OpenAI API

OpenAI API OpenAI API

Disclaimer: This post has been translated to English using a machine translation model. Please, let me know if you find any mistakes.

Install the OpenAI librarylink image 35

First of all, in order to use the OpenAI API, it is necessary to install the OpenAI library. To do this, we run the following command

	
%pip install --upgrade openai
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Import the OpenAI librarylink image 36

Once the library is installed, we import it to use it in our code.

	
import openai
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Get an API Keylink image 37

To use the OpenAI API, it is necessary to obtain an API Key. To do this, we go to the OpenAI page and register. Once registered, we navigate to the API Keys section and create a new API Key.

open ai api key

Once we have it, we tell the OpenAI API what our API Key is.

	
api_key = "Pon aquí tu API key"
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We create our first chatbotlink image 38

With the OpenAI API, it's very easy to create a simple chatbot, to which we will pass a prompt, and it will return a response.

First, we need to choose which model we are going to use. In my case, I will use the gpt-3.5-turbo-1106 model, which is a good model for this post as of today, since what we are going to do does not require using the best model. OpenAI has a list with all their models and a page with the prices

	
model = "gpt-3.5-turbo-1106"
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Now we need to create a client that will communicate with the OpenAI API.

	
client = openai.OpenAI(api_key=api_key, organization=None)
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As we can see, we have passed our API Key. Additionally, you can pass the organization, but in our case, it is not necessary.

We create the prompt

	
promtp = "Cuál es el mejor lenguaje de programación para aprender?"
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And now we can ask OpenAI for a response.

	
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
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Let's see how the response is.

	
type(response), response
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(openai.types.chat.chat_completion.ChatCompletion,
ChatCompletion(id='chatcmpl-8RaHCm9KalLxj2PPbLh6f8A4djG8Y', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='No hay un "mejor" lenguaje de programación para aprender, ya que depende de tus intereses, objetivos y el tipo de desarrollo que te interese. Algunos lenguajes populares para empezar a aprender a programar incluyen Python, JavaScript, Java, C# y Ruby. Estos lenguajes son conocidos por su sintaxis clara y su versatilidad, lo que los hace buenos candidatos para principiantes. También es útil investigar qué lenguajes son populares en la industria en la que te gustaría trabajar, ya que el conocimiento de un lenguaje en demanda puede abrirte más oportunidades laborales. En resumen, la elección del lenguaje de programación para aprender dependerá de tus preferencias personales y de tus metas profesionales.', role='assistant', function_call=None, tool_calls=None))], created=1701584994, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint='fp_eeff13170a', usage=CompletionUsage(completion_tokens=181, prompt_tokens=21, total_tokens=202)))
	
print(f"response.id = {response.id}")
print(f"response.choices = {response.choices}")
for i in range(len(response.choices)):
print(f"response.choices[{i}] = {response.choices[i]}")
print(f" response.choices[{i}].finish_reason = {response.choices[i].finish_reason}")
print(f" response.choices[{i}].index = {response.choices[i].index}")
print(f" response.choices[{i}].message = {response.choices[i].message}")
content = response.choices[i].message.content.replace(' ', ' ')
print(f" response.choices[{i}].message.content = {content}")
print(f" response.choices[{i}].message.role = {response.choices[i].message.role}")
print(f" response.choices[{i}].message.function_call = {response.choices[i].message.function_call}")
print(f" response.choices[{i}].message.tool_calls = {response.choices[i].message.tool_calls}")
print(f"response.created = {response.created}")
print(f"response.model = {response.model}")
print(f"response.object = {response.object}")
print(f"response.system_fingerprint = {response.system_fingerprint}")
print(f"response.usage = {response.usage}")
print(f" response.usage.completion_tokens = {response.usage.completion_tokens}")
print(f" response.usage.prompt_tokens = {response.usage.prompt_tokens}")
print(f" response.usage.total_tokens = {response.usage.total_tokens}")
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response.id = chatcmpl-8RaHCm9KalLxj2PPbLh6f8A4djG8Y
response.choices = [Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='No hay un "mejor" lenguaje de programación para aprender, ya que depende de tus intereses, objetivos y el tipo de desarrollo que te interese. Algunos lenguajes populares para empezar a aprender a programar incluyen Python, JavaScript, Java, C# y Ruby. Estos lenguajes son conocidos por su sintaxis clara y su versatilidad, lo que los hace buenos candidatos para principiantes. También es útil investigar qué lenguajes son populares en la industria en la que te gustaría trabajar, ya que el conocimiento de un lenguaje en demanda puede abrirte más oportunidades laborales. En resumen, la elección del lenguaje de programación para aprender dependerá de tus preferencias personales y de tus metas profesionales.', role='assistant', function_call=None, tool_calls=None))]
response.choices[0] = Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='No hay un "mejor" lenguaje de programación para aprender, ya que depende de tus intereses, objetivos y el tipo de desarrollo que te interese. Algunos lenguajes populares para empezar a aprender a programar incluyen Python, JavaScript, Java, C# y Ruby. Estos lenguajes son conocidos por su sintaxis clara y su versatilidad, lo que los hace buenos candidatos para principiantes. También es útil investigar qué lenguajes son populares en la industria en la que te gustaría trabajar, ya que el conocimiento de un lenguaje en demanda puede abrirte más oportunidades laborales. En resumen, la elección del lenguaje de programación para aprender dependerá de tus preferencias personales y de tus metas profesionales.', role='assistant', function_call=None, tool_calls=None))
response.choices[0].finish_reason = stop
response.choices[0].index = 0
response.choices[0].message = ChatCompletionMessage(content='No hay un "mejor" lenguaje de programación para aprender, ya que depende de tus intereses, objetivos y el tipo de desarrollo que te interese. Algunos lenguajes populares para empezar a aprender a programar incluyen Python, JavaScript, Java, C# y Ruby. Estos lenguajes son conocidos por su sintaxis clara y su versatilidad, lo que los hace buenos candidatos para principiantes. También es útil investigar qué lenguajes son populares en la industria en la que te gustaría trabajar, ya que el conocimiento de un lenguaje en demanda puede abrirte más oportunidades laborales. En resumen, la elección del lenguaje de programación para aprender dependerá de tus preferencias personales y de tus metas profesionales.', role='assistant', function_call=None, tool_calls=None)
response.choices[0].message.content =
No hay un "mejor" lenguaje de programación para aprender, ya que depende de tus intereses, objetivos y el tipo de desarrollo que te interese. Algunos lenguajes populares para empezar a aprender a programar incluyen Python, JavaScript, Java, C# y Ruby. Estos lenguajes son conocidos por su sintaxis clara y su versatilidad, lo que los hace buenos candidatos para principiantes. También es útil investigar qué lenguajes son populares en la industria en la que te gustaría trabajar, ya que el conocimiento de un lenguaje en demanda puede abrirte más oportunidades laborales. En resumen, la elección del lenguaje de programación para aprender dependerá de tus preferencias personales y de tus metas profesionales.
response.choices[0].message.role = assistant
response.choices[0].message.function_call = None
response.choices[0].message.tool_calls = None
response.created = 1701584994
response.model = gpt-3.5-turbo-1106
response.object = chat.completion
response.system_fingerprint = fp_eeff13170a
response.usage = CompletionUsage(completion_tokens=181, prompt_tokens=21, total_tokens=202)
response.usage.completion_tokens = 181
response.usage.prompt_tokens = 21
response.usage.total_tokens = 202

As we can see, it returns a lot of information

For example response.choices[0].finish_reason = stop means that the model has stopped generating text because it has reached the end of the prompt. This is very useful for debugging, as the possible values are stop which means that the API returned the complete message, length which means that the model's output was incomplete because it was longer than max_tokens or the model's token limit, function_call which means that the model decided to call a function, content_filter which means that the content was omitted due to OpenAI's content limitations, and null which means that the API response was incomplete.

It also provides us with token information to keep track of the money spent.

Parameterslink image 39

When requesting a response from OpenAI, we can pass it a series of parameters to get a more suitable response to what we want. Let's see which parameters we can pass it.

  • messages: List of messages that have been sent to the chatbot
  • model: Model we want to use
  • frequency_penalty: Frequency penalty. The higher the value, the less likely the model is to repeat the same response
  • max_tokens: Maximum number of tokens that the model can return
  • n: Number of responses we want the model to return
  • presence_penalty: Presence penalty. The higher the value, the less likely the model is to repeat the same response
  • seed: Seed for text generation
  • stop: List of tokens indicating that the model should stop generating text
  • stream: If True, the API will return a response each time the model generates a token. If False, the API will return a response when the model has generated all tokens.
  • temperature: The higher the value, the more creative the model will be
  • top_p: The higher the value, the more creative the model will be
  • user: User ID of the person talking to the chatbot
  • timeout: Maximum time we want to wait for the API to return a response

Let's see some

Messageslink image 40

We can pass the API a list of messages that have been sent to the chatbot. This is useful for passing the conversation history to the chatbot, so it can generate a more appropriate response based on the conversation. And to condition the chatbot's response based on what was said previously.

Additionally, we can pass a system message to indicate how it should behave.

Conversation Historylink image 41

Let's see an example of the conversation history, first we ask how they are doing

	
promtp = "Hola, soy MaximoFN, ¿Cómo estás?"
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Hola MaximoFN, soy un modelo de inteligencia artificial diseñado para conversar y ayudar en lo que necesites. ¿En qué puedo ayudarte hoy?

It has responded that it doesn't have feelings and asked how it can help us. So if I ask it now what my name is, it won't be able to answer.

	
promtp = "¿Me puedes decir cómo me llamo?"
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Lo siento, no tengo esa información. Pero puedes decírmelo tú.

To solve this, we pass the conversation history

	
promtp = "¿Me puedes decir cómo me llamo?"
response = client.chat.completions.create(
model=model,
messages=[
{"role": "user", "content": "Hola, soy MaximoFN, ¿Cómo estás?"},
{"role": "assistant", "content": "Hola MaximoFN, soy un modelo de inteligencia artificial diseñado para conversar y ayudar en lo que necesites. ¿En qué puedo ayudarte hoy?"},
{"role": "user", "content": f"{promtp}"},
],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Tu nombre es MaximoFN.
Conditioning through exampleslink image 42

Now let's look at an example of how to condition the chatbot's response based on what it has been told previously. Now we ask it how to get the list of files in a directory in the terminal.

	
promtp = "¿Cómo puedo listar los archivos de un directorio en la terminal?"
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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En la terminal de un sistema operativo Unix o Linux, puedes listar los archivos de un directorio utilizando el comando `ls`. Por ejemplo, si quieres listar los archivos del directorio actual, simplemente escribe `ls` y presiona Enter. Si deseas listar los archivos de un directorio específico, puedes proporcionar la ruta del directorio después del comando `ls`, por ejemplo `ls /ruta/del/directorio`. Si deseas ver más detalles sobre los archivos, puedes usar la opción `-l` para obtener una lista detallada o `-a` para mostrar también los archivos ocultos.

