langchain_community.embeddings.itrex.QuantizedBgeEmbeddings

class langchain_community.embeddings.itrex.QuantizedBgeEmbeddings[source]

Bases: BaseModel, Embeddings

Leverage Itrex runtime to unlock the performance of compressed NLP models.

Please ensure that you have installed intel-extension-for-transformers.

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 {})

onnx_file_name: Optional[str] =

File name of onnx optimized model which is exported by itrex. (default “int8-model.onnx”)

Example

from langchain_community.embeddings import QuantizedBgeEmbeddings

model_name = "Intel/bge-small-en-v1.5-sts-int8-static-inc"
encode_kwargs = {'normalize_embeddings': True}
hf = QuantizedBgeEmbeddings(
    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 if the input data cannot be parsed to form a valid model.

async aembed_documents(texts: List[str]) List[List[float]]

Asynchronous Embed search docs.

Parameters

texts (List[str]) –

Return type

List[List[float]]

async aembed_query(text: str) List[float]

Asynchronous Embed query text.

Parameters

text (str) –

Return type

List[float]

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

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

Input:

texts: List[str] = List of text documents to embed.

Output:

List[List[float]] = The embeddings of each text document.

Parameters

texts (List[str]) –

Return type

List[List[float]]

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

Embed query text.

Parameters

text (str) –

Return type

List[float]

load_model() None[source]
Return type

None

Examples using QuantizedBgeEmbeddings