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Joy Maitra
เข้าร่วมเมื่อ 30 มี.ค. 2012
No Code RAG chatbot || Langflow RAG chatbot
Title: How to Build a RAG Application with Langflow & OpenAI ChatGPT - No Coding Needed!
Description: Are you ready to create powerful AI-driven applications without writing a single line of code? In this step-by-step video tutorial, I’ll show you exactly how to use Langflow and OpenAI’s ChatGPT to build a Retrieval-Augmented Generation (RAG) application with ease!
🔍 What is RAG?
RAG, or Retrieval-Augmented Generation, is a cutting-edge AI technique that combines retrieval-based systems and generative models like ChatGPT. It’s perfect for applications that need to pull in accurate information from external sources (like databases or websites) and then generate human-like responses.
🚀 Why Use Langflow and OpenAI ChatGPT?
Langflow is a powerful framework that simplifies the process of building RAG applications, allowing you to visually design workflows without any coding. With OpenAI’s ChatGPT model integrated, you can create intelligent apps capable of answering questions, providing summaries, or pulling in relevant information dynamically.
💡 In This Video, You’ll Learn:
How to set up Langflow and integrate it with OpenAI’s GPT models.
Step-by-step guidance on creating a RAG pipeline.
How to use Langflow’s drag-and-drop interface to structure your data retrieval process.
How to create seamless AI applications that combine data retrieval with powerful language generation.
Tips and tricks for enhancing your RAG application’s performance.
👨💻 Perfect for:
Developers and AI enthusiasts looking to build intelligent systems without the need for extensive programming.
Businesses that want to create custom AI-powered applications for knowledge management, customer service, or content generation.
Anyone interested in learning more about combining retrieval systems with generative AI.
🔧 Tools Used:
Langflow: A low-code platform for building RAG applications.
OpenAI ChatGPT: State-of-the-art language model used for generating responses.
No Coding Required: This tutorial is designed for beginners and professionals alike-no technical background needed!
🔎 Keywords:
RAG application, Retrieval-Augmented Generation, OpenAI ChatGPT, Langflow tutorial, no coding AI, build AI apps without coding, low-code AI, data retrieval with AI, AI for beginners, AI development without programming.
Don’t forget to Like, Subscribe, and hit the Notification Bell for more tutorials on AI, machine learning, and low-code solutions! Got questions or want to see more? Drop a comment below and I’ll be happy to help!
Description: Are you ready to create powerful AI-driven applications without writing a single line of code? In this step-by-step video tutorial, I’ll show you exactly how to use Langflow and OpenAI’s ChatGPT to build a Retrieval-Augmented Generation (RAG) application with ease!
🔍 What is RAG?
RAG, or Retrieval-Augmented Generation, is a cutting-edge AI technique that combines retrieval-based systems and generative models like ChatGPT. It’s perfect for applications that need to pull in accurate information from external sources (like databases or websites) and then generate human-like responses.
🚀 Why Use Langflow and OpenAI ChatGPT?
Langflow is a powerful framework that simplifies the process of building RAG applications, allowing you to visually design workflows without any coding. With OpenAI’s ChatGPT model integrated, you can create intelligent apps capable of answering questions, providing summaries, or pulling in relevant information dynamically.
💡 In This Video, You’ll Learn:
How to set up Langflow and integrate it with OpenAI’s GPT models.
Step-by-step guidance on creating a RAG pipeline.
How to use Langflow’s drag-and-drop interface to structure your data retrieval process.
How to create seamless AI applications that combine data retrieval with powerful language generation.
Tips and tricks for enhancing your RAG application’s performance.
👨💻 Perfect for:
Developers and AI enthusiasts looking to build intelligent systems without the need for extensive programming.
Businesses that want to create custom AI-powered applications for knowledge management, customer service, or content generation.
Anyone interested in learning more about combining retrieval systems with generative AI.
🔧 Tools Used:
Langflow: A low-code platform for building RAG applications.
OpenAI ChatGPT: State-of-the-art language model used for generating responses.
No Coding Required: This tutorial is designed for beginners and professionals alike-no technical background needed!
🔎 Keywords:
RAG application, Retrieval-Augmented Generation, OpenAI ChatGPT, Langflow tutorial, no coding AI, build AI apps without coding, low-code AI, data retrieval with AI, AI for beginners, AI development without programming.
Don’t forget to Like, Subscribe, and hit the Notification Bell for more tutorials on AI, machine learning, and low-code solutions! Got questions or want to see more? Drop a comment below and I’ll be happy to help!
