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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# How to Protect Sensitive Data by Running LLMs Locally with Ollama

**[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 10 min read · 2 upvotes · 0 comments

## Summary

A practical guide to running LLMs locally using Ollama to avoid sending sensitive data to cloud providers. Covers installation, pulling models, calling Ollama from Python via its native SDK, the OpenAI-compatible SDK, and LangChain integration. Demonstrates a provider-agnostic factory pattern for switching between cloud and local models, and shows how to build a LangGraph ReAct agent backed by a local model. Uses FinanceGPT (a personal finance app processing bank statements and tax forms) as a real-world example. Also discusses tradeoffs including response quality, inference speed, hardware requirements, and function calling reliability.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.freecodecamp.org/news/protect-sensitive-data-with-local-llms/>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#data-privacy](https://daily.dev/tags/data-privacy), [#langchain](https://daily.dev/tags/langchain), [#ollama](https://daily.dev/tags/ollama), [#langgraph](https://daily.dev/tags/langgraph)

[View this post on daily.dev](https://daily.dev/posts/how-to-protect-sensitive-data-by-running-llms-locally-with-ollama-krjyxqjkt)

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