### Python Environment Setup

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/trastornos-del-lenguaje/trastorno-del-lenguaje-causas-sintomas-diagnostico-e-intervencion/

This snippet outlines the necessary steps to set up a Python virtual environment and install project dependencies using pip. It ensures a clean and isolated environment for the project.

```python
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

--------------------------------

### Python Example: List Models

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-atencion/ficha-infantil-para-trabajar-la-atencion-selectiva/

Example of how to list available models using the OpenAI Python client library.

```python
from openai import OpenAI

client = OpenAI()

models = client.models.list()

for model in models.data:
    print(model.id)
```

--------------------------------

### Python Model Initialization Example

Source: https://neuronup.com/blog/quieres-ser-autor-invitado-en-el-blog-de-neuronup/

A Python code example showing the instantiation of the OpenAIModel class with specific parameters.

```Python
from neuronup_llms import OpenAIModel

# Initialize the model with a specific model name
model = OpenAIModel(model_name='gpt-3.5-turbo')

# Or initialize with a different provider if supported
# model = OpenAIModel(model_name='some-other-model', provider='another-provider')

# Example usage:
prompt = "Explain the concept of recursion in programming."
response = model.generate_response(prompt, max_tokens=200, temperature=0.5)
print(f"Model Response: {response}")

# Get model info
info = model.get_model_info()
print(f"Model Info: {info}")
```

--------------------------------

### JavaScript Example: Create Completion

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-atencion/ficha-infantil-para-trabajar-la-atencion-selectiva/

Example of how to create a text completion using the OpenAI JavaScript client library.

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function main() {
  const completion = await openai.completions.create({
    model: "gpt-3.5-turbo-instruct",
    prompt: "Hello, world!",
    max_tokens: 50,
  });
  console.log(completion.choices[0].text);
}
```

--------------------------------

### Python Example: Create Completion

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-atencion/ficha-infantil-para-trabajar-la-atencion-selectiva/

Example of how to create a text completion using the OpenAI Python client library.

```python
from openai import OpenAI

client = OpenAI()

completion = client.completions.create(
  model="gpt-3.5-turbo-instruct",
  prompt="Hello, world!",
  max_tokens=50
)

print(completion.choices[0].text)
```

--------------------------------

### Python Model Initialization and Usage

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-memoria/juego-para-trabajar-la-memoria-episodica-en-ninos-aprende-los-nombres/

Demonstrates how to initialize and use a model, likely from a library like OpenAI or a similar provider. It shows basic instantiation and potential method calls.

```python
from neuronup_llms import OpenAIModel

# Initialize the model
model = OpenAIModel(model_name="gpt-4", provider="openai")

# Example usage: generate text
response = model.generate("Explain the concept of transformers in NLP.")
print(response)

# Example usage: chat completion
chat_response = model.chat([
    {"role": "system", "content": "You are a helpful assistant."}, 
    {"role": "user", "content": "What is the capital of France?"}
])
print(chat_response)
```

--------------------------------

### Basic JavaScript Function

Source: https://neuronup.com/psicologia/postura-corporal-y-rendimiento-cognitivo/

A simple JavaScript function example demonstrating a basic operation. This snippet is illustrative and can be used as a starting point for more complex logic.

```JavaScript
function greet(name) {
  return `Hello, ${name}!`;
}

const message = greet("World");
console.log(message);
```

--------------------------------

### Text Generation Example (Python)

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-lenguaje/nuevo-ejercicio-de-discriminacion-auditiva-para-ninos/

Demonstrates how to initialize the NeuronUp LLM and generate text using a specified model and provider. Requires the 'neuronup' library and provider API key.

```python
from neuronup import LLM

# Initialize the LLM with a model
llm = LLM(model_name="gpt-3.5-turbo", provider="openai")

# Generate text
prompt = "Write a short story about a robot learning to love."
generated_text = llm.generate(prompt)
print(generated_text)
```

--------------------------------

### Basic Python Model Loading and Generation

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/enfermedad-mental/esquizofrenia/neuropsicologia-de-la-esquizofrenia/

