Summary of Riccardo Carlesso's Talk on News Crawler via Langchain.RB and Gemini APIs

In this presentation, Riccardo Carlesso, Developer Advocate at Google Cloud, discusses a project involving a news crawler built with Langchain.RB and the Gemini APIs. The talk emphasizes how to keep Large Language Models (LLMs) updated with today’s news, addressing the inherent challenges of LLMs, such as outdated data and hallucinations. Carlesso demonstrates a solution that not only retrieves current news articles but also analyzes and summarizes them by topic.

Key Points Discussed:

• Introduction to LLMs and Their Limitations:

  • LLMs traditionally excel at processing historical data but struggle with real-time updates. Carlesso highlights the transition from traditional data input methods to those that leverage real-time crawling for current news.

• Embeddings Concept Explained:

  • Carlesso explains embeddings as vector representations of data that allow for semantic searching. He presents examples of embedding usage in comparing textual items and improves search accuracy in the context of a Ruby application.

• Implementation of News Crawler:

  • The news crawler is implemented by aggregating articles from multiple RSS feeds. Carlesso showcases the simplicity of embedding the article titles and describes the integration with a vector database (using PGVector) for similarity searches.

• RAG vs. Function Calling:

  • The presentation contrasts traditional Retrieval-Augmented Generation (RAG) methods with new function calling capabilities. RAG has been widely adopted, but Carlesso argues that it is becoming outdated, while function calling presents a more efficient means to interact with LLMs by dynamically fetching and injecting up-to-date information into models.

• Live Demos:

  • Carlesso shares active demonstrations of the tool's functionality, showing how users query articles related to specific topics and retrieve summaries. He explains the retrieval of specific articles based on user queries, including parameters for filtering articles.

• Code Integration and Real-time Flexibility:

  • He discusses the integration of Ruby code with the LLM through tools that communicate via JSON, and how simple adjustments can yield significant changes in tool performance or output.

Main Takeaways:

• Carlesso urges developers to think about utilizing current technologies to build applications that stay relevant and efficient.
• The seamless integration and function calling capabilities enhance the usability of LLMs, broadening their application in real-time scenarios.
• He highlights the potential for scalability and customization in news retrieval applications, allowing developers to tailor responses based on their unique needs.

This talk ultimately illustrates an innovative approach to keeping AI systems updated with relevant information by combining existing technologies in a new framework.