OpenAI Document Embedding for QA Project using Pinecone

This project demonstrates the use of the OpenAI API to embed documents into a vector database for question-answering tasks.

Introduction

The goal of this project is to create a system that can efficiently search and retrieve answers from a large collection of documents using the OpenAI API. By embedding the documents into a vector database, we can perform similarity-based searches to find the most relevant document for a given question.

Features

• Document Preprocessing: The project includes a preprocessing step to clean and prepare the documents before embedding.
• OpenAI API Integration: The OpenAI API is utilized to generate document embeddings.
• Vector Database: The project utilizes a vector database, we will use Pinecone, to store and index the document embeddings.
• Question-Answering: Given a question, the system searches the vector database for the most similar document and retrieves the answer.

Future Enhancements

• User Interface: Develop a web-based or command-line interface for easier interaction with the system.
• Incremental Updates: Implement a mechanism to handle incremental updates to the document collection and embeddings.
• Performance Optimization: Explore techniques to optimize the search and retrieval process for large-scale document collections.

Conclusion

This project demonstrates the integration of the OpenAI API with a vector database for efficient question-answering tasks. By leveraging document embeddings, we can quickly search and retrieve answers from a large corpus of documents. The project provides a foundation for building more advanced QA systems with additional features and optimizations.