QuantizedBiEncoderEmbeddings#

class langchain_community.embeddings.optimum_intel.QuantizedBiEncoderEmbeddings[source]#

Bases: BaseModel, Embeddings

Quantized bi-encoders embedding models.

Please ensure that you have installed optimum-intel and ipex.

Input:

model_name: str = Model name. max_seq_len: int = The maximum sequence length for tokenization. (default 512) pooling_strategy: str =

“mean” or “cls”, pooling strategy for the final layer. (default “mean”)

query_instruction: Optional[str] =: An instruction to add to the query before embedding. (default None)
document_instruction: Optional[str] =: An instruction to add to each document before embedding. (default None)
padding: Optional[bool] =: Whether to add padding during tokenization or not. (default True)
model_kwargs: Optional[Dict] =: Parameters to add to the model during initialization. (default {})
encode_kwargs: Optional[Dict] =: Parameters to add during the embedding forward pass. (default {})

Example:

from langchain_community.embeddings import QuantizedBiEncoderEmbeddings

model_name = “Intel/bge-small-en-v1.5-rag-int8-static” encode_kwargs = {‘normalize_embeddings’: True} hf = QuantizedBiEncoderEmbeddings(

model_name, encode_kwargs=encode_kwargs, query_instruction=”Represent this sentence for searching relevant passages: “

)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

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

Asynchronous Embed search docs.

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

Asynchronous Embed query text.

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

Embed a list of text documents using the Optimized Embedder model.

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

Embed query text.

load_model() → None[source]#

Examples using QuantizedBiEncoderEmbeddings