agentutility(7)$ man embedding-similarity
/embedding-similarity
PRICE / CALL
$0.005
USDC · base mainnet · scheme: exact
METHOD
POST
CLUSTER
wordmintCATEGORY
uncategorized
STATUS
● live
NAME
embedding-similarity — measures how semantically similar two strings are: embeds both via venice (default model: text-embedding-bge-m3) and returns the cosine s…
SYNOPSIS
POST https://x402.agentutility.ai/embedding-similarity
Content-Type: application/json
X-PAYMENT: <signed-transferWithAuthorization>
{ ... }↳ first call →
402 Payment Required. Sign USDCtransferWithAuthorization, retry with theX-PAYMENT header.DESCRIPTION
Measures how semantically similar two strings are: embeds both via Venice (default model: text-embedding-bge-m3) and returns the cosine similarity as a single float in [-1, 1]. Useful for paraphrase detection, dedup, and cheap retrieval routing. Use it as an embedding similarity, semantic match, vector compare, or are-these-strings-similar check.
INPUT — request schema
| property | type | description | req? |
|---|---|---|---|
| text_a | string | First text. Up to 30,000 chars. | required |
| text_b | string | Second text. Up to 30,000 chars. | required |
| model | string | Venice embedding model. Default 'text-embedding-bge-m3'. | optional |
OUTPUT — response shape
| field | type | description |
|---|---|---|
| text_a | string | Echoes back the first input string that was embedded for comparison. |
| text_b | string | Echoes back the second input string that was embedded for comparison. |
| similarity | string | Cosine similarity between the two embeddings as a float in [-1, 1]; 1 means identical direction. |
| model | string | Venice embedding model used to produce the vectors, defaults to text-embedding-bge-m3. |
| dimensions | string | Number of dimensions in each embedding vector returned by the model. |
| source | string | Upstream embedding provider that generated the vectors, typically venice. |
EXAMPLES — two ways to call
EXAMPLE 1 · curl
curl -X POST https://x402.agentutility.ai/embedding-similarity \
-H 'Content-Type: application/json' \
-d '{ }'first response =
402 Payment Required with payment requirements; sign + retry with X-PAYMENT.EXAMPLE 2 · mcp
# Install the MCP package for this endpoint's cluster npx -y @agentutility/mcp-<cluster> # Required: EVM private key with USDC on Base export X402_PRIVATE_KEY=0x... # Then call the embedding-similarity tool from your MCP-aware agent.
MCP server handles payment automatically — your coding agent just calls the tool by name.
METADATA
- tags
- wordmintembeddingscosine-similaritysemantic-searchvector-compareparaphrase-detectiondedupembedding-similarity
- methods
- POST
- cluster
- wordmint
- price
- $0.005 USDC per call
ADJACENT — other endpoints in wordmint
| endpoint | description | price |
|---|---|---|
| brand-tagline | Generates brand taglines and slogans for launch pages, X bios, email copy, and product cards. | $0.005 |
| brand-tagline-generate | Generates tagline options for a brand or startup from its name, concept, audience, and tone. | $0.005 |
| card-resolve | Normalizes free-form graded card text into a canonical card object. | $0.005 |
| content-simhash | Fingerprints text with a 64-bit SimHash for near-duplicate detection, computed entirely locally. | $0.005 |
| cron-parse | Cron parser. | $0.005 |
| detect-language | Language detector / language identification. | $0.005 |
| dictionary-define | Looks up English word definitions with pronunciation, part of speech, and synonyms. | $0.005 |
| extract-entities | Named entity recognition (NER) / entity extractor. | $0.005 |
SEE ALSO