Embeddings

from praisonaiagents import embedding

result = embedding("Hello, world!")
print(f"Dimensions: {len(result.embeddings[0])}")  # 1536

from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant.",
    knowledge=["AI agents can use tools to accomplish tasks."],
    knowledge_config={
        "embedder": {
            "provider": "openai",
            "config": {"model": "text-embedding-3-small"}
        }
    }
)

response = agent.chat("What can AI agents do?")

from praisonaiagents import Agent

agent = Agent(
    instructions="You remember conversations.",
    memory=True,
    memory_config={"embedding_model": "text-embedding-3-small"}
)

agent.chat("My name is Alice.")
response = agent.chat("What is my name?")  # Uses embedding for retrieval

from praisonaiagents import embedding

result = embedding("Hello, world!", model="text-embedding-3-small")
print(f"Dimensions: {len(result.embeddings[0])}")  # 1536

from praisonaiagents import embedding

result = embedding(
    ["Hello", "World", "AI agents"],
    model="text-embedding-3-small"
)

print(f"Generated {len(result.embeddings)} embeddings")

from praisonaiagents import get_dimensions

dims = get_dimensions("text-embedding-3-small")  # 1536
dims = get_dimensions("text-embedding-3-large")  # 3072

from praisonaiagents import embedding

result = embedding(
    "Hello world",
    model="text-embedding-3-large",
    dimensions=256  # Reduce dimensions
)

print(f"Dimensions: {len(result.embeddings[0])}")  # 256

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())

# Recommended: Direct import from praisonaiagents
from praisonaiagents import embedding, EmbeddingResult, get_dimensions
from praisonaiagents import aembedding  # async version

# Plural aliases (OpenAI style)
from praisonaiagents import embeddings, aembeddings

# Short aliases
from praisonaiagents import embed, aembed

# Alternative: Import from embedding submodule
from praisonaiagents.embedding import embedding, aembedding

from praisonaiagents import EmbeddingResult         # canonical
from praisonai import EmbeddingResult               # same class
from praisonai.capabilities import EmbeddingResult  # same class