Update README.md

README.md

@@ -29,6 +29,8 @@ Our model is capable of:
 
 # News
 
+- 2024-08-18: 👀 We released a **new [end-to-end Visual RAG huggingface demo](https://huggingface.co/spaces/bokesyo/MiniCPMV-RAG-PDFQA)**, which supports **both retrieval and generation**, which means, you can use our system to **answer your questions within a long PDF** now!
+
 - 2024-08-17: 👊 We open-sourced [cleaned version of training codebase](https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train) for MiniCPM-Visual-Embedding, which supports **deepspeed zero stage 1,2** and **large batchsize** like `4096` for full-parameter training to turn VLMs into dense retrievers. We also developed methods to filter training datasets and generating queries using unlablled datasets. We supports **multi-nodes, multi-GPUs** high-efficiency **evaluation** on large retrieval datasets. With such efforts, we support up to `20B` VLM contrastive learning with `4096` batch size. We have tested that one can train a VLM dense retriever with only **1 GPU, but with batch size of `4096`**. 
 
 - 2024-07-14: 🤗 We released **online huggingface demo**! Try our [online demo](https://huggingface.co/spaces/bokesyo/MiniCPM_Visual_Document_Retriever_Demo)!
@@ -164,7 +166,7 @@ If you find our work useful, please consider cite us:
 
 ```bibtex
 @misc{RhapsodyEmbedding2024,
-  author = {Rhapsody Group},
+  author = {Rhapsody Group, OpenBMB},
   title = {Memex: OCR-free Visual Document Embedding Model as Your Personal Librarian},
   year = {2024},
   howpublished = {\url{https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0}},