Build a Powerful PDF Q&A RAG System with Langflow, OpenAI & Chroma DB! (No Code/Low Code Tutorial)

Рет қаралды 1,311

Күн бұрын

Welcome to Generative Geek! In this video, I'll walk you through how to build a powerful PDF-based Question and Answer (Q&A) RAG (Retrieval Augmented Generation) system using Langflow, OpenAI, and Chroma DB, all without writing any advanced code. Langflow offers a no-code/low-code solution that simplifies the process of creating complex LangChain-based LLM applications with an intuitive drag-and-drop interface.
What You'll Learn:
- Introduction to Langflow: Discover the capabilities of this no-code platform.
- Setting Up Your Environment: Quick guide to get you started with Langflow, OpenAI, and Chroma DB.
- Building the PDF Q&A RAG System: Step-by-step tutorial on how to use Langflow to create a robust Q&A RAG system.
- Integrating OpenAI: Learn how to harness the power of OpenAI for intelligent Q&A.
- Using Chroma DB: Understand how to leverage Chroma DB for efficient data management.
Chapters:
(00:00) Introduction
(02:20) Installing Langflow
(05:15) Error fix
(06:50) Quick UI walkthrough of Langflow
(08:46) File reader Component
(09:07) Text Splitter Component
(09:45) Connecting two components
(10:15) Chroma Vector Store component
(11:15) OpenAI Embeddings Component
(11:40) Creating a Global Variable in Langflow
(13:50) Adding various input components
(15:00) Building the Prompt Template
(17:00) Adding memory to Chatbot
(19:30) Adding Chroma Vector Search
(22:50) Adding OpenAI Component
(24:50) Playground and Debug
Why Watch This Video?
- No Code/Low Code: Perfect for beginners and non-programmers.
- Comprehensive Tutorial: Detailed instructions to guide you through each step.
- Quick Learning: Build your RAG application in less than 20 minutes!
Useful Links:
- Langflow Github - github.com/langflow-ai/langflow
- OpenAI API Key - platform.openai.com
Follow Me:
- LinkedIn: / vaibhavpandey
If you found this video helpful, don't forget to like, share, and subscribe for more tutorials on generative AI, Python, and cutting-edge technology! Leave your comments below and let me know what you think or what you'd like to see next.
More from Generative Geek:
Experiments with LLM Apps - • Experiments with LLM Apps
Building RAG Applications - • Building RAG Application
Learn OpenAI Embeddings - • Learn OpenAI Embeddings
Get Started with ChromaDB - • Get Started with Chrom...
Pandas Basics to Pro - • Pandas - Beginner to Pro
Python and AI - • Python and AI

Пікірлер: 7

@UshaJain-z8n 8 күн бұрын

Thank you for the video, I am facing an issue with Vector Search. I don't have an option on langflow for "Vector Search". Please help me with the steps.

@generativegeek 8 күн бұрын

which version of langflow are you on?

@jlfguthrie Ай бұрын

Thank you for a very good tutorial. Am I understanding correctly, if I select a vector store component, like Chroma, does Langflow automatically create the Chroma database, or do I have to have a seperate Chroma db elsewhere?

@generativegeek Ай бұрын

Thank you. langflow will persist the chroma db in the path you specify. You will be able to see it within the working directory. At 10:26 in the video you can see when i bring in the chroma langflow component there is an option to specify persist directory.

@brianrowe1152 18 күн бұрын

Just asking. Why would I do this? When I can just prompt something like Meastro to just write the python code? The LLM is the interface now. I tried this and its neat, but at the end of the day, its faster to just ask Maestro to write the python. Not trying to start a riot, I just don't see why I would use this.

@generativegeek 6 күн бұрын

this is just a demonstration of what all is possible. the way to think about this is that if you know of the capabilities, you can orchestrate these type of pieces and build a solution.