# Citiview Agent Market – Text Embeddings (BGE-M3)

> 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.

## Facts

- Endpoint: GET https://agents.citiviewgroup.com/v1/embed?utm_source=zero.xyz
- Price: $0.003/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/citiview-agent-market-text-embeddings-bge-m3-5caaff76
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_h05Cj3r9mglWAc76oBHSt

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability citiview-agent-market-text-embeddings-bge-m3-5caaff76
```

Example prompt: Embed these 3 passages for me so I can store them in my vector database and run semantic search: 'Climate change affects global temperatures', 'Renewable energy reduces carbon emissions', 'Solar panels are increasingly affordable'.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "GET"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "properties": {
      "text": {
       "type": "string"
      },
      "texts": {
       "type": "string",
       "description": "Up to 100 texts; separate items with |"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/citiview-agent-market-text-embeddings-bge-m3-5caaff76/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from agents.citiviewgroup.com](https://www.zero.xyz/host/agents.citiviewgroup.com/llms.txt)