I'm trying to build a privacy-first RAG system using Ollama and local Mistral models. In your opinion, is LlamaIndex the best framework for RAG applications when you aren't using OpenAI? Some frameworks seem very heavily biased toward the GPT ecosystem, and I want to make sure I won't run into integration headaches with local setups.
3 answers
LlamaIndex is actually very model-agnostic. They have a first-class integration with Ollama and Hugging Face. You just need to swap out the llm and embed_model in the global settings. I’ve run full RAG pipelines on a local MacBook Pro using LlamaIndex and it worked flawlessly. The framework doesn't care if the API endpoint is api.openai.com or localhost:11434. In fact, their documentation has specific sections for local-only setups, which shows they take the privacy-first community seriously.
Does it support GGUF or EXL2 formats directly, or do I always have to go through a provider like Ollama? I’m trying to squeeze as much performance as possible out of my GPU and want to avoid extra overhead.
For local RAG, memory management is key. LlamaIndex allows you to store the index as a simple JSON file on your disk if you don't want to run a full vector database.
That "SimpleVectorStore" is perfect for small-scale local projects, Karen. It makes the whole application portable without needing Docker or a cloud subscription.
Scott, you can use the `LlamaCPP` module in LlamaIndex to load GGUF files directly without any server in between. This gives you the lowest possible latency. You just point the framework to your `.gguf` file path. It’s incredibly efficient and gives you full control over the context window and temperature settings right from the Python script.