linkfox-multimodal-product-similarity

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Multimodal Product Image Similarity Analysis

Multimodal 产品图片相似度分析

This skill guides you on how to analyze and group products by the visual similarity of their main images. It helps Amazon sellers identify same-style products, detect competitor lookalikes, and organize product lists into visually coherent clusters.

本技能将指导您如何根据商品主图的视觉相似度对产品进行分析和分组。它能帮助亚马逊卖家识别同款产品、检测竞品仿款，并将产品列表整理为视觉上连贯的聚类组。

Core Concepts

核心概念

Product Image Similarity Analysis uses multimodal AI to compare the main images of products and automatically group them based on visual features such as appearance, color, composition, and material. It is a post-processing tool -- it operates on product data that has already been retrieved by a preceding step (e.g., product search, product recommendations).

Similarity threshold: The

similarityThreshold

parameter controls how visually close two products must be to land in the same group. It is an integer from 0 to 100 representing a percentage. A higher value means stricter matching (only near-identical images group together); a lower value means more lenient matching (broader visual clusters). The default is 60.

Single-brand group filtering: The

includeSingleBrandGroups

flag (default

true

) controls whether groups containing products from only one brand are included in the results. Setting it to

false

filters out single-brand groups, which is useful when the user wants to focus on cross-brand visual overlaps (e.g., competitor lookalike analysis).

产品图片相似度分析采用Multimodal AI对比商品主图，并根据外观、颜色、构图和材质等视觉特征自动对产品进行分组。这是一款后处理工具——它基于前置步骤（如产品搜索、产品推荐）已获取的产品数据进行操作。

相似度阈值：

similarityThreshold

参数控制两个产品需达到的视觉接近程度才能归为同一组。它是一个0到100之间的整数，代表百分比。数值越高意味着匹配越严格（只有近乎完全相同的图片才会被归为一组）；数值越低意味着匹配越宽松（视觉聚类范围更广）。默认值为60。

单品牌组过滤：

includeSingleBrandGroups

标志（默认值为

true

）控制结果中是否包含仅含单一品牌产品的分组。将其设置为

false

可过滤掉单品牌组，这在用户希望关注跨品牌视觉重叠（如竞品仿款分析）时非常有用。

Input Data Requirement

输入数据要求

This tool requires a

products

list from a preceding step. It cannot fetch product data on its own. The typical workflow is:

Run a product search or recommendation tool to obtain a product list.
Pass that product list into this tool via
```
refResultData
```
for visual similarity grouping.

The input data must be a JSON object containing a

products

array.

本工具需要来自前置步骤的

products

列表。它无法自行获取产品数据。典型工作流如下：

运行产品搜索或推荐工具获取产品列表。
通过
```
refResultData
```
将该产品列表传入本工具进行视觉相似度分组。

输入数据必须是包含

products

数组的JSON对象。

Parameters

参数

Parameter	Type	Required	Description
similarityThreshold	integer	No	Similarity threshold (0-100), default `60` . Higher = stricter matching.
includeSingleBrandGroups	boolean	No	Whether to include groups with only one brand, default `true` . Set to `false` to focus on cross-brand similarity.
refResultData	string	No	JSON string of the preceding tool's result data containing the product list.
userInput	string	No	The original user query or instruction text.

参数	类型	是否必填	描述
similarityThreshold	integer	否	相似度阈值（0-100），默认值为 `60` 。数值越高，匹配越严格。
includeSingleBrandGroups	boolean	否	是否包含仅含单一品牌的分组，默认值为 `true` 。设置为 `false` 可聚焦跨品牌相似度分析。
refResultData	string	否	包含产品列表的前置工具结果数据的JSON字符串。
userInput	string	否	用户原始查询或指令文本。

Response Fields

响应字段

Field	Type	Description
groups	array	List of similarity groups. Each group contains `groupNumber` , `reason` , `brandCount` , and an `asins` array of product details.
analysisInfo	object	Summary: `totalProductsAnalyzed` , `totalGroupsFound` , `similarityThreshold` , `analysisTimestamp` .
tables	array	Tabular result data, each element with `data` , `columns` , and `name` .
total	integer	Total number of result items.
title	string	Result title.
type	string	Rendering style hint.
costToken	integer	Total LLM tokens consumed (input + output).

