# QuantizedBgeEmbeddings

> **Class** in `langchain_community`

📖 [View in docs](https://reference.langchain.com/python/langchain-community/embeddings/itrex/QuantizedBgeEmbeddings)

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

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

## Signature

```python
QuantizedBgeEmbeddings(
    self,
    model_name: str,
    *,
    max_seq_len: int = 512,
    pooling_strategy: str = 'mean',
    query_instruction: Optional[str] = None,
    document_instruction: Optional[str] = None,
    padding: bool = True,
    model_kwargs: Optional[Dict] = None,
    encode_kwargs: Optional[Dict] = None,
    onnx_file_name: Optional[str] = 'int8-model.onnx',
    **kwargs: Any = {},
)
```

## Description

**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:**

.. code-block:: python

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: "
)

## Extends

- `BaseModel`
- `Embeddings`

## Constructors

```python
__init__(
    self,
    model_name: str,
    *,
    max_seq_len: int = 512,
    pooling_strategy: str = 'mean',
    query_instruction: Optional[str] = None,
    document_instruction: Optional[str] = None,
    padding: bool = True,
    model_kwargs: Optional[Dict] = None,
    encode_kwargs: Optional[Dict] = None,
    onnx_file_name: Optional[str] = 'int8-model.onnx',
    **kwargs: Any = {},
) -> None
```

| Name | Type |
|------|------|
| `model_name` | `str` |
| `max_seq_len` | `int` |
| `pooling_strategy` | `str` |
| `query_instruction` | `Optional[str]` |
| `document_instruction` | `Optional[str]` |
| `padding` | `bool` |
| `model_kwargs` | `Optional[Dict]` |
| `encode_kwargs` | `Optional[Dict]` |
| `onnx_file_name` | `Optional[str]` |


## Properties

- `model_name_or_path`
- `max_seq_len`
- `pooling`
- `padding`
- `encode_kwargs`
- `model_kwargs`
- `normalize`
- `batch_size`
- `query_instruction`
- `document_instruction`
- `onnx_file_name`
- `model_config`

## Methods

- [`load_model()`](https://reference.langchain.com/python/langchain-community/embeddings/itrex/QuantizedBgeEmbeddings/load_model)
- [`embed_documents()`](https://reference.langchain.com/python/langchain-community/embeddings/itrex/QuantizedBgeEmbeddings/embed_documents)
- [`embed_query()`](https://reference.langchain.com/python/langchain-community/embeddings/itrex/QuantizedBgeEmbeddings/embed_query)

---

[View source on GitHub](https://github.com/langchain-ai/langchain-community/blob/d5ea8358933260ad48dd31f7f8076555c7b4885a/libs/community/langchain_community/embeddings/itrex.py#L9)