RAG Engine: Retrieval Augmented Generation Engine

RagE (Rag Engine) is a tool designed to facilitate the construction and training of components within the Retrieval-Augmented-Generation (RAG) model. It also offers algorithms to support retrieval and provides pipelines for evaluating models. Moreover, it fosters rapid development of question answering systems and chatbots based on the RAG model.

Currently, we have completed the basic training pipeline for the model, but there is still much to be done due to limited resources. However, with this library, we are continuously updating and developing. Additionally, we will consistently publish the models that we train.

Installation 🔥

• We recommend python 3.9 or higher, torch 2.0.0 or higher, transformers 4.31.0 or higher.

• Currently, you can only download from the source, however, in the future, we will upload it to PyPI. RagE can be installed from source with the following commands:

git clone https://github.com/anti-aii/RagE.git
cd RagE
pip install -e .

Quick start 🥮

• 1. Initialize the model
• 2. Load model from Huggingface Hub
• 3. Training
• 4. ONNX
• 5. List of pretrained models

We have detailed instructions for using our models for inference. See notebook

1. Initialize the model

Let's initialize the SentenceEmbedding model

>>> import torch 
>>> from pyvi import ViTokenizer
>>> from rage import SentenceEmbedding
>>> device= torch.device('cuda' if torch.cuda.is_available() else 'cpu')
>>> model= SentenceEmbedding(model_name= "vinai/phobert-base-v2", torch_dtype= torch.float32, aggregation_hidden_states= False, strategy_pooling= "dense_first")
>>> model.to(device)
SentenceEmbeddingConfig(model_base: {'model_type_base': 'RobertaModel', 'model_name': 'vinai/phobert-base-v2', 'type_backbone': 'mlm', 'required_grad_base_model': True, 'aggregation_hidden_states': False, 'concat_embeddings': False, 'dropout': 0.1, 'quantization_config': None}, pooling: {'strategy_pooling': 'dense_first'})

Then, we can show the number of parameters in the model.

>>> model.summary_params()
trainable params: 135588864 || all params: 135588864 || trainable%: 100.0
>>> model.summary()
+---------------------------+-------------+------------------+
|        Layer (type)       |    Params   | Trainable params |
+---------------------------+-------------+------------------+
|    model (RobertaModel)   | 134,998,272 |    134998272     |
| pooling (PoolingStrategy) |   590,592   |      590592      |
|       drp1 (Dropout)      |      0      |        0         |
+---------------------------+-------------+------------------+

Now we can use the SentenceEmbedding model to encode the input words. The output of the model will be a matrix in the shape of (batch, dim). Additionally, we can load weights that we have previously trained and saved.

>>> model.load("best_sup_general_embedding_phobert2.pt", key= False)
>>> sentences= ["Tôi đang đi học", "Bạn tên là gì?",]
>>> sentences= list(map(lambda x: ViTokenizer.tokenize(x), sentences))
>>> model.encode(sentences, batch_size= 1, normalize_embedding= "l2", return_tensors= "np", verbose= 1)
2/2 [==============================] - 0s 43ms/Sample
array([[ 0.00281098, -0.00829096, -0.01582766, ...,  0.00878178,
         0.01830498, -0.00459659],
       [ 0.00249859, -0.03076724,  0.00033016, ...,  0.01299141,
        -0.00984358, -0.00703243]], dtype=float32)

2. Load model from Huggingface Hub

First, download a pretrained model.

>>> model= SentenceEmbedding.from_pretrained('anti-ai/VieSemantic-base')

Then, we encode the input sentences and compare their similarity.

>>> sentences = ["Nó rất thú_vị", "Nó không thú_vị ."]
>>> output= model.encode(sentences, batch_size= 1, return_tensors= 'pt')
>>> torch.cosine_similarity(output[0].view(1, -1), output[1].view(1, -1)).cpu().tolist()
2/2 [==============================] - 0s 40ms/Sample
[0.5605039596557617]

3. Training

We have some examples of training for SentenceEmbedding, ReRanker, and LLM models. Additionally, you can rely on the optimal parameters we used for specific tasks and datasets. See examples

4. ONNX

It is now possible to export SentenceEmbedding and ReRanker models to the ONNX format. We also provide an API for easy usage.

We can export to .onnx format with the export_onnx function. Note that it is only supported for the SentenceEmbedding and ReRanker classes.

>>> model.export_onnx('model.onnx', opset_version= 17, test_performance= True)
**** DONE ****
2024-10-30 17:20:29.981403140 [W:onnxruntime:, inference_session.cc:2039 Initialize] Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.
******** Test Performance ********
2774/2756 [==============================] - 143s 52ms/step - time: 0.7818
Average inference time: 1.70 seconds
Total inference time: 2 minutes and 22.39 seconds

To use SentenceEmbedding or ReRanker models in ONNX format, you can use the load_onnx method to return objects of the corresponding SentenceEmbeddingOnnx or ReRankerOnnx classes.

>>> model_onnx= SentenceEmbedding.load_onnx('model.onnx')
2024-10-30 10:50:22.721487149 [W:onnxruntime:, inference_session.cc:2039 Initialize] Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.
>>> model_onnx.encode(['xin chào', 'bạn tên là gì ạ?'])
2/2 [==============================] - 0s 14ms/Sample
array([[[ 0.19600058,  0.0093571 , -0.20171645, ..., -0.12414521,
          0.1908756 , -0.02904402],
        [ 0.07333153,  0.07584963, -0.01428957, ..., -0.0851631 ,
          0.14394096, -0.28628293]]], dtype=float32)

5. List of pretrained models

This list will be updated with our prominent models. Our models will primarily aim to support Vietnamese language. Additionally, you can access our datasets and pretrained models by visiting https://huggingface.co/anti-ai.

Model Name	Model Type	#params	checkpoint
anti-ai/ViEmbedding-base	SentenceEmbedding	135.5M	model
anti-ai/BioViEmbedding-base-unsup	SentenceEmbedding	135.5M	model
anti-ai/VieSemantic-base	SentenceEmbedding	135.5M	model

Contacts

If you have any questions about this repo, please contact me ([email protected])