- BigQuery, Google Cloud Platform, AI
- Big Query, Google Cloud
- 4 min reading time
Christopher Maier
In numerous industries, efficiency and precision are of paramount importance. Managing large volumes of project data, such as daily reports, logs, descriptions, material plans, and product data sheets, often presents a complex challenge. However, with AI and BigQuery, you can effortlessly interact with your documents and retrieve the information needed in real time to make informed decisions.
This wiki explains how these technologies can lead to significant improvements using an example from the construction industry. In addition, we present a prototype developed by us that uses an innovative document and chat interface with BigQuery as a vector database.
Table of contents
What is BigQuery?
BigQuery is Google's fully managed, serverless data warehouse that enables extremely fast SQL queries. Thanks to the powerful computing infrastructure of Google, BigQuery is ideal for processing large datasets and offers real-time analyses. This is particularly advantageous for the construction industry, which requires fast access to extensive project data.
The role of vector databases in the construction industry
Vector databases store data as high-dimensional vectors and are ideal for similarity searches. In the software, vector embeddings are used to represent the text of construction documents. This enables an efficient search for the most relevant information.
What is a vector database?
A vector database is a special type of database that stores data as vectors in a multidimensional space. These vectors are mathematical objects that have a direction and a magnitude. In this context, these are typically data points that have been converted into a numerical form by algorithms.
How do vector databases work?
Multidimensional spaces:
A multidimensional space contains not only the three classical dimensions (length, width, height) but many dimensions, sometimes hundreds or thousands. Each of these dimensions represents a specific characteristic of the data.
- Coordinates in this space: Each vector in this multidimensional space has a specific number of dimensions. The coordinates of a vector indicate exactly where a data point is located in this space. For example, a vector in a three-dimensional space has the coordinates (2, 3, 5). In a space with 100 dimensions, a vector can accordingly contain 100 such numbers.
Introduction to the context cache:
The context cache is a function that stores frequently accessed data so that it does not have to be reprocessed each time. This saves costs and improves response times.
Advantages of Context Caching:
- Cost reduction: Caching frequently used data lowers operating costs because fewer tokens need to be processed with each request.
- Reduced latency: Faster access to cached data improves user experience.
- Sustainability: Less computing power means less energy consumption and leads to more sustainable operations.
Application examples for document search:
Conclusion:
AI and BigQuery offer fast, precise, and scalable solutions for data analysis and information retrieval, which can significantly improve project management across various industries. Our prototype demonstrates how these technologies can be used to develop a powerful chat interface that efficiently accesses and processes relevant documents.
Interested? Contact us today to learn more about how your processes can be optimized through the use of AI and BigQuery.
Know more?
Would you like to delve deeper into this topic? Then we would be happy to talk to you personally about the possibilities of the Google Cloud Platform (GCP).
