---
license: apache-2.0
---

Converted and quantized.

# Custom Embedding Class for Optimum ONNX Runtime

This document provides the implementation of a custom embedding class designed to work with the Optimum ONNX Runtime model.

```python
from typing import List
from llama_index.embeddings.huggingface_optimum import OptimumEmbedding
import asyncio

class CustomEmbedding:
    def __init__(self, folder_name: str):
        """Initialize the embedding model."""
        self.embed_model = OptimumEmbedding(folder_name=folder_name)

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        """Asynchronously embed a list of documents."""
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(None, self.embed_documents, texts)

    async def aembed_query(self, text: str) -> List[float]:
        """Asynchronously embed a single query."""
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(None, self.embed_query, text)

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed a list of documents."""
        return [self.embed_model.get_text_embedding(text) for text in texts]

    def embed_query(self, text: str) -> List[float]:
        """Embed a single query."""
        return self.embed_model.get_text_embedding(text)

# Example Usage
custom_embeddings = CustomEmbedding(folder_name="./optimum_model")
```

## Key Features

1. **Initialization**:
   - The `CustomEmbedding` class initializes the `OptimumEmbedding` instance with the specified `folder_name` for the preloaded model.

2. **Asynchronous Methods**:
   - `aembed_documents(texts: List[str])`: Asynchronously embeds a list of documents and returns their embeddings.
   - `aembed_query(text: str)`: Asynchronously embeds a single query and returns its embedding.

3. **Synchronous Methods**:
   - `embed_documents(texts: List[str])`: Embeds a list of documents and returns their embeddings.
   - `embed_query(text: str)`: Embeds a single query and returns its embedding.

## Usage

- **Folder Name**:
  Replace `"./optimum_model"` with the path to your locally stored Optimum ONNX Runtime model.

- **Example**:
  ```python
  # Embed a single query
  query_embedding = custom_embeddings.embed_query("Hello World!")

  # Embed multiple documents
  document_embeddings = custom_embeddings.embed_documents(["Document 1", "Document 2"])
  ```