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Citiview Agent Market – Text Embeddings (BGE-M3)
x402
Citiview Agent Market – Text Embeddings (BGE-M3) is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-14).
Converts up to 100 texts per call into 1024-dimensional multilingual semantic embeddings using BGE-M3, suitable for semantic search and RAG pipelines.
Price / call$0.003
Success—
StatusUnknown
How this service works
Text embeddings for semantic search and RAG (BGE-M3, multilingual, 1024 dimensions). Up to 100 texts per call, one flat price. Priced by size: $0.003 + 3x the model token cost. Also callable as GET with the same fields as query parameters.
Output
Returns 1024-dimensional float embedding vectors for each input text, produced by the BGE-M3 multilingual model. Each vector captures the semantic meaning of the input and can be used directly for cosine similarity, nearest-neighbor search, clustering, or as input to downstream RAG retrieval steps.
Example use cases
RAG pipeline document indexing
“I need to embed all these product descriptions so I can store them in Pinecone and retrieve the most relevant ones when a user asks a question — can you vectorize these 50 texts for me?”
Multilingual semantic search
“I have a mix of English, French, and Spanish customer reviews — can you generate embeddings for all of them so I can cluster similar feedback together regardless of language?”
Query-document similarity scoring
“Embed this user query 'best practices for API rate limiting' and these 5 documentation snippets so I can compute cosine similarity and find the most relevant doc.”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.com/v1/embed?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need multilingual, high-quality 1024-dimensional dense embeddings from BGE-M3 at a flat per-call price regardless of batch size (up to 100 texts). It is especially well-suited for RAG pipelines, multilingual corpora, and scenarios where you want a single API call to handle a batch of passages. Prefer it over OpenAI embeddings when you want a flat-rate, non-token-based pricing model or need BGE-M3's specific multilingual representation quality.
Side effects
- More than 100 texts submitted in one call — batch limit exceeded
- Malformed pipe-separated texts string causing parsing errors
- Empty input text resulting in zero or degenerate vectors
- Payment failure or insufficient USDC balance blocking the x402 call
- Text exceeding model's token context window producing truncated embeddings
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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