Welcome to the AWS Pi Day 2024 repository, where you can explore various applications and examples using Amazon Bedrock, fine-tuning, and Retrieval-Augmented Generation (RAG)! 🎉

• Getting_started.ipynb - An introduction to Amazon Bedrock and its usage with the Python SDK and UI. Discover the serverless experience and how to integrate foundation models (FMs) using AWS tools. 🌐

• Bedrock_KB.ipynb - A deep dive into building a Q&A application using the Knowledge Bases for Amazon Bedrock - Retrieve API. Learn to connect to your data for enhanced, context-aware responses from Anthropic Claude V2. 🔍

• Vectors_RAG_Aurora.ipynb - Explore the basics of vector embeddings and the use of Amazon Aurora as a vector store on AWS with Bedrock. 📊

• Resume_Screening_App - Streamline your recruitment process with a Streamlit application for effective résumé screening. 📂

• Bedrock_Claude_Chat - An end-to-end sample chatbot using the Anthropic company's LLM Claude, one of the foundational models provided by Amazon Bedrock for generative AI. 💬

• FineTuning_Bedrock - Examples related to fine-tuning Bedrock models, including code for fine-tuning Meta Llama 2 for text summarization. 🛠️

Amazon Bedrock supports a range of industry-leading foundation models. Choose the model that best suits your unique goals and begin innovating. 🎯

To get started:

1. Clone this repository.
2. Install the dependencies listed in requirements.txt.
3. Explore the notebooks and applications provided.

For detailed setup, refer to each example and application's specific documentation.

Here's a quick overview of what you'll find in this repository:

• 📚 Notebooks
  • Getting_started.ipynb
  • Bedrock_KB.ipynb
  • Bedrock_KB_Langchain_with_op.ipynb
  • Vectors_RAG_Aurora.ipynb
  • ...and more!
• 📁 Applications
  • Bedrock_Claude_Chat
  • Resume_Screening_App
  • ...and more!

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions

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Licensed under the MIT-0 License. View License.