Join Mark and Allen, your favorite Two Voice Devs, as they explore the exciting (and sometimes frustrating!) world of Gemini 2.0's search grounding capabilities and how to use it with LangChainJS! Allen shares his recent holiday project: a deep dive into Google's latest AI tools, including the revamped search grounding feature, and how he made it work seamlessly across Gemini 1.5 and 2.0. We'll show you the code and demonstrate the differences between using search grounding and not, using real-world examples. Learn how to build your own powerful, grounded AI applications and stay ahead of the curve in the rapidly changing AI landscape!
In this episode, you'll discover:
[00:00:00] Introduction to Two Voice Devs and what we've been up to
[00:00:24] Allen discusses tackling bug fixes and updates with Gemini 2.0 and LangChain
[00:00:51] The new Gemini 2.0 Search Grounding Tool: what's new? What does it mean to be "agentic"?
[00:02:13] Allen dives into the Google Search Tool, understanding the differences between 1.5 and 2.0, and building a layer for easy use in LangChain
[00:03:06] Allen walks us through the code! The magic of setting up a model with or without search capabilities in LangChainJS
[00:04:48] Using output parsers and annotating your results in LangChainJS
[00:05:53] Similarities between Perplexity's results, and how LangChainJS handles output
[00:06:46] Running the same query with and without grounding, and the dramatic difference in the response (Who won the Nobel Prize for Physics in 2024?)
[00:08:26] A closer look at how LangChainJS presents its source references and how to use them in your projects.
[00:12:55] Taking advantage of tools that Google is providing
[00:13:20] The goal of keeping backward compatibility for developers
[00:15:39] Exploring how this is a version of RAG and how that compares to using external data sources
[00:16:50] What are data sources in VertexAI and how they relate?
[00:19:14] What is the cost? How is Google pricing the search capability?
[00:20:59] More to come soon from Allen with LangChainJS!
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