Let's see what it answers if we condition it now with examples of short responses.

	
promtp = "¿Cómo puedo listar los archivos de un directorio en la terminal?"
response = client.chat.completions.create(
model=model,
messages=[
{"role": "user", "content": "Obtener las 10 primeras líneas de un archivo"},
{"role": "assistant", "content": "head -n 10"},
{"role": "user", "content": "Encontrar todos los archivos con extensión .txt"},
{"role": "assistant", "content": "find . -name '*.txt"},
{"role": "user", "content": "Dividir un archivo en varias páginas"},
{"role": "assistant", "content": "split -l 1000"},
{"role": "user", "content": "Buscar la dirección IP 12.34.56.78"},
{"role": "assistant", "content": "nslookup 12.34.56.78"},
{"role": "user", "content": "Obtener las 5 últimas líneas de foo.txt"},
{"role": "assistant", "content": "tail -n 5 foo.txt"},
{"role": "user", "content": "Convertir ejemplo.png en JPEG"},
{"role": "assistant", "content": "convert example.png example.jpg"},
{"role": "user", "content": "Create a git branch named 'new-feature"},
{"role": "assistant", "content": "git branch new-feature"},
{"role": "user", "content": f"{promtp}"},
],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Puede usar el comando `ls` en la terminal para listar los archivos de un directorio. Por ejemplo:
```
ls
```
Muestra los archivos y directorios en el directorio actual.

We have managed to get a shorter response.

Conditioning with system messagelink image 43

We can pass a system message to indicate how it should behave.

	
promtp = "¿Cómo puedo listar los archivos de un directorio en la terminal?"
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "Eres un experto asistente de terminal de ubuntu que responde solo con comandos de terminal"},
{"role": "user", "content": f"{promtp}"},
],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Puedes listar los archivos de un directorio en la terminal usando el comando `ls`. Por ejemplo, para listar los archivos del directorio actual, simplemente escribe `ls` y presiona Enter. Si quieres listar los archivos de un directorio específico, puedes utilizar `ls` seguido de la ruta del directorio. Por ejemplo, `ls /ruta/del/directorio`.
	
promtp = "¿Cómo puedo listar los archivos de un directorio en la terminal?"
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "Eres un experto asistente de terminal de ubuntu que responde solo con comandos de terminal"},
{"role": "user", "content": "Obtener las 10 primeras líneas de un archivo"},
{"role": "assistant", "content": "head -n 10"},
{"role": "user", "content": "Encontrar todos los archivos con extensión .txt"},
{"role": "assistant", "content": "find . -name '*.txt"},
{"role": "user", "content": "Dividir un archivo en varias páginas"},
{"role": "assistant", "content": "split -l 1000"},
{"role": "user", "content": "Buscar la dirección IP 12.34.56.78"},
{"role": "assistant", "content": "nslookup 12.34.56.78"},
{"role": "user", "content": "Obtener las 5 últimas líneas de foo.txt"},
{"role": "assistant", "content": "tail -n 5 foo.txt"},
{"role": "user", "content": "Convertir ejemplo.png en JPEG"},
{"role": "assistant", "content": "convert example.png example.jpg"},
{"role": "user", "content": "Create a git branch named 'new-feature"},
{"role": "assistant", "content": "git branch new-feature"},
{"role": "user", "content": f"{promtp}"},
],
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
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Puedes listar los archivos de un directorio en la terminal utilizando el comando "ls". Por ejemplo, para listar los archivos en el directorio actual, puedes ejecutar el comando "ls". Si deseas listar los archivos de otro directorio, simplemente especifica el directorio después del comando "ls", por ejemplo "ls /ruta/al/directorio".

Maximum number of response tokenslink image 44

We can limit the number of tokens that the model can return. This is useful to prevent the model from exceeding the response we want.

	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
max_tokens = 50,
)
content = response.choices[0].message.content.replace(' ', ' ')
print(content)
print(f" response.choices[{i}].finish_reason = {response.choices[i].finish_reason}")
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La respuesta a esta pregunta puede variar dependiendo de los intereses y objetivos individuales, ya que cada lenguaje de programación tiene sus propias ventajas y desventajas. Sin embargo, algunos de los lenguajes más
response.choices[0].finish_reason = length

As we can see, the response is cut off mid-sentence because it would exceed the token limit. Additionally, the stopping reason is now length instead of stop.

Model Creativity through Temperaturelink image 45

We can make the model more creative through temperature. The higher the value, the more creative the model will be.

	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
temperature = 0
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
temperature = temperature,
)
content_0 = response.choices[0].message.content.replace(' ', ' ')
print(content_0)
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No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los intereses y objetivos individuales. Algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C#. Cada uno tiene sus propias ventajas y desventajas, por lo que es importante investigar y considerar qué tipo de desarrollo de software te interesa antes de elegir un lenguaje para aprender.
	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
temperature = 1
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
temperature = temperature,
)
content_1 = response.choices[0].message.content.replace(' ', ' ')
print(content_1)
Copy
	
No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los objetivos y preferencias individuales del programador. Sin embargo, algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C++. Estos lenguajes son relativamente fáciles de aprender y tienen una amplia gama de aplicaciones en la industria de la tecnología. Es importante considerar qué tipo de proyectos o campos de interés te gustaría explorar al momento de elegir un lenguaje de programación para aprender.
	
print(content_0)
print(content_1)
Copy
	
No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los intereses y objetivos individuales. Algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C#. Cada uno tiene sus propias ventajas y desventajas, por lo que es importante investigar y considerar qué tipo de desarrollo de software te interesa antes de elegir un lenguaje para aprender.
No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los objetivos y preferencias individuales del programador. Sin embargo, algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C++. Estos lenguajes son relativamente fáciles de aprender y tienen una amplia gama de aplicaciones en la industria de la tecnología. Es importante considerar qué tipo de proyectos o campos de interés te gustaría explorar al momento de elegir un lenguaje de programación para aprender.

Model Creativity through top_plink image 46

We can make the model more creative using the top_p parameter. The higher the value, the more creative the model will be.

	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
top_p = 0
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
top_p = top_p,
)
content_0 = response.choices[0].message.content.replace(' ', ' ')
print(content_0)
Copy
	
No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los intereses y objetivos individuales. Algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C#. Cada uno tiene sus propias ventajas y desventajas, por lo que es importante investigar y considerar qué tipo de desarrollo de software te interesa antes de elegir un lenguaje para aprender.
	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
top_p = 1
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
top_p = top_p,
)
content_1 = response.choices[0].message.content.replace(' ', ' ')
print(content_1)
Copy
	
El mejor lenguaje de programación para aprender depende de los objetivos del aprendizaje y del tipo de programación que se quiera realizar. Algunos lenguajes de programación populares para principiantes incluyen Python, Java, JavaScript y Ruby. Sin embargo, cada lenguaje tiene sus propias ventajas y desventajas, por lo que es importante considerar qué tipo de proyectos o aplicaciones se quieren desarrollar antes de elegir un lenguaje de programación para aprender. Python es a menudo recomendado por su facilidad de uso y versatilidad, mientras que JavaScript es ideal para la programación web.
	
print(content_0)
print(content_1)
Copy
	
No hay un "mejor" lenguaje de programación para aprender, ya que la elección depende de los intereses y objetivos individuales. Algunos lenguajes populares para principiantes incluyen Python, JavaScript, Java y C#. Cada uno tiene sus propias ventajas y desventajas, por lo que es importante investigar y considerar qué tipo de desarrollo de software te interesa antes de elegir un lenguaje para aprender.
El mejor lenguaje de programación para aprender depende de los objetivos del aprendizaje y del tipo de programación que se quiera realizar. Algunos lenguajes de programación populares para principiantes incluyen Python, Java, JavaScript y Ruby. Sin embargo, cada lenguaje tiene sus propias ventajas y desventajas, por lo que es importante considerar qué tipo de proyectos o aplicaciones se quieren desarrollar antes de elegir un lenguaje de programación para aprender. Python es a menudo recomendado por su facilidad de uso y versatilidad, mientras que JavaScript es ideal para la programación web.

Number of responseslink image 47

We can ask the API to return more than one response. This is useful for getting multiple responses from the model so we can choose the one we like best. For this, we will set the temperature and top_p parameters to 1 to make the model more creative.

	
promtp = "¿Cuál es el mejor lenguaje de programación para aprender?"
temperature = 1
top_p = 1
response = client.chat.completions.create(
model = model,
messages = [{"role": "user", "content": f"{promtp}"}],
temperature = temperature,
top_p = top_p,
n = 4
)
content_0 = response.choices[0].message.content.replace(' ', ' ')
content_1 = response.choices[1].message.content.replace(' ', ' ')
content_2 = response.choices[2].message.content.replace(' ', ' ')
content_3 = response.choices[3].message.content.replace(' ', ' ')
print(content_0)
print(content_1)
print(content_2)
print(content_3)
Copy
	
