Table of Contents

Vector Search Using Ollama for Retrieval-Augmented Generation (RAG)

How Vector Search Powers Retrieval-Augmented Generation (RAG) From Search to Context

The Flow of Meaning

Putting It All Together

What Is Retrieval-Augmented Generation (RAG)? The Retrieve-Read-Generate Architecture Explained

Why Retrieval-Augmented Generation (RAG) Improves LLM Accuracy

The Broader Picture: A Hybrid of Search and Generation

Key Takeaway

How to Build a RAG Pipeline with FAISS and Ollama (Local LLM) Step 1: Implementing HNSW Vector Search with FAISS for RAG

Step 2: Prompt Engineering for Retrieval-Augmented Generation (RAG)

Step 3: Generating Grounded Answers with Ollama Local LLM

Adding Feedback Loops to Improve Retrieval Accuracy

Putting It All Together

Configuring Your Development Environment: Setting Up Ollama and FAISS for a Local RAG Pipeline Optional Dependencies

Local LLM Setup (Ollama)

Implementation Walkthrough Configuration (config.py)

Integrating Ollama with FAISS Vector Search…