Fully Local RAG with Ollama

A "Fully Local" RAG system is the ultimate solution for data privacy and disconnected environments (like submarines or high-security labs). Ollama provides the foundational engine for this architecture.

The Local Stack

1. LLM: Llama 3 or Mistral (via Ollama).
2. Embeddings: Nomic-Embed-Text (via Ollama).
3. Vector DB: Chroma (running on local disk).
4. Orchestration: LangChain (running in your Python script).

Implementation Walkthrough

1. Start the Models

ollama serve
ollama pull llama3
ollama pull nomic-embed-text

2. Connect the Orchestrator

from langchain_ollama import OllamaLLM, OllamaEmbeddings
from langchain_chroma import Chroma

llm = OllamaLLM(model="llama3")
embeddings = OllamaEmbeddings(model="nomic-embed-text")

# Initialize Chroma locally
db = Chroma(persist_directory="./local_db", embedding_function=embeddings)

Performance Optimization

Local hardware (GPUs/CPUs) is the bottleneck.

• Quantization: Use "Q4_0" or "Q5_K_M" model versions to save VRAM.
• Context Management: Limit local context to 4k-8k tokens, as larger contexts significantly slow down inference on consumer hardware.

Pros vs. Cons

Feature	Local RAG	Cloud RAG (Bedrock/OpenAI)
Privacy	🔒 Maximum	⚠️ Limited by TOS
Cost	💸 Hardware only	💰 Per token price
Scale	🐢 Slower on large docs	⚡ Instant scaling
Setup	🛠️ Complex hardware reqs	☁️ API Key only

Use Cases for Local RAG

• Confidential HR documents.
• Proprietary source code.
• Remote areas with no internet.
• Developer experimentation and testing.

Exercises

1. Install Ollama and pull Llama 3.
2. Ask it to summarize a local markdown file.
3. Measure the "Tokens per second." Is it fast enough for a real user?