Cosine Similarity (Bag-of-Words) — KiHustle Bot Bazaar

x402

Cosine Similarity (Bag-of-Words) — KiHustle Bot Bazaar is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).

Computes cosine similarity between two text strings using a bag-of-words (BoW) representation

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

Returns a JSON object with a similarity result and status field. The similarity score reflects how close the two input texts are based on overlapping vocabulary weighted by cosine distance using a bag-of-words model. A score of 1.0 indicates identical term distributions; 0.0 indicates no overlap.

Example use cases

Deduplicating blog post drafts

“I have two article drafts and I want to know how similar they are before publishing — first one is 'Top 10 AI side hustle ideas for 2024' and the second is 'Best ways to earn with AI in 2024'. Can you score their similarity?”

Matching job description to resume

“Compare this job posting and this resume summary and tell me how closely they match using cosine similarity: job is 'Seeking a data analyst skilled in Python and dashboards' and resume says 'Experienced in Python, data visualization, and reporting tools'.”

Checking plagiarism in submitted content

“I need to check if these two paragraphs are too similar to each other before I post them — can you run a cosine similarity check on them? Paragraph A: 'Build automated income using AI agents.' Paragraph B: 'Create passive revenue streams by automating tasks with AI.'”

Technical details

Endpoint

POSThttps://kihustle.tech/bot-bazaar/api/v1/cosine-similarity-bow?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a fast, lightweight, lexical similarity measure between two short-to-medium text strings and do not need semantic embedding-based comparison. It is ideal for deduplication, keyword matching, and basic relevance scoring where vocabulary overlap is a sufficient proxy for similarity. Prefer semantic embedding endpoints when deep semantic meaning matters more than word overlap.

Side effects

Missing text_a or text_b fields returns an error
Empty string inputs may yield a zero-similarity result or error
Very long texts may cause unexpected truncation or timeout
Non-UTF-8 or special character inputs may cause parsing failures
Response schema is minimal — numeric score format not guaranteed beyond 'result' and 'status' fields

Input

Schema · Request body

Field	Type	Description
text_a	string
text_b	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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