---
language:
- en
tags:
- information retrieval
- embedding model
- visual information retrieval
metrics:
- recall
pipeline_tag: feature-extraction
license: apache-2.0
---

# OCR-free Visual Document Embedding Model as Your Personal Librarian

The model only takes images as document-side inputs and produce vectors representing document pages. `minicpm-visual-embedding-v0` is trained with over 200k query-visual document pairs, including textual document, visual document, arxiv figures, industry documents, textbooks, ebooks, etc. The performance of `minicpm-visual-embedding-v0` is on a par with our ablation text embedding model on text-oriented documents, and an advantages on visually-intensive documents.

![Memex Archtechture](images/memex.png)

# News

- 2024-06-27: 🚀 We released our first visual embedding model checkpoint minicpm-visual-embedding-v0 on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).

- 2024-05-08: 🌍 We [open-sourced](https://github.com/RhapsodyAILab/minicpm-visual-embedding) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code. 

# Get started

Pip install all dependencies:

```
Pillow==10.1.0
timm==0.9.10
torch==2.1.2
torchvision==0.16.2
transformers==4.36.0
sentencepiece==0.1.99
numpy==1.26.0
```

First you are suggested to git clone this huggingface repo or download repo with `huggingface_cli`.

```bash
git lfs install
git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0
```

or 

```bash
huggingface-cli download RhapsodyAI/minicpm-visual-embedding-v0
```

```python
from transformers import AutoModel
from transformers import AutoTokenizer
from PIL import Image
import torch

device = 'cuda:0'

# Load model, be sure to substitute `model_path` by your model path 
model_path = '/local/path/to/model'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model.to(device)

# Load image to PIL.Image object
image_1 = Image.open('/local/path/to/images/memex.png').convert('RGB')
image_2 = Image.open('/local/path/to/images/us2020.png').convert('RGB')
image_3 = Image.open('/local/path/to/images/hard_negative.png').convert('RGB')

# User query
query_instruction = 'Represent this query for retrieving relavant document: '
query = 'Who was elected as president of United States in 2020?'
query_full = query_instruction + query

# Embed image documents
with torch.no_grad():
    p_reps = model(text=['', '', ''], image=[image_1, image_2, image_3], tokenizer=tokenizer)

# Embed text queries
with torch.no_grad():
    q_reps = model(text=[query_full], image=[None], tokenizer=tokenizer) # [B, s, d]

# Calculate similarities
scores = torch.matmul(q_reps, p_reps.T)
print(scores)
```

# Limitations

- This checkpoint is an alpha version, and may not be strong in your tasks, for bad case, please create an issue to let us know, many thanks!

- The modeling script `modeling_minicpmv` on `huggingface` is not standard yet, the inference code could be further improved.

- The inference speed is low, because vision encoder uses `timm`, which does not yet support `flash-attn`.


# Citation

If you find our work useful, please consider cite us:

```bibtex
@misc{RhapsodyEmbedding2024,
  author = {RhapsodyAI},
  title = {OCR-free Visual Document Embedding Model as Your Personal Librarian},
  year = {2024},
  howpublished = {\url{https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0}},
  note = {Accessed: 2024-06-28}
}
```