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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: https://bit.ly/4eyKPbs Best Repo for you: https://bit.ly/4bWraQA AI Agents With Memory Video: https://www.youtube.com/live/L49AxDcO... 😎 Reach Me Follow me on LinkedIn:   / richmondalake   Follow me on Twitter:   / richmondalake   #anthropic #claude #artificialintelligence #machinelearning #aiengineering