Retrieval Augmented Generation (RAG): A simple RAG pipeline based on Gemini & Chromadb & Gradio
Join this channel to get access to membership perks :
/ @muratkarakayaakademi
---------------------------------- RELATED LINKS--------------------------------------------------------------------
For all tutorials: muratkarakaya.net
Colab Notebook: https://colab.research.google.com/dri...
Retrieval Augmented Generation (RAG) playlist: https://colab.research.google.com/dri...
• Retrieval Augmented Generation (RAG)
Github pages: https://kmkarakaya.github.io/Deep-Lea...
Github Repo: https://github.com/kmkarakaya/Deep-Le...
ChatGPT playlist: • All About ChatGPT
--------------------------------------------------------------------------------------------------------
Related Tutorial Playlists in English:
All Tutorials in English: https://www.youtube.com/c/MuratKaraka...
All About Transformers: • All About Transformers
Classification with Keras Tensorflow: • Classification with Keras / Tensorflow
Word Embedding in Keras: • Word Embedding in Keras
Applied Machine Learning with Python: • Applied Machine Learning with Python
How to evaluate a TensorFlow Keras model by using correct performance metrics? • How to evaluate a TensorFlow Keras mo...
-------------------------------------- TUTORIAL CONTENT-------------------------------------
Retrieval Augmented Generation (RAG): A simple RAG pipeline based on Gemini & Chromadb & Gradio
🚀In this comprehensive tutorial series, we delve into the world of developing a Retrieval Augmented Generation (RAG) application. If you're looking to create a chatbot using advanced technologies like GEMINI and Chromadb, you're in the right place! This video is designed for everyone interested in building a RAG system, whether you're an experienced developer or just starting out.
In the first three parts of our series:
Text Generation and Chat Coding with GEMINI API: Learn how to implement and use the GEMINI API to create dynamic text-based interactions.
*Building Vector Storage and Similarity Search with Persistent Chromadb:* Discover how to efficiently store and retrieve vectors using Chromadb.
In this fourth part, titled "SIMPLE RAG APPLICATION BASED ON GEMINI & CHROMADB," we aim to build a functional RAG pipeline using these powerful tools. Here's what you can expect:
*Key Steps Covered in the Video:*
1. *Building a Knowledge Base from Scratch with Persistent Chromadb:* Learn how to create a robust knowledge base from multiple documents.
2. *Uploading Multiple Documents and Creating a Knowledge Base:* A step-by-step guide to uploading and organizing your documents.
3. *Testing the Knowledge Base:* Methods to ensure your knowledge base is working correctly.
4. *Loading a Knowledge Base from Persistent Chromadb:* Learn how to efficiently load your knowledge base.
5. *LLM Connection: Chat API with Google GEMINI:* Integrate the Google GEMINI model for better interaction.
6. *Creating a RAG Pipeline for the Existing Knowledge Base:* Develop a seamless pipeline to use your knowledge base with GEMINI.
7. *A Simple Loop for User Interaction:* Implement a user-friendly loop.
8. *A Gradio Interface for RAG:* Create an intuitive interface using Gradio for a better user experience.
All these steps will be implemented and coded with Python on Google Colab, making it easy to follow along and replicate the process.
Join our community of developers and tech enthusiasts! Don’t forget to like, share, and subscribe for our latest tutorials and tech insights.
SIMPLE RAG APPLICATION BASED ON GEMINI & CHROMADB
#RAG #Chatbot #Chromadb #Gemini #Coding #Python #GoogleColab #MachineLearning #ArtificialIntelligence #MuratKarakayaAcademy
Watch video Retrieval Augmented Generation (RAG): A simple RAG pipeline based on Gemini & Chromadb & Gradio online without registration, duration hours minute second in high quality. This video was added by user Murat Karakaya Akademi 01 January 1970, don't forget to share it with your friends and acquaintances, it has been viewed on our site 240 once and liked it 7 people.