How to implement RAG in Snowflake AI Cortex | A Step-by-Step Tutorial | Hindi | 11 minutes
Welcome to Coders Academy!
In this video, we explore Retrieval Augmented Generation (RAG) and its importance, especially concerning the cutoff dates of LLM models.
Learn how RAG allows AI models to use external, custom information to generate accurate and context-aware responses.
We break down the RAG process into its core steps: Retrieval, Augmentation, and Generation.
See a detailed walk-through of a typical RAG pipeline implemented within Snowflake AI Cortex.
The demo uses ergonomic data and showcases how to leverage Snowflake's capabilities for AI projects, including the Vector data type and functions like cortex.embed_text and VECTOR_COSINE_SIMILARITY.
Learn how to generate vector embeddings for your data and retrieve relevant documents based on a user query.
Learn how to construct prompts for an LLM using the retrieved context and generate responses using functions like Cortex.complete and models like Llama2.
Watch the full RAG functionality demonstrated with sample queries and results directly in a
Snowflake Notebook
Code - https://github.com/toashishagarwal/Snowflake-AI/blob/main/4%20RAG/demoRAG.ipynb
Chapters
0:01 Introduction & Welcome
00:27 What is RAG and Why Do We Need It? (LLM Limitations)
01:22 RAG Definition and Steps (Retrieval, Augmentation, Generation)
02:07 Overview of the RAG Pipeline
03:44 Demo of RAG pipeline in Snowflake
05:02 How to generate and Insert Vector/Embeddings (cortex.embed_text) in Snowflake
06:05 Writing the Document Retrieval Function in python & Using VECTOR_COSINE_SIMILARITY
07:42 Writing the Main RAG Function, Creating Context and Prompt for the LLM, Using Cortex.complete with Llama2
09:17 Testing the RAG Functionality
09:32 Demo Results and Sample Queries
10:46 Conclusion & Summary
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#Python #MachineLearning #AI #GenerativeAI #DeepLearning #DataScience
Видео How to implement RAG in Snowflake AI Cortex | A Step-by-Step Tutorial | Hindi | 11 minutes канала Coders Academy
In this video, we explore Retrieval Augmented Generation (RAG) and its importance, especially concerning the cutoff dates of LLM models.
Learn how RAG allows AI models to use external, custom information to generate accurate and context-aware responses.
We break down the RAG process into its core steps: Retrieval, Augmentation, and Generation.
See a detailed walk-through of a typical RAG pipeline implemented within Snowflake AI Cortex.
The demo uses ergonomic data and showcases how to leverage Snowflake's capabilities for AI projects, including the Vector data type and functions like cortex.embed_text and VECTOR_COSINE_SIMILARITY.
Learn how to generate vector embeddings for your data and retrieve relevant documents based on a user query.
Learn how to construct prompts for an LLM using the retrieved context and generate responses using functions like Cortex.complete and models like Llama2.
Watch the full RAG functionality demonstrated with sample queries and results directly in a
Snowflake Notebook
Code - https://github.com/toashishagarwal/Snowflake-AI/blob/main/4%20RAG/demoRAG.ipynb
Chapters
0:01 Introduction & Welcome
00:27 What is RAG and Why Do We Need It? (LLM Limitations)
01:22 RAG Definition and Steps (Retrieval, Augmentation, Generation)
02:07 Overview of the RAG Pipeline
03:44 Demo of RAG pipeline in Snowflake
05:02 How to generate and Insert Vector/Embeddings (cortex.embed_text) in Snowflake
06:05 Writing the Document Retrieval Function in python & Using VECTOR_COSINE_SIMILARITY
07:42 Writing the Main RAG Function, Creating Context and Prompt for the LLM, Using Cortex.complete with Llama2
09:17 Testing the RAG Functionality
09:32 Demo Results and Sample Queries
10:46 Conclusion & Summary
Follow me-
http://instagram.com/codersAcademyAI
#Python #MachineLearning #AI #GenerativeAI #DeepLearning #DataScience
Видео How to implement RAG in Snowflake AI Cortex | A Step-by-Step Tutorial | Hindi | 11 minutes канала Coders Academy
RAG Retrieval Augmented Generation Snowflake Snowflake AI Cortex AI LLM Large Language Model Vector Database Vector Embeddings Embeddings cortex.embed_text VECTOR_COSINE_SIMILARITY Llama2 Tutorial How to Demo Data Science Machine Learning AI Tutorial Snowflake Tutorial RAG Tutorial Python SQL Notebook External Information Custom Data
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27 мая 2025 г. 12:07:10
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