# """
# Embeddings utilities - now uses the new embeddings architecture.
# This module provides backward compatibility for existing code while
# using the new direct import embeddings architecture.
# """
# import logging
# import os
# from selfmemory.configs import SelfMemoryConfig
# logger = logging.getLogger(__name__)
# # Global embedding provider instance
# _embedding_provider = None
# def _get_embedding_provider():
# """Get or create the global embedding provider using new architecture."""
# global _embedding_provider
# if _embedding_provider is None:
# config = SelfMemoryConfig()
# # Determine provider from config or environment
# provider = getattr(config.embedding, "provider", "ollama").lower()
# # Use direct imports based on provider
# if provider == "openai":
# from selfmemory.embeddings.configs import OpenAIEmbedderConfig
# from selfmemory.embeddings.openai import OpenAIEmbedding
# embedding_config = OpenAIEmbedderConfig(
# api_key=getattr(
# config.embedding, "api_key", os.getenv("OPENAI_API_KEY")
# ),
# model=getattr(config.embedding, "model", "text-embedding-3-small"),
# )
# _embedding_provider = OpenAIEmbedding(embedding_config)
# elif provider == "azure_openai":
# from selfmemory.embeddings.azure_openai import AzureOpenAIEmbedding
# from selfmemory.embeddings.configs import AzureOpenAIEmbedderConfig
# embedding_config = AzureOpenAIEmbedderConfig(
# api_key=getattr(
# config.embedding, "api_key", os.getenv("AZURE_OPENAI_API_KEY")
# ),
# azure_endpoint=getattr(
# config.embedding,
# "azure_endpoint",
# os.getenv("AZURE_OPENAI_ENDPOINT"),
# ),
# model=getattr(config.embedding, "model", "text-embedding-3-small"),
# deployment_name=getattr(config.embedding, "deployment_name", None),
# )
# _embedding_provider = AzureOpenAIEmbedding(embedding_config)
# elif provider == "huggingface":
# from selfmemory.embeddings.configs import HuggingFaceEmbedderConfig
# from selfmemory.embeddings.huggingface import HuggingFaceEmbedding
# embedding_config = HuggingFaceEmbedderConfig(
# model=getattr(
# config.embedding, "model", "sentence-transformers/all-MiniLM-L6-v2"
# ),
# api_key=getattr(
# config.embedding, "api_key", os.getenv("HUGGINGFACE_API_KEY")
# ),
# use_local=getattr(config.embedding, "use_local", True),
# )
# _embedding_provider = HuggingFaceEmbedding(embedding_config)
# elif provider == "mock":
# from selfmemory.embeddings.configs import MockEmbedderConfig
# from selfmemory.embeddings.mock import MockEmbedding
# embedding_config = MockEmbedderConfig(
# embedding_dims=getattr(config.embedding, "embedding_dims", 768)
# )
# _embedding_provider = MockEmbedding(embedding_config)
# else: # Default to ollama
# from selfmemory.embeddings.configs import OllamaEmbedderConfig
# from selfmemory.embeddings.ollama import OllamaEmbedding
# embedding_config = OllamaEmbedderConfig(
# model=getattr(config.embedding, "model", "nomic-embed-text"),
# ollama_base_url=getattr(
# config.embedding, "ollama_base_url", "http://localhost:11434"
# ),
# )
# _embedding_provider = OllamaEmbedding(embedding_config)
# return _embedding_provider
# def get_embeddings(text: str) -> list[float]:
# """
# Get embeddings vector for the given text using the configured provider.
# Args:
# text: The input text to get embeddings for
# Returns:
# List of float values representing the embedding vector
# Raises:
# ValueError: If text is empty
# Exception: If embedding generation fails
# """
# if not text or not text.strip():
# raise ValueError("Text cannot be empty")
# try:
# provider = _get_embedding_provider()
# return provider.embed(text.strip())
# except Exception as e:
# logger.error(f"Failed to generate embeddings: {str(e)}")
# raise Exception(f"Embedding generation failed: {str(e)}") from e
# def generate_embeddings(text: str) -> dict:
# """
# Generate embeddings for the given text using the configured provider.
# This function is kept for backward compatibility.
# Args:
# text: The input text to generate embeddings for
# Returns:
# Dictionary containing embeddings and metadata
# Raises:
# Exception: If embedding generation fails
# """
# try:
# logger.debug(f"Generating embeddings for text of length {len(text)}")
# embeddings = get_embeddings(text)
# return {"embeddings": [embeddings]}
# except Exception as e:
# logger.error(f"Failed to generate embeddings: {str(e)}")
# raise Exception(f"Embedding generation failed: {str(e)}") from e
# # Backwards compatibility functions
# def get_embedding_client():
# """Get the embedding provider instance for backward compatibility."""
# return _get_embedding_provider()
# if __name__ == "__main__":
# test_text = "Hello, this is a test."
# try:
# embedding_vector = get_embeddings(test_text)
# print(f"Generated embedding vector with {len(embedding_vector)} dimensions")
# print(f"First 5 values: {embedding_vector[:5]}")
# except Exception as e:
# print(f"Error: {e}")
