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Agent Tools – Text Similarity (BGE-M3 Cosine)
x402
Agent Tools – Text Similarity (BGE-M3 Cosine) is a paid API for AI agents from agents.crowdspark.me, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-31).
Computes semantic similarity between two texts using BGE-M3 embeddings and returns a cosine score plus a qualitative band (near_duplicate, high, related, weak, unrelated).
Price / call$0.003
Success—
StatusUnknown
How this service works
Pay-per-call utilities for AI agents over x402, USDC on Base.
Output
A JSON object containing: the cosine similarity score (float 0–1), a qualitative band ('near_duplicate', 'high', 'related', 'weak', or 'unrelated'), the embedding model used ('baai/bge-m3'), the metric ('cosine'), the number of embedding dimensions (1024), and character counts for both input texts. Texts are not stored.
Example use cases
RAG chunk relevance scoring
“Score how closely this user question matches a retrieved knowledge-base passage so I can decide whether to include it in the context: question is 'What are the refund policies for digital goods?' and the chunk is 'Digital purchases are non-refundable except in cases of technical failure verified by support.'”
Product listing deduplication
“I have two product descriptions and I need to know if they're near-duplicates before merging them into my catalog: first is 'Sony WH-1000XM5 wireless noise-cancelling headphones, 30h battery' and second is 'Sony WH1000XM5 Headphones – ANC, Bluetooth, 30 hours playtime'. Are they the same item?”
Resume-to-job-description matching
“Tell me how well this resume summary matches the job description — give me a cosine similarity score and the band. Resume: 'Experienced Python developer with 5 years in backend APIs and cloud infrastructure.' Job: 'We are looking for a senior backend engineer proficient in Python and cloud platforms.'”
Technical details
Endpoint
POSThttps://agents.crowdspark.me/v1/text/similarity?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need multilingual semantic similarity with a high-quality dense embedding model (BGE-M3, 1024 dimensions) and want a ready-made qualitative band alongside the raw score. Ideal for RAG pipeline chunk ranking, deduplication workflows, and semantic matching tasks where storing texts is undesirable. Prefer it over keyword-based matching when meaning matters more than token overlap, and over heavier LLM-based comparisons when cost and latency are a concern at $0.003 per call.
Side effects
- Input text exceeds 8,000 characters — likely a 400 or payload-too-large error
- Missing 'a' or 'b' field in JSON body — validation error returned
- Network timeout for very long texts near the character limit
- Payment not attached or insufficient USDC — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| a | string | First text, max 8,000 characters |
| b | string | Second text, max 8,000 characters |
Output
Schema · Response
{
"type": "json",
"example": {
"band": "high",
"chars": {
"a": 23,
"b": 27
},
"model": "baai/bge-m3",
"metric": "cosine",
"dimensions": 1024,
"similarity": 0.8123
}
}Tags
x402
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