Online AI services are a great way to search through and parse long documents, but they come with some drawbacks.Your data is held by someone else, you use up tokens or require a subscription to get access, and you need internet access for it to work.By switching to a local-only approach, I can ensure that no sensitive documents can be leaked accidentally, and I don't have to worry about whether my private data is being used to train cloud models.
A research workflow locally Collect, summarize, embed, and retrieve, all locally When I set out to create a local AI research assistant, the goal was to be able to collect a body of information and then use an AI to sift through it rather than relying on what an AI "knows." Once a particular resource has been collected—say a PDF—a local model will produce a tidy summary and automatically extract highlights, sources, and pertinent questions.The documents will then be embedded (a process of chunking them up and turning them into vectors) so that an AI can easily search based on intent or semantic meaning rather than literal keyword matching.Related How Google Search Results Have Changed Through the Years From clean to messy.
Posts 1 By Joe Fedewa Once everything is up and running, the entire setup will function a bit like my own personal NotebookLM.The software setup Ollama for inference, a small embedding model, and a RAG Close There are three major parts to this setup: an inference layer, an embedding model, and an LLM that supports retrieval augmented generation (RAG).I used Ollama for the inference layer because it simplifies the llama.cpp setup process considerably.
It allows you to pull a huge range of AI models and run them with one or two commands, and it is compatible with most of the models I want to use.To handle the embedding process, I'm currently experimenting with EmbeddingGemma.The major limitation is that it can't handle images.
If you need to handle images, or if you just want to try something else, Qwen3-VL-Embedding is a reasonable choice.Both have variants that are small enough to run on any modern GPU.For now, I'm toggling the embedder manually, though eventually I'm going to create a watcher process that runs automatically whenever a new file is added.
The interface can be whatever you'd like—AnythingLLM and Open WebUI are easy, popular choices because they handle most of the tedious stuff behind the scenes.I'm using AnythingLLM for now.A local database is private and inexpensive Stop wasting tokens on RAG Close If you're paying for some kind of cloud-based AI service, whether it is a fixed subscription or a pay-as-you-go approach, it doesn't make sense to spend money on something that you can do for free locally.
Once everything is up and running, your only ongoing cost is the cost of electricity.Of course, you're also limited by your hardware.If you have a moderately powerful GPU, your local AI will be pretty fast.
If you're only using a CPU, you should expect it to be slower.I'm not limited to my desktop either.I've configured Ollama to be accessible to any device on my network, which means I can just connect my laptop or my phone.
Results are sent to and from my gaming PC, which runs the "brain" of the setup on a 5070 Ti.Privacy is less of an issue AI is great in many ways, but you can't really treat the cloud models as private.There are a few big problems.
The first is basic: if you upload something to an AI service, it will be stored on a web server for an indeterminate period of time.If something goes wrong and the AI company is compromised, your file could be too.Another issue is training.
Most of the major AI providers allow you to opt out of training new AI on your conversations, but it is easy to leave that setting enabled by accident.If you're working with a file that needs to be kept private, then that is an unacceptable possibility.There is also the issue of accidental sharing.
Both ChatGPT and Claude work in a way that could allow a search engine to index conversations, meaning someone could theoretically land on your shared conversation from Google or DuckDuckGo.Raw reasoning quality and speed are a bit worse The major performance deficit is reasoning.Frontier models, which are usually cloud-only, are better at thinking and synthesizing than small local models when you give them a hard analytical question.
You can’t hand local AI a pile of documents and expect it to draw novel scientific conclusions; there are just too many bottlenecks.It powers research on all of my AI projects My archive is only just getting started, but I've integrated it into my voice-based AI assistant too.That means everything I download and add can be accessed by talking with it.
The entire setup feels very futuristic.I can sit down, ask my Discord bot or my home assistant about some specific research I provided, and they'll speak to me about it in real time.It can then answer questions about the content organically.
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