add embed-anything to readme (#138)

README.md

@@ -222,7 +222,8 @@ Several community projects and ressources have been developed around ColPali to
 |---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Byaldi        | [`Byaldi`](https://github.com/AnswerDotAI/byaldi) is [RAGatouille](https://github.com/AnswerDotAI/RAGatouille)'s equivalent for ColPali, leveraging the `colpali-engine` package to facilitate indexing and storing embeddings.                    |
 | PyVespa       | [`PyVespa`](https://pyvespa.readthedocs.io/en/latest/examples/colpali-document-retrieval-vision-language-models-cloud.html) allows interaction with [Vespa](https://vespa.ai/), a production-grade vector database, with detailed ColPali support. |
-| Candle        | [Candle](https://github.com/huggingface/candle/tree/main/candle-examples/examples/colpali) enables ColPali inference with an efficient ML framework for Rust.                                                                                      |
+| Candle        | [Candle](https://github.com/huggingface/candle/tree/main/candle-examples/examples/colpali) enables ColPali inference with an efficient ML framework for Rust.  
+| EmbedAnything | [`EmbedAnything`](https://github.com/StarlightSearch/EmbedAnything) Allows end-to-end ColPali inference with both Candle and ONNX backend. |
 | DocAI         | [DocAI](https://github.com/PragmaticMachineLearning/docai) uses ColPali with GPT-4o and Langchain to extract structured information from documents.                                                                                                |
 | VARAG         | [VARAG](https://github.com/adithya-s-k/VARAG) uses ColPali in a vision-only and a hybrid RAG pipeline.                                                                                                                                             |
 | ColBERT Live! | [`ColBERT Live!`](https://github.com/jbellis/colbert-live/) enables ColPali usage with vector databases supporting large datasets, compression, and non-vector predicates.                                                                         |
@@ -241,6 +242,7 @@ Several community projects and ressources have been developed around ColPali to
 | Finance Report Analysis with ColPali and Gemini   | [Jaykumaran (LearnOpenCV)](https://github.com/spmallick/learnopencv/tree/master/Multimodal-RAG-with-ColPali-Gemini)                             |
 | Multimodal Retrieval-Augmented Generation (RAG) with Document Retrieval (ColPali) and Vision Language Models (VLMs) | [Sergio Paniego](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms) |
 | Document Similarity Search with ColPali           | [Frank Sommers](https://colab.research.google.com/github/fsommers/documentai/blob/main/Document_Similarity_with_ColPali_0_2_2_version.ipynb)    |
+| End-to-end ColPali inference with EmbedAnything | [Akshay Ballal (EmbedAnything)](https://colab.research.google.com/drive/1-Eiaw8wMm8I1n69N1uKOHkmpw3yV22w8?usp=sharing)
 
 ### Other resources