Building a RAG Pipeline with Anthropic Claude Sonnet 3.5

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
Notebook: bit.ly/4eyKPbs
Best Repo for you: bit.ly/4bWraQA
AI Agents With Memory Video: ruclips.net/user/liveL49AxDcO...
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[@SanjaySingh-gj2kq]

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

[@skohari]

Glad I was recommended this! Brilliant video

[@lalpremi]

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

[@dewijones92]

great

[@richmond_a]

Thanks for Watching
Links
Notebook: bit.ly/4eyKPbs
Best Repo for you: bit.ly/4bWraQA
AI Agents With Memory Video: ruclips.net/user/liveL49AxDcOURU?si=uBj8SuvCIJdVKUoz

[@divyansh4620]

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