AI Agent智能应用从0到1定制开发（友客fx）

# 安装必要的库
pip install tensorflow nltk

# 示例数据集
intents = [
    {"tag": "greeting", "patterns": ["Hi", "Hello", "Hey", "Hi there"], "response": ["Hello!", "Hi there!", "Hey!"]},
    # 添加更多意图...
]

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

nltk.download('punkt')
nltk.download('stopwords')

def clean_up_sentence(sentence):
    # Tokenize the pattern
    words = word_tokenize(sentence)
    # Remove stop words from the sentence
    words = [word for word in words if not word in stopwords.words("english")]
    # Normalize the words
    words = [word.lower() for word in words]
    # Create a linear array of words
    return words

def recognize_intent(intents, sentence):
    sentence_words = clean_up_sentence(sentence)
    # Create a dictionary of words in the intents patterns
    pattern_dict = {}
    for intent in intents:
        for pattern in intent['patterns']:
            pattern_words = clean_up_sentence(pattern)
            for word in pattern_words:
                if word not in pattern_dict:
                    pattern_dict[word] = [intent['tag']]
                else:
                    pattern_dict[word].append(intent['tag'])
    # Find the intent with the most words in common
    matched_intent = ''
    score = 0
    for word in sentence_words:
        if word in pattern_dict:
            for intent in pattern_dict[word]:
                if matched_intent != intent:
                    matched_intent = intent
                    score += 1
    # Return the intent with the highest score
    return matched_intent

def generate_response(intent, intents):
    tag = intents[intents.index(intent)]
    return tag['response'][randint(0, len(tag['response']) - 1)]

import random

def chatbot_response(intents, input_text):
    intent = recognize_intent(intents, input_text)
    if intent == 'None':
        return "I did not understand, can you try another way to ask?"
    else:
        return generate_response(intent, intents)

def chat_with_chatbot(intents):
    print("Hi, I am your AI Agent. How can I help you?")
    while True:
        input_text = input("You: ")
        if input_text.lower() in ['bye', 'exit', 'stop']:
            print("AI Agent: Bye! Have a nice day.")
            break
        response = chatbot_response(intents, input_text)
        print(f"AI Agent: {response}")

if __name__ == "__main__":
    chat_with_chatbot(intents)

# 这是一个示例，实际训练模型需要更复杂的代码
import tensorflow as tf

# 假设你已经有了训练数据
train_data = ...
train_labels = ...

model = tf.keras.models.Sequential([
    # 添加层...
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10)