LOCAL DOCUMENT AI

Use local AI with private documents

A genuinely local document workflow can keep the document, retrieval step and model inference on your own machine. The important part is checking every layer rather than assuming that a local model makes the whole pipeline local.

Short documents can fit directly into context

If the extracted text fits within the model's usable context window, an app can place the full document into the conversation. This is simple but memory use rises with context length.

Long documents often use retrieval

Retrieval-augmented generation, or RAG, breaks a source into searchable chunks and selects relevant parts for the model. It can make large collections practical, but poor retrieval can omit the section that contains the answer.

Scanned PDFs need OCR first

An image of text is not the same as selectable text. Scanned contracts, letters and receipts may need OCR before a text model can work with them reliably.

Check where embeddings and retrieval happen

The text model can be local while embeddings or document indexing use a remote API. Review the document application's configuration before treating sensitive material as offline.

LM Studio supports offline document chat

LM Studio currently documents local document chat and RAG as offline-capable after model files are downloaded. It states that attached documents and document processing stay on the machine.

Verify answers against the source

RAG does not make hallucinations impossible. Ask the model to identify the relevant passage and verify important claims against the original document.

Source: LM Studio offline operation and document chat guidance.