{"id":1160355,"date":"2025-01-13T19:03:52","date_gmt":"2025-01-13T11:03:52","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1160355.html"},"modified":"2025-01-13T19:03:54","modified_gmt":"2025-01-13T11:03:54","slug":"python%e5%a6%82%e4%bd%95%e5%86%99%e5%af%b9%e8%af%9dai","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1160355.html","title":{"rendered":"python\u5982\u4f55\u5199\u5bf9\u8bddai"},"content":{"rendered":"

\"python\u5982\u4f55\u5199\u5bf9\u8bddai\"<\/p>\n

Python\u5199\u5bf9\u8bddAI<\/a>\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\u5e93\u3001\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u3001\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u548c\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\u5982GPT-3\uff0c\u662f\u76ee\u524d\u6700\u6d41\u884c\u548c\u6709\u6548\u7684\u65b9\u6cd5\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u6765\u5f00\u53d1\u5bf9\u8bddAI\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528NLP\u5e93<\/h2>\n<\/p>\n

1\u3001NLTK\u5e93<\/h3>\n<\/p>\n

NLTK\uff08Natural Language Toolkit\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u9002\u7528\u4e8e\u5904\u7406\u548c\u5206\u6790\u4eba\u7c7b\u8bed\u8a00\u6570\u636e\u3002NLTK\u63d0\u4f9b\u4e86\u5927\u91cf\u6587\u672c\u5904\u7406\u5e93\u3001\u5206\u7c7b\u5668\u548c\u8bed\u6599\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6784\u5efa\u7b80\u5355\u7684\u5bf9\u8bdd\u7cfb\u7edf\u3002<\/p>\n<\/p>\n

import nltk<\/p>\n
from nltk.chat.util import Chat, reflections<\/p>\n
\u9884\u5b9a\u4e49\u7684\u5bf9\u8bdd\u6a21\u5f0f<\/strong><\/h2>\n
pairs = [<\/p>\n
    [<\/p>\n
        r"my name is (.*)",<\/p>\n
        ["Hello %1, How are you today ?",]<\/p>\n
    ],<\/p>\n
    [<\/p>\n
        r"hi|hey|hello",<\/p>\n
        ["Hello", "Hey there",]<\/p>\n
    ],<\/p>\n
    [<\/p>\n
        r"what is your name ?",<\/p>\n
        ["I am a bot created by NLTK. You can call me NLTKBot.",]<\/p>\n
    ],<\/p>\n
    [<\/p>\n
        r"how are you ?",<\/p>\n
        ["I'm doing good, how about You ?",]<\/p>\n
    ]<\/p>\n
]<\/p>\n
\u521b\u5efa\u804a\u5929\u673a\u5668\u4eba<\/strong><\/h2>\n
chatbot = Chat(pairs, reflections)<\/p>\n
\u5f00\u59cb\u5bf9\u8bdd<\/strong><\/h2>\n
chatbot.converse()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001spaCy\u5e93<\/h3>\n<\/p>\n
spaCy\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684NLP\u5e93\uff0c\u4e13\u6ce8\u4e8e\u9ad8\u6548\u548c\u5feb\u901f\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3002\u867d\u7136spaCy\u672c\u8eab\u5e76\u4e0d\u63d0\u4f9b\u5bf9\u8bdd\u7cfb\u7edf\u7684\u76f4\u63a5\u652f\u6301\uff0c\u4f46\u5b83\u53ef\u4ee5\u7528\u4e8e\u9884\u5904\u7406\u548c\u5206\u6790\u6587\u672c\u6570\u636e\uff0c\u4e3a\u5bf9\u8bdd\u7cfb\u7edf\u7684\u6784\u5efa\u63d0\u4f9b\u57fa\u7840\u3002<\/p>\n<\/p>\n
import spacy<\/p>\n
nlp = spacy.load("en_core_web_sm")<\/p>\n
def respond_to_greeting(text):<\/p>\n
    doc = nlp(text)<\/p>\n
    for token in doc:<\/p>\n
        if token.lower_ in ["hi", "hello", "hey"]:<\/p>\n
            return "Hello! How can I help you today?"<\/p>\n
    return "I'm sorry, I didn't understand that."<\/p>\n
text = "Hi, how are you?"<\/p>\n
response = respond_to_greeting(text)<\/p>\n
