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ClarisTalk

In this episode of Claris Talk AI, hosts Cris Ippolite and Matt Navarre delve into the concept of vector databases and their importance in semantic search and chatbot functionality. They discuss the process of converting words into numbers through embeddings, using specifically trained language models called embeddings models. The hosts explain how these models extract features from words and plot them in a multi-dimensional space, where the distance between the plotted points indicates their similarity. Podcast custom GPT: https://chatgpt.com/g/g-iAiq4Ryjv-cl-... Cris and Matt also explore the potential for integrating semantic search capabilities into FileMaker databases, either by storing vectors in a separate database or potentially within FileMaker itself using container fields. They touch on the importance of re-ranking search results based on semantic distances and the possibilities this could bring to FileMaker developers. The conversation also covers the rapid advancements in AI and the need for regular discussions to stay informed. Cris shares his experience using the Gemini 1.5 Pro model from Google, which offers a 1 million token context window, enabling users to analyze large amounts of data, such as court transcripts or even entire movies, for various purposes. SHOW NOTES: Book recommendation: "AI for Good: Applications in Sustainability, Humanitarian Action and Health" by various PhDs Libby app for accessing audiobooks from local libraries Upcoming FileMaker conferences: Rome FileMaker Week (October 7-13), Engage EU, dot FMP Berlin, and Reconnect in Brisbane (September) Gemini 1.5 Pro model from Google, offering a 1 million token context window, now generally available Cris Ippolite will be participating in a Claris AI webinar on April 10 TIMESTAMPS: 00:00 Introduction and overview of the episode 01:28 The importance of semantic search and its relevance to Claris FileMaker developers 02:33 Matt Navarre's FRM Search Results tool and plans for a semantic search update 07:02 Lexical searches vs. semantic searches 10:25 The process of converting words into numbers through embeddings 16:12 Plotting words in a multi-dimensional space to determine similarity 20:23 Semantic search in chatbots and matching customer needs with business solutions 21:47 Storing vectors in a separate database or within FileMaker using container fields 26:51 Matt Navarre's love for JSON and its versatility in FileMaker development 29:38 Upcoming FileMaker conferences and guest appearances on the podcast 37:00 The rapid advancements in AI and the need for regular discussions to stay informed 43:51 Book recommendation: "AI for Good" and the importance of educating oneself about AI technology 48:48 A Wheel of Fortune contestant solves a puzzle with just one letter, stumping AI models 52:53 Gemini 1.5 Pro model from Google and its 1 million token context window 58:58 Analyzing movies and TV shows using the Gemini 1.5 Pro model for various purposes Follow Cris:    / @isolutionsai     / crisippolite     / isolutionsai   https://www.isolutionsai.com –––– Previous Episodes: Gabriel Woodger:    • Bonus Episode: Gabriel Woodger talks with ...   Ernest Koe & Joris Aarts:    • Special Guests Ernest Koe and Joris Aarts   Context: It's not what you think:    • Context: It's not what you think.   Prompt Templates:    • ClarisTalk Episode 8... and Claris Talk AI...