This project is aimed at creating a Retrieval Augmented (RAG) pipeline to enable users to ask questions from my Resume and Work Portfolio. The user can ask any question about my career - work experience, projects, academics, accomplishments, skills and more.
In the attached notebook, we will follow three approaches to implement Retrieval Augmented Generation (RAG):
- Basic RAG Pipeline:
- Basic Indexing of Documents using LlamaIndex.
- Querying from the Created Index.
Source:https://learn.deeplearning.ai/building-evaluating-advanced-rag/lesson/2/advanced-rag-pipeline
- Advanced RAG Pipeline - Sentence Window Retrieval:
- Creating Sentence Window Based Index (Breaks down documents into smaller chunks like sentences).
- Querying most relevant chunks along with surrounding context.
Source: https://learn.deeplearning.ai/building-evaluating-advanced-rag/lesson/2/advanced-rag-pipeline
Source: https://learn.deeplearning.ai/building-evaluating-advanced-rag/lesson/2/advanced-rag-pipeline
- Advanced RAG Pipeline - Auto-merging retrieval:
- Creating Automerging Retrieval Based Index.
- Querying using auto-merging retrieval which merges information from multiple sources or segments of text to create a more comprehensive and contextually relevant response to a query.
Source: https://learn.deeplearning.ai/building-evaluating-advanced-rag/lesson/2/advanced-rag-pipeline