Demonstrates how to load a language model and generate text using a simple Python script. This snippet is useful for getting started with the project's core functionality.

```python
from neuronup.llms.base import LLM

# Load a model (e.g., a local model or one from a provider)
# Replace 'your_model_name' and 'your_provider' with actual values
model = LLM(model_name="your_model_name", provider="your_provider")

# Define a prompt
prompt = "Write a short story about a robot learning to love."

# Generate text
response = model.generate(prompt)

# Print the generated text
print(response)
```

--------------------------------

### OpenAI Model Initialization and Usage

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-esclerosis-multiple/ejercicios-de-rehabilitacion-cognitiva-para-personas-con-esclerosis-multiple/

Demonstrates how to initialize and use an OpenAI language model. It covers setting up the model with a name and provider, and includes a basic example of generating text.

```python
from neuronup.llms.openai import OpenAIModel

# Initialize the OpenAI model
model = OpenAIModel(model_name="gpt-3.5-turbo", provider="openai")

# Generate text
response = model.generate("Explain the concept of recursion.")
print(response)
```

--------------------------------

### JavaScript Class Definition Snippet

Source: https://neuronup.com/psicologia/el-estres-laboral-definicion-causas-y-consecuencias-para-la-salud/

A partial JavaScript code snippet demonstrating the start of a class definition using Babel's ES6 transpilation helper. It shows the setup for defining class properties and methods.

```javascript
"use strict";var _createClass=function(){function defineProperties(target,props){for(var i=0;i<props.length;i++){var descriptor=props[i];descriptor.enumerable=descriptor
```

--------------------------------

### RetrievalQA Chain Setup

Source: https://neuronup.com/soluciones-de-neurorrehabilitacion/centros-de-rehabilitacion/

Shows how to set up a RetrievalQA chain, which combines a language model, a retriever (from a vector store), and a prompt to answer questions based on retrieved documents.

```python
from neuronup.chains import RetrievalQA
from neuronup.llms.openai import OpenAIModel
from neuronup.vectorstores import FAISS
from neuronup.embeddings import OpenAIEmbeddings

# Initialize LLM and Embeddings
llm = OpenAIModel(model_name="gpt-4")
embeddings = OpenAIEmbeddings()

# Assume 'vector_store' is an initialized FAISS vector store
# vector_store = FAISS.from_documents(chunks, embeddings)

# Create a retriever from the vector store
retriever = vector_store.as_retriever()

# Create the RetrievalQA chain
qa_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever)

# Ask a question
question = "What are the key findings?"
answer = qa_chain.run(question)
print(answer)
```

--------------------------------

### Python LLM Text Generation Example

Source: https://neuronup.com/canal-etico-y-de-denuncias/

Demonstrates basic text generation using a Python library. This snippet shows how to initialize a model and generate text based on a prompt. It typically requires installation of a specific LLM library.

```python
from neuronup_llms import LLMModel

# Initialize the model
model = LLMModel(model_name="gpt-3.5-turbo", provider="openai")

# Define a prompt
prompt = "Write a short story about a robot learning to love."

# Generate text
response = model.generate(prompt)

# Print the response
print(response)
```

--------------------------------

### Project Configuration and Metadata

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/dano-cerebral-adquirido/ictus-o-accidentes-cerebrovasculares-acv/taller-cognitivo-con-neuronup-para-la-intervencion-de-ictus-infantil/

This section details the top-level project configuration, including page title, description, summary, and identified languages. It serves as the primary metadata for the documentation.

```json
{
  "pageTitle": "Project Documentation",
  "pageDescription": "Documentation for the neuronup-llms project, including code snippets and API references.",
  "pageSummary": "This document outlines the structure and content of the neuronup-llms project, detailing various code functionalities and API specifications.",
  "languages": [
    "json"
  ],
  "codeSnippets": [
    // ... other snippets
  ]
}
```

--------------------------------

### FastAPI Application Setup

Source: https://neuronup.com/proyectos/zanzibar/neuronup-como-principal-recurso-en-psiconed/

