Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Overview
OpenAI provides state-of-the-art embedding models including text-embedding-3-small and text-embedding-3-large.
Quick Start
from praisonaiagents import embedding
result = embedding("Hello world", model="text-embedding-3-small")
print(f"Dimensions: {len(result.embeddings[0])}")
CLI Usage
praisonai embed "Hello world" --model text-embedding-3-small
Setup
export OPENAI_API_KEY="sk-..."
Available Models
| Model | Dimensions | Max Tokens | Use Case |
|---|
text-embedding-3-small | 1536 | 8191 | Cost-effective, general purpose |
text-embedding-3-large | 3072 | 8191 | Highest quality |
text-embedding-ada-002 | 1536 | 8191 | Legacy model |
Custom Dimensions
OpenAI’s v3 models support dimension reduction:
from praisonaiagents import embedding
# Reduce to 256 dimensions for efficiency
result = embedding("Hello world", model="text-embedding-3-large", dimensions=256)
print(f"Dimensions: {len(result.embeddings[0])}") # 256
Batch Embeddings
from praisonaiagents import embedding
texts = ["Hello", "World", "AI agents"]
result = embedding(texts, model="text-embedding-3-small")
print(f"Generated {len(result.embeddings)} embeddings")
Async Usage
import asyncio
from praisonaiagents import aembedding
async def main():
result = await aembedding("Hello world", model="text-embedding-3-small")
print(f"Dimensions: {len(result.embeddings[0])}")
asyncio.run(main())
Pricing
| Model | Price per 1M tokens |
|---|
| text-embedding-3-small | $0.02 |
| text-embedding-3-large | $0.13 |
| text-embedding-ada-002 | $0.10 |
