In today’s data-driven landscape, businesses are constantly challenged to draw meaning from the ocean of data. Whether it is customer feedback, social media interactions, or product reviews, there are literally thousands of forms in which this data can manifest itself.
With this vast volume of information, traditional keyword-based search approaches are usually not that effective, especially if the data is increasing at a rapid pace. This is because they largely rely on “exact match” methods, which often results in businesses missing the real opportunity to comprehend customers’ sentiments and preferences.
This lack of a stronger, more eloquent search mechanism creates the need for a better solution that can seamlessly handle the complexities of natural language as well as data processing. This is where the Vector search technology is a viable solution.
What is Vector Search?
Vector search is a method to transform data —both unstructured and structured—like text, images, and audio into numerical forms called “vectors” that capture semantic meaning, allowing businesses to carry out more advanced searches to obtain richer insights. The fully managed, serverless data warehouse, Google BigQuery offers a scalable platform to deploy vector search, enabling organizations to manage extremely large data sets efficiently.
Need For Vector Search?
Vector search helps in addressing common data-related problems in business, including:
Information Overload
Businesses generate an immense volume of data. Sifting through this manually is very time-consuming and inefficient.
Inefficient Search
The traditional search methods depend heavily on keyword-based exact matches which are more than likely to miss insights especially when the wording is different, or synonyms are used in customer feedback/support questions.
Contextual Awareness
The context and subtlety of words pertaining to customer feedback or support queries can be hard to understand, hampering insights.
How Vector Search works?
Embedding
The text is converted into vector embeddings using techniques like Word2Vec, GloVe or even advanced models like BERT or Sentence Transformers. Each piece of text represents a high-dimensional vector.
Indexing
After all these representations, the vectors are indexed in such a way that the search is effective and retrieval is easy.
Similarity Search
At the time of query, the text of the query is converted to a vector as well. The system looks for the closest vectors in the index and returns results that are semantically similar, even when those results do not share the same keywords.
Implementing Vector Search in Google BigQuery
Step 1: Preparation
Gather the data you would like to analyze. It can be a group of customer reviews, a support ticket, or any other text relevant to your use case.
Step 2: Creating Embeddings
You can now use the AI Platform in Google Cloud to do vector embeddings. For instance, you can plug in pre-trained models such as BERT through TensorFlow or PyTorch, which are likely to provide higher-quality embeddings.
Step 3: BigQuery – Storing your Embeddings
When you have your embeddings, you can store them in BigQuery. Set up a table that includes the original text as well as its vector representation.
Step 4: Vector Search
You can use BigQuery’s support for approximate nearest neighbor search to perform a vector search and use SQL queries to find the vectors that are most similar to your query vector.
Conclusion
Advanced techniques used to understand the semantic meaning of text allow organizations to gain deeper insights for better decision-making. As the landscape of data evolves, embracing technologies such as vector search will be important in staying ahead of the curve.ty for each aspect, improving responsiveness.