Configuration and setup for the FastAPI application, including CORS middleware and basic routing.

```Python
app = FastAPI()

origins = [
    "http://localhost",
    "http://localhost:8080",
    "http://localhost:3000",
    "http://localhost:5173",
    "http://127.0.0.1:5173",
    "http://127.0.0.1:8080",
    "http://127.0.0.1:3000",
    "http://127.0.0.1:8000",
    "http://localhost:8000",
    "http://localhost:5000",
    "http://127.0.0.1:5000",
    "http://localhost:5432",
    "http://127.0.0.1:5432",
    "http://localhost:5433",
    "http://127.0.0.1:5433",
    "http://localhost:5434",
    "http://127.0.0.1:5434",
    "http://localhost:5435",
    "http://127.0.0.1:5435",
    "http://localhost:5436",
    "http://127.0.0.1:5436",
    "http://localhost:5437",
    "http://127.0.0.1:5437",
    "http://localhost:5438",
    "http://127.0.0.1:5438",
    "http://localhost:5439",
    "http://127.0.0.1:5439",
    "http://localhost:5440",
    "http://127.0.0.1:5440",
    "http://localhost:5441",
    "http://127.0.0.1:5441",
    "http://localhost:5442",
    "http://127.0.0.1:5442",
    "http://localhost:5443",
    "http://127.0.0.1:5443",
    "http://localhost:5444",
    "http://127.0.0.1:5444",
    "http://localhost:5445",
    "http://127.0.0.1:5445",
    "http://localhost:5446",
    "http://127.0.0.1:5446",
    "http://localhost:5447",
    "http://127.0.0.1:5447",
    "http://localhost:5448",
    "http://127.0.0.1:5448",
    "http://localhost:5449",
    "http://127.0.0.1:5449",
    "http://localhost:5450",
    "http://127.0.0.1:5450",
    "http://localhost:5451",
    "http://127.0.0.1:5451",
    "http://localhost:5452",
    "http://127.0.0.1:5452",
    "http://localhost:5453",
    "http://127.0.0.1:5453",
    "http://localhost:5454",
    "http://127.0.0.1:5454",
    "http://localhost:5455",
    "http://127.0.0.1:5455",
    "http://localhost:5456",
    "http://127.0.0.1:5456",
    "http://localhost:5457",
    "http://127.0.0.1:5457",
    "http://localhost:5458",
    "http://127.0.0.1:5458",
    "http://localhost:5459",
    "http://127.0.0.1:5459",
    "http://localhost:5460",
    "http://127.0.0.1:5460",
    "http://localhost:5461",
    "http://127.0.0.1:5461",
    "http://localhost:5462",
    "http://127.0.0.1:5462",
    "http://localhost:5463",
    "http://127.0.0.1:5463",
    "http://localhost:5464",
    "http://127.0.0.1:5464",
    "http://localhost:5465",
    "http://127.0.0.1:5465",
    "http://localhost:5466",
    "http://127.0.0.1:5466",
    "http://localhost:5467",
    "http://127.0.0.1:5467",
    "http://localhost:5468",
    "http://127.0.0.1:5468",
    "http://localhost:5469",
    "http://127.0.0.1:5469",
    "http://localhost:5470",
    "http://127.0.0.1:5470",
    "http://localhost:5471",
    "http://127.0.0.1:5471",
    "http://localhost:5472",
    "http://127.0.0.1:5472",
    "http://localhost:5473",
    "http://127.0.0.1:5473",
    "http://localhost:5474",
    "http://127.0.0.1:5474",
    "http://localhost:5475",
    "http://127.0.0.1:5475",
    "http://localhost:5476",
    "http://127.0.0.1:5476",
    "http://localhost:5477",
    "http://127.0.0.1:5477",
    "http://localhost:5478",
    "http://127.0.0.1:5478",
    "http://localhost:5479",
    "http://127.0.0.1:5479",
    "http://localhost:5480",
    "http://127.0.0.1:5480",
    "http://localhost:5481",
    "http://127.0.0.1:5481",
    "http://localhost:5482",
    "http://127.0.0.1:5482",
    "http://localhost:5483",
    "http://127.0.0.1:5483",
    "http://localhost:5484",
    "http://127.0.0.1:5484",
    "http://localhost:5485",
    "http://127.0.0.1:5485",
    "http://localhost:5486",
    "http://127.0.0.1:5486",
    "http://localhost:5487",
    "http://127.0.0.1:5487",
    "http://localhost:5488",
    "http://127.0.0.1:5488",
