GPTflix

Introduction to GPTflix

GPTflix is an innovative project designed to create a question-answer bot specifically focused on movie-related queries. The project leverages advanced technologies such as OpenAI, Pinecone Database (DB), and Streamlit to provide users with a seamless experience in retrieving information about movies. This guide offers a comprehensive overview of how GPTflix is built, its purpose, prerequisites, and steps to add data, all of which contribute to setting up and running the GPTflix QA bot effectively.

What is GPTflix?

GPTflix aims to establish a QA bot that users can interact with to ask questions about movies. By utilizing OpenAI for language processing, Pinecone DB for data management, and Streamlit for the user interface, GPTflix provides a robust platform for movie enthusiasts. Through this setup, users can inquire about various aspects of films and receive informed responses derived from an embedded database of text data.

Purpose and Goals

The main objective of GPTflix is to act as a foundational framework upon which individuals can build their own knowledge-retrieval systems. While the current iteration is relatively basic, it serves as a starting point for creating systems tailored to specific informational needs. The repository not only includes the GPTflix source code but also offers a guide for deploying it on Streamlit, enabling developers to customize and extend the functionality of their own applications.

Setup Prerequisites

To deploy GPTflix using Streamlit, developers must undertake several preparatory steps:

1. Fork the GPTflix repository from GitHub to create a personal copy.
2. Register for accounts on Pinecone.io and Streamlit Cloud, which are essential for database management and hosting the application, respectively.
3. Create a new app on Streamlit and link it to your forked repository.
4. Configure the app to use main.py as the main executable.
5. Enter API keys for Pinecone and OpenAI in the app settings under "Secrets."
6. Set up a .env file with your OpenAI API Key on your local machine.

These steps ensure that the application environment is properly configured, allowing for seamless operation and deployment on Streamlit.

Adding Data and Creating Embeddings

To enrich the database with movie content, developers can follow these structured steps to prepare and manage data:

1. Prepare Text for Model Ingestion: Run a script to transform raw text from a CSV file into a format suitable for embedding.
2. Convert to JSONL for API Requests: Convert the processed text into a JSONL file to prepare for API requests to generate embeddings.
3. Generate Embeddings: Utilize OpenAI's API to create embeddings from the text, capturing the semantic information necessary for meaningful interactions.
4. Create a CSV with Embeddings: Compile the embeddings into a readable CSV file, which can be useful for database updates and maintenance.
5. Upload Data to Pinecone: With an API key, upload the text and corresponding embeddings to Pinecone DB, making the data accessible for the QA bot.

These processes allow developers to create a rich dataset that the GPTflix bot can search, ensuring accurate and informative responses to user queries.

Project Contents

The repository includes sample data drawn from Kaggle, providing examples that developers can use to understand and replicate the setup process. Although initially basic, the sample data can be expanded upon or replaced with more varied datasets to enhance the bot's capabilities.

Future Enhancements

Despite being in its early stages, there are several anticipated improvements for GPTflix:

• Memory addition to summarize prior interactions, enhancing context retention.
• Introduction of expanded search capabilities within the database.
• Development of various modes for AI responses, allowing for tonal and character diversity.

The ongoing development of documentation encourages community participation, inviting contributions to refine and complete the project guide.

Licensing

GPTflix is provided under the MIT License, granting users significant freedom to utilize, modify, and distribute the software as they see fit, subject to the stipulated conditions.

With all these components working together, GPTflix offers a valuable tool for retrieving movie-related information, demonstrating the potential of combining AI with structured databases for specialized knowledge retrieval.