In this journey, I explore automating PDF autofill using LangChain and LangFlow. Leveraging LangFlow and OpenAI, I streamline the employment form completion process, demonstrating steps to install LangFlow and set up a PostgreSQL table. Despite encountering challenges in prototyping with LangFlow, the exploration progresses to auto-fill PDFs. After extracting form fields and LLaMA model setup, I employ LangChain to fetch PostgreSQL data. Concluding with Python manipulation to interpolate and update the PDF, the process achieves seamless auto-fill. Dive into the details, overcome challenges, and witness the power of LangChain and LangFlow in revolutionizing PDF automation.

In this post, I extend the use case from my previous post to demonstrate building a ChatBot for PDF files using LangChain. In the preparation phase, I install Chroma, an open-source embedding database, and ingest a PDF file using PyPDFLoader. I then split the document into chunks and use Chroma's default embeddings. Due to a potential issue, I provide an alternative embedding approach. Next, I load a local LLaMA model, prepare for question-answering, and run queries using RetrievalQAWithSourcesChain. I also touch on running with OpenBLAS for optimization. The guide empowers users to explore personalized question-answering over their PDF documents.