A step-by-step guide to building a complete local RAG pipeline using FAISS for vector search and Ollama for LLM inference. Covers the full workflow: embedding documents with SentenceTransformers, storing and querying vectors via FAISS Flat and HNSW indexes, constructing prompts with strict vs. synthesis modes, and generating
Table of contents
Vector Search Using Ollama for Retrieval-Augmented Generation (RAG)How Vector Search Powers Retrieval-Augmented Generation (RAG)What Is Retrieval-Augmented Generation (RAG)?How to Build a RAG Pipeline with FAISS and Ollama (Local LLM)Configuring Your Development Environment: Setting Up Ollama and FAISS for a Local RAG PipelineImplementation WalkthroughIntegrating Ollama with FAISS Vector Search for RAGRunning a Local RAG Pipeline with Ollama and FAISSTiny Gotchas and TipsHow to Run a Local RAG System with Ollama and FAISSExample OutputWhat You Learned: Building a Production-Ready Local RAG System with Ollama and FAISSSummarySort: