Project Introduction: OpenAI ChatGPT Knowledge Base Chatbot
The "OpenAI ChatGPT Knowledge Base Chatbot" is a versatile project designed to harness the power of advanced artificial intelligence to interact with users on a personalized level, making use of custom data sources. The aim is to create a chatbot that can engage in meaningful conversations, utilizing both user-specific data and a deep understanding of context.
Technical Stack
This project is built using a robust and modern technology stack that includes:
- Node.js and Python: These are used for building the core backend of the application, providing a seamless and efficient server-side functionality.
- MongoDB: A NoSQL database that is used to store user data and conversation history, ensuring that interactions with the chatbot can be personalized and saved for future reference.
- React: This JavaScript library is used for building the frontend of the web application, offering a dynamic and responsive user interface.
- OpenAI ChatGPT Turbo 3.5: The advanced model that powers the conversational capabilities of the chatbot, enabling it to understand and respond to user queries with high accuracy.
- OpenAI Ada Model: Utilized for embedding, this model allows the integration of user-specific data into the chatbot's responses.
- Vectorization: This technique is employed to provide the chatbot with long-term and short-term memory capabilities, enhancing the quality of interactions over time.
- Pincones Database: For embedding purposes, aiding in the efficient retrieval and use of data.
Features
The chatbot comes equipped with several key features designed to boost its functionality and user experience:
- User Management System: Facilitates the registration and management of users, ensuring that the system can personalize interactions based on individual user data.
- Long-term, Permanent Conversations: Users can engage in ongoing dialogues with the chatbot, with each session building upon previous interactions to deliver a more cohesive and personalized experience.
- Custom Indexes: Users have the option to integrate their own data sources, including files in PDF and TXT formats or websites, which the chatbot can then use to provide informed responses.
- Automatic Retry on API Errors: Enhances reliability by automatically retrying operations in the event of API errors, minimizing interruptions in service.
- Change and View Model Parameters: Users have the ability to adjust and monitor various model parameters, such as temperature and top_p, enabling fine-tuning of the chatbot's response behavior to better meet user needs.
This project brings together the power of machine learning and user-centric design to deliver a state-of-the-art chatbot experience, offering a unique blend of intelligence, flexibility, and user interactivity.