El mejor lenguaje de programación para aprender depende de tus objetivos y del tipo de aplicaciones que te interese desarrollar. Algunos de los lenguajes más populares para aprender son:
1. Python: Es un lenguaje de programación versátil, fácil de aprender y con una amplia comunidad de desarrolladores. Es ideal para principiantes y se utiliza en una gran variedad de aplicaciones, desde desarrollo web hasta inteligencia artificial.
2. JavaScript: Es el lenguaje de programación más utilizado en el desarrollo web. Es imprescindible para aquellos que quieren trabajar en el ámbito del desarrollo frontend y backend.
3. Java: Es un lenguaje de programación muy popular en el ámbito empresarial, por lo que aprender Java puede abrirte muchas puertas laborales. Además, es un lenguaje estructurado que te enseñará conceptos importantes de la programación orientada a objetos.
4. C#: Es un lenguaje de programación desarrollado por Microsoft que se utiliza especialmente en el desarrollo de aplicaciones para Windows. Es ideal para aquellos que quieran enfocarse en el desarrollo de aplicaciones de escritorio.
En resumen, el mejor lenguaje de programación para aprender depende de tus intereses y objetivos personales. Es importante investigar y considerar qué tipos de aplicaciones te gustaría desarrollar para elegir el lenguaje que más se adapte a tus necesidades.
El mejor lenguaje de programación para aprender depende de los objetivos y necesidades individuales. Algunos de los lenguajes de programación más populares y ampliamente utilizados incluyen Python, JavaScript, Java, C++, Ruby y muchos otros. Python es a menudo recomendado para principiantes debido a su sintaxis simple y legible, mientras que JavaScript es esencial para el desarrollo web. Java es ampliamente utilizado en el desarrollo de aplicaciones empresariales y Android, y C++ es comúnmente utilizado en sistemas embebidos y juegos. En última instancia, el mejor lenguaje de programación para aprender dependerá de lo que quiera lograr con su habilidades de programación.
El mejor lenguaje de programación para aprender depende de los intereses y objetivos individuales de cada persona. Algunos de los lenguajes más populares y bien documentados para principiantes incluyen Python, JavaScript, Java y C#. Python es conocido por su simplicidad y versatilidad, mientras que JavaScript es esencial para el desarrollo web. Java y C# son lenguajes ampliamente utilizados en la industria y proporcionan una base sólida para aprender otros lenguajes. En última instancia, la elección del lenguaje dependerá de las metas personales y la aplicación deseada.
El mejor lenguaje de programación para aprender depende de los intereses y objetivos de cada persona. Algunos lenguajes populares para principiantes incluyen Python, Java, JavaScript, C++ y Ruby. Python es frecuentemente recomendado para aprender a programar debido a su sintaxis sencilla y legible, mientras que Java es utilizado en aplicaciones empresariales y Android. JavaScript es fundamental para el desarrollo web, y C++ es comúnmente utilizado en aplicaciones de alto rendimiento. Ruby es conocido por su facilidad de uso y flexibilidad. En última instancia, la elección del lenguaje dependerá de qué tipo de desarrollo te interesa y qué tipo de proyectos deseas realizar.

Retrain OpenAI Modellink image 48

OpenAI offers the possibility of retraining its API models to get better results with our own data. This has the following advantages

  • Higher quality results are obtained for our data
  • In a prompt, we can give it examples to make it behave as we want, but only a few. This way, by retraining it, we can provide many more.+ Token savings due to shorter instructions. Since we have already trained it for our use case, we can give it fewer instructions to solve our tasks.
  • Lower latency requests. When calling our own models, we will have less latency.

Data Preparationlink image 49

The OpenAI API asks us to provide the data in a jsonl file in the following format

{
      "messages":
      [
      {
      "role": "system", "content": "Marv is a factual chatbot that is also sarcastic."
      },
      {
      ¿Cuál es la capital de Francia?
      },
      {
      "Paris, as if everyone doesn't know that already."
      }
      It seems like you might have accidentally sent an incomplete or incorrect Markdown text. Could you please provide the actual Markdown content you want to translate? I'm ready to help with the translation once you share it!
      ]
      }
      {
      "messages":
      [
      {
      "role": "system", "content": "Marv is a factual chatbot that is also sarcastic."
      },
      {
      "Who wrote 'Romeo and Juliet'?"
      },
      {
      "Oh, just some guy named William Shakespeare. Ever heard of him?"
      }
      ]
      }
      {
      "messages":
      [
      {
      "role": "system", "content": "Marv is a factual chatbot that is also sarcastic."
      },
      {
      ¿A qué distancia se encuentra la Luna de la Tierra?
      },
      {
      "Around 238,900 miles. Give or take a few, like that really matters."
      }
      ]
      It seems like you have provided an incomplete or incorrect Markdown text to translate. Please provide the full Markdown content so I can assist you with the translation.

With a maximum of 4096 tokens

Data Validationlink image 50

To save myself some work, I've been passing all my posts to chatgpt one by one and asking it to generate 10 FAQs for each in CSV format, because I was unsure if it would be able to generate a format like the one requested in jsonl. And it has generated a CSV with the following format for each post

prompt,completion
      What does the Introduction to Python cover in the provided material? "The Introduction to Python covers topics such as data types, operators, use of functions and classes, handling iterable objects, and using modules. [More information](https://maximofn.com/python/)"
      What are the basic data types in Python? Python has 7 basic data types: text (`str`), numeric (`int`, `float`, `complex`), sequences (`list`, `tuple`, `range`), mapping (`dict`), sets (`set`, `frozenset`), booleans (`bool`), and binary (`bytes`, `bytearray`, `memoryview`). [More information](https://maximofn.com/python/)
      What are and how are operators used in Python? "Operators in Python are special symbols used to perform operations such as addition, subtraction, multiplication, and division between variables and values. They also include logical operators for comparisons. [More information](https://maximofn.com/python/)"
      How is a function defined and used in Python? In Python, a function is defined using the `def` keyword, followed by the function name and parentheses. Functions can have parameters and return values. They are used to encapsulate logic that can be reused throughout the code. [More information](https://maximofn.com/python/)What are classes in Python and how are they used? "Classes in Python are the foundation of object-oriented programming. They allow you to create objects that encapsulate data and functionalities. Classes are defined using the keyword `class`, followed by the class name. [More information](https://maximofn.com/python/)"
      ...

Each CSV has 10 FAQs

I'm going to write a code that takes each CSV and generates two new jsonls, one for training and another for validation.

	
import os
CSVs_path = "openai/faqs_posts"
percetn_train = 0.8
percetn_validation = 0.2
jsonl_train = os.path.join(CSVs_path, "train.jsonl")
jsonl_validation = os.path.join(CSVs_path, "validation.jsonl")
# Create the train.jsonl and validation.jsonl files
with open(jsonl_train, 'w') as f:
f.write('')
with open(jsonl_validation, 'w') as f:
f.write('')
for file in os.listdir(CSVs_path): # Get all files in the directory
if file.endswith(".csv"): # Check if file is a csv
csv = os.path.join(CSVs_path, file) # Get the path to the csv file
number_of_lines = 0
csv_content = []
for line in open(csv, 'r'): # Read all lines in the csv file
if line.startswith('prompt'): # Skip the first line
continue
number_of_lines += 1 # Count the number of lines
csv_content.append(line) # Add the line to the csv_content list
number_of_train = int(number_of_lines * percetn_train) # Calculate the number of lines for the train.jsonl file
number_of_validation = int(number_of_lines * percetn_validation) # Calculate the number of lines for the validation.sjonl file
for i in range(number_of_lines):
prompt = csv_content[i].split(',')[0]
response = ','.join(csv_content[i].split(',')[1:]).replace(' ', '').replace('"', '')
if i > 0 and i <= number_of_train:
# add line to train.jsonl
with open(jsonl_train, 'a') as f:
f.write(f'{"{"}"messages": [{"{"}"role": "system", "content": "Eres un amable asistente dispuesto a responder."{"}"}, {"{"}"role": "user", "content": "{prompt}"{"}"}, {"{"}"role": "assistant", "content": "{response}"{"}"}]{"}"} ')
elif i > number_of_train and i <= number_of_train + number_of_validation:
# add line to validation.csv
with open(jsonl_validation, 'a') as f:
f.write(f'{"{"}"messages": [{"{"}"role": "system", "content": "Eres un amable asistente dispuesto a responder."{"}"}, {"{"}"role": "user", "content": "{prompt}"{"}"}, {"{"}"role": "assistant", "content": "{response}"{"}"}]{"}"} ')
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Once I have the two jsonls, I run a code provided by OpenAI to check the jsonls

First we validate the training ones

	
from collections import defaultdict
import json
# Format error checks
format_errors = defaultdict(int)
with open(jsonl_train, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
for ex in dataset:
if not isinstance(ex, dict):
format_errors["data_type"] += 1
continue
messages = ex.get("messages", None)
if not messages:
format_errors["missing_messages_list"] += 1
continue
for message in messages:
if "role" not in message or "content" not in message:
format_errors["message_missing_key"] += 1
if any(k not in ("role", "content", "name", "function_call") for k in message):
format_errors["message_unrecognized_key"] += 1
if message.get("role", None) not in ("system", "user", "assistant", "function"):
format_errors["unrecognized_role"] += 1
content = message.get("content", None)
function_call = message.get("function_call", None)
if (not content and not function_call) or not isinstance(content, str):
format_errors["missing_content"] += 1
if not any(message.get("role", None) == "assistant" for message in messages):
format_errors["example_missing_assistant_message"] += 1
if format_errors:
print("Found errors:")
for k, v in format_errors.items():
print(f"{k}: {v}")
else:
print("No errors found")
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No errors found

And now the validation ones

	
# Format error checks
format_errors = defaultdict(int)
with open(jsonl_validation, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
for ex in dataset:
if not isinstance(ex, dict):
format_errors["data_type"] += 1
continue
messages = ex.get("messages", None)
if not messages:
format_errors["missing_messages_list"] += 1
continue
for message in messages:
if "role" not in message or "content" not in message:
format_errors["message_missing_key"] += 1
if any(k not in ("role", "content", "name", "function_call") for k in message):
format_errors["message_unrecognized_key"] += 1
if message.get("role", None) not in ("system", "user", "assistant", "function"):
format_errors["unrecognized_role"] += 1
content = message.get("content", None)
function_call = message.get("function_call", None)
if (not content and not function_call) or not isinstance(content, str):
format_errors["missing_content"] += 1
if not any(message.get("role", None) == "assistant" for message in messages):
format_errors["example_missing_assistant_message"] += 1
if format_errors:
print("Found errors:")
for k, v in format_errors.items():
print(f"{k}: {v}")
else:
print("No errors found")
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No errors found

Token Calculationlink image 51

The maximum number of tokens for each example must be 4096, so if we have longer examples only the first 4096 tokens will be used. Therefore, we are going to count the number of tokens in each jsonl to know how much it will cost to retrain the model.

But first, we need to install the tiktoken library, which is the tokenizer used by OpenAI and will also help us determine how many tokens each CSV contains, and therefore, how much it will cost to retrain the model.

