Text Embeddings

Vector embeddings for semantic search and RAG applications

Generate high-quality vector embeddings for semantic search, RAG applications, and AI-powered features using Cxmpute's distributed embedding service.

Overview

Cxmpute's Text Embeddings service converts text into dense vector representations that capture semantic meaning. These embeddings are essential for building semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications.

Key Features

High-Quality Embeddings: State-of-the-art embedding models
Multiple Models: Various embedding models optimized for different use cases
Batch Processing: Efficient processing of multiple texts
Global Network: Low-latency access worldwide
OpenAI Compatible: Familiar API structure

Quick Start

Basic Request

Shell

curl -X POST https://cxmpute.cloud/api/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "X-User-Id: YOUR_USER_ID" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nomic-embed-text",
    "input": "Cxmpute provides distributed AI inference services."
  }'

Python Example

Python

import requests

url = "https://cxmpute.cloud/api/v1/embeddings"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "X-User-Id": "YOUR_USER_ID",
    "Content-Type": "application/json"
}

data = {
    "model": "nomic-embed-text",
    "input": "This is a sample text for embedding"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

embedding = result["data"][0]["embedding"]
print(f"Embedding dimension: {len(embedding)}")

API Reference

Endpoint

HTTP

POST /v1/embeddings

Parameters

Parameter	Type	Required	Description
`model`	string	Yes	Embedding model name
`input`	string/array	Yes	Text(s) to embed
`truncate`	boolean	No	Truncate input to model's max length

Available Models

Model	Dimension	Description
`nomic-embed-text`	768	General-purpose embeddings
`all-minilm-l6-v2`	384	Fast, lightweight
`bge-large-en-v1.5`	1024	High-quality English

Response Format

JSON

{
  "object": "list",
  "data": [
    {
      "object": "embedding", 
      "embedding": [0.1, -0.2, 0.3, ...],
      "index": 0
    }
  ],
  "model": "nomic-embed-text",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Use Cases

1. Semantic Search

Build search systems that understand meaning, not just keywords.

2. Recommendation Systems

Recommend content based on semantic similarity.

3. RAG Applications

Enhance LLM responses with relevant context retrieval.

4. Document Clustering

Group similar documents automatically.

5. Text Classification

Classify text using embedding-based similarity.

Pricing

During our testnet phase, all services are completely free for all users! Pricing for the mainnet launch is to be determined (TBD).

Join our Discord community to stay updated on pricing announcements, give feedback, and connect with other developers building with Cxmpute.

Support

Discord: Community support
Contact: Contact us

Ready to build semantic applications? Start with our high-quality embeddings and create intelligent search, recommendation, and AI systems!