Python Tutorial: Translate Long Documents Using AI

Table of Contents

1. Introduction
2. Setting up the Application
3. Configuring the Input and Output Languages
4. Splitting the Text into Segments
5. Counting the Tokens
6. Creating the System Prompt
7. Running the Translation Loop
8. Concatenating the Translated Text
9. Finalizing the Results
10. Conclusion

Introduction

In this article, we will explore how to build a simple application using the OpenAI API for language translation. Specifically, we will focus on translating a document, text, or article from one language to another. We will learn how to input the text, split it into segments, translate each segment, and then recombine the translated segments to obtain the final translated text. We will also discuss the importance of segmenting the text and managing token counts to avoid exceeding the limits of the language model.

Setting up the Application

To get started, we need to set up our development environment and Create a new folder for our translation example. We will use Jupyter Notebook, which allows us to work with code snippets and explanations in a single document. After setting up our notebook, we will configure the input and output languages and specify the path for the input data.

Configuring the Input and Output Languages

In order to perform the translation, we need to specify the input and output languages. While the language model can auto-detect the input language, it is useful to explicitly state it. In our example, the input language will be English, and the output language will be French. We will also define the path for the input data, which is the text we want to translate.

Splitting the Text into Segments

Next, we need to split the text into segments to avoid overloading the language model. Language models typically have limits on how much text can be processed in one go, so splitting the text into smaller segments is necessary. We will use a split STRING, such as a full stop or a double newline, to divide the text into segments. By splitting the text, we can effectively manage the amount of data we input and obtain accurate translations.

Counting the Tokens

Tokens are the individual units of text that the language model processes. As tokens can incur costs, we want to keep track of the token count to manage our expenses. By counting the tokens in the text, we can estimate the cost and predict the resources required for the translation. In our example, we will use the OpenAI tokenizer, Tiktoken, to break the text into tokens and determine the token count.

Creating the System Prompt

To ensure clear communication with the language model, we need to provide a system prompt. The system prompt is an instruction given to the model as to what we want it to do. In our case, we want to specify that our tool is a translation tool and that we expect it to receive a string in the input language and return the same string in the output language. We will also emphasize the importance of maintaining the original formatting, content, style, and tone in the translated text.

Running the Translation Loop

Once we have set up the system prompt, we can start the translation process. We will loop through the segmented text and send each segment along with the system prompt to the language model for translation. The language model will process the messages and generate translations for each segment. We will Collect the results and keep track of the tokens consumed during the translation process.

Concatenating the Translated Text

After translating each segment, we need to combine the translated segments to obtain the final translated text. By concatenating the results, we can ensure that the entire text is properly translated and reconstructed. We will use the join function in Python to merge the segments into a single string.

Finalizing the Results

Once we have the translated text, we can finalize the results. We will print the translated text, along with any Relevant statistics such as token count and segments completed. Additionally, we will provide an overview of the total tokens consumed during the translation process.

Conclusion

In this article, we have learned how to build a simple application for language translation using the OpenAI API. We have explored the necessary steps, such as setting up the application, configuring the input and output languages, splitting the text into segments, counting the tokens, creating the system prompt, running the translation loop, concatenating the translated text, and finalizing the results. By following these steps, we can effectively translate text from one language to another.