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Agent Data Tools: Text Embedding
x402
Agent Data Tools: Text Embedding is a paid API for AI agents from agent-data-tools.revmesh2074.workers.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).
Converts input text (up to 4,000 characters) into a 1024-dimensional numerical embedding vector using the BAAI BGE-M3 model
Price / call$0.01
Success—
StatusUnknown
How this service works
A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication.
Output
Returns a JSON object containing the model name ('model': '@cf/baai/bge-m3'), a 1024-dimensional floating-point array ('embedding': [float, ...]) representing the semantic content of the input text, and a dimension count ('dimensions': 1024). The embedding can be used directly for cosine similarity, nearest-neighbor search, or storage in a vector database.
Example use cases
RAG pipeline document chunking
“I have this paragraph I want to index in my vector store: 'The Federal Reserve raised interest rates by 25 basis points in its latest meeting, citing persistent inflation concerns.' Can you embed it so I can store it alongside my other document chunks?”
Semantic similarity between two phrases
“I need to check how semantically similar two pieces of text are — can you embed both 'machine learning model training' and 'AI neural network optimization' so I can compute their cosine similarity?”
Query vectorization for search
“Embed this user search query so I can run a nearest-neighbor lookup against my knowledge base: 'What are the side effects of ibuprofen when taken with blood thinners?'”
Technical details
Endpoint
GEThttps://agent-data-tools.revmesh2074.workers.dev/ai/embed?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a pay-per-call, no-account embedding service using the multilingual BGE-M3 model at $0.002 per call with x402 USDC micropayments on Base. Ideal for AI agents that need on-demand embeddings without managing API keys or subscriptions, and that already use the Agent Data Tools suite for crypto, web, or business data. BGE-M3 provides strong multilingual and cross-lingual retrieval performance at 1024 dimensions.
Side effects
- Text exceeds 4,000 character limit — embedding may be truncated or rejected
- Missing required 'text' query parameter returns an error
- Payment failure in x402 protocol results in no charge and no response
- Model inference timeout on very dense or unusual text
- Network errors to Cloudflare Workers endpoint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | Text to embed, up to 4,000 characters |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| model | string | |
| embedding | array | |
| dimensions | number |
Tags
x402
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