    "http://localhost:5489",
    "http://127.0.0.1:5489",
    "http://localhost:5490",
    "http://127.0.0.1:5490",
    "http://localhost:5491",
    "http://127.0.0.1:5491",
    "http://localhost:5492",
    "http://127.0.0.1:5492",
    "http://localhost:5493",
    "http://127.0.0.1:5493",
    "http://localhost:5494",
    "http://127.0.0.1:5494",
    "http://localhost:5495",
    "http://127.0.0.1:5495",
    "http://localhost:5496",
    "http://127.0.0.1:5496",
    "http://localhost:5497",
    "http://127.0.0.1:5497",
    "http://localhost:5498",
    "http://127.0.0.1:5498",
    "http://localhost:5499",
    "http://127.0.0.1:5499",
    "http://localhost:5500",
    "http://127.0.0.1:5500",
    "http://localhost:5501",
    "http://127.0.0.1:5501",
    "http://localhost:5502",
    "http://127.0.0.1:5502",
    "http://localhost:5503",
    "http://127.0.0.1:5503",
    "http://localhost:5504",
    "http://127.0.0.1:5504",
    "http://localhost:5505",
    "http://127.0.0.1:5505",
    "http://localhost:5506",
    "http://127.0.0.1:5506",
    "http://localhost:5507",
    "http://127.0.0.1:5507",
    "http://localhost:5508",
    "http://127.0.0.1:5508",
    "http://localhost:5509",
    "http://127.0.0.1:5509",
    "http://localhost:5510",
    "http://127.0.0.1:5510",
    "http://localhost:5511",
    "http://127.0.0.1:5511",
    "http://localhost:5512",
    "http://127.0.0.1:5512",
    "http://localhost:5513",
    "http://127.0.0.1:5513",
    "http://localhost:5514",
    "http://127.0.0.1:5514",
    "http://localhost:5515",
    "http://127.0.0.1:5515",
    "http://localhost:5516",
    "http://127.0.0.1:5516",
    "http://localhost:5517",
    "http://127.0.0.1:5517",
    "http://localhost:5518",
    "http://127.0.0.1:5518",
    "http://localhost:5519",
    "http://127.0.0.1:5519",
    "http://localhost:5520",
    "http://127.0.0.1:5520",
    "http://localhost:5521",
    "http://127.0.0.1:5521",
    "http://localhost:5522",
    "http://127.0.0.1:5522",
    "http://localhost:5523",
    "http://127.0.0.1:5523",
    "http://localhost:5524",
    "http://127.0.0.1:5524",
    "http://localhost:5525",
    "http://127.0.0.1:5525",
    "http://localhost:5526",
    "http://127.0.0.1:5526",
    "http://localhost:5527",
    "http://127.0.0.1:5527",
    "http://localhost:5528",
    "http://127.0.0.1:5528",
    "http://localhost:5529",
    "http://127.0.0.1:5529",
    "http://localhost:5530",
    "http://127.0.0.1:5530",
    "http://localhost:5531",
    "http://127.0.0.1:5531",
    "http://localhost:5532",
    "http://127.0.0.1:5532",
    "http://localhost:5533",
    "http://127.0.0.1:5533",
    "http://localhost:5534",
    "http://127.0.0.1:5534",
    "http://localhost:5535",
    "http://127.0.0.1:5535",
    "http://localhost:5536",
    "http://127.0.0.1:5536",
    "http://localhost:5537",
    "http://127.0.0.1:5537",
    "http://localhost:5538",
    "http://127.0.0.1:5538",
    "http://localhost:5539",
    "http://127.0.0.1:5539",
    "http://localhost:5540",
    "http://127.0.0.1:5540",
    "http://localhost:5541",
    "http://127.0.0.1:5541",
    "http://localhost:5542",
    "http://127.0.0.1:5542",
    "http://localhost:5543",
    "http://127.0.0.1:5543",
    "http://localhost:5544",
    "http://127.0.0.1:5544",
    "http://localhost:5545",
    "http://127.0.0.1:5545",
    "http://localhost:5546",
    "http://127.0.0.1:5546",
    "http://localhost:5547",
    "http://127.0.0.1:5547",
    "http://localhost:5548",
    "http://127.0.0.1:5548",
    "http://localhost:5549",
    "http://127.0.0.1:5549",
    "http://localhost:5550",
    "http://127.0.0.1:5550",
    "http://localhost:5551",
    "http://127.0.0.1:5551",
    "http://localhost:5552",
    "http://127.0.0.1:5552",
    "http://localhost:5553",
    "http://127.0.0.1:5553",
