Smith",
"email": "jane.smith@example.com",
"Department": "Marketing"
}
]
Input JSON:
{
"employee": [
{
"empId": "E001",
"firstName": "John",
"surname": "Doe",
"Department": "Sales",
"contact": {
"email": "john.doe@example.com",
"phone": "555-1234"
}
},
{
"empId": "E002",
"firstName": "Jane",
"surname": "Smith",
"Department": "Marketing",
"contact": {
"email": "jane.smith@example.com",
"phone": "555-5678"
}
}
]
}
Advantages of the filter generation approach
User-friendly: Specify transformations in plain language without writing complex code.
Time saving: Quickly perform transformations on large data sets.
Consistent results: Ensure consistent transformations across all data inputs.
Conclusion.
While applying dynamic AI to each JSON entry can be useful in certain cases, it often leads to inefficiencies and inconsistent results. Using AI to generate a transformation filter provides a faster, more consistent, and more efficient solution for transforming JSON data.
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