字段	类型	描述
groups	array	相似度分组列表。每个分组包含 `groupNumber` 、 `reason` 、 `brandCount` 以及包含产品详情的 `asins` 数组。
analysisInfo	object	摘要信息： `totalProductsAnalyzed` 、 `totalGroupsFound` 、 `similarityThreshold` 、 `analysisTimestamp` 。
tables	array	表格形式的结果数据，每个元素包含 `data` 、 `columns` 和 `name` 。
total	integer	结果项总数。
title	string	结果标题。
type	string	渲染样式提示。
costToken	integer	消耗的LLM总令牌数（输入+输出）。

Group Item (asins array element)

分组项（asins数组元素）

Field	Type	Description
asin	string	Product ASIN
productId	string	Product ID
brand	string	Brand name
price	number	Price
rating	number	Rating score
ratings	integer	Number of ratings
monthlySalesUnits	integer	Monthly sales units
monthlySalesRevenue	number	Monthly sales revenue
monthlySalesUnitsGrowthRate	number	Monthly sales growth rate
imageUrl	string	Main image URL
productImageUrls	array	All product image URLs
imagePrompt	string	AI-generated image description
asinUrl	string	Product detail page URL
availableDate	string	Listing date
color	string	Color
material	string	Material

字段	类型	描述
asin	string	产品ASIN
productId	string	产品ID
brand	string	品牌名称
price	number	价格
rating	number	评分
ratings	integer	评价数量
monthlySalesUnits	integer	月度销量
monthlySalesRevenue	number	月度销售额
monthlySalesUnitsGrowthRate	number	月度销量增长率
imageUrl	string	主图URL
productImageUrls	array	所有产品图片URL
imagePrompt	string	AI生成的图片描述
asinUrl	string	产品详情页URL
availableDate	string	上架日期
color	string	颜色
material	string	材质

API Usage

API 使用方法

This tool calls the LinkFox tool gateway API. See

references/api.md

for endpoint details, request parameters, and response structure. You can also execute

scripts/multimodal_analyze_product_similarity.py

directly to run analyses.

本工具调用LinkFox工具网关API。端点详情、请求参数和响应结构请参见

references/api.md

。您也可以直接执行

scripts/multimodal_analyze_product_similarity.py

来运行分析。

Usage Examples

使用示例

1. Group search results by visual similarity (default threshold) After obtaining a product list from a search tool, pass the results to this tool to cluster visually similar items:

User: "Group these products by how similar they look."
Action: Call the API with refResultData set to the preceding product list JSON, using the default similarityThreshold of 60.

2. Find near-identical products (strict matching)

User: "Which of these products have almost the same main image?"
Action: Call the API with similarityThreshold set to 85 or higher for strict visual matching.

3. Cross-brand competitor lookalike detection

User: "Show me groups where different brands have similar-looking products."
Action: Call the API with includeSingleBrandGroups set to false to filter out single-brand clusters.

4. Broad visual clustering (lenient threshold)

User: "Roughly categorize these products by appearance."
Action: Call the API with similarityThreshold set to 40 for broad grouping.

5. Combined: strict similarity across brands

User: "Find products from different brands that look nearly identical."
Action: Call the API with similarityThreshold set to 80 and includeSingleBrandGroups set to false.

1. 按视觉相似度对搜索结果进行分组（默认阈值） 从搜索工具获取产品列表后，将结果传入本工具以对视觉相似的商品进行聚类：

用户："将这些产品按外观相似度分组。"
操作：调用API，将refResultData设置为前置产品列表的JSON，使用默认的similarityThreshold（60）。

2. 查找近乎完全相同的产品（严格匹配）

用户："这些产品中哪些的主图几乎完全相同？"
操作：调用API，将similarityThreshold设置为85或更高以进行严格视觉匹配。

3. 跨品牌竞品仿款检测

用户："展示不同品牌拥有相似外观产品的分组。"
操作：调用API，将includeSingleBrandGroups设置为false以过滤掉单品牌聚类组。

4. 宽泛视觉聚类（宽松阈值）

用户："大致按外观对这些产品进行分类。"
操作：调用API，将similarityThreshold设置为40以进行宽泛分组。

5. 组合使用：跨品牌严格相似度匹配

用户："找出不同品牌中外观几乎完全相同的产品。"
操作：调用API，将similarityThreshold设置为80，includeSingleBrandGroups设置为false。

