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LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications. The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts. One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks. The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics. The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable. Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.

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Scripps Networks Interactive is a leading developer of engaging lifestyle content in the home, food and travel categories for television, the internet and emerging platforms. Their U.S. lifestyle portfolio comprises popular television and internet brands HGTV, DIY Network, Food Network, Cooking Channel, Travel Channel and Great American Country, which collectively engage more than 190 million consumers each month, and HGTV was among the top 10 cable networks in 2016. Managing the asset and instance-level metadata for Scripps' historical media portfolio is critical to their business. It affects everything from broadcast availability to viewing behavior to the ability to syndicate content to non-linear and international broadcast partners. In this session, the speakers will discuss: Applying graph technology to federate relevant metadata across multiple digital asset management systems into a cohesive multi-media library Leveraging the advantages of graph DB over traditional relational databases Facilitating modeling via graph technology of complex, multi-dimensional relationships between multiple domains (shows, seasons, episodes, formats & versions)

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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