Building a Private RAG Pipeline with Open Source LLMs
Data privacy is paramount. Learn how to build a completely offline, secure RAG pipeline using Llama 3, Ollama, and local vector databases.
Sending sensitive corporate documents to OpenAI or Anthropic isn't an option for everyone. For industries like healthcare, finance, and legal, data privacy is non-negotiable. Fortunately, the open-source ecosystem has matured enough to build powerful, local RAG pipelines that rival their cloud-hosted counterparts.
The Stack: Privacy First
We can now build a fully functional "Chat with your Data" system that runs entirely within your firewall. Here is the battle-tested stack we recommend:
- LLM: Llama 3 (8B or 70B) running via Ollama. These models have reached a level of reasoning that is sufficient for most RAG tasks.
- Embeddings: Nomic-Embed-Text. Good embeddings are the heart of RAG; if your retrieval is bad, your generation will be bad. Nomic offers state-of-the-art performance for retrieval tasks.
- Vector DB: Qdrant or ChromaDB running locally in Docker. These databases are optimized for high-dimensional search and can handle millions of vectors with millisecond latency.
- Orchestration: LangChain or LlamaIndex to glue it all together.
Why Go Local?
This architecture ensures that zero data leaves your perimeter. There are no API calls to external servers, no data logging by third parties, and no risk of your proprietary information leaking into a public model's training set. We have deployed similar systems for clients who need absolute peace of mind. This philosophy of "privacy by default" is also central to Pacibook.com, where user data is protected by similar architectural principles.