To install it, run the following command

pip install tiktoken

We create a few necessary functions

	
import tiktoken
import numpy as np
encoding = tiktoken.get_encoding("cl100k_base")
def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3
return num_tokens
def num_assistant_tokens_from_messages(messages):
num_tokens = 0
for message in messages:
if message["role"] == "assistant":
num_tokens += len(encoding.encode(message["content"]))
return num_tokens
def print_distribution(values, name):
print(f" #### Distribution of {name}:")
print(f"min:{min(values)}, max: {max(values)}")
print(f"mean: {np.mean(values)}, median: {np.median(values)}")
print(f"p5: {np.quantile(values, 0.1)}, p95: {np.quantile(values, 0.9)}")
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# Warnings and tokens counts
n_missing_system = 0
n_missing_user = 0
n_messages = []
convo_lens = []
assistant_message_lens = []
with open(jsonl_train, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
for ex in dataset:
messages = ex["messages"]
if not any(message["role"] == "system" for message in messages):
n_missing_system += 1
if not any(message["role"] == "user" for message in messages):
n_missing_user += 1
n_messages.append(len(messages))
convo_lens.append(num_tokens_from_messages(messages))
assistant_message_lens.append(num_assistant_tokens_from_messages(messages))
print("Num examples missing system message:", n_missing_system)
print("Num examples missing user message:", n_missing_user)
print_distribution(n_messages, "num_messages_per_example")
print_distribution(convo_lens, "num_total_tokens_per_example")
print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
n_too_long = sum(l > 4096 for l in convo_lens)
print(f" {n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning")
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Num examples missing system message: 0
Num examples missing user message: 0
#### Distribution of num_messages_per_example:
min:3, max: 3
mean: 3.0, median: 3.0
p5: 3.0, p95: 3.0
#### Distribution of num_total_tokens_per_example:
min:67, max: 132
mean: 90.13793103448276, median: 90.0
p5: 81.5, p95: 99.5
#### Distribution of num_assistant_tokens_per_example:
min:33, max: 90
mean: 48.66379310344828, median: 48.5
p5: 41.0, p95: 55.5
0 examples may be over the 4096 token limit, they will be truncated during fine-tuning

As we can see in the training set, no message exceeds 4096 tokens

	
# Warnings and tokens counts
n_missing_system = 0
n_missing_user = 0
n_messages = []
convo_lens = []
assistant_message_lens = []
with open(jsonl_validation, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
for ex in dataset:
messages = ex["messages"]
if not any(message["role"] == "system" for message in messages):
n_missing_system += 1
if not any(message["role"] == "user" for message in messages):
n_missing_user += 1
n_messages.append(len(messages))
convo_lens.append(num_tokens_from_messages(messages))
assistant_message_lens.append(num_assistant_tokens_from_messages(messages))
print("Num examples missing system message:", n_missing_system)
print("Num examples missing user message:", n_missing_user)
print_distribution(n_messages, "num_messages_per_example")
print_distribution(convo_lens, "num_total_tokens_per_example")
print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
n_too_long = sum(l > 4096 for l in convo_lens)
print(f" {n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning")
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Num examples missing system message: 0
Num examples missing user message: 0
#### Distribution of num_messages_per_example:
min:3, max: 3
mean: 3.0, median: 3.0
p5: 3.0, p95: 3.0
#### Distribution of num_total_tokens_per_example:
min:80, max: 102
mean: 89.93333333333334, median: 91.0
p5: 82.2, p95: 96.8
#### Distribution of num_assistant_tokens_per_example:
min:41, max: 57
mean: 48.2, median: 49.0
p5: 42.8, p95: 51.6
0 examples may be over the 4096 token limit, they will be truncated during fine-tuning

No message in the validation set exceeds 4096 tokens either.

Calculation of the Costlink image 52

Another very important thing is to know how much it will cost us to do this fine-tuning.

	
# Pricing and default n_epochs estimate
MAX_TOKENS_PER_EXAMPLE = 4096
TARGET_EPOCHS = 3
MIN_TARGET_EXAMPLES = 100
MAX_TARGET_EXAMPLES = 25000
MIN_DEFAULT_EPOCHS = 1
MAX_DEFAULT_EPOCHS = 25
with open(jsonl_train, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
convo_lens = []
for ex in dataset:
messages = ex["messages"]
convo_lens.append(num_tokens_from_messages(messages))
n_epochs = TARGET_EPOCHS
n_train_examples = len(dataset)
if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
n_epochs = min(MAX_DEFAULT_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
n_epochs = max(MIN_DEFAULT_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)
n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)
print(f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training")
print(f"By default, you'll train for {n_epochs} epochs on this dataset")
print(f"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens")
tokens_for_train = n_epochs * n_billing_tokens_in_dataset
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Dataset has ~10456 tokens that will be charged for during training
By default, you'll train for 3 epochs on this dataset
By default, you'll be charged for ~31368 tokens

As of the time of writing this post, the cost to train gpt-3.5-turbo is $0.0080 per 1000 tokens, so we can determine how much the training will cost.

	
pricing = 0.0080
num_tokens_pricing = 1000
training_price = pricing * (tokens_for_train // num_tokens_pricing)
print(f"Training price: ${training_price}")
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Training price: $0.248
	
# Pricing and default n_epochs estimate
MAX_TOKENS_PER_EXAMPLE = 4096
TARGET_EPOCHS = 3
MIN_TARGET_EXAMPLES = 100
MAX_TARGET_EXAMPLES = 25000
MIN_DEFAULT_EPOCHS = 1
MAX_DEFAULT_EPOCHS = 25
with open(jsonl_validation, 'r', encoding='utf-8') as f:
dataset = [json.loads(line) for line in f]
convo_lens = []
for ex in dataset:
messages = ex["messages"]
convo_lens.append(num_tokens_from_messages(messages))
n_epochs = TARGET_EPOCHS
n_train_examples = len(dataset)
if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
n_epochs = min(MAX_DEFAULT_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
n_epochs = max(MIN_DEFAULT_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)
n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)
print(f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training")
print(f"By default, you'll train for {n_epochs} epochs on this dataset")
print(f"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens")
tokens_for_validation = n_epochs * n_billing_tokens_in_dataset
Copy
	
Dataset has ~1349 tokens that will be charged for during training
By default, you'll train for 6 epochs on this dataset
By default, you'll be charged for ~8094 tokens
	
validation_price = pricing * (tokens_for_validation // num_tokens_pricing)
print(f"Validation price: ${validation_price}")
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Validation price: $0.064
	
total_price = training_price + validation_price
print(f"Total price: ${total_price}")
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Total price: $0.312

If our calculations are correct, we see that the retraining of gpt-3.5-turbo will cost us $0.312

Traininglink image 53

Once we have everything ready, we need to upload the jsonls to the OpenAI API to retrain the model. To do this, we run the following code

	
result = client.files.create(file=open(jsonl_train, "rb"), purpose="fine-tune")
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type(result), result
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(openai.types.file_object.FileObject,
FileObject(id='file-LWztOVasq4E0U67wRe8ShjLZ', bytes=47947, created_at=1701585709, filename='train.jsonl', object='file', purpose='fine-tune', status='processed', status_details=None))
	
print(f"result.id = {result.id}")
print(f"result.bytes = {result.bytes}")
print(f"result.created_at = {result.created_at}")
print(f"result.filename = {result.filename}")
print(f"result.object = {result.object}")
print(f"result.purpose = {result.purpose}")
print(f"result.status = {result.status}")
print(f"result.status_details = {result.status_details}")
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result.id = file-LWztOVasq4E0U67wRe8ShjLZ
result.bytes = 47947
result.created_at = 1701585709
result.filename = train.jsonl
result.object = file
result.purpose = fine-tune
result.status = processed
result.status_details = None
	
jsonl_train_id = result.id
print(f"jsonl_train_id = {jsonl_train_id}")
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jsonl_train_id = file-LWztOVasq4E0U67wRe8ShjLZ

We do the same with the validation set

	
result = client.files.create(file=open(jsonl_validation, "rb"), purpose="fine-tune")
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print(f"result.id = {result.id}")
print(f"result.bytes = {result.bytes}")
print(f"result.created_at = {result.created_at}")
print(f"result.filename = {result.filename}")
print(f"result.object = {result.object}")
print(f"result.purpose = {result.purpose}")
print(f"result.status = {result.status}")
print(f"result.status_details = {result.status_details}")
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result.id = file-E0YOgIIe9mwxmFcza5bFyVKW
result.bytes = 6369
result.created_at = 1701585730
result.filename = validation.jsonl
result.object = file
result.purpose = fine-tune
result.status = processed
result.status_details = None
	
jsonl_validation_id = result.id
print(f"jsonl_train_id = {jsonl_validation_id}")
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jsonl_train_id = file-E0YOgIIe9mwxmFcza5bFyVKW

Once they are uploaded, we proceed to train our own OpenAI model. For this, we use the following code

	
result = client.fine_tuning.jobs.create(model = "gpt-3.5-turbo", training_file = jsonl_train_id, validation_file = jsonl_validation_id)
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type(result), result
Copy
	
(openai.types.fine_tuning.fine_tuning_job.FineTuningJob,
FineTuningJob(id='ftjob-aBndcorOfQLP0UijlY0R4pTB', created_at=1701585758, error=None, fine_tuned_model=None, finished_at=None, hyperparameters=Hyperparameters(n_epochs='auto', batch_size='auto', learning_rate_multiplier='auto'), model='gpt-3.5-turbo-0613', object='fine_tuning.job', organization_id='org-qDHVqEZ9tqE2XuA0IgWi7Erg', result_files=[], status='validating_files', trained_tokens=None, training_file='file-LWztOVasq4E0U67wRe8ShjLZ', validation_file='file-E0YOgIIe9mwxmFcza5bFyVKW'))
	
print(f"result.id = {result.id}")
print(f"result.created_at = {result.created_at}")
print(f"result.error = {result.error}")
print(f"result.fine_tuned_model = {result.fine_tuned_model}")
print(f"result.finished_at = {result.finished_at}")
print(f"result.hyperparameters = {result.hyperparameters}")
print(f" n_epochs = {result.hyperparameters.n_epochs}")
print(f" batch_size = {result.hyperparameters.batch_size}")
print(f" learning_rate_multiplier = {result.hyperparameters.learning_rate_multiplier}")
print(f"result.model = {result.model}")
print(f"result.object = {result.object}")
print(f"result.organization_id = {result.organization_id}")
print(f"result.result_files = {result.result_files}")
print(f"result.status = {result.status}")
print(f"result.trained_tokens = {result.trained_tokens}")
print(f"result.training_file = {result.training_file}")
print(f"result.validation_file = {result.validation_file}")
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result.id = ftjob-aBndcorOfQLP0UijlY0R4pTB
result.created_at = 1701585758
result.error = None
result.fine_tuned_model = None
result.finished_at = None
result.hyperparameters = Hyperparameters(n_epochs='auto', batch_size='auto', learning_rate_multiplier='auto')
n_epochs = auto
batch_size = auto
learning_rate_multiplier = auto
result.model = gpt-3.5-turbo-0613
result.object = fine_tuning.job
result.organization_id = org-qDHVqEZ9tqE2XuA0IgWi7Erg
result.result_files = []
result.status = validating_files
result.trained_tokens = None
result.training_file = file-LWztOVasq4E0U67wRe8ShjLZ
result.validation_file = file-E0YOgIIe9mwxmFcza5bFyVKW
	
fine_tune_id = result.id
print(f"fine_tune_id = {fine_tune_id}")
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fine_tune_id = ftjob-aBndcorOfQLP0UijlY0R4pTB

