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
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