haive.core.models.embeddings
============================

.. py:module:: haive.core.models.embeddings

.. autoapi-nested-parse::

   Haive Embeddings Module.

   This module provides comprehensive abstractions and implementations for working with
   text embedding models from various providers. Embeddings are vector representations
   of text that capture semantic meaning, enabling similarity search, clustering,
   and other NLP applications.

   The module supports a wide range of embedding providers with a consistent interface
   for configuration and use.

   Supported Cloud Providers:
       - Azure OpenAI: Microsoft's hosted OpenAI embedding models
       - OpenAI: Direct OpenAI embedding models
       - Cohere: Specialized embedding models from Cohere
       - Jina AI: Jina AI embedding models
       - Google Vertex AI: Google Cloud's machine learning platform
       - AWS Bedrock: Amazon's foundation model service
       - Cloudflare Workers AI: Cloudflare's AI model hosting
       - Voyage AI: Specialized embedding models from Voyage AI
       - Anyscale: Anyscale embedding models

   Supported Local/Self-hosted Providers:
       - HuggingFace: Open-source embedding models from the HuggingFace model hub
       - SentenceTransformers: Efficient sentence embedding models
       - FastEmbed: Lightweight embedding models optimized for CPU
       - Ollama: Local embedding models via Ollama
       - LlamaCpp: Local embedding models via llama.cpp

   Key Components:
       - Base Classes: Abstract base classes for embedding configurations
       - Provider Types: Enumeration of supported embedding providers
       - Provider Implementations: Provider-specific configuration classes
       - Factory Functions: Simplified creation of embedding instances
       - Security: Secure handling of API keys with environment variable resolution
       - Caching: Efficient caching of embeddings for performance optimization

   Typical usage example::

       from haive.core.models.embeddings import create_embeddings, OpenAIEmbeddingConfig

       # Configure an embedding model
       config = OpenAIEmbeddingConfig(
           model="text-embedding-3-small"
       )

       # Create the embeddings
       embeddings = create_embeddings(config)

       # Generate embeddings
       doc_vectors = embeddings.embed_documents(["Document text"])
       query_vector = embeddings.embed_query("Query text")



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/haive/core/models/embeddings/base/index
   /autoapi/haive/core/models/embeddings/filter/index
   /autoapi/haive/core/models/embeddings/provider_types/index


Functions
---------

.. autoapisummary::

   haive.core.models.embeddings.test_config_classes_exist


Package Contents
----------------

.. py:function:: test_config_classes_exist()

   Placeholder test function.