Collection of notebooks to understand world of Retrieval Augmented Generation (RAG), one of the most popular applications of LLMs
00_Quick_Start_Guide.ipynb: Warm up notebook to help you set up your env file and to test LLM calls
01_Tokenization.ipynb: let's understand the world of tokens in LLMs
02_Let's_talk_Embeddings.ipynb: Full fledged notebook to appreciate the power of embeddings from scratch
03_Basic_RAG.ipynb: Build a RAG pipeline using LangChain, Key steps in building a RAG application, Document loaders, Strategies for Data Chunking, Building Vector Stores, Retrieval Techniques and their Importance
04_Advanced_RAG_Retriever.ipynb: Advanced Retrival Strategies for better retrieval
05_Advanced_RAG_MVR.ipynb: Parent Document Retriver or Multi Vector Retriver
06_Advanced_RAG_Cross_encoder.ipynb: Cross encoder based reranking of retieved documents
07_Advanced_RAG_Query_Expansion.ipynb: Query expansion technique to increase the chance of getting user question answered
08_RAG_using_OpenSource_LLMs.ipynb: Building RAG pipelines using open source LLMs [WIP]
09_RAG_Evaluation.ipynb: Let's understand how to evaluate the RAG pipeline using various cool metrics
10_Agentic_RAG.ipynb : Empowering your your RAG pipeline using Autonomous AI Agents [WIP]