filmov
tv
GenAI Vector Embeddings Explained: Create, Store, Search | ChromaDB & FAISS | Learn RAG from Scratch
Показать описание
Welcome to the FreeBirds Crew! 🚀 In this third video of our Learn RAG from Scratch Playlist, learn how to create and store vector embeddings in a vector database like ChromaDB and FAISS.
I will demonstrate how to implement them using Python and LangChain. Discover:
1. What are Vector Embeddings and their types (Text, Image, Audio).
2. How to create embeddings and store them in VectorDBs.
3. Differences between Vector Search and Semantic Search with real coding examples with python, langchain and hugging face library.
By the end of this tutorial, you'll understand key concepts like similarity search, semantic search, and how to build small projects using LangChain on various data types. Perfect for beginners and experts working on RAG (Retrieval Augmented Generation) and LLM projects.
👉 Don’t forget to like, subscribe, and hit the bell icon for more AI tutorials and project walkthroughs.
📂 Playlist: Learn RAG from Scratch
Join this channel to get access to perks:
Playlists that make you skilled up
📱Follow US on social media.
⚡️ Do like, comment, share, and subscribe to our YouTube channel for more videos and projects.
Krish Naik genai,
Krish Naik llm,
Krish naik rag,
krish naik vector databases,
Krish Naik Python tutorial,
Krish naik explainable ai,
Krish Naik data science,
Krish Naik statistics,
machine learning full course,
machine learning tutorial,
machine learning interview questions,
machine learning projects in Python,
data science for beginners,
data science project,
data science full course,
data science interview questions,
data science interview,
machine learning interview questions,
statistics interview questions,
Python interview questions,
interview questions,
leetcode questions,
interview preparation for FAANG,
FAANG Interview questions,
Google data science Interview questions,
Amazon data scientist interview,
Meta data scientist interview questions,
I will demonstrate how to implement them using Python and LangChain. Discover:
1. What are Vector Embeddings and their types (Text, Image, Audio).
2. How to create embeddings and store them in VectorDBs.
3. Differences between Vector Search and Semantic Search with real coding examples with python, langchain and hugging face library.
By the end of this tutorial, you'll understand key concepts like similarity search, semantic search, and how to build small projects using LangChain on various data types. Perfect for beginners and experts working on RAG (Retrieval Augmented Generation) and LLM projects.
👉 Don’t forget to like, subscribe, and hit the bell icon for more AI tutorials and project walkthroughs.
📂 Playlist: Learn RAG from Scratch
Join this channel to get access to perks:
Playlists that make you skilled up
📱Follow US on social media.
⚡️ Do like, comment, share, and subscribe to our YouTube channel for more videos and projects.
Krish Naik genai,
Krish Naik llm,
Krish naik rag,
krish naik vector databases,
Krish Naik Python tutorial,
Krish naik explainable ai,
Krish Naik data science,
Krish Naik statistics,
machine learning full course,
machine learning tutorial,
machine learning interview questions,
machine learning projects in Python,
data science for beginners,
data science project,
data science full course,
data science interview questions,
data science interview,
machine learning interview questions,
statistics interview questions,
Python interview questions,
interview questions,
leetcode questions,
interview preparation for FAANG,
FAANG Interview questions,
Google data science Interview questions,
Amazon data scientist interview,
Meta data scientist interview questions,
0:04:23
Vector Databases simply explained! (Embeddings & Indexes)
0:08:29
A Beginner's Guide to Vector Embeddings
0:05:12
OpenAI Embeddings Explained in 5 Minutes
0:17:01
GenAI Vector Embeddings Explained: Create, Store, Search | ChromaDB & FAISS | Learn RAG from Scr...
0:36:23
Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP
0:03:22
Vector databases are so hot right now. WTF are they?
0:18:41
OpenAI Embeddings and Vector Databases Crash Course
0:06:52
Vector Database Explained | What is Vector Database?
0:08:38
What are Word Embeddings?
0:36:01
End To End LLM Langchain Project using Pinecone Vector Database #genai
0:00:58
How Does Rag Work? - Vector Database and LLMs #datascience #naturallanguageprocessing #llm #gpt
0:04:43
The Biggest Misconception about Embeddings
0:08:12
What is a Vector Database?
0:16:12
Word Embedding and Word2Vec, Clearly Explained!!!
0:04:34
Retrieval Augmented Generation (RAG) | Embedding Model, Vector Database, LangChain, LLM
0:34:23
Let’s Build a Movie Recommendations Next.js App with Vector Embeddings (Generative AI for beginners)...
0:08:03
RAG Explained
0:11:31
Vertex AI Embeddings API + Matching Engine: Grounding LLMs made easy
3:19:26
Gen AI Course | Gen AI Tutorial For Beginners
0:29:04
RAG, semantic search, embedding, vector... Find out what the terms used with Generative AI mean!
0:19:47
What are Vector Embeddings? | Pinecone DB | ChatGPT
0:12:44
LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners
0:00:55
Vector Databases (Pinecone)
0:06:36
What is Retrieval-Augmented Generation (RAG)?
Комментарии