มุมมอง: 148
วีดีโอ
Ollama - run any model locally || Run any model Locally
มุมมอง 113หลายเดือนก่อน
In this video I am assuming that by now viewers are already aware how to install Ollama. With Ollama you not necessarily need to serves only the models available in Ollama. I this we video I have tried to capture how to serve custom models through Ollama.
Super chatbot - talk to Multiple LLM || Ultimate Chatbot Comparison Tool
มุมมอง 2052 หลายเดือนก่อน
Welcome to our latest video where we dive deep into the world of Large Language Models (LLMs) and introduce you to an innovative tool that is changing the game for developers and AI enthusiasts alike. Our custom-built chatbot, designed using Streamlit and Python, is not just any chatbot. It's a powerful platform that allows you to interact with, compare, and analyze the responses from some of t...
No Code Generative AI application with Langflow || Langflow tutorial
มุมมอง 2304 หลายเดือนก่อน
Dive into our comprehensive Langflow tutorial and learn how to create a simple chatbot from scratch! Discover how Langflow's powerful tools can be further utilized for building advanced generative AI applications and RAG applications. Perfect for beginners and seasoned developers alike, this step-by-step guide will elevate your AI development skills. Watch now and start your journey with Langfl...
LangGraph create own LLM Agent || LangGrapg Multiagent
มุมมอง 2805 หลายเดือนก่อน
Delve deep into the realm of Lang graph, the groundbreaking framework designed to empower LLM agents! In our comprehensive video guide, we unravel the intricate functions and features of this versatile tool, providing a step-by-step exploration of its capabilities. From crafting linear chains to orchestrating decision-making processes, Lang graph offers a robust platform for building intelligen...
Chat with images and scanned pdf || Azure AI vision to chatbot
มุมมอง 2625 หลายเดือนก่อน
"Embark on a journey into the realm of AI-driven communication with ChatGPT 4 Vision! In this illuminating TH-cam video, we unveil the transformative capabilities of ChatGPT 4 Vision, enabling seamless conversations enriched with visual content. Dive deep into the world of image-based communication as we demonstrate how to effortlessly chat using images, revolutionizing the way you interact onl...
Document Summarization with OpenAI || 4 ways to summarize your local PDF
มุมมอง 2886 หลายเดือนก่อน
Unlock the power of document summarization with OpenAI API! 🚀 In this comprehensive guide, learn how to harness the potential of cutting-edge AI technology to create a document summarization app using Streamlit. Dive deep into four distinct techniques for summarization, empowering you to efficiently distill lengthy texts and PDFs into concise, actionable insights. Whether you're a developer, en...
RAG to SQL chatbot with streamlit || RAG use case || Data Analytics with LLM
มุมมอง 1.6K7 หลายเดือนก่อน
Unlock the full potential of your data analytics projects by learning how to efficiently generate SQL queries using the revolutionary Retrieval-Augmented Generation (RAG) technique. In this comprehensive tutorial, we delve into the practical applications of RAG for enhancing data retrieval and manipulation, making it an invaluable tool for data analysts, developers, and anyone looking to stream...
Data Engineering with large language model || Streamlit llm chatbot || Chat with private Data
มุมมอง 2768 หลายเดือนก่อน
In this video I have demonstrated a project on how to execute data engineering task with LLM. I have used Mistral AI for the LLM. Making use of the streamlit chatbot we can create a user interface to accept users input in simple english, convert the input into a python code and execute it on the data, uploaded by the user. Github link: github.com/JYTDemo/data-engineering-chatbot
Generate code with Microsoft Phi 2 | Data Engineering with LLM
มุมมอง 1818 หลายเดือนก่อน
"Unlock the power of Language Model for Code Generation! 🚀 In this tutorial, dive deep into the world of Large Language Models (LLM) and learn how to effortlessly generate code using advanced natural language processing. Whether you're a seasoned developer or a coding enthusiast, this step-by-step guide will walk you through the process, demystifying the magic behind LLM (Microsoft Phi 2). Acce...
4 ways to run LLM locally || How to run MPT-7B locally || Run StabilityAI 3B model locally
มุมมอง 3209 หลายเดือนก่อน
"Unlock the power of Large Language Models (LLMs) with my latest TH-cam video! 🚀 Learn 4 diverse and effective methods to run LLMs locally, empowering you with the flexibility and control you need. Whether you're a developer, researcher, or enthusiast, this step-by-step guide will demystify the process and enhance your LLM experience. Watch now to harness the full potential of these cutting-edg...
