# Image-Text Retrieval Embdding with LightningDOT

*author: David Wang*


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## Description

This operator extracts features for image or text with [LightningDOT](https://arxiv.org/abs/2103.08784) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity.


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## Code Example

Load an image from path './teddy.jpg' to generate an image embedding. 

Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. 

*Write a pipeline with explicit inputs/outputs name specifications:*

```python

from towhee import pipe, ops, DataCollection

img_pipe = (
    pipe.input('url')
    .map('url', 'img', ops.image_decode.cv2_rgb())
    .map('img', 'vec', ops.image_text_embedding.lightningdot(model_name='lightningdot_base', modality='image'))
    .output('img', 'vec')
)

text_pipe = (
    pipe.input('text')
    .map('text', 'vec', ops.image_text_embedding.lightningdot(model_name='lightningdot_base', modality='text'))
    .output('text', 'vec')
)

DataCollection(img_pipe('./teddy.jpg')).show()
DataCollection(text_pipe('A teddybear on a skateboard in Times Square.')).show()
```

<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***lightningdot(model_name, modality)***

**Parameters:**


​   ***model_name:*** *str*

​   The model name of LightningDOT. Supported model names: 
- lightningdot_base
- lightningdot_coco_ft
- lightningdot_flickr_ft     

​   ***modality:*** *str*

​   Which modality(*image* or *text*) is used to generate the embedding. 

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## Interface

An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.


**Parameters:**

​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)*  or *str*

​  The data (image or text based on specified modality) to generate embedding.	



**Returns:** *numpy.ndarray*

​   The data embedding extracted by model.





# More Resources

- [Sparse and Dense Embeddings - Zilliz blog](https://zilliz.com/learn/sparse-and-dense-embeddings): Learn about sparse and dense embeddings, their use cases, and a text classification example using these embeddings.
- [Building A Trademark Image Search System with Milvus - Zilliz blog](https://zilliz.com/learn/image-based-trademark-similarity-search-system): Learn how to use a vector database to build your own trademark image similarity search system that could save you from intellectual property lawsuits.
- [Training Your Own Text Embedding Model - Zilliz blog](https://zilliz.com/learn/training-your-own-text-embedding-model): Explore how to train your text embedding model using the `sentence-transformers` library and generate our training data by leveraging a pre-trained LLM.
- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts.
- [Hybrid Search: Combining Text and Image for Enhanced Search Capabilities - Zilliz blog](https://zilliz.com/learn/hybrid-search-combining-text-and-image): Milvus enables hybrid sparse and dense vector search and multi-vector search capabilities, simplifying the vectorization and search process.
- [Build a Multimodal Search System with Milvus - Zilliz blog](https://zilliz.com/blog/how-vector-dbs-are-revolutionizing-unstructured-data-search-ai-applications): Implementing a Multimodal Similarity Search System Using Milvus, Radient, ImageBind, and Meta-Chameleon-7b
- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.