We can see that in status it showed validating_files. Since the fine-tuning takes a while, we can keep checking on the process using the following code

	
result = client.fine_tuning.jobs.retrieve(fine_tuning_job_id = fine_tune_id)
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type(result), result
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(openai.types.fine_tuning.fine_tuning_job.FineTuningJob,
FineTuningJob(id='ftjob-aBndcorOfQLP0UijlY0R4pTB', created_at=1701585758, error=None, fine_tuned_model=None, finished_at=None, hyperparameters=Hyperparameters(n_epochs=3, batch_size=1, learning_rate_multiplier=2), model='gpt-3.5-turbo-0613', object='fine_tuning.job', organization_id='org-qDHVqEZ9tqE2XuA0IgWi7Erg', result_files=[], status='running', trained_tokens=None, training_file='file-LWztOVasq4E0U67wRe8ShjLZ', validation_file='file-E0YOgIIe9mwxmFcza5bFyVKW'))
	
print(f"result.id = {result.id}")
print(f"result.created_at = {result.created_at}")
print(f"result.error = {result.error}")
print(f"result.fine_tuned_model = {result.fine_tuned_model}")
print(f"result.finished_at = {result.finished_at}")
print(f"result.hyperparameters = {result.hyperparameters}")
print(f" n_epochs = {result.hyperparameters.n_epochs}")
print(f" batch_size = {result.hyperparameters.batch_size}")
print(f" learning_rate_multiplier = {result.hyperparameters.learning_rate_multiplier}")
print(f"result.model = {result.model}")
print(f"result.object = {result.object}")
print(f"result.organization_id = {result.organization_id}")
print(f"result.result_files = {result.result_files}")
print(f"result.status = {result.status}")
print(f"result.trained_tokens = {result.trained_tokens}")
print(f"result.training_file = {result.training_file}")
print(f"result.validation_file = {result.validation_file}")
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result.id = ftjob-aBndcorOfQLP0UijlY0R4pTB
result.created_at = 1701585758
result.error = None
result.fine_tuned_model = None
result.finished_at = None
result.hyperparameters = Hyperparameters(n_epochs=3, batch_size=1, learning_rate_multiplier=2)
n_epochs = 3
batch_size = 1
learning_rate_multiplier = 2
result.model = gpt-3.5-turbo-0613
result.object = fine_tuning.job
result.organization_id = org-qDHVqEZ9tqE2XuA0IgWi7Erg
result.result_files = []
result.status = running
result.trained_tokens = None
result.training_file = file-LWztOVasq4E0U67wRe8ShjLZ
result.validation_file = file-E0YOgIIe9mwxmFcza5bFyVKW

We create a loop that waits for the training to finish

	
import time
result = client.fine_tuning.jobs.retrieve(fine_tuning_job_id = fine_tune_id)
status = result.status
while status != "succeeded":
time.sleep(10)
result = client.fine_tuning.jobs.retrieve(fine_tuning_job_id = fine_tune_id)
status = result.status
print("Job succeeded!")
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Job succeeded

As the training has finished, we request the information about the process again.

	
result = client.fine_tuning.jobs.retrieve(fine_tuning_job_id = fine_tune_id)
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print(f"result.id = {result.id}")
print(f"result.created_at = {result.created_at}")
print(f"result.error = {result.error}")
print(f"result.fine_tuned_model = {result.fine_tuned_model}")
print(f"result.finished_at = {result.finished_at}")
print(f"result.hyperparameters = {result.hyperparameters}")
print(f" n_epochs = {result.hyperparameters.n_epochs}")
print(f" batch_size = {result.hyperparameters.batch_size}")
print(f" learning_rate_multiplier = {result.hyperparameters.learning_rate_multiplier}")
print(f"result.model = {result.model}")
print(f"result.object = {result.object}")
print(f"result.organization_id = {result.organization_id}")
print(f"result.result_files = {result.result_files}")
print(f"result.status = {result.status}")
print(f"result.trained_tokens = {result.trained_tokens}")
print(f"result.training_file = {result.training_file}")
print(f"result.validation_file = {result.validation_file}")
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result.id = ftjob-aBndcorOfQLP0UijlY0R4pTB
result.created_at = 1701585758
result.error = None
result.fine_tuned_model = ft:gpt-3.5-turbo-0613:personal::8RagA0RT
result.finished_at = 1701586541
result.hyperparameters = Hyperparameters(n_epochs=3, batch_size=1, learning_rate_multiplier=2)
n_epochs = 3
batch_size = 1
learning_rate_multiplier = 2
result.model = gpt-3.5-turbo-0613
result.object = fine_tuning.job
result.organization_id = org-qDHVqEZ9tqE2XuA0IgWi7Erg
result.result_files = ['file-dNeo5ojOSuin7JIkNkQouHLB']
result.status = succeeded
result.trained_tokens = 30672
result.training_file = file-LWztOVasq4E0U67wRe8ShjLZ
result.validation_file = file-E0YOgIIe9mwxmFcza5bFyVKW

Let's look at some interesting data

	
fine_tuned_model = result.fine_tuned_model
finished_at = result.finished_at
result_files = result.result_files
status = result.status
trained_tokens = result.trained_tokens
print(f"fine_tuned_model = {fine_tuned_model}")
print(f"finished_at = {finished_at}")
print(f"result_files = {result_files}")
print(f"status = {status}")
print(f"trained_tokens = {trained_tokens}")
Copy
	
fine_tuned_model = ft:gpt-3.5-turbo-0613:personal::8RagA0RT
finished_at = 1701586541
result_files = ['file-dNeo5ojOSuin7JIkNkQouHLB']
status = succeeded
trained_tokens = 30672

We can see that they have given the name ft:gpt-3.5-turbo-0613:personal::8RagA0RT to our model, its status is now succeeded, and it has used 30672 tokens, while we had predicted

	
tokens_for_train, tokens_for_validation, tokens_for_train + tokens_for_validation
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(31368, 8094, 39462)

That is, it has used fewer tokens, so the training has cost us less than we had forecasted, specifically

	
real_training_price = pricing * (trained_tokens // num_tokens_pricing)
print(f"Real training price: ${real_training_price}")
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Real training price: $0.24

In addition to this information, if we go to the finetune page of OpenAI, we can see that our model is there.

open ai finetune

We can also see how much the training has cost us.

open ai finetune cost

As we can see, it has only been $0.25

And finally, let's see how long it took to complete this training. We can check what time it started.

open ai finetune start

And what time did it end?

open ai finetune end

So it has taken roughly about 10 minutes

Test of the modellink image 54

Inside the playground of OpenAI we can test our model, but we are going to do it through the API as we have learned here.

	
promtp = "¿Cómo se define una función en Python?"
response = client.chat.completions.create(
model = fine_tuned_model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
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type(response), response
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(openai.types.chat.chat_completion.ChatCompletion,
ChatCompletion(id='chatcmpl-8RvkVG8a5xjI2UZdXgdOGGcoelefc', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)', role='assistant', function_call=None, tool_calls=None))], created=1701667535, model='ft:gpt-3.5-turbo-0613:personal::8RagA0RT', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=54, prompt_tokens=16, total_tokens=70)))
	
print(f"response.id = {response.id}")
print(f"response.choices = {response.choices}")
for i in range(len(response.choices)):
print(f"response.choices[{i}] = {response.choices[i]}")
print(f" response.choices[{i}].finish_reason = {response.choices[i].finish_reason}")
print(f" response.choices[{i}].index = {response.choices[i].index}")
print(f" response.choices[{i}].message = {response.choices[i].message}")
content = response.choices[i].message.content.replace(' ', ' ')
print(f" response.choices[{i}].message.content = {content}")
print(f" response.choices[{i}].message.role = {response.choices[i].message.role}")
print(f" response.choices[{i}].message.function_call = {response.choices[i].message.function_call}")
print(f" response.choices[{i}].message.tool_calls = {response.choices[i].message.tool_calls}")
print(f"response.created = {response.created}")
print(f"response.model = {response.model}")
print(f"response.object = {response.object}")
print(f"response.system_fingerprint = {response.system_fingerprint}")
print(f"response.usage = {response.usage}")
print(f" response.usage.completion_tokens = {response.usage.completion_tokens}")
print(f" response.usage.prompt_tokens = {response.usage.prompt_tokens}")
print(f" response.usage.total_tokens = {response.usage.total_tokens}")
Copy
	
response.id = chatcmpl-8RvkVG8a5xjI2UZdXgdOGGcoelefc
response.choices = [Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)', role='assistant', function_call=None, tool_calls=None))]
response.choices[0] = Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)', role='assistant', function_call=None, tool_calls=None))
response.choices[0].finish_reason = stop
response.choices[0].index = 0
response.choices[0].message = ChatCompletionMessage(content='Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)', role='assistant', function_call=None, tool_calls=None)
response.choices[0].message.content =
Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)
response.choices[0].message.role = assistant
response.choices[0].message.function_call = None
response.choices[0].message.tool_calls = None
response.created = 1701667535
response.model = ft:gpt-3.5-turbo-0613:personal::8RagA0RT
response.object = chat.completion
response.system_fingerprint = None
response.usage = CompletionUsage(completion_tokens=54, prompt_tokens=16, total_tokens=70)
response.usage.completion_tokens = 54
response.usage.prompt_tokens = 16
response.usage.total_tokens = 70
	
print(content)
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Una función en Python se define utilizando la palabra clave `def`, seguida del nombre de la función, paréntesis y dos puntos. El cuerpo de la función se indenta debajo. [Más información](https://maximofn.com/python/)

We have a model that not only solves the answer for us, but also provides a link to the documentation of our blog

Let's see how it behaves with an example that clearly has nothing to do with the blog

	
promtp = "¿Cómo puedo cocinar pollo frito?"
response = client.chat.completions.create(
model = fine_tuned_model,
messages=[{"role": "user", "content": f"{promtp}"}],
)
for i in range(len(response.choices)):
content = response.choices[i].message.content.replace(' ', ' ')
print(f"{content}")
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Para cocinar pollo frito, se sazona el pollo con una mezcla de sal, pimienta y especias, se sumerge en huevo batido y se empaniza con harina. Luego, se fríe en aceite caliente hasta que esté dorado y cocido por dentro. [Más información](https://maximofn.com/pollo-frito/)

As can be seen, it gives us the link https://maximofn.com/pollo-frito/ which does not exist. Therefore, even though we have retrained a chatGPT model, we need to be careful with its responses and not trust it 100%.

