Building Beyond the Buzz: LLMs, Langchain, and Vertex AI

TL;DR

Learn how semantic search powered by LMs can provide relevant and accurate information based on user queries, leading to valuable insights and improved search experiences.

Transcript

I'm a customer engineer at Google and today I'm here with two intentions with this talk first intention is that I looked through the agenda and I saw well there's actually not all the sessions that go on gen and L so I'm here to sort of fulfill your needs for B Bingo a little bit um then the second intention is that I'm actually here to share some ... Read More

Key Insights

• 🙈 Semantic search powered by LMs can bridge the gap between technology and business value by providing accurate and relevant information to users.
• 🙈 LMs excel at structuring unstructured data and building a semantic understanding of knowledge databases, enabling applications like document summarization and fact extraction.
• 😫 By leveraging LMs, businesses can improve search experiences, gain valuable insights from large data sets, and enhance knowledge management processes.
• 🙈 Chunking and prompt engineering are important techniques for controlling and improving the output of LMs in semantic search applications.
• 🙈 Multishot fine-tuning and providing positive examples can significantly enhance the quality and performance of LMs in structured output tasks.

Questions & Answers

Q: What is the difference between traditional keyword search and semantic search?

Traditional keyword search matches documents based on the specified keywords in a query, while semantic search aims to understand the user's intent and provide documents or information that align with that intent. It goes beyond keyword matching and considers context and meaning.

Q: How can LMs be used to structure unstructured data?

LMs can take unstructured data, such as free text or images, and generate more structured outputs. For example, they can summarize long texts, extract key facts or information, and compare or analyze information in a more organized manner.

Q: What are the benefits of using semantic search in knowledge databases?

Semantic search allows for a deeper understanding of the context and connections within a knowledge database. It enables better search recommendations, targeted queries, and classifications, making it easier to find relevant information and improve knowledge management.

Q: How can businesses implement semantic search using LMs?

The speaker suggests using an architecture that consists of an ingestion layer for processing documents, an extraction layer for handling user queries, and a vector database for storing and searching document embeddings. This architecture enables the creation of a semantic agent that can navigate and interact with the knowledge base.

Summary & Key Takeaways

• The speaker, a customer engineer at Google, aims to fulfill the audience's need for information on semantic search and share experiences in bringing gen visions to life using Google technology.

• Semantic search goes beyond traditional keyword search by understanding the user's query and providing relevant documents or information that matches their intent.

• LMs excel at structuring unstructured data and building a semantic understanding of knowledge databases, making them valuable for tasks like document summarization and answering complex queries.