Introduction
If you’re looking to transform the way you interact with unstructured data, you’ve come to the right place! In this blog, you’ll discover how the exciting field of Generative AI, specifically tools like Vector Search and large language models (LLMs), are revolutionizing search capabilities.
You will learn the power of vector search and additionally, you will explore techniques for rapid ingestion of unstructured data, such as web pages, to enhance your search and chat systems efficiently.
A typical conversational search solution for querying public pages involves the following steps:
- Crawl and load web pages content: Extract and organize web page content for further processing.
- Create document chunks and vector embeddings: Divide web page content into smaller segments and generate vector representations of each chunk.
- Store document chunks and embeddings in a secure location: Securely store text chunks and vector embeddings for efficient retrieval.
- Build a vector search index to store the embeddings for later querying: Construct a Vector Search index to efficiently search and retrieve vector embeddings based on similarity.
- Continuously update the vector search index with new page contents: Regularly update the index with new web page content to maintain relevance.
- Perform search queries on the vector search index to retrieve relevant web page content: Leverage the vector search index to identify and retrieve relevant web page content in response to search queries.
Maintaining the data ingestion process over time can be daunting, especially when dealing with thousands of web pages.
Fear not, we’ve got your back.
Our solution
We streamline the data ingestion process, making it effortless to deploy a conversational search solution that draws insights from the specified webpages. Our approach leverages a combination of Google Cloud products, including Vertex AI Vector Search, Vertex AI Text Embedding Model, Cloud Storage, Cloud Run, and Cloud Logging.
Benefits:
- Easy deployment: Guided steps ensure seamless integration into your Google Cloud project.
- Flexible configuration: Customize project, region, index-prefix, index, and endpoint names to suit your needs.
- Real-time monitoring: Cloud Logging provides comprehensive visibility into the data ingestion pipeline.
- Scalable storage: Cloud Storage securely stores text chunks and embeddings for efficient retrieval.