Generating Images with DALL-E 3link image 55

To generate images with DALL-E 3, we have to use the following code

	
response = client.images.generate(
model="dall-e-3",
prompt="a white siamese cat",
size="1024x1024",
quality="standard",
n=1,
)
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type(response), response
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(openai.types.images_response.ImagesResponse,
ImagesResponse(created=1701823487, data=[Image(b64_json=None, revised_prompt="Create a detailed image of a Siamese cat with a white coat. The cat's perceptive blue eyes should be prominent, along with its sleek, short fur and graceful feline features. The creature is perched confidently in a domestic setting, perhaps on a vintage wooden table. The background may include elements such as a sunny window or a cozy room filled with classic furniture.", url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-qDHVqEZ9tqE2XuA0IgWi7Erg/user-XXh0uD53LAOCBxspbc83Hlcj/img-T81QvQ1nB8as0vl4NToILZD4.png?st=2023-12-05T23%3A44%3A47Z&se=2023-12-06T01%3A44%3A47Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-05T19%3A58%3A58Z&ske=2023-12-06T19%3A58%3A58Z&sks=b&skv=2021-08-06&sig=nzDujTj3Y3THuRrq2kOvASA5xP73Mm8HHlQuKKkLYu8%3D')]))
	
print(f"response.created = {response.created}")
for i in range(len(response.data)):
print(f"response.data[{i}] = {response.data[i]}")
print(f" response.data[{i}].b64_json = {response.data[i].b64_json}")
print(f" response.data[{i}].revised_prompt = {response.data[i].revised_prompt}")
print(f" response.data[{i}].url = {response.data[i].url}")
Copy
	
response.created = 1701823487
response.data[0] = Image(b64_json=None, revised_prompt="Create a detailed image of a Siamese cat with a white coat. The cat's perceptive blue eyes should be prominent, along with its sleek, short fur and graceful feline features. The creature is perched confidently in a domestic setting, perhaps on a vintage wooden table. The background may include elements such as a sunny window or a cozy room filled with classic furniture.", url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-qDHVqEZ9tqE2XuA0IgWi7Erg/user-XXh0uD53LAOCBxspbc83Hlcj/img-T81QvQ1nB8as0vl4NToILZD4.png?st=2023-12-05T23%3A44%3A47Z&se=2023-12-06T01%3A44%3A47Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-05T19%3A58%3A58Z&ske=2023-12-06T19%3A58%3A58Z&sks=b&skv=2021-08-06&sig=nzDujTj3Y3THuRrq2kOvASA5xP73Mm8HHlQuKKkLYu8%3D')
response.data[0].b64_json = None
response.data[0].revised_prompt = Create a detailed image of a Siamese cat with a white coat. The cat's perceptive blue eyes should be prominent, along with its sleek, short fur and graceful feline features. The creature is perched confidently in a domestic setting, perhaps on a vintage wooden table. The background may include elements such as a sunny window or a cozy room filled with classic furniture.
response.data[0].url = https://oaidalleapiprodscus.blob.core.windows.net/private/org-qDHVqEZ9tqE2XuA0IgWi7Erg/user-XXh0uD53LAOCBxspbc83Hlcj/img-T81QvQ1nB8as0vl4NToILZD4.png?st=2023-12-05T23%3A44%3A47Z&se=2023-12-06T01%3A44%3A47Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-05T19%3A58%3A58Z&ske=2023-12-06T19%3A58%3A58Z&sks=b&skv=2021-08-06&sig=nzDujTj3Y3THuRrq2kOvASA5xP73Mm8HHlQuKKkLYu8%3D

We can see a very interesting piece of information that we cannot see when using DALL-E 3 through the OpenAI interface, and that is the prompt that has been passed to the model.

	
response.data[0].revised_prompt
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"Create a detailed image of a Siamese cat with a white coat. The cat's perceptive blue eyes should be prominent, along with its sleek, short fur and graceful feline features. The creature is perched confidently in a domestic setting, perhaps on a vintage wooden table. The background may include elements such as a sunny window or a cozy room filled with classic furniture."

With that prompt, it generated the following image

	
import requests
url = response.data[0].url
# img_data = requests.get(url).content
with open('openai/dall-e-3.png', 'wb') as handler:
handler.write(requests.get(url).content)
Copy
dall-e 3

Since we have the prompt that OpenAI used, we will actually try to use it to generate a similar cat but with green eyes.

	
revised_prompt = response.data[0].revised_prompt
gree_eyes = revised_prompt.replace("blue", "green")
response = client.images.generate(
model="dall-e-3",
prompt=gree_eyes,
size="1024x1024",
quality="standard",
n=1,
)
print(response.data[0].revised_prompt)
image_url = response.data[0].url
image_path = 'openai/dall-e-3-green.png'
with open(image_path, 'wb') as handler:
handler.write(requests.get(image_url).content)
Copy
	
A well-defined image of a Siamese cat boasting a shiny white coat. Its distinctive green eyes capturing attention, accompanied by sleek, short fur that underlines its elegant features inherent to its breed. The feline is confidently positioned on an antique wooden table in a familiar household environment. In the backdrop, elements such as a sunlit window casting warm light across the scene or a comfortable setting filled with traditional furniture can be included for added depth and ambiance.
dall-e-3-green

Although the color of the cat has changed and not just the eyes, the position and background are very similar.

In addition to the prompt, the other variables we can modify are

  • model: Allows choosing the image generation model, possible values are dalle-2 and dalle-3
  • size: Allows changing the size of the image, possible values are 256x256, 512x512, 1024x1024, 1792x1024, 1024x1792 pixels
  • quality: Allows changing the image quality, possible values are standard or hd
  • response_format: Allows changing the response format, possible values are url or b64_json* n: Allows changing the number of images we want the model to return. With DALL-E 3, we can only request one image.
  • style: Allows changing the style of the image, possible values are vivid or natural

So we are going to generate a high-quality image

	
response = client.images.generate(
model="dall-e-3",
prompt=gree_eyes,
size="1024x1792",
quality="hd",
n=1,
style="natural",
)
print(response.data[0].revised_prompt)
image_url = response.data[0].url
image_path = 'openai/dall-e-3-hd.png'
with open(image_path, 'wb') as handler:
handler.write(requests.get(image_url).content)
display(Image(image_path))
Copy
	
Render a portrait of a Siamese cat boasting a pristine white coat. This cat should have captivating green eyes that stand out. Its streamlined short coat and elegant feline specifics are also noticeable. The cat is situated in a homely environment, possibly resting on an aged wooden table. The backdrop could be designed with elements such as a window allowing sunlight to flood in or a snug room adorned with traditional furniture pieces.
dall-e-3-hd

Visionlink image 56

We are going to use the vision model with the following image

panda

What looks like a panda here in small size, but if we see it up close, it's harder to see the panda.

panda

To use the vision model, we have to use the following code

	
prompt = "¿Ves algún animal en esta imagen?"
image_url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTU376h7oyFuEABd-By4gQhfjEBZsaSyKq539IqklI4MCEItVm_b7jtStTqBcP3qzaAVNI"
response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
}
],
max_tokens=300,
)
print(response.choices[0].message.content)
Copy
	
Lo siento, no puedo ayudar con la identificación o comentarios sobre contenido oculto en imágenes.

He can't find the panda, but it's not the goal of this post for him to see the panda; we only want to explain how to use the GPT4 vision model, so we won't delve any deeper into this topic.

We can pass several images at once

	
image_url1 = "https://i0.wp.com/www.aulapt.org/wp-content/uploads/2018/10/ilusiones-%C3%B3pticas.jpg?fit=649%2C363&ssl=1"
image_url2 = "https://i.pinimg.com/736x/69/ed/5a/69ed5ab09092880e38513a8870efee10.jpg"
prompt = "¿Ves algún animal en estas imágenes?"
display(Image(url=image_url1))
display(Image(url=image_url2))
response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt,
},
{
"type": "image_url",
"image_url": {
"url": image_url1,
},
},
{
"type": "image_url",
"image_url": {
"url": image_url2,
},
},
],
}
],
max_tokens=300,
)
print(response.choices[0].message.content)
Copy
	
<IPython.core.display.Image object>
	
<IPython.core.display.Image object>
	
Sí, en ambas imágenes se ven figuras de animales. Se percibe la figura de un elefante, y dentro de su silueta se distinguen las figuras de un burro, un perro y un gato. Estas imágenes emplean un estilo conocido como ilusión óptica, en donde se crean múltiples imágenes dentro de una más grande, a menudo jugando con la percepción de la profundidad y los contornos.

Text to Speechlink image 57

We can generate audio from text with the following code

	
speech_file_path = "openai/speech.mp3"
text = "Hola desde el blog de MaximoFN"
response = client.audio.speech.create(
model="tts-1",
voice="alloy",
input=text,
)
response.stream_to_file(speech_file_path)
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We can choose

  • model: Allows choosing the audio generation model. The possible values are tts-1 and tts-1-hd* voice: Allows choosing the voice that the model will use, possible values are alloy, echo, fable, onyx, nova and shimmer

Speech to text (Whisper)link image 58

We can transcribe audio using Whisper with the following code

	
audio_file = "MicroMachines.mp3"
audio_file= open(audio_file, "rb")
transcript = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
)
print(transcript.text)
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This is the Micromachine Man presenting the most midget miniature motorcade of micromachines. Each one has dramatic details, terrific trim, precision paint jobs, plus incredible micromachine pocket play sets. There's a police station, fire station, restaurant, service station, and more. Perfect pocket portables to take anyplace. And there are many miniature play sets to play with, and each one comes with its own special edition micromachine vehicle and fun fantastic features that miraculously move. Raise the boat lift at the airport, marina, man the gun turret at the army base, clean your car at the car wash, raise the toll bridge. And these play sets fit together to form a micromachine world. Micromachine pocket play sets so tremendously tiny, so perfectly precise, so dazzlingly detailed, you'll want to pocket them all. Micromachines and micromachine pocket play sets sold separately from Galoob. The smaller they are, the better they are.