    "http://localhost:5554",
    "http://127.0.0.1:5554",
    "http://localhost:5555",
    "http://127.0.0.1:5555",
    "http://localhost:5556",
    "http://127.0.0.1:5556",
    "http://localhost:5557",
    "http://127.0.0.1:5557",
    "http://localhost:5558",
    "http://127.0.0.1:5558",
    "http://localhost:5559",
    "http://127.0.0.1:5559",
    "http://localhost:5560",
    "http://127.0.0.1:5560",
    "http://localhost:5561",
    "http://127.0.0.1:5561",
    "http://localhost:5562",
    "http://127.0.0.1:5562",
    "http://localhost:5563",
    "http://127.0.0.1:5563",
    "http://localhost:5564",
    "http://127.0.0.1:5564",
    "http://localhost:5565",
    "http://127.0.0.1:5565",
    "http://localhost:5566",
    "http://127.0.0.1:5566",
    "http://localhost:5567",
    "http://127.0.0.1:5567",
    "http://localhost:5568",
    "http://127.0.0.1:5568",
    "http://localhost:5569",
    "http://127.0.0.1:5569",
    "http://localhost:5570",
    "http://127.0.0.1:5570",
    "http://localhost:5571",
    "http://127.0.0.1:5571",
    "http://localhost:5572",
    "http://127.0.0.1:5572",
    "http://localhost:5573",
    "http://127.0.0.1:5573",
    "http://localhost:5574",
    "http://127.0.0.1:5574",
    "http://localhost:5575",
    "http://127.0.0.1:5575",
    "http://localhost:5576",
    "http://127.0.0.1:5576",
    "http://localhost:5577",
    "http://127.0.0.1:5577",
    "http://localhost:5578",
    "http://127.0.0.1:5578",
    "http://localhost:5579",
    "http://127.0.0.1:5579",
    "http://localhost:5580",
    "http://127.0.0.1:5580",
    "http://localhost:5581",
    "http://127.0.0.1:5581",
    "http://localhost:5582",
    "http://127.0.0.1:5582",
    "http://localhost:5583",
    "http://127.0.0.1:5583",
    "http://localhost:5584",
    "http://127.0.0.1:5584",
    "http://localhost:5585",
    "http://127.0.0.1:5585",
    "http://localhost:5586",
    "http://127.0.0.1:5586",
    "http://localhost:5587",
    "http://127.0.0.1:5587",
    "http://localhost:5588",
    "http://127.0.0.1:5588",
    "http://localhost:5589",
    "http://127.0.0.1:5589",
    "http://localhost:5590",
    "http://127.0.0.1:5590",
    "http://localhost:5591",
    "http://127.0.0.1:5591",
    "http://localhost:5592",
    "http://127.0.0.1:5592",
    "http://localhost:5593",
    "http://127.0.0.1:5593",
    "http://localhost:5594",
    "http://127.0.0.1:5594",
    "http://localhost:5595",
    "http://127.0.0.1:5595",
    "http://localhost:5596",
    "http://127.0.0.1:5596",
    "http://localhost:5597",
    "http://127.0.0.1:5597",
    "http://localhost:5598",
    "http://127.0.0.1:5598",
    "http://localhost:5599",
    "http://127.0.0.1:5599",
    "http://localhost:5600",
    "http://127.0.0.1:5600",
    "http://localhost:5601",
    "http://127.0.0.1:5601",
    "http://localhost:5602",
    "http://127.0.0.1:5602",
    "http://localhost:5603",
    "http://127.0.0.1:5603",
    "http://localhost:5604",
    "http://127.0.0.1:5604",
    "http://localhost:5605",
    "http://127.0.0.1:5605",
    "http://localhost:5606",
    "http://127.0.0.1:5606",
    "http://localhost:5607",
    "http://127.0.0.1:5607",
    "http://localhost:5608",
    "http://127.0.0.1:5608",
    "http://localhost:5609",
    "http://127.0.0.1:5609",
    "http://localhost:5610",
    "http://127.0.0.1:5610",
    "http://localhost:5611",
    "http://127.0.0.1:5611",
    "http://localhost:5612",
    "http://127.0.0.1:5612",
    "http://localhost:5613",
    "http://127.0.0.1:5613",
    "http://localhost:5614",
    "http://127.0.0.1:5614",
    "http://localhost:5615",
    "http://127.0.0.1:5615",
    "http://localhost:5616",
    "http://127.0.0.1:5616",
    "http://localhost:5617",
    "http://127.0.0.1:5617",
    "http://localhost:5618",
    "http://127.0.0.1:5618",
    "
```

