RAG-Agents-Bootcamp

Welcome to the RAG and Agents Bootcamp repository! This repository contains all the Jupyter notebooks and sample data used throughout the bootcamp. Each module is organized in its own folder, with relevant projects and sample data included.

Repository Structure

• module 2

  • chromadb-vector-store-using-llamaindex: Notebook demonstrating how to use ChromaDB for vector storage with LlamaIndex.
  • llama-index-project: Project showcasing the integration of LlamaIndex.
  • sample_data.pdf: Sample data that can be used in Module 2.

• module 3

  • advanced-rag-cross-encoder-reranking-langhchain: Notebook on advanced retrieval-augmented generation with cross-encoders.
  • notebook-hybrid-search-rag: Hybrid search techniques for RAG.
  • notebookbuilding-a-pdf-based-qa-system-with-langchain: Steps to build a PDF-based Q&A system with Langchain.
  • sample_data.pdf: Additional sample data for Module 3.
  • sample_data2.pdf: More sample data for Module 3.

• module 4

  • advanced-rag-fine-tune-embeddings: Fine-tuning embeddings for advanced RAG.
  • evaluating-and-optimizing-rag-pipelines-with-beyondllm: Methods to evaluate and optimize RAG pipelines with BeyondLLM.
  • sample_data.pdf: Sample data used in Module 4.

• module 5

  • genai-stack-api: API integration with GenAI Stack.
  • sample_data.pdf`: Sample data used in Module 5.

• module 6

  • notebook-autogen-project: AutoGen project notebook.
  • notebook-crewai-project: CrewAI project demonstration.

• module 7

  • customer-feedback-agent: Building a customer feedback agent.
  • sample_data.pdf`: Sample data used in Module 7.