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ollama-rag

Create RAG systems with Ollama, LangChain, and LlamaIndex.

Updated Aug 23, 2026
Search Tags:#embeddings#langchain#llm#rag#ollama#llamaindex
One-click install
npx skills add https://github.com/cuba6112/skillfactory --skill ollama-rag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ollama-rag
Source: https://github.com/cuba6112/skillfactory/tree/main/.claude/skills/ollama-rag
Command: npx skills add https://github.com/cuba6112/skillfactory --skill ollama-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of building Retrieval Augmented Generation (RAG) systems by leveraging Ollama for both local and cloud-based Large Language Models (LLMs) and embedding models.

Core Features & Use Cases

  • Local & Cloud LLMs: Utilize powerful models like DeepSeek-V3.2 (GPT-5 level) or Qwen3-Coder (1M context) via Ollama, with or without local hardware.
  • RAG Frameworks: Integrates seamlessly with LangChain and LlamaIndex for document Q&A, knowledge bases, and agentic RAG.
  • Embedding Models: Supports various embedding models for accurate document retrieval.
  • Use Case: Quickly set up a RAG system to answer questions from a large codebase or a collection of technical documents using a local Ollama model.

Quick Start

Use the ollama-rag skill to build a RAG system using LangChain and a local 'nemotron-3-nano' model.

Frequently Asked Questions about ollama-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG system using a local LLM with Ollama?▼

Build a RAG system using Ollama by integrating local models like Nemotron-3-Nano with LangChain or LlamaIndex for document Q&A and knowledge base construction using various embedding models.

Can I use LangChain and LlamaIndex with Ollama for document Q&A?▼

Yes, you can use LangChain and LlamaIndex with Ollama for document Q&A. This integration enables the creation of Retrieval Augmented Generation systems and knowledge bases using both local and cloud-based models.

Does Ollama support cloud-based LLMs like DeepSeek-V3.2 for RAG applications?▼

Yes, Ollama supports cloud-based LLMs like DeepSeek-V3.2 for RAG applications. You can utilize these powerful models with or without local hardware to process large contexts or technical documents.

What embedding models are available for Retrieval Augmented Generation with Ollama?▼

Available embedding models for Retrieval Augmented Generation with Ollama include Snowflake-Arctic-Embed2. These models ensure accurate document retrieval when building knowledge bases with LangChain or LlamaIndex.

What is the best way to set up a knowledge base from a large codebase using a local model?▼

The best way to set up a knowledge base from a large codebase is using a local Ollama model like Nemotron-3-Nano. Integrate it with LlamaIndex or LangChain to accurately retrieve and answer questions from technical documents.

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