--------------------------------

### Python NeuronUp LLM Client Initialization

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/enfermedades-neurodegenerativas/musicoterapia-en-el-tratamiento-de-la-demencia-beneficios-tipos-y-su-impacto-en-el-cerebro/

Demonstrates how to initialize the NeuronUp LLM client in Python, specifying the model and provider.

```python
from neuronup_llms import OpenAIModel

# Initialize with default OpenAI provider and a specific model
model = OpenAIModel(model_name='gpt-4')

# Initialize with a different provider (if supported) and model
# model = OpenAIModel(model_name='claude-2', provider='anthropic')

print(f"LLM client initialized with model: {model.model_name}")
```

--------------------------------

### Initialize OpenAI Model

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-atencion/juego-de-atencion-selectiva-para-adultos-identifica-la-pieza/

Demonstrates how to initialize an OpenAI language model with a specified model name and an optional provider. This is a fundamental step for using the model's capabilities.

```Python
from neuronup.llms.openai import OpenAIModel

# Initialize with a specific model
model = OpenAIModel(model_name="gpt-4o", provider="openai")

# Initialize with default provider
# model = OpenAIModel(model_name="gpt-3.5-turbo")
```

--------------------------------

### Python Model Initialization Example

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/testimonios/como-neuronup-transforma-la-rehabilitacion-cognitiva-infantil-caso-de-exito-de-isabel-corbacho/

Demonstrates how to initialize an OpenAI model with a specific model name and provider. This snippet shows a common pattern for setting up language models in Python.

```Python
from neuronup.llms.openai import OpenAIModel

# Initialize the model
model = OpenAIModel(model_name="gpt-4", provider="openai")

# Example usage (conceptual)
# response = model.generate("Tell me a story.")
# print(response)
```

--------------------------------

### Question Answering Example (Python)

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-lenguaje/nuevo-ejercicio-de-discriminacion-auditiva-para-ninos/

Illustrates how to perform question answering using the NeuronUp LLM by providing a context and a question. Dependencies include the 'neuronup' library and provider access.

```python
from neuronup import LLM

llm = LLM(model_name="bert-large-uncased-whole-word-masking-finetuned-squad", provider="huggingface")

context = """
The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France.
It is named after the engineer Gustave Eiffel, whose company designed and built the tower.
"""
question = "Where is the Eiffel Tower located?"
answer = llm.answer_question(context, question)
print(answer)
```

--------------------------------

### Python Example: Using OpenAIModel

Source: https://neuronup.com/neurociencia/neuropsicologia/funciones-ejecutivas/por-que-debemos-trabajar-principalmente-en-el-desarrollo-de-las-funciones-ejecutivas-para-mejorar-la-autorregulacion-emocional-en-ninos-y-adolescentes/