print(response)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h2>\n<\/p>\n
1\u3001\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668<\/h3>\n<\/p>\n
\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u662f\u4e00\u79cd\u7b80\u5355\u800c\u6709\u6548\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u9002\u7528\u4e8e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u3002\u5728\u5bf9\u8bdd\u7cfb\u7edf\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668\u6765\u8bc6\u522b\u7528\u6237\u8f93\u5165\u7684\u610f\u56fe\u3002<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
from sklearn.naive_bayes import MultinomialNB<\/p>\n
\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
train_data = [<\/p>\n
    ("Hello", "greeting"),<\/p>\n
    ("Hi", "greeting"),<\/p>\n
    ("How are you?", "greeting"),<\/p>\n
    ("What is your name?", "question"),<\/p>\n
    ("Tell me a joke", "command")<\/p>\n
]<\/p>\n
\u63d0\u53d6\u7279\u5f81<\/strong><\/h2>\n
vectorizer = CountVectorizer()<\/p>\n
X_train = vectorizer.fit_transform([text for text, label in train_data])<\/p>\n
y_train = [label for text, label in train_data]<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model = MultinomialNB()<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
def predict_intent(text):<\/p>\n
    X_test = vectorizer.transform([text])<\/p>\n
    return model.predict(X_test)[0]<\/p>\n
text = "Hello"<\/p>\n
intent = predict_intent(text)<\/p>\n
print(intent)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u652f\u6301\u5411\u91cf\u673a<\/h3>\n<\/p>\n
\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u76d1\u7763\u5b66\u4e60\u7b97\u6cd5\uff0c\u9002\u7528\u4e8e\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u5728\u5bf9\u8bdd\u7cfb\u7edf\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528SVM\u6765\u5206\u7c7b\u7528\u6237\u8f93\u5165\u7684\u6587\u672c\u5e76\u751f\u6210\u76f8\u5e94\u7684\u54cd\u5e94\u3002<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
from sklearn.svm import SVC<\/p>\n
\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
train_data = [<\/p>\n
    ("Hello", "greeting"),<\/p>\n
    ("Hi", "greeting"),<\/p>\n
    ("How are you?", "greeting"),<\/p>\n
    ("What is your name?", "question"),<\/p>\n
    ("Tell me a joke", "command")<\/p>\n
]<\/p>\n
\u63d0\u53d6\u7279\u5f81<\/strong><\/h2>\n
vectorizer = CountVectorizer()<\/p>\n
X_train = vectorizer.fit_transform([text for text, label in train_data])<\/p>\n
y_train = [label for text, label in train_data]<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model = SVC(kernel='linear')<\/p>\n
model.fit(X_train, y_train)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
def predict_intent(text):<\/p>\n
    X_test = vectorizer.transform([text])<\/p>\n
    return model.predict(X_test)[0]<\/p>\n
text = "Hi"<\/p>\n
intent = predict_intent(text)<\/p>\n
print(intent)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6df1\u5ea6\u5b66\u4e60\u6280\u672f<\/h2>\n<\/p>\n
1\u3001RNN\u548cLSTM<\/h3>\n<\/p>\n
\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\u548c\u957f\u77ed\u671f\u8bb0\u5fc6\uff08LSTM\uff09\u7f51\u7edc\u662f\u5904\u7406\u5e8f\u5217\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u53ef\u4ee5\u7528\u4e8e\u6784\u5efa\u590d\u6742\u7684\u5bf9\u8bdd\u7cfb\u7edf\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
