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---
license: apache-2.0
---
# 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"])
```
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