Vector databases have become increasingly popular, thanks to the rise of large language models (LLMs). These databases offer numerous benefits, such as scalability, performance optimization, and a range of powerful indexing and searching capabilities. One notable application of vector databases is content moderation, where they can play a crucial role in filtering out harmful content.
A Demo From Redis's Vector DB Features
How is it Done??
- The process begins with curating a collection of 'bad' imagery, achieved by gathering and augmenting harmful images and generating CLIP embeddings for them. These embeddings are then stored in the vector database. Additionally, a separate index is maintained for known good images.
- To identify harmful imagery effectively, an approximate nearest neighbor (ANN) algorithm is utilized.
- Unfamiliar images are compared against the 'bad' index, and a distance threshold is established based on retrieved matches to determine the image's safety. However, the solution doesn't stop there; it has been further developed to improve its effectiveness.
- For each uploaded image, sets of images are retrieved from both the 'good' and 'bad' indices. This allows for the calculation of aggregate information, such as the average distance from the 'good' and 'bad' image sets.
- With this data, a model is trained to assign a "goodness" propensity score to every image. Based on these scores, images are categorized as harmful, requiring further investigation, or safe for circulation on the platform.
There is More
This content moderation flow using vector databases can also be extended to videos. Keyframes are extracted from videos through scene change detection, and frames around these areas are sampled. Similar to images, the extracted keyframes undergo the content moderation process using the vector database. The entire video is then assigned the lowest propensity score from its keyframes.
By employing vector databases for content moderation, platforms like Social Media can maintain a safer environment, expedite the moderation process, and provide an enhanced user experience.
It's exciting to witness the diverse applications of vector databases, and I look forward to sharing more insights on their potential and our machine learning developments. Stay connected!
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