How to Build an AI Document Chatbot in 10 Minutes ? |🦜️ 🔗LangFlow & Flowise

hello everyone so today I want to show you a platform which you can create a powerful application without writing a single line of a code this platform provides a drag and drop component which you can play around and connect components to build and Powerful applications this can be used by the developer to understand the workflow and create and prototype also it can be used for a non-coders to easily create without adding a single line of the code there are two platform I came across which is Lang flow and flowwise as flowise is built on top of the lag flow and has a similar interface so I will be showing you about Lang Syne today I can make a different video for and flowwise also in my coming next videos so stay tuned so to install the Lang flow first you need to get n terminal I'm using Ubuntu for now but you can use common prompt in a windows so first you need a python for that if you haven't installed a python you need to install it first then if you look at the repo of the link below a is an installation guide so we'll follow it to install lanflow we just need to do PP install link flow I have already installed the Lang flow so it said requirement already satisfied but it will take some time in your case some of you might come to an issue of an build fill in a Windows that's because of your C plus plus compiler to solve that problem you need to install a visual studio Big C plus plus otherwise you are good to go after you have successfully installed the link flow just type lag flow and there will be the IP address which is localhost opening at the Port 7860 you can follow the link to go to the UI of nlang flow the Lang flow is simple drag and drop interface so let's see some of the component we have an agents like CSV agent Json agent SQL agents and some chains like conversation chains La llm chains and also with Johnny promzin in The Lorax exam we can see CSV loader PDF loader text loader and web-based loader so if you have seen my previous video that all the component that we use to build our PDF and website chatbot so let's see a quick prototype for a chatbot to create a simple chat board like a chats apt we require and conversation chain so just drag drop here the conversation change required a llm the llm is basically large language model so the language model can be of GPT open AI or plan D5 as we have seen in my previous video as of now the lag flow doesn't support open source model or it does there are some models like Lama and other that you can install yourself and try it on so if you see in llms we can see C Transformer chat open Ai and open AI only the chat open AI is basically chargpt API and open AI is the older GB3 model like DaVinci you can select any one of it if you have an API key point so if I drag an open AI we can see a red button yeah this indicates if everything is configured correctly Now to create an conversation chain we just connect open AI with llm in a conversation change now if everything is done you might require an API key for this so for inference just click on this button now you can type anything here and get in response the link flow also provides API interface which you can use in production to see that you can select an code button above here then there are two apis for Python apis and for python code for an API you just need an API route here and the function that does something like this if you see here we just need an Json file that is loaded here to get this Json file you can just export from here giving any name you like after you are done with drag and drops everything then you can download the Json file of an lag flow and you are good to go I didn't have an open AI key as I just completed before making this video so I will not be able to test but it will work fine in your case so to create a PDF chat like in my previous video we require a text splitter to break our PDF into chunks PDF loader to load our PDF emittings to create emailings from the chunks and Vector store to store our embeddings the victory store here we got 4 5 chroma quadrant and wave 8. here you can use chroma or files as of now as in my previous video we have used files to do everything so we'll use it here now let's drop the component one by one 's done you need some copper of more in the previous example we got a conversation change but but it has only the condition of llms and memory but in our case we require something to interface our Vector so here instead of the conversation change we require an Vector store agent and to get the information about the vector we require backage store info you can get that from the toolkits down below so I forgot about an llms we can use open AI for now now let's connect the dots first we require an PDF loader as of now I have taken text loader for an example you can you replace the text loader to an Pi PDF loader to load and PDF the text loader is passed to an document where we speed the character of N Text the split rate takes are stored in our document after doing an embeddings so let's connect it to then twice with embeddings the files is a vectorate store so we require something to get the information about n5s in that case we have used Vector store info so let's get the information of n files through and practice store info we have an llms now weightage store isn't the battery store agent requires a vector store info and llms for llms we have used open AI so we'll connect this with open Ai llms and the js2 info in Vector store info that's all so you have built yourself a PDF chat board which you can talk to in the similar example if you want to talk to an website you just need to replace text loader with web-based loader and for npdf you require Pi PDF loader there are other loaders such as CSP loader Facebook Chat loader and no sun iot loaders also get book loaders so you can now play around with this to create your one prototype after you've done everything you can just export it in a Json format and use that file for your production or inference so that's all for this video thank you and if you like my video please like share and subscribe and have a good day

Channel: Whispering AI

Views: 4,243

Keywords: langchain, langflow, flowise, gpt, autogpt, tutorial, step by step, flowise ai, langchain flowise, langflow ai, langflow tutorial, how to install langflow, lang chain gradio, langchain demo, langchain tutorial, langchain explained, langchain hugging face, langchain chat gpt, chat models, create chart with llm, langchain cookbook, langchain qa documents, lang chain chroma, what is langchain, ui for langchain, langchain ui, user interface for langchain, ui flow for langchain

Id: C86-GnE0HPg

Length: 8min 21sec (501 seconds)

Published: Fri Jun 02 2023

Reddit Comments

[u/adlx]

Last time I tried Langflow it installed several gigas of dependencies, even it installed nvidia stuff and Cuda. Didn't make any sense to me that a UI /flow library would need all that. Did they solve that?