tessl/pypi-portkey-ai

Python client library for the Portkey API - Control Panel for AI Apps with unified API signature, automated fallbacks, retries, load balancing, semantic caching, virtual keys, and comprehensive observability features.

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Embeddings

Name: tessl/pypi-portkey-ai
Author: tessl

Text embedding generation for converting text into numerical vectors for semantic similarity, search, and machine learning applications. Supports multiple providers through Portkey's unified interface.

Capabilities

Embedding Creation

Creates vector embeddings from input text using the specified model. Supports various embedding models from different providers with consistent interface.

class Embeddings:
    def create(
        self,
        *,
        input: str,
        model: Optional[str] = "portkey-default",
        dimensions: Union[int, NotGiven] = NOT_GIVEN,
        encoding_format: Union[str, NotGiven] = NOT_GIVEN,
        user: Union[str, NotGiven] = NOT_GIVEN,
        **kwargs
    ) -> CreateEmbeddingResponse:
        """
        Create embeddings for the given input text.

        Args:
            input: Input text to create embeddings for
            model: Embedding model to use (defaults to "portkey-default")
            dimensions: Number of dimensions for the embedding vectors (model-dependent)
            encoding_format: Format for encoding the embeddings (e.g., "float", "base64")
            user: User identifier for tracking and analytics
            **kwargs: Additional provider-specific parameters

        Returns:
            CreateEmbeddingResponse: Response containing embedding vectors and metadata
        """

class AsyncEmbeddings:
    async def create(
        self,
        *,
        input: str,
        model: Optional[str] = "portkey-default",  
        dimensions: Union[int, NotGiven] = NOT_GIVEN,
        encoding_format: Union[str, NotGiven] = NOT_GIVEN,
        user: Union[str, NotGiven] = NOT_GIVEN,
        **kwargs
    ) -> CreateEmbeddingResponse:
        """Async version of create method."""

Usage Examples

from portkey_ai import Portkey

# Initialize client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",
    virtual_key="VIRTUAL_KEY"  
)

# Create embeddings for single text
response = portkey.embeddings.create(
    input="Hello, world!",
    model="text-embedding-ada-002"
)

# Access the embedding vector
embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")

# Create embeddings with specific dimensions
response = portkey.embeddings.create(
    input="Text to embed",
    model="text-embedding-3-small",
    dimensions=512
)

# Batch embedding creation (provider-dependent)
texts = [
    "First text to embed",
    "Second text to embed", 
    "Third text to embed"
]

for text in texts:
    response = portkey.embeddings.create(
        input=text,
        model="text-embedding-ada-002",
        user="user-123"
    )
    print(f"Embedding for '{text}': {len(response.data[0].embedding)} dimensions")

Async Usage

import asyncio
from portkey_ai import AsyncPortkey

async def create_embeddings():
    portkey = AsyncPortkey(
        api_key="PORTKEY_API_KEY",
        virtual_key="VIRTUAL_KEY"
    )
    
    response = await portkey.embeddings.create(
        input="Async embedding creation",
        model="text-embedding-ada-002"
    )
    
    return response.data[0].embedding

# Run async function
embedding = asyncio.run(create_embeddings())

Types

class CreateEmbeddingResponse:
    """Response from embedding creation request"""
    object: str  # "list"
    data: List[EmbeddingObject]
    model: str
    usage: EmbeddingUsage
    _headers: Optional[dict]  # Response headers for debugging

class EmbeddingObject:
    """Individual embedding object"""
    object: str  # "embedding"
    embedding: List[float]  # The embedding vector
    index: int  # Index in the input list

class EmbeddingUsage:
    """Token usage information"""
    prompt_tokens: int
    total_tokens: int

Install with Tessl CLI

npx tessl i tessl/pypi-portkey-ai