Content Moderationlink image 59

We can obtain the category of a text among the classes sexual, hate, harassment, self-harm, sexual/minors, hate/threatening, violence/graphic, self-harm/intent, self-harm/instructions, harassment/threatening and violence. For this, we use the following code with the previously transcribed text.

	
text = transcript.text
response = client.moderations.create(input=text)
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type(response), response
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(openai.types.moderation_create_response.ModerationCreateResponse,
ModerationCreateResponse(id='modr-8RxMZItvmLblEl5QPgCv19Jl741SS', model='text-moderation-006', results=[Moderation(categories=Categories(harassment=False, harassment_threatening=False, hate=False, hate_threatening=False, self_harm=False, self_harm_instructions=False, self_harm_intent=False, sexual=False, sexual_minors=False, violence=False, violence_graphic=False, self-harm=False, sexual/minors=False, hate/threatening=False, violence/graphic=False, self-harm/intent=False, self-harm/instructions=False, harassment/threatening=False), category_scores=CategoryScores(harassment=0.0003560568729881197, harassment_threatening=2.5426568299735663e-06, hate=1.966094168892596e-05, hate_threatening=6.384455986108151e-08, self_harm=7.903140613052528e-07, self_harm_instructions=6.443992219828942e-07, self_harm_intent=1.2202733046251524e-07, sexual=0.0003779272665269673, sexual_minors=1.8967952200910076e-05, violence=9.489082731306553e-05, violence_graphic=5.1929731853306293e-05, self-harm=7.903140613052528e-07, sexual/minors=1.8967952200910076e-05, hate/threatening=6.384455986108151e-08, violence/graphic=5.1929731853306293e-05, self-harm/intent=1.2202733046251524e-07, self-harm/instructions=6.443992219828942e-07, harassment/threatening=2.5426568299735663e-06), flagged=False)]))
	
print(f"response.id = {response.id}")
print(f"response.model = {response.model}")
for i in range(len(response.results)):
print(f"response.results[{i}] = {response.results[i]}")
print(f" response.results[{i}].categories = {response.results[i].categories}")
print(f" response.results[{i}].categories.harassment = {response.results[i].categories.harassment}")
print(f" response.results[{i}].categories.harassment_threatening = {response.results[i].categories.harassment_threatening}")
print(f" response.results[{i}].categories.hate = {response.results[i].categories.hate}")
print(f" response.results[{i}].categories.hate_threatening = {response.results[i].categories.hate_threatening}")
print(f" response.results[{i}].categories.self_harm = {response.results[i].categories.self_harm}")
print(f" response.results[{i}].categories.self_harm_instructions = {response.results[i].categories.self_harm_instructions}")
print(f" response.results[{i}].categories.self_harm_intent = {response.results[i].categories.self_harm_intent}")
print(f" response.results[{i}].categories.sexual = {response.results[i].categories.sexual}")
print(f" response.results[{i}].categories.sexual_minors = {response.results[i].categories.sexual_minors}")
print(f" response.results[{i}].categories.violence = {response.results[i].categories.violence}")
print(f" response.results[{i}].categories.violence_graphic = {response.results[i].categories.violence_graphic}")
print(f" response.results[{i}].category_scores = {response.results[i].category_scores}")
print(f" response.results[{i}].category_scores.harassment = {response.results[i].category_scores.harassment}")
print(f" response.results[{i}].category_scores.harassment_threatening = {response.results[i].category_scores.harassment_threatening}")
print(f" response.results[{i}].category_scores.hate = {response.results[i].category_scores.hate}")
print(f" response.results[{i}].category_scores.hate_threatening = {response.results[i].category_scores.hate_threatening}")
print(f" response.results[{i}].category_scores.self_harm = {response.results[i].category_scores.self_harm}")
print(f" response.results[{i}].category_scores.self_harm_instructions = {response.results[i].category_scores.self_harm_instructions}")
print(f" response.results[{i}].category_scores.self_harm_intent = {response.results[i].category_scores.self_harm_intent}")
print(f" response.results[{i}].category_scores.sexual = {response.results[i].category_scores.sexual}")
print(f" response.results[{i}].category_scores.sexual_minors = {response.results[i].category_scores.sexual_minors}")
print(f" response.results[{i}].category_scores.violence = {response.results[i].category_scores.violence}")
print(f" response.results[{i}].category_scores.violence_graphic = {response.results[i].category_scores.violence_graphic}")
print(f" response.results[{i}].flagged = {response.results[i].flagged}")
Copy
	
response.id = modr-8RxMZItvmLblEl5QPgCv19Jl741SS
response.model = text-moderation-006
response.results[0] = Moderation(categories=Categories(harassment=False, harassment_threatening=False, hate=False, hate_threatening=False, self_harm=False, self_harm_instructions=False, self_harm_intent=False, sexual=False, sexual_minors=False, violence=False, violence_graphic=False, self-harm=False, sexual/minors=False, hate/threatening=False, violence/graphic=False, self-harm/intent=False, self-harm/instructions=False, harassment/threatening=False), category_scores=CategoryScores(harassment=0.0003560568729881197, harassment_threatening=2.5426568299735663e-06, hate=1.966094168892596e-05, hate_threatening=6.384455986108151e-08, self_harm=7.903140613052528e-07, self_harm_instructions=6.443992219828942e-07, self_harm_intent=1.2202733046251524e-07, sexual=0.0003779272665269673, sexual_minors=1.8967952200910076e-05, violence=9.489082731306553e-05, violence_graphic=5.1929731853306293e-05, self-harm=7.903140613052528e-07, sexual/minors=1.8967952200910076e-05, hate/threatening=6.384455986108151e-08, violence/graphic=5.1929731853306293e-05, self-harm/intent=1.2202733046251524e-07, self-harm/instructions=6.443992219828942e-07, harassment/threatening=2.5426568299735663e-06), flagged=False)
response.results[0].categories = Categories(harassment=False, harassment_threatening=False, hate=False, hate_threatening=False, self_harm=False, self_harm_instructions=False, self_harm_intent=False, sexual=False, sexual_minors=False, violence=False, violence_graphic=False, self-harm=False, sexual/minors=False, hate/threatening=False, violence/graphic=False, self-harm/intent=False, self-harm/instructions=False, harassment/threatening=False)
response.results[0].categories.harassment = False
response.results[0].categories.harassment_threatening = False
response.results[0].categories.hate = False
response.results[0].categories.hate_threatening = False
response.results[0].categories.self_harm = False
response.results[0].categories.self_harm_instructions = False
response.results[0].categories.self_harm_intent = False
response.results[0].categories.sexual = False
response.results[0].categories.sexual_minors = False
response.results[0].categories.violence = False
response.results[0].categories.violence_graphic = False
response.results[0].category_scores = CategoryScores(harassment=0.0003560568729881197, harassment_threatening=2.5426568299735663e-06, hate=1.966094168892596e-05, hate_threatening=6.384455986108151e-08, self_harm=7.903140613052528e-07, self_harm_instructions=6.443992219828942e-07, self_harm_intent=1.2202733046251524e-07, sexual=0.0003779272665269673, sexual_minors=1.8967952200910076e-05, violence=9.489082731306553e-05, violence_graphic=5.1929731853306293e-05, self-harm=7.903140613052528e-07, sexual/minors=1.8967952200910076e-05, hate/threatening=6.384455986108151e-08, violence/graphic=5.1929731853306293e-05, self-harm/intent=1.2202733046251524e-07, self-harm/instructions=6.443992219828942e-07, harassment/threatening=2.5426568299735663e-06)
response.results[0].category_scores.harassment = 0.0003560568729881197
response.results[0].category_scores.harassment_threatening = 2.5426568299735663e-06
response.results[0].category_scores.hate = 1.966094168892596e-05
response.results[0].category_scores.hate_threatening = 6.384455986108151e-08
response.results[0].category_scores.self_harm = 7.903140613052528e-07
response.results[0].category_scores.self_harm_instructions = 6.443992219828942e-07
response.results[0].category_scores.self_harm_intent = 1.2202733046251524e-07
response.results[0].category_scores.sexual = 0.0003779272665269673
response.results[0].category_scores.sexual_minors = 1.8967952200910076e-05
response.results[0].category_scores.violence = 9.489082731306553e-05
response.results[0].category_scores.violence_graphic = 5.1929731853306293e-05
response.results[0].flagged = False

The transcribed audio does not fall into any of the previous categories, let's try with another text.

	
text = "I want to kill myself"
response = client.moderations.create(input=text)
for i in range(len(response.results)):
print(f"response.results[{i}].categories.harassment = {response.results[i].categories.harassment}")
print(f"response.results[{i}].categories.harassment_threatening = {response.results[i].categories.harassment_threatening}")
print(f"response.results[{i}].categories.hate = {response.results[i].categories.hate}")
print(f"response.results[{i}].categories.hate_threatening = {response.results[i].categories.hate_threatening}")
print(f"response.results[{i}].categories.self_harm = {response.results[i].categories.self_harm}")
print(f"response.results[{i}].categories.self_harm_instructions = {response.results[i].categories.self_harm_instructions}")
print(f"response.results[{i}].categories.self_harm_intent = {response.results[i].categories.self_harm_intent}")
print(f"response.results[{i}].categories.sexual = {response.results[i].categories.sexual}")
print(f"response.results[{i}].categories.sexual_minors = {response.results[i].categories.sexual_minors}")
print(f"response.results[{i}].categories.violence = {response.results[i].categories.violence}")
print(f"response.results[{i}].categories.violence_graphic = {response.results[i].categories.violence_graphic}")
print()
print(f"response.results[{i}].category_scores.harassment = {response.results[i].category_scores.harassment}")
print(f"response.results[{i}].category_scores.harassment_threatening = {response.results[i].category_scores.harassment_threatening}")
print(f"response.results[{i}].category_scores.hate = {response.results[i].category_scores.hate}")
print(f"response.results[{i}].category_scores.hate_threatening = {response.results[i].category_scores.hate_threatening}")
print(f"response.results[{i}].category_scores.self_harm = {response.results[i].category_scores.self_harm}")
print(f"response.results[{i}].category_scores.self_harm_instructions = {response.results[i].category_scores.self_harm_instructions}")
print(f"response.results[{i}].category_scores.self_harm_intent = {response.results[i].category_scores.self_harm_intent}")
print(f"response.results[{i}].category_scores.sexual = {response.results[i].category_scores.sexual}")
print(f"response.results[{i}].category_scores.sexual_minors = {response.results[i].category_scores.sexual_minors}")
print(f"response.results[{i}].category_scores.violence = {response.results[i].category_scores.violence}")
print(f"response.results[{i}].category_scores.violence_graphic = {response.results[i].category_scores.violence_graphic}")
print()
print(f"response.results[{i}].flagged = {response.results[i].flagged}")
Copy
	
response.results[0].categories.harassment = False
response.results[0].categories.harassment_threatening = False
response.results[0].categories.hate = False
response.results[0].categories.hate_threatening = False
response.results[0].categories.self_harm = True
response.results[0].categories.self_harm_instructions = False
response.results[0].categories.self_harm_intent = True
response.results[0].categories.sexual = False
response.results[0].categories.sexual_minors = False
response.results[0].categories.violence = True
response.results[0].categories.violence_graphic = False
response.results[0].category_scores.harassment = 0.004724912345409393
response.results[0].category_scores.harassment_threatening = 0.00023778305330779403
response.results[0].category_scores.hate = 1.1909247405128554e-05
response.results[0].category_scores.hate_threatening = 1.826493189582834e-06
response.results[0].category_scores.self_harm = 0.9998544454574585
response.results[0].category_scores.self_harm_instructions = 3.5801923647937883e-09
response.results[0].category_scores.self_harm_intent = 0.99969482421875
response.results[0].category_scores.sexual = 2.141016238965676e-06
response.results[0].category_scores.sexual_minors = 2.840671520232263e-08
response.results[0].category_scores.violence = 0.8396497964859009
response.results[0].category_scores.violence_graphic = 2.7347923605702817e-05
response.results[0].flagged = True