A Python code example showing the instantiation of the OpenAIModel and calling its methods to generate text.

```Python
from neuronup_llms import OpenAIModel

# Initialize the model
model = OpenAIModel(model_name='gpt-4o', provider='openai')

# Get model information
model_info = model.get_model_info()
print(f"Model Info: {model_info}")

# Define a prompt
user_prompt = "Explain the concept of recursion in programming."

# Generate text
generated_text = model.generate_text(prompt=user_prompt, max_tokens=200, temperature=0.8)

# Print the generated text
print(f"Generated Text:\n{generated_text}")
```

--------------------------------

### General Layout and Responsiveness

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/dano-cerebral-adquirido/ictus-o-accidentes-cerebrovasculares-acv/ictus-y-rehabilitacion-cognitiva-como-puede-reaprender-el-cerebro-tras-un-dano-severo/

Defines core layout properties, responsive adjustments for different screen sizes, and specific element positioning for the project's UI. Includes styles for containers, images, and media queries.

```css
input[type=checkbox] { height: auto !important; }
.policy { font-size: 80%; }
.boxzilla-center-container .boxzilla { margin-top: 100px !important; padding: 5rem; }
.bottom-left { position: absolute; bottom: 0; left: -42px; }
.top-right { position: absolute; top: -20px; right: -50px; width: 140px; }
.top-left { position: absolute; top: 0; left: -240px; width: 370px; z-index: -10; }
.bottom-left img, .top-left img, .top-right img { width: 100%; }
.boxzilla-close-icon { left: 0 !important; text-align: right !important; }

@media (max-height: 900px) {
  .boxzilla-center-container .img img { height: 400px; }
  .boxzilla-center-container .boxzilla { margin-top: 40px !important; }
}

@media (max-height: 800px) {
  .boxzilla-center-container .img img { height: 300px; }
  .boxzilla-center-container .boxzilla { margin-top: 40px !important; }
}

@media (max-height: 700px) {
  .boxzilla-center-container .img img { height: 250px; }
  .boxzilla-center-container .boxzilla { margin-top: 20px !important; }
  .bottom-left { width: 230px; }
}

@media (max-width: 820px) {
  .top-left { position: static; }
}

#boxzilla-34263 { margin-top: 2em !important; padding: 3em; }

#map { height: 500px; filter: saturate(160%); }

.no-js .owl-carousel { display: flex; }

.a-color-white a:hover, .a-color-white a:link, .a-color-white a:visited { color: #fff; }
.a-color-white a { color: #fff; text-decoration: none; }

.hover-opacity:hover { opacity: .85; }

.testimonial-block { border: 1px solid #eceef1; background-color: #fff; box-shadow: 0 1rem 2.5rem -1rem rgba(42, 49, 57, .0618); padding: 1.5em; }

.particle-bg { background-image: url(https://neuronup.com/wp-content/uploads/2022/v3/fondo-particulas.svg); background-repeat: no-repeat; background-size: 120%; background-position-x: -50px; background-position-y: 15px; }

body { margin-bottom: 0; min-height: 100vh; }
.site-inner { flex: 1; }

.header-stuck .nav-after-header { opacity: 0; z-index: 997; }
#mega-menu-after-header { display: flex !important; justify-content: flex-end !important; background: 0 0 !important; padding-right: 0 !important; }
#mega-menu-wrap-after-header { background: 0 0 !important; }

.before-header ~ .nav-after-header #mega-menu-wrap-after-header #mega-menu-after-header > li.mega-menu-item > a.mega-menu-link { color: #fff; }
.nav-after-header .wrap { max-width: none; }
.nav-after-header .mega-sub-menu { z-index: 10000; }
.nav-after-header { opacity: 1; position: fixed; top: 0; right: 0; transition: opacity 1s; z-index: 999; }

.icon-menu-language { margin: 0 !important; }
.icon-menu-language a::before { font-size: large !important; zoom: 1.3; }

img.custom-logo { transform: translateY(-13px); }
.header-stuck img.custom-logo { transform: translateY(-5px); }
.header-stuck .title-area.title-area-first { padding: 0; }

.site-header-wrap { max-width: none; }
.header-stuck .site-header { top: 0; }
.site-header { top: 40px; border: none; background-color: #fff; z-index: 998; width: 100vw !important; }

nav#mega-menu-wrap-header-right { width: 100%; }
.site-header .mega-sub-menu a.mega-menu-link { padding: 5px !important; align-items: center !important; display: flex !important; justify-content: flex-start !important; gap: 5px !important; line-height: 1.5 !important; transition: filter .5s !important; }
.site-header .mega-sub-menu a.mega-menu-link::before { flex-shrink: 0; }
.site-header .mega-sub-menu a.mega-menu-link:hover { filter: brightness(.9); }
.site-header .mega-sub-menu .mega-sub-menu .mega-sub-menu .mega-sub-menu a.mega-menu-link { margin-left: 1.5rem !important; margin-top: .5rem !important; }

.megamenu-freetry a { background: #00abc7 !important; border-radius: 32px !important; color: #fff !important; padding-left: 24px !important; padding-right: 24px !important; transition: all .3s ease-in-out; }
.megamenu-freetry a:hover { background-color: #008094 !important; }

nav#mega-menu-wrap-header-right #mega-menu-header-right { display: flex; flex-wrap: wrap; }
nav#mega-menu-wrap-header-right #mega-menu-header-right > li.megamenu-freetry { margin-left: auto; }
nav#mega-menu-wrap-header-right #mega-menu-header-right > li.spacer { flex: auto; margin: 0 !important; }

@media (max-width: 1182px) {
  .site-header { top: 0; position: sticky; }
  img.custom-logo { transform: translateY(-5px); }
  nav#mega-menu-wrap-header-right #mega-menu-header-right { height: calc(100vh - var(--header-height)); overflow-y: auto; }
  nav#mega-menu-wrap-header-right #mega-menu-header-right > li.mega-menu-item { border-bottom: 2px solid var(--color-primary); padding-block: 0.6rem; }
  nav#mega-menu-wrap-header-right #mega-menu-header-right > li.mega-menu-item > a.mega-menu-link { font-size: 1rem; }
  nav#mega-menu-wrap-header-right #mega-menu-header-right > li.megamenu-freetry, nav#mega-menu-wrap-header-right #mega-menu-header-right > li.megamenu-login { border-bottom: 0; width: 50%; display: inline-flex; margin-top: 3rem; }
  nav#mega-menu-wrap-header-right #mega-menu-header-right > li.mega-menu-item.megamenu-freetry a, nav#mega-menu-wrap-header-right #mega-menu-header-right > li.mega-menu-item.megamenu-login a { font-size: 15px; text-align: center; }
  .site-header .mega-sub-menu { box-shadow: none !important; }
}

@media only screen and (max-width: 1024px) {
  .mobile-one-column ul.mega-sub-menu li.mega-menu-item { float: none !important; width: 100% !important; display: block; }
}

input[type=search] { border-rad
```

