Meng Lin's Byte-Wise Words

  With LLM 🤗 and LangChain 🦜️🔗, the possibilities are endless. Let me show you what LangChain is capable of and provide some examples. I hope this inspires you to build great tools and businesses. Bear in mind, no one knows your domain better than you do, but with AI, together we can create truly remarkable applications that add value to the world.  📝 Summarization: This is probably the most common use of LangChain. It can generate summaries of text documents like news articles, research papers, or legal documents. Example: github.com/summarizepaper/summarizepaper ❓ Question Answering over Docs: Use LangChain to create question-answering systems that provide answers to factual questions on various topics like weather, current events, historical figures, etc. Example: github.com/mlin6436/chatpdf 🔍 Evaluation: LangChain offers metrics to assess the accuracy, fluency, informativeness and identity the strengths and weaknesses of LangChain applications. Example: github.com/lan...

Are you ready to unlock the full potential of ChatGPT and revolutionise how your organisation understands and interacts with your data? Learn how LangChain uses ChatGPT to make querying external resources a breeze, whether it's PDFs, emails, Confluence, and so much more! Following my last post on talking with a PDF file https://lnkd.in/eim2TFkg, here is a deeper dive into how to utilise LangChain to build your own personalised context-aware chat bot! Here's how it works: First, LangChain processes your PDF file, transforming it into a structured text format, so that it can be chunked as PDF files tend to be large, and have the embeddings generated. Next, you input your query using everyday language, such as asking a question or describing what you're looking for. LangChain then uses similarity search algorithms to compare and provide results based on how closely related your query and the content extracted from the PDF. It's like having a smart assistant that understand...