bigfunctions > generate_categories
generate_categories¶
Call or Deploy generate_categories
?
✅ You can call this generate_categories
bigfunction directly from your Google Cloud Project (no install required).
- This
generate_categories
function is deployed inbigfunctions
GCP project in 39 datasets for all of the 39 BigQuery regions. You need to use the dataset in the same region as your datasets (otherwise you may have a function not found error). - Function is public, so it can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
- You may prefer to deploy the BigFunction in your own project if you want to build and manage your own catalog of functions. This is particularly useful if you want to create private functions (for example calling your internal APIs). Discover the framework
Public BigFunctions Datasets:
Region | Dataset |
---|---|
eu |
bigfunctions.eu |
us |
bigfunctions.us |
europe-west1 |
bigfunctions.europe_west1 |
asia-east1 |
bigfunctions.asia_east1 |
... | ... |
Description¶
Signature
generate_categories(items)
Description
Return categories
of items
.
Using GenAi, this function generates a hierarchy of categories and subcategories that best represents the given items
.
(items
: must be a json array of strings or or objects).
Result is a json with the following schema:
{
categories: [
{
name: string,
subcategories: [
string
]
}
]
}
Examples¶
Categorize User Reviews of Nickel App in App Store
select bigfunctions.eu.generate_categories(
(
select to_json(array_agg(content))
from bigfunctions.eu.get_appstore_reviews(
'https://apps.apple.com/fr/app/nickel-compte-pour-tous/id1119225763'
)
)
)
select bigfunctions.us.generate_categories(
(
select to_json(array_agg(content))
from bigfunctions.us.get_appstore_reviews(
'https://apps.apple.com/fr/app/nickel-compte-pour-tous/id1119225763'
)
)
)
select bigfunctions.europe_west1.generate_categories(
(
select to_json(array_agg(content))
from bigfunctions.europe_west1.get_appstore_reviews(
'https://apps.apple.com/fr/app/nickel-compte-pour-tous/id1119225763'
)
)
)
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| categories |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {
"categories": [
{
"name": "Account Management",
"subcategories": [
"Login Issues",
...
]
},
...
]
}
|
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Need help using generate_categories
?
The community can help! Engage the conversation on Slack
For professional suppport, don't hesitate to chat with us.
Found a bug using generate_categories
?
If the function does not work as expected, please
- report a bug so that it can be improved.
- or open the discussion with the community on Slack.
For professional suppport, don't hesitate to chat with us.
Use cases¶
The generate_categories
function is useful for automatically categorizing text data, particularly when you have a large collection of items and want to understand the main themes or topics discussed. Here are a few specific use cases:
-
Customer Feedback Analysis: Analyze customer reviews, support tickets, or survey responses to identify common issues, compliments, or feature requests. The function can group similar feedback into categories, making it easier to understand customer sentiment and prioritize areas for improvement. The provided example shows how to categorize app store reviews, highlighting topics like "Account Management," which might include subcategories like "Login Issues."
-
Product Categorization: Automatically categorize products in an e-commerce setting based on their descriptions. This can improve search functionality, recommend related products, or organize product catalogs. For example, if the input
items
are product descriptions like "vintage leather jacket," "stylish bomber jacket," and "warm winter coat," the function might generate categories like "Outerwear" with subcategories "Jackets" and "Coats." -
Topic Modeling for Research: Analyze a collection of research papers, articles, or social media posts to identify the main topics discussed. This can be helpful for literature reviews, trend analysis, or identifying areas for further research.
-
Content Tagging: Automatically tag articles, blog posts, or other content with relevant keywords or categories, making it easier for users to discover content that interests them.
-
Support Ticket Routing: Categorize incoming support tickets based on their descriptions to automatically route them to the appropriate support team.
In essence, any scenario where you need to group a large number of text items into meaningful categories could benefit from the generate_categories
function. The use of GenAI allows the function to infer categories and subcategories even without predefined categories, making it flexible and adaptable to various data types.
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