Mastering OpenAI: Extracting Answers from Multiple Files

Table of Contents

1. Introduction
2. Importing Packages
3. Constructing the Data Frame
4. Reading the Files
5. Calculating Number of Tokens
6. Appending Data to the Data Frame
7. Computing Document Embeddings
8. Comparing Documents
9. User Query and Context
10. Calling the Chat Completion Endpoint
11. Conclusion

Introduction

In this article, we will learn how to extract answers from multiple files using OpenAI GPT-3 and Python. We will utilize the tick token and Python to accomplish this task. The process involves importing necessary packages, constructing a data frame, reading the files, calculating the number of tokens, and comparing the documents to find Relevant answers to a specific query. We will also cover how to set user context and utilize the chat completion endpoint to get accurate responses. So let's dive in and explore this fascinating topic!

1. Importing Packages

Before we begin, we need to import the required packages. These include pandas, numpy, os, tick token, and openAI. These packages are essential for data manipulation, file handling, and leveraging the OpenAI GPT-3 model for natural language processing.

2. Constructing the Data Frame

To organize the data effectively, we will construct a data frame. This data frame will consist of three columns: file name, content, and the number of tokens used by each file. We will use pandas to Create this data frame and populate it with the necessary information.

3. Reading the Files

Next, we need to Read the files from a specific directory. We will use the listdir function to get all the files present in the directory. Then, we will open each file and extract its content. We will filter out only the text files and store their content for further processing.

4. Calculating Number of Tokens

After extracting the content, we need to calculate the number of tokens used in each file. We will utilize the tick token library to encode the content and get the number of tokens. This will help us understand the length and complexity of each file.

5. Appending Data to the Data Frame

Once we have the file name, content, and the number of tokens for each file, we will append this information to the data frame. This will allow us to easily access and analyze the data later on. We will use the append function and set the appropriate values for each column.

6. Computing Document Embeddings

To compare the documents effectively, we need to compute the document embeddings. Document embeddings represent the semantic meaning and context of each document. We will define a function to calculate the embeddings for each row of the data frame. This function will utilize the OpenAI GPT-3 model to generate embeddings for the content of each document.

7. Comparing Documents

Once we have computed the document embeddings, we can compare the similarity between the user query and the documents. We will define a function that takes the user query as input and calculates the similarity between the query and each document using the dot product. We will sort the results in descending order to prioritize the most relevant documents.

8. User Query and Context

Now that we have the groundwork laid out, we can use an actual user query to find the most relevant documents. We will pass the user query to the function and compute the document embeddings. We can specify the number of top-ranked documents We Are interested in. We will append the user query and context to the messages object for the chat completion endpoint.

9. Calling the Chat Completion Endpoint

Finally, we will call the chat completion endpoint using the OpenAI GPT-3 model (GBPT 3.5 Turbo). We will pass the model and the messages object containing the user query and context. The completion API will generate a response that resembles a professor providing a concise answer to the user's query. We will extract and print the response for further analysis.

10. Conclusion

In this article, we have explored how to extract answers from multiple files using OpenAI GPT-3 and Python. We have covered the step-by-step process, including importing packages, constructing a data frame, reading files, calculating tokens, computing document embeddings, comparing documents, setting user context, and calling the chat completion endpoint. By leveraging the power of OpenAI GPT-3, we can find accurate and relevant answers to complex queries.