With AI technology becoming more prevalent in the business industry, everyone is trying to find ways to apply it to their industry in order to develop new features or to build upon and improve existing ones.
One feature of particular note is vector embedding, a method of converting existing forms of data like images or words into their numerical equivalents without having them lose their initial ties and meaning.
But how can this be applied to help businesses? That is what we are here to find out.
What Is Vector Embedding?
Vector embedding, as mentioned above, is a way to turn existing data into vectors that AI technology can process better in order to produce better and more accurate results and to gain the ability of providing an answer through “context clues”, in a sense.
This allows users to search for queries that are loosely tied around their input, perform comparisons between two examples and even create whole knowledge bases that can later serve as a basis for training new AI models on.
Its current use is that of an essential tool often found in search engines that offer image searching as an example, but have a more widely applicable use for enabling AI models to better understand the data that they are being trained on.
How Bicom Systems Plans To Use This Method
Our product offer, in particular, is quite robust, with a lot of features behind it that may leave some people overwhelmed when trying to take it all in, especially for new users who may have a hard time navigating our documentation and general knowledge base.
That is why our teams are hard at work trying to apply the vector embedding method and shaping it into a versatile search feature that can help people find what they need from different databases, be it our documentation, gloCOM chat history or similar, all done through the help of a customer-safe AI model.
We are currently looking into open-source AI models that can provide a desirable level of result quality without putting too much strain on resources while respecting customer data privacy.
In time, this process should allow us to build up a rich and detailed knowledge base of our entire product suite which should enable our users to find what they need a lot easier.
How It Is Implemented
This may sound complex, but the method of implementation can be visualized through a few simple steps:
1. Data Retrieval: Fetching the relevant data.
2. Data Preprocessing: Chunking the data.
3. AI Model Integration: Utilizing an AI model for vector embedding.
4. Vectorization: Transforming data into vectors.
5. Vector Database Integration: Storing the vectors in a dedicated database.
The search process is even more intuitive, only utilizing 3 steps:
1. User Query: Inputting a search query
2. Vector Database Query: Retrieving results from the vector database based on number of matching vectors
3. Result Penetration: Displaying results ordered by vector similarity
Privacy Concerns
The concept itself is great enough and should help reduce the tedium of trying to find a specific feature through screenshots and semantic clues and we wish for this to have a wider application for all users of the Bicom Systems product suite.
That wish, however, does come with a few challenges considering the robust clientele that our partners handle, especially ones that hold privacy to a very high degree like hospitals, banks, law enforcement agencies and more.
They cannot exactly feed confidential information to this database without breaching some rather heavy laws, a hurdle that one of OpenAIs base vector embedding models faces.
We, however, believe that we have managed to avoid this hurdle by providing a tool with a similar function, but one that can be set up in a way where it works with custom sets of privacy rules that these institutions have to follow, making it a more favorable solution in the open market.
Going Forward With Vector Embedding Technology
The technology behind vector embedding feels like it has a lot of untapped potential waiting to be discovered and utilized to further improve business workflow in different aspects.
As we devised this technology through the use of existing open source sentence transformers from SBERT in an offline setting, we had uncovered a few potential use cases later down the line as well as received a surprising compatibility on our AI model with multiple languages without lowering search result quality too drastically.
These tests have given us a pipeline to work toward, or at the very least a few ideas that may prove fruitful, like:
• Further search optimization and refinement
• Utilizing other open source models in an offline setting to enhance result quality and feed the knowledge base further
• Using existing and future research to construct an offline vector embedding model of our own.
• Applying the gathered knowledge and tools to repurpose the AI as an advanced search engine within gloCOM for optimal results.
Though, currently we do not have a particular focus on which of these to pursue further without delving into more research, we do have a few ideas in mind and are waiting on partner input before making our decision on how best to tackle this monumental task.
If this has piqued your interest or if you would simply like to be part of these conversations, feel free to give us a call if you have any inquiries. Our teams will gladly answer any of your questions.