Automated Text-Generation Pipeline with Langchain, Large Language Models and Vector Stores

This paper investigates the application and effectiveness of Langchain, a framework designed to streamline the development of applications powered by large language models (LLMs). The study focuses on demonstrating Langchain's capabilities through its implementation in a text-generation pipeline. This pipeline integrates LLMs with vector stores to automate key processes such as text extraction, summarization, and question-answering. Through this practical application, the paper explores how Langchain facilitates the creation of a sophisticated system that provides users with concise, contextually relevant responses to their queries. The findings highlight Langchain's potential to simplify the development of complex LLM-based solutions for knowledge retrieval and text generation.