Vector Databases 101: Understanding the Basics and Beyond! || Memory for LLM
มุมมอง 1459 หลายเดือนก่อน
Certainly! Crafting a compelling SEO description is crucial for attracting viewers to your TH-cam video. Here's a suggestion for your vector database video: 🚀 Unlock the Power of Vector Databases! 🚀 Dive into the fundamentals of vector databases with [Your Name/Channel Name]. 🎥 Whether you're a beginner or looking to enhance your knowledge, this video breaks down the basics of vector databases,...
OpenAI RAG Chatbot | Chat with PDF locally
มุมมอง 8K10 หลายเดือนก่อน
🌐 Ready to elevate your chatbot game? Join us on a coding adventure as we show you how to build a Responsive and Adaptive Chatbot using the power of Python, Streamlit, Langchain, an open-source vector store database, and the incredible OpenAI API! 🚀 In this hands-on tutorial, we'll guide you through the process of creating a chatbot that not only understands natural language but adapts its resp...
Chatbot for local LLM with memory and serving API endpoint | Memory-Enhanced Chatbots for LLM
มุมมอง 37310 หลายเดือนก่อน
Dive into the buzz around Generative AI models in our latest video! 🌐 Explore the world of OpenSource models, where repositories are ablaze with innovation. 🔥 Hugging Face, the largest repo, boasts over 3k models waiting to be explored. In this tutorial, we guide you through the process of executing a Language Model (LLM) on your local machine, complete with a chat interface and advanced memory...
Chatbot using python || How to run ChatGPT locally || Chatbot for LLM with streamlit
มุมมอง 34810 หลายเดือนก่อน
Chatbot using python || How to run ChatGPT locally || Chatbot for LLM with streamlit
How to run LLM on google-colab | free GPU for LLM | Google colab setup for LLM
มุมมอง 6K11 หลายเดือนก่อน
How to run LLM on google-colab | free GPU for LLM | Google colab setup for LLM
How to run a llm locally | Run Mistral 7B on local machine | generate code using LLM
มุมมอง 4.5K11 หลายเดือนก่อน
How to run a llm locally | Run Mistral 7B on local machine | generate code using LLM
GenAI Comparison | Mistral 7B vs CodeLLama vs Zephyr
มุมมอง 485ปีที่แล้ว
GenAI Comparison | Mistral 7B vs CodeLLama vs Zephyr
Sahayak Demo || Tool to make sql query on Excel || OpenSource Tool
มุมมอง 302 ปีที่แล้ว
Sahayak Demo || Tool to make sql query on Excel || OpenSource Tool
Use case - Blockchain in Media and entertainment industry||Blockchain simply explained
มุมมอง 6256 ปีที่แล้ว
Use case - Blockchain in Media and entertainment industry||Blockchain simply explained
Voice controlled Home Automation|| raspberry pi || ESP8266|| Python
มุมมอง 1.1K6 ปีที่แล้ว
Voice controlled Home Automation|| raspberry pi || ESP8266|| Python
I forgot to add this, I also tried running open llms on colab with GPU but again the response time is horrible.
Hi sir, very deatiled explanation, how about the data privacy sir, if I use open AI? We are trying to build custom chatbot with open llms but as you said, the response is very pathetic. Does the performance improve if we run it on GPU instance ? Here the major concern is data privacy, so hesitant to use Azure open AI. May I know your suggestions on our usecase ? Thank you.
Thank you for commenting!! I do understand ur concern. Well from experience what I see is major companies are trusting Azure open ai with their data. MS promises not to use the enterprise data for traing the model. Also there are platforms like secure GPT for additional security. Regarding open source models that runs in the local system ae not good enough. However LLama 70B seems to give good results, I access it through Groq.
This is good, Joy. But, can we have something like ChatAll, or GodMode browser? ChatAll and Godmode have a little cluttered UI, and I cannot use live online models like ChatGPT, Claude 3.5, Gemini, etc. in conjunction with local offline ollama models, or models from llmstudio. Thanks!
That's a great idea, thanks for sharing, will try to create something on that line soon !
@@joymaitra5414 will watch out for an update!
you have not attached any link with it
does it handle large amount of data?? like more that 3L data
Thanks for commenting, in this approach we are not using LLM to deal with data, we are generating the query only.