Now if it detects that the text is self_harm_intent

Assistantslink image 60

OpenAI gives us the possibility to create assistants, so we can create them with the characteristics that we want, for example, an assistant expert in Python, and use it as if it were a particular model of OpenAI. This means we can use it for a query and have a conversation with it, and after some time, use it again with a new query in a new conversation.

When working with assistants, we will have to create them, create a thread, send them the message, execute them, wait for them to respond, and view the response.

asistentes

Create the assistantlink image 61

First we create the assistant

	
code_interpreter_assistant = client.beta.assistants.create(
name="Python expert",
instructions="Eres un experto en Python. Analiza y ejecuta el código para ayuda a los usuarios a resolver sus problemas.",
tools=[{"type": "code_interpreter"}],
model="gpt-3.5-turbo-1106"
)
Copy
	
type(code_interpreter_assistant), code_interpreter_assistant
Copy
	
(openai.types.beta.assistant.Assistant,
Assistant(id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', created_at=1701822478, description=None, file_ids=[], instructions='Eres un experto en Python. Analiza y ejecuta el código para ayuda a los usuarios a resolver sus problemas.', metadata={}, model='gpt-3.5-turbo-1106', name='Python expert', object='assistant', tools=[ToolCodeInterpreter(type='code_interpreter')]))
	
code_interpreter_assistant_id = code_interpreter_assistant.id
print(f"code_interpreter_assistant_id = {code_interpreter_assistant_id}")
Copy
	
code_interpreter_assistant_id = asst_A2F9DPqDiZYFc5hOC6Rb2y0x

When creating the assistant, the variables we have are

  • name: name of the assistant
  • instructions: Instructions for the assistant. Here we can explain how the assistant should behave.* tools: Tools that the assistant can use. Currently only code_interpreter and retrieval are available.
  • model: Model that the assistant will use

This assistant is already created and we can use it as many times as we want. To do this, we need to create a new thread, so if in the future someone else wants to use it because it proves useful, by creating a new thread, they will be able to use it as if they were using the original assistant. They would only need the assistant's ID.

Thread or threadlink image 62

A thread represents a new conversation with the assistant, so even if time has passed, as long as we have the thread ID, we can continue the conversation. To create a new thread, we need to use the following code

	
thread = client.beta.threads.create()
Copy
	
type(thread), thread
thread_id = thread.id
print(f"thread_id = {thread_id}")
Copy
	
thread_id = thread_nfFT3rFjyPWHdxWvMk6jJ90H

We upload a filelink image 63

Let's create a .py file that we will ask the interpreter to explain

	
import os
python_code = os.path.join("openai", "python_code.py")
code = "print('Hello world!')"
with open(python_code, "w") as f:
f.write(code)
Copy

We upload it to the OpenAI API using the client.files.create function, which we already used when we did fine-tuning of a chatGPT model and uploaded the jsonls. Only before, in the purpose variable, we passed fine-tuning since the files we were uploading were for fine-tuning, and now we pass assistants since the files we are going to upload are for an assistant.

	
file = client.files.create(
file=open(python_code, "rb"),
purpose='assistants'
)
Copy
	
type(file), file
Copy
	
(openai.types.file_object.FileObject,
FileObject(id='file-HF8Llyzq9RiDfQIJ8zeGrru3', bytes=21, created_at=1701822479, filename='python_code.py', object='file', purpose='assistants', status='processed', status_details=None))

Send a message to the assistantlink image 64

We create the message that we are going to send to the assistant, and also indicate the file ID about which we want to ask.

	
message = client.beta.threads.messages.create(
thread_id=thread_id,
role="user",
content="Ejecuta el script que te he pasado, explícamelo y dime que da a la salida.",
file_ids=[file.id]
)
Copy

Run the assistantlink image 65

We run the assistant telling it to resolve the user's doubt

	
run = client.beta.threads.runs.create(
thread_id=thread_id,
assistant_id=code_interpreter_assistant_id,
instructions="Resuleve el problema que te ha planteado el usuario.",
)
Copy
	
type(run), run
Copy
	
(openai.types.beta.threads.run.Run,
Run(id='run_WZxT1TUuHT5qB1ZgD34tgvPu', assistant_id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', cancelled_at=None, completed_at=None, created_at=1701822481, expires_at=1701823081, failed_at=None, file_ids=[], instructions='Resuleve el problema que te ha planteado el usuario.', last_error=None, metadata={}, model='gpt-3.5-turbo-1106', object='thread.run', required_action=None, started_at=None, status='queued', thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H', tools=[ToolAssistantToolsCode(type='code_interpreter')]))
	
run_id = run.id
print(f"run_id = {run_id}")
Copy
	
run_id = run_WZxT1TUuHT5qB1ZgD34tgvPu

Wait for it to finish processinglink image 66

While the assistant is analyzing, we can check the status.

	
run = client.beta.threads.runs.retrieve(
thread_id=thread_id,
run_id=run_id
)
Copy
	
type(run), run
Copy
	
(openai.types.beta.threads.run.Run,
Run(id='run_WZxT1TUuHT5qB1ZgD34tgvPu', assistant_id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', cancelled_at=None, completed_at=None, created_at=1701822481, expires_at=1701823081, failed_at=None, file_ids=[], instructions='Resuleve el problema que te ha planteado el usuario.', last_error=None, metadata={}, model='gpt-3.5-turbo-1106', object='thread.run', required_action=None, started_at=1701822481, status='in_progress', thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H', tools=[ToolAssistantToolsCode(type='code_interpreter')]))
	
run.status
Copy
	
'in_progress'
	
while run.status != "completed":
time.sleep(1)
run = client.beta.threads.runs.retrieve(
thread_id=thread_id,
run_id=run_id
)
print("Run completed!")
Copy
	
Run completed!

Process the responselink image 67

Once the assistant has finished, we can see the response.

	
messages = client.beta.threads.messages.list(
thread_id=thread_id
)
Copy
	
type(messages), messages
Copy
	
(openai.pagination.SyncCursorPage[ThreadMessage],
SyncCursorPage[ThreadMessage](data=[ThreadMessage(id='msg_JjL0uCHCPiyYxnu1FqLyBgEX', assistant_id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', content=[MessageContentText(text=Text(annotations=[], value='La salida del script es simplemente "Hello world!", ya que la única instrucción en el script es imprimir esa frase. Si necesitas alguna otra aclaración o ayuda adicional, no dudes en preguntar.'), type='text')], created_at=1701822487, file_ids=[], metadata={}, object='thread.message', role='assistant', run_id='run_WZxT1TUuHT5qB1ZgD34tgvPu', thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H'), ThreadMessage(id='msg_nkFbq64DTaSqxIAQUGedYmaX', assistant_id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', content=[MessageContentText(text=Text(annotations=[], value='El script proporcionado contiene una sola línea que imprime "Hello world!". Ahora procederé a ejecutar el script para obtener su salida.'), type='text')], created_at=1701822485, file_ids=[], metadata={}, object='thread.message', role='assistant', run_id='run_WZxT1TUuHT5qB1ZgD34tgvPu', thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H'), ThreadMessage(id='msg_bWT6H2f6lsSUTAAhGG0KXoh7', assistant_id='asst_A2F9DPqDiZYFc5hOC6Rb2y0x', content=[MessageContentText(text=Text(annotations=[], value='Voy a revisar el archivo que has subido y ejecutar el script proporcionado. Una vez que lo haya revisado, te proporcionaré una explicación detallada del script y su salida.'), type='text')], created_at=1701822482, file_ids=[], metadata={}, object='thread.message', role='assistant', run_id='run_WZxT1TUuHT5qB1ZgD34tgvPu', thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H'), ThreadMessage(id='msg_RjDygK7c8yCqYrjnUPfeZfUg', assistant_id=None, content=[MessageContentText(text=Text(annotations=[], value='Ejecuta el script que te he pasado, explícamelo y dime que da a la salida.'), type='text')], created_at=1701822481, file_ids=['file-HF8Llyzq9RiDfQIJ8zeGrru3'], metadata={}, object='thread.message', role='user', run_id=None, thread_id='thread_nfFT3rFjyPWHdxWvMk6jJ90H')], object='list', first_id='msg_JjL0uCHCPiyYxnu1FqLyBgEX', last_id='msg_RjDygK7c8yCqYrjnUPfeZfUg', has_more=False))
	
for i in range(len(messages.data)):
for j in range(len(messages.data[i].content)):
print(f"messages.data[{i}].content[{j}].text.value = {messages.data[i].content[j].text.value}")
Copy
	
messages.data[0].content[0].text.value = La salida del script es simplemente "Hello world!", ya que la única instrucción en el script es imprimir esa frase.
Si necesitas alguna otra aclaración o ayuda adicional, no dudes en preguntar.
messages.data[1].content[0].text.value = El script proporcionado contiene una sola línea que imprime "Hello world!". Ahora procederé a ejecutar el script para obtener su salida.
messages.data[2].content[0].text.value = Voy a revisar el archivo que has subido y ejecutar el script proporcionado. Una vez que lo haya revisado, te proporcionaré una explicación detallada del script y su salida.
messages.data[3].content[0].text.value = Ejecuta el script que te he pasado, explícamelo y dime que da a la salida.

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