from keras.models import Sequential<\/p>\n
from keras.layers import LSTM, Dense, Embedding<\/p>\n
\u8bad\u7ec3\u6570\u636e<\/strong><\/h2>\n
train_data = [<\/p>\n
    ("Hello", "greeting"),<\/p>\n
    ("Hi", "greeting"),<\/p>\n
    ("How are you?", "greeting"),<\/p>\n
    ("What is your name?", "question"),<\/p>\n
    ("Tell me a joke", "command")<\/p>\n
]<\/p>\n
\u521b\u5efa\u8bcd\u6c47\u8868<\/strong><\/h2>\n
vocabulary = set(word for text, label in train_data for word in text.split())<\/p>\n
word_to_index = {word: i for i, word in enumerate(vocabulary)}<\/p>\n
\u51c6\u5907\u6570\u636e<\/strong><\/h2>\n
X_train = np.array([[word_to_index[word] for word in text.split()] for text, label in train_data])<\/p>\n
y_train = np.array([0 if label == "greeting" else 1 for text, label in train_data])<\/p>\n
\u521b\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential()<\/p>\n
model.add(Embedding(input_dim=len(vocabulary), output_dim=8, input_length=max(len(x) for x in X_train)))<\/p>\n
model.add(LSTM(16))<\/p>\n
model.add(Dense(1, activation='sigmoid'))<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X_train, y_train, epochs=10)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
def predict_intent(text):<\/p>\n
    X_test = np.array([[word_to_index[word] for word in text.split()]])<\/p>\n
    return model.predict(X_test)[0]<\/p>\n
text = "Hi"<\/p>\n
intent = predict_intent(text)<\/p>\n
print(intent)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001Seq2Seq\u6a21\u578b<\/h3>\n<\/p>\n
Seq2Seq\u6a21\u578b\u662f\u4e00\u79cd\u7528\u4e8e\u5e8f\u5217\u5230\u5e8f\u5217\u4efb\u52a1\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u673a\u5668\u7ffb\u8bd1\u548c\u5bf9\u8bdd\u751f\u6210\u3002<\/p>\n<\/p>\n
from keras.models import Model<\/p>\n
from keras.layers import Input, LSTM, Dense<\/p>\n
\u5b9a\u4e49\u6a21\u578b<\/strong><\/h2>\n
encoder_inputs = Input(shape=(None, num_encoder_tokens))<\/p>\n
encoder = LSTM(latent_dim, return_state=True)<\/p>\n
encoder_outputs, state_h, state_c = encoder(encoder_inputs)<\/p>\n
encoder_states = [state_h, state_c]<\/p>\n
decoder_inputs = Input(shape=(None, num_decoder_tokens))<\/p>\n
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)<\/p>\n
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)<\/p>\n
decoder_dense = Dense(num_decoder_tokens, activation='softmax')<\/p>\n
decoder_outputs = decoder_dense(decoder_outputs)<\/p>\n
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=batch_size, epochs=epochs, validation_split=0.2)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
def decode_sequence(input_seq):<\/p>\n
    states_value = encoder_model.predict(input_seq)<\/p>\n
    target_seq = np.zeros((1, 1, num_decoder_tokens))<\/p>\n
    target_seq[0, 0, target_token_index['\\t']] = 1.<\/p>\n
    stop_condition = False<\/p>\n
    decoded_sentence = ''<\/p>\n
    while not stop_condition:<\/p>\n
        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)<\/p>\n
        sampled_token_index = np.argmax(output_tokens[0, -1, :])<\/p>\n
        sampled_char = reverse_target_char_index[sampled_token_index]<\/p>\n
        decoded_sentence += sampled_char<\/p>\n