Display Rules

展示规则

Present grouping results clearly: Show each similarity group with its group number, the reason for grouping, brand count, and a table of products within the group.
Show product images when possible: If image URLs are available, include them to help users visually verify the grouping.
Highlight cross-brand groups: When the user cares about competitor analysis, emphasize groups containing multiple brands.
Analysis summary: Always present the analysis summary (total products analyzed, total groups found, similarity threshold used, timestamp).
No subjective advice: Present the grouping data objectively. Do not inject business recommendations unless the user asks.
Large result sets: When there are many groups, show the most significant ones first (e.g., groups with the most products or the most brands) and inform the user about additional groups.
Error handling: When a request fails, explain the reason based on the response message and suggest adjustments (e.g., check that the input product data is valid, adjust the threshold).

清晰呈现分组结果：展示每个相似度分组的组号、分组原因、品牌数量以及组内产品表格。
尽可能展示产品图片：若图片URL可用，应包含图片以帮助用户直观验证分组结果。
突出跨品牌分组：当用户关注竞品分析时，强调包含多个品牌的分组。
分析摘要：始终展示分析摘要（分析的产品总数、找到的分组总数、使用的相似度阈值、时间戳）。
不提供主观建议：客观呈现分组数据。除非用户要求，否则不要加入业务建议。
大型结果集：当分组数量较多时，先展示最重要的分组（如产品数量最多或品牌数量最多的分组），并告知用户还有其他分组。
错误处理：当请求失败时，根据响应消息解释原因并建议调整（如检查输入产品数据是否有效、调整阈值）。

Important Limitations

重要限制

Post-processing only: This tool cannot fetch product data on its own. It must receive product data from a preceding step.
No database storage: Results are not stored in a database. Do not use database query tools for secondary analysis on the output.
Input format: The input must be a JSON object containing a
```
products
```
array.
Direct to summary: After this tool completes, pass the results directly to the summary stage. Do not perform additional intermediate data computations.

仅支持后处理：本工具无法自行获取产品数据。它必须接收来自前置步骤的产品数据。
无数据库存储：结果不会存储在数据库中。请勿使用数据库查询工具对输出进行二次分析。
输入格式：输入必须是包含
```
products
```
数组的JSON对象。
直接进入摘要阶段：本工具完成后，直接将结果传入摘要阶段。请勿执行额外的中间数据计算。

User Expression & Scenario Quick Reference

用户表述与场景速查

Applicable -- Visual similarity analysis on product lists:

User Says	Scenario
"Group these by how they look"	Visual clustering
"Find similar-looking products", "find lookalikes"	Similarity detection
"Which products look the same"	Image deduplication
"Show me competitor copycats"	Cross-brand lookalike analysis
"Cluster by appearance / color / style"	Visual categorization
"Are there duplicates in this list"	Image-based dedup
"Same-style products from different brands"	Cross-brand similarity

Not applicable -- Needs beyond image similarity:

Text-based product comparison (titles, descriptions, keywords)
Price or sales-based grouping without visual component
Product search or discovery (this tool only post-processes existing lists)
Review analysis, listing optimization, advertising strategy

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in

references/api.md

. Do not interrupt the user's flow.

For more high-quality, professional cross-border e-commerce skills, set LinkFox Skills.

适用场景——对产品列表进行视觉相似度分析：

用户表述	场景
"按外观对这些产品分组"	视觉聚类
"找出外观相似的产品"、"找出仿款"	相似度检测
"哪些产品外观相同"	图片去重
"展示竞品仿款"	跨品牌仿款分析
"按外观/颜色/风格聚类"	视觉分类
"这个列表里有重复项吗"	基于图片的去重
"不同品牌的同款产品"	跨品牌相似度分析

不适用场景——超出图片相似度的需求：

基于文本的产品对比（标题、描述、关键词）
无视觉维度的价格或销量分组
产品搜索或发现（本工具仅对现有列表进行后处理）
评论分析、Listing优化、广告策略

反馈：

当出现以下任一情况时，自动检测并通过Feedback API提交反馈：

本技能描述的功能或用途与实际行为不符
本技能的结果不符合用户意图
用户表达了对本技能的不满或赞扬
任何您认为可以改进的地方

按照

references/api.md

中的指定方式调用反馈API。请勿中断用户流程。

如需更多高质量、专业的跨境电商技能，请访问LinkFox Skills。