GenAI RAG with LlamaIndex, Ollama and Elasticsearch

What You Will Learn

From Search to RAG

You will expand your knowledge of semantic search and learn to apply it for RAG: starting with retrieving relevant parts, then generating substantiated responses based on them. You'll discover how LlamaIndex integrates your data with LLM, and why the size and overlap of "chunks" are important for accuracy.

Creating a Pipeline

With FastAPI, you will implement uploading and processing PDFs: extracting text, formatting JSON, splitting, creating embeddings, and indexing in Elasticsearch, with minimal boilerplate code thanks to LlamaIndex.

Working with Elasticsearch

You will create an index for resumes with vectors and metadata. You'll learn to distinguish between vector search and keyword search, understand how vector fields are stored, and how to explore documents and results through Kibana.

Interface on Streamlit

You will create a simple chat interface on Streamlit for natural language interaction. Enable debug mode to see which fragments were used for responses and apply metadata (e.g., filtering by name) to enhance accuracy.

Processing and Formatting JSON

You will extract text from PDFs using PyMuPDF, then create a neat JSON via Ollama (Mistral), preserving structure and characters. You'll master handling formatting errors and methods for reliable prompt engineering.

Improving Response Quality

You will study practical techniques to increase accuracy:

• adjusting chunk sizes and overlaps, top-K sampling;
• adding metadata (role, skills, location) for hybrid filters;
• experimenting with embedding models and prompts;
• using structured responses (e.g., JSON lists).

Docker Environment

You will assemble the entire stack in Docker Compose: FastAPI, Elasticsearch, Kibana, Streamlit, and Ollama (Mistral), to deploy the system locally with a predictable configuration.

Bonus: Production Patterns

You will learn how to scale the prototype to production level:

• store uploads in a data lake (e.g., S3) and process them through queues (Kafka/SQS);
• automatically scale workers for chunking and embeddings;
• switch LLM backends (e.g., Bedrock or OpenAI) via a unified API;
• store chat history in MongoDB/Postgres and replace Streamlit with a React/Next.js interface.