Note: If there's any error like "No module named 'xxx'", please use command "pip install xxx" to repair.

Feel free to add my QQ: 793729558 to discuss with me.
Also you can add the QQ group: 647303915 to discuss together.

My trained model is saved in my Baidu Net Disk.
The model below is trained in qingyun corpus.

I have offered 2 demo programs: "demoCmd.py" and "demoWeChat.py".
Use command " python demoCmd.py --model="xxx.pkl" --device="cpu"(or "cuda") " to run the cmd demo program.

model is your model path;
device is your program running environment, "cpu" for CPU or "cuda" for GPU;

Also you can run "demoWeChat.py" to use the chatbot to reply your WeChat message. The parameters is the same as above.

from model.nnModel import *
from model.corpusSolver import *
import torch

dataClass = Corpus('./corpus/qingyun.tsv', maxSentenceWordsNum=25)

First parameter is your corpus path;
maxSentenceWordsNum will ignore the data whose words number of question or answer is too big;

Also you can load your corpus. Only your file content formats need to be consistent:

Q A
Q A
...

Every line is a question and a answer with a '\t' split.
The corpus comes from https://github.com/codemayq/chinese_chatbot_corpus, you can get larger corpus from this Github.
Thanks very much for the corpus summarized and processed by the author.

First you need to create a Seq2Seq object.

model = Seq2Seq(dataClass, featureSize=256, hiddenSize=256, 
                attnType='L', attnMethod='general', 
                encoderNumLayers=3, decoderNumLayers=2, 
                encoderBidirectional=True, 
                device=torch.device('cuda:0'))

First parameter is your corpus class object.
featureSize is your word vector size;
hiddenSize is your RNN hidden state size;
attnType is your attention type. It can be 'B' for using Bahdanau Attention Structure or 'L' for using Luong Structure;
attnMethod is Luong Attention Method. It can be 'dot', 'general' or 'concat'.
encoderNumLayers is the layer number of your encoder RNN;
decoderNumlayers is the layer number of your decoder RNN;
encoderBidirectional is if your encoder RNN is bidirectional;
device is your building environment. If using CPU, then device=torch.device('cpu'); if using GPU, then device=torch.device('cuda:0');

Then you can train your model.

model.train(batchSize=1024, epoch=500)

batchSize is the number of data used for each train step;
epoch is the total iteration number of your training data;

And the log will be print like follows:

...
After iters 6540: loss = 0.844; train bleu: 0.746, embAve: 0.754; 2034.582 qa/s; remaining time: 48096.110s;
After iters 6550: loss = 0.951; train bleu: 0.734, embAve: 0.755; 2034.518 qa/s; remaining time: 48092.589s;
After iters 6560: loss = 1.394; train bleu: 0.735, embAve: 0.759; 2034.494 qa/s; remaining time: 48088.128s;
...

Finally you need to save your model for future use.

model.save('model.pkl')

First parameter is the name of model saved.

Ok, I know you are too lazy to train your own model. Also you can download my trained model in section 1.1.

First you need to create a Chatbot object.

chatbot = Chatbot('model.pkl')

First parameter is your model path;

Then you can use the greedy search to generate the answer.

chatbot.predictByGreedySearch("你好啊")

First parameter is your question;

It will return the answer like "你好,我就开心了". Also you can plot the attention by showAttention=True. Or you can use the beam search to generate the answer.

chatbot.predictByBeamSearch("什么是ai", isRandomChoose=True, beamWidth=10)

First parameter is your question;
isRandomChoose determines whether probability sampling is performed in the final beamwidth answers.
beamWidth is the search width in beam search;

It will return the answer like "反正不是苹果". Also you can show the probabilities of the beamwidth answers by showInfo=True.

First you need to calculate 4 variables:

id2word: a list of word, and the first two words have to be "<SOS>" and "<EOS>", e.g., ["<SOS>", "<EOS>", "你", "天空", "人工智能", "**", ...];
word2id: a dict with the key of word and the value of id, corresponding to id2word, e.g., {"<SOS>":0, "<EOS>":1, "你":2, "天空":3, "人工智能":4, "**":5, ...};
wordNum: the total number of words. It is equal to len(id2word) or len(word2id);
wordEmb: the word embedding array with shape (wordNum, featureSize) and the word order need to be consistent with id2word or word2id; you can random initialize the vector or "<SOS>" and "<EOS>";

Then add first three variables as parameters when you load the data.

dataClass = Corpus(..., id2word=id2word, word2id=word2id, wordNum=wordNum)

Next you need to create the word embedding object.

embedding = torch.nn.Embedding.from_pretrained(torch.tensor(wordEmb))

Finally add the embedding parameter when you create the Seq2Seq object.

model = Seq2Seq(..., embedding=embedding)

Also you can download a trained word embedding from https://github.com/Embedding/Chinese-Word-Vectors.
Thanks very much for the trained word embedding provided by the author.

For other functions such as data enhance, etc, please dig for yourselves.

[1] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.
[2] Luong M T, Pham H, Manning C D. Effective approaches to attention-based neural machine translation[J]. arXiv preprint arXiv:1508.04025, 2015.
[3] https://pytorch.apachecn.org/docs/1.0/#/