Grocery and Fresh Food Stockout Substitution Tool-Calling Trajectory Dataset
#Tool Argument Generation #Function Calling #Substitute Product Search #Tool Call Sequence Generation #Grocery Delivery #Stockout Handling #Product Substitution #Order Fulfillment
- 6530
- 0.4GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
This dataset covers grocery and fresh food delivery scenarios involving out-of-stock items, customer substitution preferences, and candidate replacements. It captures how requirements such as brand, price, product category, and dietary restrictions can be converted into parameters for substitute searches and confirmation actions. With source context and ordered target tool calls, it supports function-calling training, stockout workflow automation, and improvements to fulfillment in on-demand delivery.
Dataset Insights
Sample Examples
{}
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| source_context | object | Original stockout information, customer instructions, and candidate substitute products provided as input. |
| target_tool_calls | array | Ordered tool invocations and parameter values derived from the source context to search for and confirm a substitute. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- It covers out-of-stock item details, customer substitution requirements, and candidate replacement products. It also includes tool calls and arguments derived from these scenarios to search for and confirm substitutes.
- What is the dataset schema?
- The supplied schema includes a source_context object for scenario inputs and a target_tool_calls array for ordered target invocations. This structure supports training on tool arguments and call sequences. Check the product listing for record counts, file formats, and annotation quality details.
- Which training and application tasks does it support?
- It can support function-calling training, tool argument generation, substitute product search, and automation of stockout handling in on-demand grocery delivery. It can also help evaluate whether models follow substitution constraints such as brand, price, category, or dietary restrictions.
- How can I access the data, and what are the licensing terms?
- Access methods, downloadable formats, and license scope depend on the latest information from the product listing or data provider. No specific license terms were provided here, so confirm restrictions on commercial use, redistribution, and deployment before use.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Grocery and Fresh Food Stockout Substitution Tool-Calling Trajectory Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/a7873a7eb7bd4ade3ddfe7491ff36383},
urldate={2026-10-26},
keywords={grocery stockout dataset,food substitution data,delivery tool calling,function calling training,tool argument generation,substitute search data,product substitution preferences,order fulfillment dataset},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder