Best Ollama Models for RAG

Building a RAG (Retrieval Augmented Generation) pipeline with Ollama? Choosing the right model is critical — both for generating embeddings and for answering questions based on retrieved context. Here are the best Ollama models for RAG in 2026.

What is RAG?

RAG combines a language model with a document retrieval system. Instead of relying purely on the model’s training data, RAG retrieves relevant chunks from your own documents and feeds them to the model as context. This means accurate, up-to-date answers grounded in your own data.

A RAG pipeline needs two types of models: an embedding model to convert documents into searchable vectors, and a language model to generate answers from the retrieved context.

Best Embedding Models for RAG

1. Nomic Embed Text — Best Overall Embedding Model

Nomic Embed Text is the most popular embedding model on Ollama and for good reason. It produces high-quality embeddings, runs fast, and integrates easily with popular RAG frameworks like LangChain, LlamaIndex, and ChromaDB.

ollama pull nomic-embed-text

Dimensions: 768
RAM required: 2GB minimum

2. MXBAi Embed Large — Best for Accuracy

If embedding quality is your priority, MXBAi Embed Large consistently ranks at the top of embedding benchmarks. It’s slower than Nomic but produces more accurate retrieval results, especially for technical or domain-specific documents.

ollama pull mxbai-embed-large

Dimensions: 1024
RAM required: 2GB minimum

Best Language Models for RAG

1. Llama 3.1 8B — Best Overall RAG Model

Llama 3.1 8B is the most reliable model for answering questions from retrieved context. Its large 128K context window means you can pass substantial amounts of retrieved text without truncating, and it follows instructions to answer only from the provided context.

ollama run llama3.1

Best for: General RAG, large document sets
Context window: 128K tokens
RAM required: 8GB minimum

2. Mistral 7B — Best for Speed

Mistral 7B is fast and accurate when answering from retrieved context. It’s less likely than smaller models to hallucinate when the answer isn’t in the provided documents, making it dependable for production RAG pipelines.

ollama run mistral

Best for: Fast RAG pipelines
Context window: 32K tokens
RAM required: 8GB minimum

3. Qwen2.5 14B — Best for Complex Documents

Qwen2.5 14B handles complex, technical documents particularly well. If your RAG pipeline works with legal, financial, or scientific content, its deeper reasoning ability produces more accurate answers than smaller models.

ollama run qwen2.5:14b

Best for: Technical/complex RAG
Context window: 128K tokens
RAM required: 16GB minimum

4. Phi-4 — Best for Low-Resource RAG

Phi-4’s small footprint makes it ideal for RAG pipelines running on resource-constrained machines. It answers from context accurately and is less prone to hallucination than similarly sized models.

ollama run phi4

Best for: Low-spec machines
Context window: 16K tokens
RAM required: 6GB minimum

Recommended RAG Stack

Quick Start: RAG with Ollama and LangChain

from langchain_community.llms import Ollama
from langchain_community.embeddings import OllamaEmbeddings

llm = Ollama(model="llama3.1")
embeddings = OllamaEmbeddings(model="nomic-embed-text")

From here you can connect any vector store (ChromaDB, FAISS, Qdrant) and build a complete local RAG pipeline.

Our Recommendation

For most RAG use cases, the Nomic Embed Text + Llama 3.1 8B combination is the sweet spot. It runs on a standard 16GB RAM machine, delivers excellent retrieval accuracy, and integrates with all major RAG frameworks.

See our guide on using Ollama for embeddings and RAG for a full implementation walkthrough.