IpexLLMBgeEmbeddings#

class langchain_community.embeddings.ipex_llm.IpexLLMBgeEmbeddings[source]#

Bases: BaseModel, Embeddings

Wrapper around the BGE embedding model with IPEX-LLM optimizations on Intel CPUs and GPUs.

To use, you should have the ipex-llm and sentence_transformers package installed. Refer to here for installation on Intel CPU.

Example on Intel CPU:

from langchain_community.embeddings import IpexLLMBgeEmbeddings

embedding_model = IpexLLMBgeEmbeddings(
    model_name="BAAI/bge-large-en-v1.5",
    model_kwargs={},
    encode_kwargs={"normalize_embeddings": True},
)

Refer to here for installation on Intel GPU.

Example on Intel GPU:

from langchain_community.embeddings import IpexLLMBgeEmbeddings

embedding_model = IpexLLMBgeEmbeddings(
    model_name="BAAI/bge-large-en-v1.5",
    model_kwargs={"device": "xpu"},
    encode_kwargs={"normalize_embeddings": True},
)

Initialize the sentence_transformer.

param cache_folder: str | None = None#: Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.

param embed_instruction: str = ''#: Instruction to use for embedding document.

param encode_kwargs: Dict[str, Any] [Optional]#: Keyword arguments to pass when calling the encode method of the model.

param model_kwargs: Dict[str, Any] [Optional]#: Keyword arguments to pass to the model.

param model_name: str = 'BAAI/bge-small-en-v1.5'#: Model name to use.

param query_instruction: str = 'Represent this question for searching relevant passages: '#: Instruction to use for embedding query.

async aembed_documents(texts: list[str]) → list[list[float]]#

Asynchronous Embed search docs.

Parameters:: texts (list[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: list[list[float]]

async aembed_query(text: str) → list[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: list[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Compute doc embeddings using a HuggingFace transformer model.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Compute query embeddings using a HuggingFace transformer model.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using IpexLLMBgeEmbeddings