Descriptions
GenAI RAG with LlamaIndex, Ollama and Elasticsearch, Retrieval-Augmented Generation (RAG) is the next practical step after semantic indexing and search. In this course, you’ll build a complete, local-first RAG pipeline that ingests PDFs, stores chunked vectors in Elasticsearch, retrieves the right context, and generates grounded answers with the Mistral LLM running locally via Ollama. We’ll work end-to-end on a concrete scenario: searching student CVs to answer questions like “Who worked in Ireland?” or “Who has Spark experience?”. You’ll set up a Dockerized stack (FastAPI, Elasticsearch, Kibana, Streamlit, Ollama) and wire it together with LlamaIndex so you can focus on the logic, not boilerplate. Along the way, you’ll learn where RAG shines, where it struggles (precision/recall, hallucinations), and how to design for production. By the end, you’ll have a working app: upload PDFs → extract text → produce clean JSON → chunk & embed → index into Elasticsearch → query via Streamlit → generate answers with Mistral, fully reproducible on your machine.
What you’ll learn
- Revisit semantic search and extend it to RAG: retrieve relevant chunks first, then generate grounded answers. See how LlamaIndex connects your data to the LLM and why chunk size and overlap matter for recall and precision.
- Use FastAPI to accept PDF uploads and trigger the ingestion flow: text extraction, JSON shaping, chunking, embeddings, and indexing into Elasticsearch, all orchestrated with LlamaIndex to minimize boilerplate.
- Create an index for CV chunks with vectors and metadata. Understand similarity search vs. keyword queries, how vector fields are stored, and how to explore documents and scores with Kibana.
- Build a simple Streamlit UI to ask questions in natural language. Toggle debug mode to inspect which chunks supported the answer, and use metadata filters (e.g., by person) to boost precision for targeted queries.
Specificatoin of GenAI RAG with LlamaIndex, Ollama and Elasticsearch
- Publisher : Learn Data Engineering
- Teacher : Andreas Kretz
- Language : English
- Level : All Levels
- Number of Course : 21
- Duration : 1 hours and 49 minutes
Content of GenAI RAG with LlamaIndex, Ollama and Elasticsearch
Pictures
Sample Clip
Installation Guide
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