Semantic-Search-using-Paddle

🎨 Language

• English
• 中文

📝 Description

This code is used Paddle to do a semantic search.

There two types that you can use:

• Monolingual You can use monolingual models that trained on each languages, and search by setting the languague which needs to be show. Maybe this way can get a higher accuracy, but it is limited that if you select the language, it only shows the given language.
  • For Chinese text: hfl/roberta-wwm-ext-large
  • For English text: ernie-2.0-large-en
• Multilingual You can use multilingual models that trained on different languages together, and search without setting the language. In this way, it is more common for users to search something.
  • For Chinese and English text: ernie-m-large

You need to install milvus and mysql to store vector and other information that you need:

• For storing vector: milvus (Open-source, highly scalable, and blazing fast)
• For storing other information: mysql (of course, you can choose the database you like)

⚙ Environment

• It used 1 * NVIDIA Tesla V100 32G to train model(Recommended). Ensure that CUDA is installed.
• Of course you can use CPU to train model

🛠 Requirements

• Python 3.9
• paddlepaddle 2.3.1
  • If you need a CPU only version, please install the this version
  • Else if you need a GPU version, please install the right version that based on your GPU and CUDA. For example: paddlepaddle-gpu==2.3.1.post112
• paddlenlp 2.3.4

If you want to deploy models, you also need to install:

• pymilvus 2.1.0
• pymysql 1.0.2
• fastapi 0.79
• uvicorn 0.18.2

📚 Files

• There are four folders:
  • 1-preprocess To preprocess the raw data into the data that models can receive
  • 2-pretrain To pretrain the model, just like domain-adaptive pretraining. But it need higher GPUs
  • 3-finetune To finetune the model, make it useful for your task
  • 4-deploy To deploy the trained model on VPS, and set up API
• It used Jupyter Notebook to easily start. Of course you can convert .ipynb to .py
• The step is the index of the files
• For example, you will run the file started with 1-xxx.ipynb, and then run the file started with 2-xxx.ipynb
• The files in 3-finetune/models , 4-deploy/search_engine/models folders are fake files! You must get these files after saving your model

├─1-preprocess                  # Step 1: preprocess
│  └─data                         # Store raw data
│      ├─Chinese                    # Store raw Chinese text
│      ├─English                    # Store raw English text
│      └─multilingual               # Store multilingual text
├─2-pretrain                    # Step 2: pretrain
│  └─data                         # Store data after preprocessing
│      ├─Chinese                    # Store Chinese text after preprocessing
│      ├─English                    # Store English text after preprocessing
│      └─multilingual               # Store multilingual text after preprocessing
├─3-finetune                    # Step 3: finetune
│  ├─data                         # Store data after preprocessing
│  │  ├─Chinese                     # Store Chinese text after preprocessing
│  │  ├─English                     # Store English text after preprocessing
│  │  └─multilingual                # Store multilingual text after preprocessing
│  └─models                       # Store models after training
│      ├─ernie-m-large              # Store model after training
│      │  └─infer_model               # Store static model
│      └─roberta-wwm-ext-large      # Store model after training
│          └─infer_model              # Store static model
└─4-deploy                      # Step 4: deploy
    ├─milvus                      # Milvus database, an empty folder, it will generate files after running docker compose
    ├─mysql                       # MySQL database, an empty folder, it will generate files after running docker compose
    └─search_engine               # Search Engine
        ├─0-init_database           # To init Milvus and MySQL databases. For example: create table, collection and so on
        │  └─data                     # Store data after preprocessing
        │      ├─Chinese                # Store Chinese text after preprocessing
        │      ├─English                # Store English text after preprocessing
        │      └─multilingual           # Store multilingual text after preprocessing
        ├─models                    # Store model that needed in deploy and API model
        │  ├─Chinese                  # Store Chinese monolingual model
        │  ├─English                  # Store English monolingual model
        │  └─multilingual             # Store multilingual model
        └─routers                   # Store routers
            ├─search                  # Store search python scripts
            └─sen_to_vec              # Store sentence to vector python scripts

📖 Data

• There are only some example text in data folder

• You need to convert your data into a csv file, which split by \t (in fact, it is called tsv file)

• example data (multilingual text):

  publication_number_sear	title	abstract_ab	ipc_main_stat
  EN0008	title8	Snap my psyche like a twig	H
  CN0001A	标题1	橘黄色的日落 吞没在海平线	A
  ...	...	...	...
  CN0013A	标题13	这座城市有我的思念和喜欢	D
  EN0011	title11	Do you ever get a little bit tired of life	F

• You have to confirm that there is no \t in text and labels. Important!!!

• You don't need to split the data into train data and test data

🎯 To Run (multilingual)

Maybe there are something you have to change. For example: path

Step 1: preprocess

• Step 1(Optional): run 0-take_a_look.ipynb. Take a look at the raw data
• Step 2: run 1-filter_field. Filter your data and choose the fields that you need
• Step 3(Optional): run 2-data_visualization.ipynb. Take a look at the data after preprocessing
• Step 4(Optional): run 3-data_concat.ipynb. Run this if you need multilingual text

After running these, you can get the data after preprocessing. Please copy these files into below steps.

Step 2: pretrain

This work takes a long time to pretrain. There is nothing in this step, because I have not time and GPUs to pretrain.

If you want to pretrain, you should take some work in this.

Step 3: finetune

• Step 1: run 1.2-train_multilingual.ipynb. Train the model and save model and params
  • If you use monolingual, please run 1.1-train_monolingual.ipynb
• Step 2: run 2.2-to_static_multilingual.ipynb. Get the infer model
  • If you use monolingual, please run 2.1-to_static_monolingual.ipynb
• Step 3(Optional): run 3.2-infer_multilingual.ipynb. A test to infer
  • If you use monolingual, please run 3.1-infer_monolingual.ipynb

📢 Deploy

After getting the infer model, you can deploy it by using FastAPI or other API framework.

Make sure you have get the docker and docker compose.

• Docker 20.10.17
• Docker Compose 2.6.0

cd to your deploy folder and make sure that the docker-compose.yml in it.

Please copy your files into search_engine/models/Chinese or search_engine/models/English or search_engine/models/multilingual and make them in your model folder like:

• model.get_pooled_embedding.pdiparams
• model.get_pooled_embedding.pdmodel
• sentencepiece.bpe.model (only for ernie-m)
• special_tokens_map.json
• tokenizer_config.json
• vocab.txt

Run: docker compose up -d

• Visit: localhost:1234/docs to read docs
• Visit: localhost:9000 for Portainer
• Visit: localhost:19000 for Attu

💡 Others

Docs about PaddlePaddle, PaddleNLP, FastAPI and Milvus

• PaddlePaddle
• PaddleNLP
• FastAPI
• Milvus