Build a local RAG with Ollama, OpenAI, and Weaviate

Why Choose Garranto Academy for Your Local RAG Training?

Garranto Academy provides hands-on RAG training with expert guidance, practical projects, and industry-focused examples. You gain end-to-end skills to confidently build, deploy, and optimize local RAG systems for business-ready AI solutions.

Course Overview:

The Build a Local RAG System with Ollama, OpenAI, and Weaviate course is an intensive 3-day, hands-on program designed for developers and technical professionals who want to build secure, private Retrieval-Augmented Generation (RAG) systems. This practical training guides participants through deploying a local LLM using Ollama, generating high-quality embeddings with OpenAI, and implementing vector search and semantic retrieval through Weaviate. Learners will ingest and structure documents, apply retrieval pipelines, generate accurate grounded responses, and build an authenticated front-end interface for interactive querying. By the end of the program, participants will have a fully containerized and locally hosted RAG architecture secured with environment-based authentication, ready to integrate with enterprise datasets and real-world applications.

What You'll Learn in Our Local RAG (Ollama + OpenAI + Weaviate) Course?

Course Objectives:

Upon successful completion of this course, learners will be able to:

• Describe RAG architecture and where retrieval improves generation.

• Install and configure Ollama for running a local LLM.

• Design a chunking and metadata strategy for embeddings.

• Configure Weaviate schema, ingest documents, and run vector search.

• Build a Python pipeline that retrieves, prompts, and generates grounded answers.

• Evaluate and tune retrieval (top-k, thresholds) and prompts for quality.

• Implement a minimal authenticated UI to query the RAG system.

• Package services with Docker Compose and apply basic security and ops guardrails.

Prerequisites

• Python basics and command-line usage

• OpenAI API key ready

• Docker installed plus Git familiarity

Course Outlines:

Module 1.1 — Local RAG with Ollama: Concepts

• Key Concepts: RAG components and flow; local vs hosted LLMs; Ollama models, context windows, and limits; prompt templates for retrieval; environment setup and GPU/CPU considerations; data privacy and keys management; minimal ops: logs, health checks.

Module 1.2 — Local RAG with Ollama: Hands-On Lab

• Scenario: Stand up a local LLM service suitable for RAG generation.

-Steps: Initialize project repo and Python venv; install Ollama and pull a compact model; create a prompt template for answer synthesis; expose a simple generate() function; add .env handling for secrets; verify latency and token limits with sample prompts.

• Deliverables: Project scaffold with working local LLM; prompt template; run logs screenshot.

Module 2.1 — Embeddings and Weaviate: Concepts

• Key Concepts: OpenAI embeddings API basics and dimensionality; text chunking strategies (size, overlap); metadata design (source, title, page, tags); Weaviate schema classes and properties; vector search modes (cosine, dot); ingestion workflows and idempotency; rate limits and batching.

Module 2.2 — Embeddings and Weaviate: Hands-On Lab

• Scenario: Ingest a small corpus and make it searchable via vectors.

• Steps: Start Weaviate (Docker) with persistent volume; define a schema for “DocumentChunk”; write a chunker for PDFs/Markdown; generate embeddings via OpenAI and upsert to Weaviate with metadata; test similarity queries (top-k) and inspect results; add simple re-chunk/re-ingest script.

• Deliverables: Ingestion script; Weaviate schema file; index report with corpus stats.

Module 3.1 — Retrieval and generation pipeline: Concepts

• Key Concepts: Retriever design (filters, top-k); minimal reranking options (when/why); constructing the final prompt with citations; safety: answer length, refusal patterns, and source-attribution; evaluation basics (exact match for FAQs, groundedness checks, spot tests); observability essentials.

Module 3.2 — Retrieval and generation pipeline: Hands-On Lab

• Scenario: Wire up retrieve-then-generate with source citations.

• Steps: Implement retrieve() against Weaviate with filters; assemble context with de-dup and length caps; build synthesize() using Ollama and the prompt template; return answers + citations; add simple evaluation script (sample questions, hit rate, answer length); tune top-k and chunk size.

• Deliverables: End-to-end RAG pipeline module; evaluation report (sample metrics, decisions).

Module 4.1 — Frontend with authentication: Concepts

• Key Concepts: Choosing a minimal UI (Streamlit) for speed; authentication options (username/password, environment-based secrets); role scoping (viewer/admin); basic rate limiting and input validation; error handling and user feedback; packaging with Docker Compose.

Module 4.2 — Frontend with authentication: Hands-On Lab

• Scenario: Ship a simple, authenticated app to query the RAG system.

• Steps: Build a Streamlit UI: login form, query box, results with citations; add simple auth (e.g., hashed credentials from env/.toml); validate inputs and set max query length; expose pipeline via internal module call; add request logging; create Docker Compose file (Ollama, Weaviate, app); smoke-test end to-end.

• Deliverables: Authenticated Streamlit app; Docker Compose stack; README with run and .env instructions.

Course Outcomes:

Upon completing the "BUILD A LOCAL RAG WITH OLLAMA, OPENAI, AND WEAVIATE" course, participants will:

• Understand the full architecture and operational workflow of modern RAG systems.

• Install, configure, and run local LLMs using Ollama for offline inference.

• Generate embeddings with the OpenAI API and integrate them into Weaviate for semantic search.

• Apply effective chunking and metadata strategies to optimize vector storage and retrieval.

• Build an end-to-end Python RAG pipeline that produces grounded, cited responses.

• Evaluate and optimize retrieval quality through top-k tuning and prompt refinement.

• Deploy a secure, authenticated Streamlit interface and containerize the entire RAG system with Docker Compose.

Key Benefits of Building a Local RAG with Ollama, OpenAI, and Weaviate

Building a local RAG system enhances privacy, speed, and control over your data while delivering accurate, contextual AI responses. It empowers teams to create secure, high-performance AI solutions without relying on cloud dependencies.

How a RAG System Can Transform Your AI Applications

A RAG system boosts the accuracy and reliability of AI by combining LLM reasoning with real-time, verified data retrieval. It transforms your applications with grounded responses, reduced hallucinations, and scalable knowledge integration.