Weaviate + LangChain for LLM apps presented by Erika Cardenas

We are super excited to publish this overview of how LangChain and Weaviate work together from Erika Cardenas! Erika explains some of the key concepts behind this technology such as Sequential Chains, CombineDocuments, and Tool Use such as Weaviate! Erika also explains how these tools help overcome limitations of LLMs such as Hallucination and Limited Input Context. Erika then presents the ChatVectorDB chain and the code needed to build your own chatbot, demoing it by chatting with the Weaviate Podcast transcripts! We really hope you enjoy the video and are more than happy to help you build these kind of applications! The fact verification chain is sourced from this example made by jagilley - https://github.com/jagilley/fact-checker. Please leave a star on the repo! Check out the LangChain docs here - https://langchain.readthedocs.io/en/latest/. First time using Weaviate? Check out our Quickstart guid here - https://weaviate.io/developers/weaviate/quickstart. Check out the Weaviate Podcast Search Repository (with Data) here - https://github.com/weaviate/weaviate-podcast-search Thank you so much for watching! Chapters 0:08 What is LangChain? 0:40 Outline 1:44 Sequential Chains 2:18 Fact Verification Example 4:08 CombineDocuments 4:40 Stuffing 4:52 Map Reduce 5:36 Refine 6:12 Map Rerank 6:40 Tool Use 7:10 ChatVectorDB 8:36 Demo!!