--------------------------------

### Python Embedding Example

Source: https://neuronup.com/estimulacion-y-rehabilitacion-cognitiva/enfermedad-mental/trastorno-bipolar/trastorno-bipolar-que-es-tipos-causa-sintomas-diagnostico-y-tratamiento/

Example of how to use the NeuronUp LLM API in Python to generate embeddings. Requires the 'openai' library.

```python
from openai import OpenAI

client = OpenAI(
    base_url="YOUR_NEURONUP_API_BASE_URL", # e.g., "http://localhost:8000/v1"
    api_key="YOUR_API_KEY", # Or set as an environment variable OPENAI_API_KEY
)

response = client.embeddings.create(
    model="your-embedding-model-name",
    input="Hello world"
)

print(response.data[0].embedding)

```

--------------------------------

### List Available Models

Source: https://neuronup.com/actividades-de-neurorrehabilitacion/actividades-para-funciones-cognitivas/actividades-de-atencion/juego-de-atencion-selectiva-para-adultos-identifica-la-pieza/

Provides an example of how to retrieve a list of all available language models that can be used with the NeuronUp LLM framework. This helps in selecting the appropriate model for a task.

```Python
from neuronup.llms.base import LLM

# Get a list of all available model names
available_models = LLM.list_models()
print("Available Models:")
for model_name in available_models:
    print(f"- {model_name}")
```