haive.core.engine.embedding.providers.HuggingFaceEmbeddingConfigΒΆ
HuggingFace embedding configuration.
ClassesΒΆ
Configuration for HuggingFace embeddings. |
Module ContentsΒΆ
- class haive.core.engine.embedding.providers.HuggingFaceEmbeddingConfig.HuggingFaceEmbeddingConfig[source]ΒΆ
Bases:
haive.core.engine.embedding.base.BaseEmbeddingConfig
Configuration for HuggingFace embeddings.
This configuration provides access to HuggingFace embedding models including sentence transformers and other transformer-based embedding models.
Examples
Basic usage:
config = HuggingFaceEmbeddingConfig( name="hf_embeddings", model="sentence-transformers/all-MiniLM-L6-v2" ) embeddings = config.instantiate()
With GPU support:
config = HuggingFaceEmbeddingConfig( name="hf_embeddings", model="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"}, encode_kwargs={"normalize_embeddings": True} )
With caching:
config = HuggingFaceEmbeddingConfig( name="hf_embeddings", model="sentence-transformers/all-MiniLM-L6-v2", use_cache=True, cache_folder="./embedding_cache" )
- embedding_typeΒΆ
Always EmbeddingType.HUGGINGFACE
- modelΒΆ
HuggingFace model name or path
- model_kwargsΒΆ
Additional arguments for model initialization
- encode_kwargsΒΆ
Additional arguments for encoding
- use_cacheΒΆ
Whether to use embedding caching
- cache_folderΒΆ
Directory for caching embeddings
- instantiate()[source]ΒΆ
Create a HuggingFace embeddings instance.
- Returns:
HuggingFaceEmbeddings instance configured with the provided parameters
- Raises:
ImportError β If required packages are not installed
ValueError β If configuration is invalid
- Return type:
Any
- classmethod validate_cache_folder(v, values)[source]ΒΆ
Set default cache folder if not specified.
- Return type:
Any