Boost Your PDF Searching with OpenAI Embeddings and Laravel

Boost Your PDF Searching with OpenAI Embeddings and Laravel

Table of Contents

1. Introduction
2. Project Overview
3. Converting PDF to Text
4. Preprocessing the Text
5. Splitting the Text into Chunks
6. Converting Chunks into Vectors
7. Storing Text and Vectors in the Database
8. Searching for Relevant Vectors
9. Retrieving Text from Vectors
10. Creating a Knowledge Base
11. Asking Questions and Generating Answers
12. Conclusion

Introduction

In this article, we will explore an exciting project that combines the power of opinion embeddings with PDF searching. If You have ever struggled with finding specific information within PDF documents, then this project is for you. We will dive into the step-by-step process of integrating opinion embeddings and performing searches on PDF documents. We will also demonstrate how to combine user inputs with PDF content to Create Prompts for open AI text models, allowing us to ask questions and receive answers Based on a knowledge base extracted from PDFs. Let's discover the world of opinion AI embeddings and how they can revolutionize PDF searching.

Project Overview

Our project involves developing an application that leverages opinion embeddings to convert PDF files into searchable vectors. Using natural language queries, users can now search through PDF documents and retrieve relevant chunks of text based on similarity scores. This is made possible by utilizing Cosine similarity to ensure accurate and efficient results.

We will start by converting the PDF to text using a PDF parsing Package. Then, we will preprocess the text by tokenizing, normalizing, and removing stop words. After preprocessing, we will split the text into chunks and convert each chunk into a vector using open AI text embedding models.

The vectors and corresponding text chunks will be stored in a database, creating our own knowledge base. When a user inputs a question, we will convert it into a vector and search through the database for relevant vectors using cosine similarity. The retrieved vectors will be combined to form a prompt, which will be used to query open AI models for answers.

Converting PDF to Text

The first step in our project is to convert PDF files into text. We will use a PDF parsing package to extract the text from the PDF file. This text will serve as the basis for our searches and vector conversions.

Preprocessing the Text

Before performing searches, we need to preprocess the text to ensure accurate results. This involves tokenizing the text, normalizing it, and removing stop words. By preprocessing the text, we can increase the accuracy and relevance of our search results.

Splitting the Text into Chunks

Since open AI has token limits, we need to split the text into chunks to ensure it fits within the limit. By splitting the text, we can process and convert each chunk into a vector separately. This allows us to retain the Context of the text while staying within the token limits.

Converting Chunks into Vectors

Using open AI text embedding models, we will convert each text chunk into a vector representation. These vectors will capture the semantic meaning of the text and allow us to perform similarity searches.

Storing Text and Vectors in the Database

The converted vectors and their corresponding text chunks will be stored in a database. This will serve as our knowledge base, allowing us to retrieve relevant text based on user queries. We will store the text, vector, and file ID in separate tables to ensure efficient retrieval.

Searching for Relevant Vectors

When a user inputs a question, we will convert it into a vector representation using open AI text embeddings. We will then search through the database for similar vectors using cosine similarity. This will retrieve the most relevant vectors based on the user's query.

Retrieving Text from Vectors

After retrieving the relevant vectors, we need to obtain the corresponding text. We will map the vector IDs to the text IDs stored in the database to retrieve the text chunks. This will allow us to combine the text chunks into a single STRING, creating a knowledge base for our prompt.

Creating a Knowledge Base

Using the user input as the question and the text chunks as the knowledge base, we will construct a prompt. This prompt will be used to query open AI text models, such as GPT-3, to generate answers based on the provided knowledge base. By combining the user input and the extracted text chunks, we can generate relevant and accurate answers.

Asking Questions and Generating Answers

With our knowledge base and prompt ready, we can now ask questions and generate answers. We will use open AI text models to answer the questions based on the prompt and the knowledge base. The generated answer will be returned to the user, providing them with the information they need from the PDF document.

Conclusion

In this article, we have explored the exciting possibilities of combining opinion embeddings and PDF searching. By leveraging cosine similarity and open AI text models, we can convert PDF files into searchable vectors and retrieve relevant text based on user queries. This opens up new avenues for finding specific information within PDF documents and provides a powerful tool for information retrieval.

By following the step-by-step process outlined in this article, you can implement your own PDF searching project and harness the power of opinion embeddings. Whether it's for academic research, data analysis, or personal use, this project has the potential to revolutionize the way we Interact with PDF documents. So, let's dive in and discover the amazing world of opinion AI embeddings.

Highlights

• Convert PDF files into searchable vectors
• Retrieve relevant text based on similarity scores
• Use cosine similarity for accurate and efficient results
• Preprocess text by tokenizing, normalizing, and removing stop words
• Split text into chunks to fit within token limits
• Convert text chunks into vector representations using open AI text embeddings
• Store vectors and text chunks in a database as a knowledge base
• Search for relevant vectors using cosine similarity
• Obtain text from vectors using text IDs
• Create prompts for open AI text models using user input and knowledge base
• Generate answers based on prompts using open AI text models

FAQ

Q: Can I use this project to search for specific information within multiple PDF documents?

A: Yes, this project allows you to upload and search through multiple PDF documents. It can handle a large number of documents and retrieve relevant information based on user queries.

Q: What is the AdVantage of using opinion embeddings for PDF searching?

A: Opinion embeddings capture the semantic meaning of the text, making searches more accurate and relevant. By using cosine similarity, we can find text chunks that are similar to the user's query, ensuring precise retrieval of information.

Q: Can I customize the prompt and knowledge base for better search results?

A: Yes, you can customize the prompt and knowledge base to suit your specific needs. By providing a tailored prompt and a comprehensive knowledge base, you can generate more accurate and informative answers.

Q: Is there a limit to the number of tokens that can be searched at once?

A: Yes, there is a token limit when using open AI text models. It's important to consider this limit and ensure that your prompts and knowledge base fit within the allowed number of tokens.