Super Great Explanation, and easy to Understandable Thank you lot !
Thank you !!
Hello sir, can you share the codes?
The code is there in the GitHub, link is provided in the description of the video
Code is there in the GitHub link provided in the description of the vodeo
Please provide pre-requisite installation otherwise as a beginner no one can recreate.
Thanks for commenting, as for pre requisite, to have python is required and VS code or Jupyter notebook is required for coding.
I've tried your code and I uploaded my own csv file however its only showing the dataframe whenever I ask any question. It doesnt compute or anything.
Hi thanks for reaching out, however the application will show the dataframe only but the changes are supposed to be in the dataframe, like if the date format is changed, also you can check the CSV generated in the backend.
There is some background noise
Thanks I will try to take care of it in the following videos. Let me know if you are not able to understand any part.
Hey, great video, thanks to you I got it working. Is there a possibility to change the code, so that not the first modelfile is downloaded? I want to download Q4 Mistral for examble but the code gives Q2. I am pretty new to this, sorry if this is a silly question.
Thank you for reaching out !! Good question it is. In another video I have shared different ways to load the models, with Llama_cpp you can manually download the model of your choice and load that for inference.
can you attach notebook?
It's available in GitHub link mentioned in the description
Thank you sir , have you used open source LLMS such as Claude 3 , Llama 3 or Mistral ? what do you think of a fine tuned model ? some companies don't like closed source models (security ...)
Thank you for commenting ! I have tried to do it with Llama 2 and Mistral , well with 7B models, the response was not so good. However, I think with the higher parameter model, it must be possible.
@@joymaitra5414 Thanks for the response 🙂
Hi, I am getting error ModuleNotFoundError: No module named 'langchain.chat_models' please help
Try uploading langchain by pip install --upgrade langchain
Thanks Joy. Once the vector DB is loaded with schema and if we have some sample questions and corresponding SQLs how can we add it to the vector DB for better results ? Also does the vector DB keeps updating itself with more interaction or how can we improve by manually adding more questions and SQLs?
Thanks for reaching out ! You can update the metadata of the records in vectordb. I have another video on Vector DB, that might be of help. Vector DBs don't have any intelligence to update itself. But we can build logics around it to handle it
does it work with image based pdf? and if doesn't how can i make one? please suggest
Thanks for reaching out!! It does not work for image based pdf, for that chatGPT vision is required. And have to pass the pages of the pdf as image in the prompt. I am making a video on that will share soon. Request you to stay connected!!
Thanks. This will help me !
Glad to hear that!
Thank you, Joy. I am a new learner on RAG. Would you mind show us a demo on using module with OpenAI (not AzureChatOpenAI) and use MySQL (with full database tables provided)? I hope can follow with you to build up the chatbot step by step successfully. Thanks!
I think the same steps will work, only instead of the sqlite make the connection to MySQL (pythonic way) and change the API key for OpenAI.
Hey Joy, Can you plz help how can we use input as book cover image and find similar book search with the same code , also provide the code for the same , dataset??
Hi Neha, thanks for commenting, but this approach will definitely not work for your requirement, this implementation is for textual inputs, you would need a multi modal LLM to interpret the image input. I will try to cover that in one of my future videos.
Can you plz provide the pdf of document that you used
Not able to get that point, from where i can get the pdf for book recommendation chat bot
You can use any pdf document, with text in it, I have used a blog post, saved as pdf.
Thank you so much, this explanation is great! This really help me a lot but, i'm stuck at adding my own gguf models to my project. Like when i'm trying to add it my code didn't detect it and downloaded the other version of the model id. Can i download the models manually from Hugging Face than downloading it from the script? Because the file i downloaded from the script, is not even a gguf file or any type of that.
Yes, that is absolutely possible, you can manually download and provide to the local file path in LlamaCpp, for CTransformers also the same is possible
@@joymaitra5414 Thank you again for helping and assisting me!
Darun session
Thank you !!
Thanks for the video, does this work for multiple documents as well with good accuracy?
Yes it works with multiple documents, accuracy is quite good
*Promosm* 😓
Can we make this as conversational?
We can, I have made another video on how to create chatbots with memory.
THIS IS amazing...
Thank you!!
I see you have some good use cases. I've been working on similar projects. How about we have a little chat and exchange notes?
Sure, you can reach out on email or LinkedIn
This was a very very veryyyyy interesting video. Please continue this
Thank you !!
