7 Advanced applications with Langchain.js

 

This chapter covers

  • Building a document summarization app
  • Implementing Retrieval-Augmented Generation (RAG)
  • Providing grounding support for AI outputs
  • Processing documents with advanced techniques

This chapter explores two sophisticated generative AI web applications that demonstrate the use of the tools and techniques that we've explored so far.

Our first project focuses on building a document summarization web application. We will build a web application capable of handling 2 document formats (PDFs and DOCX) implementing advanced semantic chunking strategies, and generating meaningful summaries. The application will also demonstrate techniques for overcoming some limitations of document summarization such as prompt compression and k-means clustering to improve context retention and summary quality.

7.1 Building a document summarization web application with LangChain.js

7.1.1 Architecture and workflow

7.1.2 Document summarization web application

7.1.3 Caveats and limitations of document summarization

7.1.4 Demonstration of the app

7.1.5 Additional considerations for summarizing documents

7.2 Building a RAG web application with LangChain.js

7.2.1 Key architectural components of RAG

7.2.2 Technical architecture overview

7.2.3 RAG system components

7.2.4 Web app demonstration

7.2.5 Adding grounding support

7.2.6 Conclusion

7.3 Summary