ChatGPT Retrieval Plugin: Difference between revisions

The ChatGPT Retrieval Plugin is a powerful addition to OpenAI's ChatGPT, enabling the AI to access and utilize data stored in a vector database like Weaviate. This capability not only enhances ChatGPT's performance by providing access to customized data but also addresses its long-standing limitation of lacking long-term memory. The plugin is part of the broader ChatGPT plugins ecosystem, which allows ChatGPT to interact with various apps and services, transforming it from a conversation tool to an AI capable of taking actions in the real world.

Recently, OpenAI introduced plugins for ChatGPT, which have significantly expanded the chatbot's capabilities. These plugins act as a bridge between the chatbot and a range of third-party resources, enabling it to leverage these resources to perform tasks based on user conversations. The Retrieval Plugin, specifically, has the potential to be widely used, as it allows users to create customized versions of ChatGPT tailored to their own data.

With the integration of plugins, ChatGPT can now perform various tasks such as ordering groceries, booking restaurants, and organizing vacations by utilizing services like Instacart, OpenTable, and Expedia. Moreover, the Zapier plugin allows ChatGPT to connect with thousands of other applications, from Google Sheets to Salesforce, thus broadening its reach.

The ChatGPT Retrieval Plugin enables users to connect ChatGPT to an instance of a vector database, allowing any information stored in the connected database to be used to answer questions and provide responses based on the details stored in the database. Additionally, the vector database can be utilized as a long-term storage solution for ChatGPT, allowing it to persist and store portions of user conversations beyond the short-lived memory of a browser tab.

 

The retrieval plugin enables ChatGPT to accomplish tasks grounded in the context of knowledge stored in a connected vector database, such as Weaviate. The process consists of two steps: prompting ChatGPT with a question to craft a query that can be sent to the vector database, and then receiving relevant information and context from the database to formulate an appropriate response.

- Connecting a vector database with proprietary data to ChatGPT, allowing it to answer specific questions based on that data

- Persisting personal documents and details to provide a personalized touch to ChatGPT's responses

- Storing conversations with ChatGPT in the attached vector database, enabling continued conversations across multiple sessions

This functionality allows for regular updates to content stored in connected vector databases, giving the model awareness of new information without the need for costly and time-consuming retraining of the large language model (LLM).