Hello I am having few queries how can I reach you out please help me
You can email me
how to make api of model
I have shared, how to create API of models in my video, you may find it helpful : th-cam.com/video/-TtE7otMHCc/w-d-xo.html
Hello, thank you so much for this video. i have a question related of summarize questions in LLM documents. For example in vector database with thousands documents with date property, and i want ask the model how much document i received in the last week?
I guess we don't need LLM for such a query, we can query the vector DB, and for how to do that ? You can watch my video on Vector DB.
@@joymaitra5414 , thank you for the beedback, but the questions are illimited, the user can ask direct questions like "what is the latest document i have?" or he can ask "how many documents i didn't read ?", i don't know how can convert this question to vectorDB query.
may be you can add the file properties as metadata attributes in the vectorDB and then make use of that information to answer the query.
Hey, please add your contact email to TH-cam. I have a project you will be interested in.
Hey good video. I am trying to recreate this, created the .env all of that is situated, however when I run ui.py using streamlit, getting error AttributeError: module 'openai' has no attribute 'error'. Can you help??
Thanks for reaching out. If you are not using azure OpenAI, there can be some errors due to libraries.
@allantauro3606 hey bro have you recreated it?
@@joymaitra5414 Thanks, Joy. But if I just have API key from normal OpenAI, how can I adjust the program to fix the module problem?
I have two queries. It would be helpful if you can assist in that. 1. Template which you are using for prompt , in the first line you have provided f string. But however in the next lines you are not giving where you have {context}, {question}. So wouldn't that make it a string rather taking context and questions as variable? Or the model will understand that? 2. My second question is that I have tested the code. It is working for most part except Chat history. Even in the same session, it is not able to remember the previous questions. It is getting updated in the chat history though. But when u explicitly ask what is my previous question or ask like explain more on previous question, it is not taking the chat history rather giving halucinated questions or says it doesnt know previous question. Kindly clarify on the above two questions
Hi, Thanks for sharing the queries, for the 1st query, you are right, need to fix that. But for the 2nd question, this demo I have shown only the chatbot, while in the other view I have demonstrated how to utilize the memory in llm. Let me know, if you have further queries. th-cam.com/video/-TtE7otMHCc/w-d-xo.html
Thank you sir for this informative video
Happy to help
Sir, thanks for the video, I just want to ask from where I could learn all these libraries and their use.
Well I find the content spread all across the internet, trying to bring them together in one place. You can share your particular queries in my channel community, I will try to share my thoughts there.
thx the video! sorry my question but im a beginner in this code. What files I see github I tried to run on Codespace with no success. Is there any other file, enviroment info I should have to try and run your RAG app? Could you pls help me regarding this as a beginner?
You need to add the openAI api key as environment variable.
hey this is really useful.can you explain how to extract text from pdfs for indic langugaes.these like to extract hindi languages we are not able to extract with regular pypdf or ocr techniques.kindly help with some codes.
Thanks for your comment!! I would definitely try to cover that in my next video !!
'promosm'
Sir can you please upload the same video for open source LLM Model- MPT 7B
Sure I will try to cover in my nxt video
I have added a video which contains how to run MPT-7B locally, let me know if that helps.
Please, Upload the same video for LLM Model - MPT 7B.
Sure, I will cover it in my nxt video.
I have added a video which contains how to run MPT-7B locally, let me know if that helps.
Sir, what type of computer do you recommend for running local GPT’s? I have PrivateGPT, but it’s painfully slow on replies.
On local system with 16GB RAM and i5 processor I find the response slow bt ok, anything less than that, I hear you, it's pathetic !
Great job bro I was assigned this task and I was struggling a lot but your tutorial helped me a lot
Happy to hear, that it was helpful...
Great one! Can you make a tutorial on, how can we finetune our custom dataset locally and use that finetuned model for getting domain-specific results locally?
I will certainly try that....
Guru Bhalo Likhechish, I hope you know who I am. Don't disclose 😜. Ami Finally toke subscribe korlam. Keep going.
Thank you !!
github link is not working
Fixed it now, Thanks for letting me know !
thanks@@joymaitra5414
Why not share google colab link itself, as Github link is giving 404
Fixed it now !!
Wonderful!!!
Thank you !!
Can you please redo the Github link?
I have put it in the video description, is it not working?
@@joymaitra5414 not working
@@joymaitra5414 returns 404. the link is broken
The GitHub link is giving 404 not found. @@joymaitra5414
fixed now !!
This is awesome! 👍
Thank you Prashant !!