LangChain No-Code PDF Q&A in 2min | OpenAI & Pinecone

flowwise is the fastest and easiest way to build large language model apps with your own data on the fly. Let's see how we can do it. In this example we will show you how to upload a PDF file and ask a few questions and have the AI ​​answer for you first let me use the PDF file document uploader and upload a new AI paper and this PDF file is about a content from the AI ​​builder and we're going to ask some questions to see if we can get an answer next I'm going to hook up a text splitter node to the PDF uploader and this will basically split the PDF into different parts and hook it up to the vector database , which is Pinecone in this case, and this will store the data in Pine Point of course, you also need to include your AI Open API Keys and your Pine Cone API Key so you can get those Open Ai Keys and your Pinecone Dashboard .

The last part, I'm going to use a conversational retriever QA chain to finish the whole flow. and this change is used specifically for questions and answers like the one we're doing here. I'm just going to plug these puzzles in and put them in there, come on and we can just ask any question right now. Let's see what they are. the advantages of lrm what it does basically it will split the PDF file into different chunks, store it in the vector database and have a string to answer the question for you, as you can see the response is successfully returned to us apart from the PDF , we can also use other document uploaders like a web Scrapper, a CSV file text file and we also have a pre-built template so you can do all sorts of things you can do through lrms