4 Building a research summarization engine

 

This chapter covers

  • Explaining what a research summarization engine is.
  • Organizing core functionality, such as web searching and scraping functions.
  • Using prompt engineering for creating web searches and summarizing results.
  • Structuring the process into individual LangChain chains.
  • Integrating various sub-chains into a comprehensive main chain.
  • Applying advanced LCEL features for parallel processing.

Building on the content summarization techniques from chapter 3, this chapter will guide you through creating a research summarization engine. This LLM application will process user queries, perform web searches, and compile a comprehensive summary of the findings. We'll develop this project step by step, starting with the basics and gradually increasing in complexity. Along the way, you will deepen your knowledge of LangChain as I introduce creating LLM chains with LangChain Expression Language (LCEL).

4.1 Overview of a research summarization engine

4.2 Setting up the project

4.3 Implementing the core functionality

4.3.1 Implementing web searching

4.3.2 Implementing web scraping

4.3.3 Instantiating the LLM client

4.3.4 JSON to Python object converter

4.4 Enhancing the architecture with query rewriting

4.5 Prompt engineering

4.5.1 Crafting Web Search Prompts

4.5.2 Crafting Summarization Prompts

4.5.3 Research Report prompt

4.6 Initial implementation

4.6.1 Importing Functions and Prompt Templates

4.6.2 Setting constants and input variables

4.6.3 Instantiating the LLM client

4.6.4 Generating the web searches and collecting the results

4.6.5 Scraping the web results

4.6.6 Summarizing the web results

4.6.7 Generating the research report

4.7 Reimplementing the research summary engine in LCEL

4.7.1 Assistant Instructions chain

4.7.2 Web Searches chain

4.7.3 Search and Summarization chain

4.7.4 Web Research chain

4.8 Summary