SECRET_KEY

ANSWERS_FILE_ID

SEARCH_FILE_ID

CLASIFICATIONS_FILE_ID

Learn the OpenAI API with Python and NLP by mastering API endpoints, text generation, moderation, embeddings, and Flask integration for real-world tasks.

Using OpenAI API for Natural Language Processing in Python.png

dockerfile.tar.gz

openai_completion

embeddings

python

flask

This course introduces OpenAI API and NLP in Python, teaching you how to use the OpenAI API for real-world natural language processing tasks. You’ll begin by exploring the OpenAI API with Python, setting up your account, and learning how to access key endpoints. The completions endpoint will be covered for generating, classifying, and transforming text.

The course then covers advanced NLP in Python techniques using other OpenAI endpoints like moderations and embeddings for in-depth text analysis. You’ll practice these methods to efficiently analyze and manipulate text data.

Finally, you will integrate your OpenAI skills with Flask to build interactive, NLP-powered applications. The course emphasizes real-world projects, helping you use OpenAI’s capabilities to solve problems in content generation, text classification, and more. After completing this course, you can apply NLP in Python using OpenAI API to create scalable solutions.

Using OpenAI API for Natural Language Processing in Python

# Overview

The chat completions endpoint can also be used to complete unattended (incomplete) sentences. For example, if we leave a sentence half-written, the chat completions endpoint can complete the sentence provided that it has the appropriate context. In this case, the value of `temperature` is usually kept low so that the completion does not disregard the previous context.

Although the tiger is a solitary beast and\n

## Example
Let’s try the above example in the code widget below:

# Finding completion of unattended sentence

from openai import OpenAI
client = OpenAI(api_key="==SECRET_KEY==")

response = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "Find completion of unattended sentence"},
    {"role": "user", "content": "Although the tiger is a solitary beast and"}
  ],
)
print(response.choices[0].message.content)

If we want to generate a focused completion of the unattended sentence, we use a lower `temperature` value. Otherwise, if we want a more creative and abstract completion, we use a higher `temperature` value. In the code above, we use a low value of the `temperature` parameter. The value of `top_p` indicates that we include all the tokens in the results. The default values of `presence_penalty` and `frequency_penalty` are used. We can experiment with these parameters by changing these values in multiple runs and trying different `engines` to see their capabilities.

# Factual responses

The API has a good grasp of the data on which it is trained and can generate answers to hypothetical questions. If we want the API to reply with less vague answers, we should tell it how to respond when it does not know the answer.

Here is an example of the prompt:
```text
Q: Who is Batman?
A: Batman is a fictional comic book character.

Q: What is torsalplexity?
A: ?

Q: What is Devz9?
A: ?

Q: Who is George Lucas?
A: George Lucas is an American film director and producer famous for creating Star Wars.
...
<more questions-answers, if needed>
...
Q: What is the meaning of xyzz?
A:
```

In the example above, we have set `?` as an answer if it is unknown. The model will follow the same pattern for unknown answers.

## Example
Let’s run the above example in the code widget below:

Learn about completing incomplete content and factual responses using the completions endpoint.

Introduction

The Completions Endpoint

Miscellaneous Endpoints

APIs Integration in Flask

Conclusion

Appendix

Exploring OpenAI API

Completing Incomplete Content and Factual Responses

Overview

Content completion

Example