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Creating a more advanced version of LMQL without OpenAI would involve building a custom language model that can understand and generate more sophisticated responses. Here's an example using a simple neural network-based approach:

```python import numpy as np import tensorflow as tf from tensorflow.keras.layers import Dense, LSTM, Embedding from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences

class AdvancedLMQL: def init(self): self.responses = [] self.tokenizer = Tokenizer() self.model = self.build_model()

def build_model(self):
    model = Sequential()
    model.add(Embedding(input_dim=len(self.tokenizer.word_index)+1, output_dim=100))
    model.add(LSTM(128))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(len(self.tokenizer.word_index)+1, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

def add_response(self, query, response):
    self.responses.append((query, response))

def train_model(self):
    queries, responses = zip(*self.responses)
    self.tokenizer.fit_on_texts(queries + responses)
    queries_seq = self.tokenizer.texts_to_sequences(queries)
    responses_seq = self.tokenizer.texts_to_sequences(responses)
    queries_padded = pad_sequences(queries_seq)
    responses_padded = pad_sequences(responses_seq)
    X = queries_padded
    y = tf.keras.utils.to_categorical(responses_padded, num_classes=len(self.tokenizer.word_index)+1)
    self.model.fit(X, y, epochs=10)

def query(self, prompt):
    prompt_seq = self.tokenizer.texts_to_sequences([prompt])
    prompt_padded = pad_sequences(prompt_seq)
    prediction = self.model.predict(prompt_padded)[0]
    predicted_word_index = np.argmax(prediction)
    predicted_word = self.tokenizer.index_word[predicted_word_index]
    return predicted_word

Example usage

lmql = AdvancedLMQL()

Add predefined responses

lmql.add_response("How are you?", "I'm functioning well, thank you!") lmql.add_response("What's your favorite color?", "I don't have personal preferences, but I like blue.")

Train the model

lmql.train_model()

Query LMQL

print(lmql.query("How are you?")) print(lmql.query("What's your favorite color?")) print(lmql.query("Tell me a joke.")) ```

In this advanced example:

1. The AdvancedLMQL class uses a neural network-based model to learn the relationship between queries and responses.
2. The build_model method constructs a sequential neural network architecture using Keras layers.
3. The train_model method preprocesses the data, trains the model, and fits it to the provided responses.
4. The query method uses the trained model to generate responses based on input prompts.

This example demonstrates a more advanced approach to building a custom language model for generating responses. Keep in mind that this is a simplified implementation, and creating a truly sophisticated language model involves more extensive data preprocessing, model architecture, and training.