        if sampled_char == '\\n' or len(decoded_sentence) > max_decoder_seq_length:<\/p>\n
            stop_condition = True<\/p>\n
        target_seq = np.zeros((1, 1, num_decoder_tokens))<\/p>\n
        target_seq[0, 0, sampled_token_index] = 1.<\/p>\n
        states_value = [h, c]<\/p>\n
    return decoded_sentence<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b<\/h2>\n<\/p>\n
1\u3001GPT-3<\/h3>\n<\/p>\n
GPT-3\uff08Generative Pre-trained Transformer 3\uff09\u662fOpenAI\u5f00\u53d1\u7684\u4e00\u79cd\u5f3a\u5927\u7684\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u53ef\u4ee5\u751f\u6210\u9ad8\u8d28\u91cf\u7684\u81ea\u7136\u8bed\u8a00\u6587\u672c\u3002\u4f7f\u7528GPT-3\u6784\u5efa\u5bf9\u8bddAI\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8c03\u7528\u5176API\u3002<\/p>\n<\/p>\n
import openai<\/p>\n
openai.api_key = 'YOUR_API_KEY'<\/p>\n
response = openai.Completion.create(<\/p>\n
  engine="davinci",<\/p>\n
  prompt="Hello, how are you?",<\/p>\n
  max_tokens=50<\/p>\n
)<\/p>\n
print(response.choices[0].text.strip())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001BERT<\/h3>\n<\/p>\n
BERT\uff08Bidirectional Encoder Representations from Transformers\uff09\u662fGoogle\u5f00\u53d1\u7684\u4e00\u79cd\u9884\u8bad\u7ec3\u8bed\u8a00\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u5404\u79cdNLP\u4efb\u52a1\u3002\u867d\u7136BERT\u4e3b\u8981\u7528\u4e8e\u7406\u89e3\u4efb\u52a1\uff0c\u4f46\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u5bf9\u8bdd\u7cfb\u7edf\u7684\u57fa\u7840\u3002<\/p>\n<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification<\/p>\n
import torch<\/p>\n
\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u548c\u5206\u8bcd\u5668<\/strong><\/h2>\n
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')<\/p>\n
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
def predict_intent(text):<\/p>\n
    inputs = tokenizer(text, return_tensors="pt")<\/p>\n
    outputs = model(inputs)<\/p>\n
    logits = outputs.logits<\/p>\n
    predicted_class = torch.argmax(logits).item()<\/p>\n
    return predicted_class<\/p>\n
text = "Hi"<\/p>\n
intent = predict_intent(text)<\/p>\n
print(intent)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001DialogFlow<\/h3>\n<\/p>\n
DialogFlow\u662fGoogle\u63d0\u4f9b\u7684\u4e00\u79cd\u57fa\u4e8e\u4e91\u7684\u5bf9\u8bdd\u7cfb\u7edf\u5f00\u53d1\u5e73\u53f0\uff0c\u5b83\u53ef\u4ee5\u8f7b\u677e\u521b\u5efa\u548c\u7ba1\u7406\u5bf9\u8bdd\u4ee3\u7406\u3002\u4f7f\u7528DialogFlow\u6784\u5efa\u5bf9\u8bddAI\u65e0\u9700\u7f16\u5199\u590d\u6742\u7684\u4ee3\u7801\uff0c\u53ea\u9700\u5728\u5e73\u53f0\u4e0a\u914d\u7f6e\u548c\u8bad\u7ec3\u4ee3\u7406\u3002<\/p>\n<\/p>\n
from google.cloud import dialogflow_v2 as dialogflow<\/p>\n
client = dialogflow.SessionsClient()<\/p>\n
session = client.session_path('YOUR_PROJECT_ID', 'YOUR_SESSION_ID')<\/p>\n
text_input = dialogflow.TextInput(text="Hello", language_code="en")<\/p>\n
query_input = dialogflow.QueryInput(text=text_input)<\/p>\n
response = client.detect_intent(session=session, query_input=query_input)<\/p>\n
print(response.query_result.fulfillment_text)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
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