Building a RAG Pipeline with Anthropic Claude Sonnet 3.5

Показать описание

In this video, we explore and test the coding capabilities of Claude Sonnet 3.5, Anthropic's latest model.

We begin by providing a diagram of a RAG (Retrieval-Augmented Generation) pipeline for data processing, embedding ingestion, retrieval, and integration with large language models.

We then use Claude Sonnet 3.5 to generate Python code to implement this pipeline using MongoDB and the LangChain library. We then run and debug the code, make necessary adjustments, and highlight the pipeline's successful execution.

Additionally, we delve into the features, capabilities, and future direction of Claude Sonnet 3.5 as documented by Anthropic.

⏱️ Timestamps
00:00 Introduction and Overview
01:07 Setting Up the instruction
02:10 Observing the Result
02:50 Debugging and Troubleshooting
12:00 Creating a Vector Search Index
15:05 Inspecting the Results
17:03 Anthropic's Claude 3.5 Sonnet
22:00 Future Developments and Applications
25:19 Conclusion and Next Steps

🔗 Links

😎 Reach Me

#anthropic #claude #artificialintelligence #machinelearning #aiengineering

Richmond Alake

Рекомендации по теме

Комментарии

Thank you, Rich for the real world use case. Other youtuber are just building snake games.

SanjaySingh-gjkq

Glad I was recommended this! Brilliant video

skohari

Thank you for sharing, very interesting, have a great day. :-)

lalpremi

Richmond, sir, I need your help. I want to enter AI Agent Development, so how can I start? I just completed high school, and in mid-August, my college will start. And zero coding exp. so please help

divyansh

Building a RAG Pipeline with Anthropic Claude Sonnet 3.5

What is Retrieval-Augmented Generation (RAG)?

Create a RAG pipeline in less than 5 minutes

Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer

4-Langchain Series-Getting Started With RAG Pipeline Using Langchain Chromadb And FAISS

Building Production-Ready RAG Applications: Jerry Liu

Build a RAG Pipeline with LlamaIndex and Anthropic Claude 3

Build your own RAG (retrieval augmented generation) AI Chatbot using Python | Simple walkthrough

Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial)

Flexible RAG: Development and evaluation strategies

Building a RAG application from scratch using Python, LangChain, and the OpenAI API

Building a RAG Based LLM App And Deploying It In 20 Minutes

'I want Llama3 to perform 10x with my private knowledge' - Local Agentic RAG w/ llama3

Build a RAG Based LLM App in 20 Minutes! | Full Langflow Tutorial

RAG + Langchain Python Project: Easy AI/Chat For Your Docs

Chatbots with RAG: LangChain Full Walkthrough

Building a RAG application using open-source models (Asking questions from a PDF using Llama2)

Step-by-Step Guide to Building a RAG LLM App with LLamA2 and LLaMAindex

Back to Basics: Understanding Retrieval Augmented Generation (RAG)

How to build Multimodal Retrieval-Augmented Generation (RAG) with Gemini

Python RAG Tutorial (with Local LLMs): AI For Your PDFs

Building a RAG Pipeline with Anthropic Claude Sonnet 3.5

ADVANCED Python AI Agent Tutorial - Using RAG

Building RAG based model using Langchain | rag langchain tutorial | rag langchain huggingface

RAG Explained