Convert Chat Threads into a Vector Database
What is the main goal for this project?
The goal of this project is to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to an existing dataset to create an AI/ML model that supports development of more content and user access with the platform's evolution.
DifferentMatters is developing the latest methodology and application to help everyday people find a sense of belonging. In order to accelerate our pace to market, we need to create content specific to our IP so that our application then becomes a portal to unique experiences for each individual.
Students will develop an AI / ML model that will create a vector database to support our development of content, and eventually lead to user-content access as our platform evolves.
This will involve several different steps for the students, including:
- Conducting background research on the simplest way to create our database for usability by non-technical users.
- Analyzing our current dataset.
- Researching the latest AI / ML techniques and how they could be applied to our data.
- Developing an AI / ML model that provides unique outcomes or insights into our data.
- Providing multiple solutions that can be applied to solve the same problem.
What tasks will learners need to complete to achieve the project goal?
By the end of the project, students should demonstrate:
- Understanding of the available dataset.
- Understanding of the latest AI / ML techniques.
- Identification of ways in which AI / ML can be applied to our company.
Final deliverables should include:
- Vector database ready to use with chat threads converted to embeddings.
- A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results, and recommended next steps.
- Source materials such as code and workbooks including user manual.
How will you support learners in completing the project?
Students will connect directly on a weekly basis with us for mentorship throughout the project. We will be able to provide answers to questions such as:
- Our current products and applications of AI / ML.
- The current data set and guidance in navigating it.
- How our business intends to evolve using our Vector Database.
- Input on choices, problems or anything else the students might encounter.