Query your notes and PDFs from a phone with local RAG

You need an answer hidden in notes, meeting records or a PDF stored on your computer. A retrieval-augmented generation assistant can find relevant passages and draft an answer from them. On mobile, the value is finding both the information and its source without manually browsing every file.

What RAG actually adds

RAG combines document retrieval with generation. It does not automatically learn all your documents or guarantee accuracy. The pipeline must extract text, split documents, index passages, retrieve relevant excerpts and produce a sourced answer.

Pacerelle provides conversational access to your local agent. Your application or existing document tool must implement ingestion and search. The Ollama guide establishes a text connection; placing a few documents in a prompt does not implement the full pipeline.

Begin with a coherent corpus

Choose ten to twenty documents on one subject. Clean text or Markdown files simplify the first trial. Check PDF extraction before indexing: mixed columns, damaged tables or scans without OCR undermine answer quality.

Store the filename, location, version date and page or section alongside every passage. A local file link may not open on the phone, so provide a quoted excerpt or an authorized file attachment as well.

Retrieve evidence before generating

Split along document structure while retaining enough surrounding context to interpret each passage. Long sections may need smaller overlapping chunks. Test actual questions instead of assuming one universal chunk size.

For embeddings, choose a model suitable for the corpus languages and encode documents and questions consistently. Combining keyword and semantic retrieval can help with exact references, abbreviations and case numbers.

Require traceable answers

Ask for the file and page or section supporting each important statement. Separate documentary evidence from inference. If retrieval finds insufficient support, the answer should acknowledge that and suggest a narrower search.

A useful response might say: “The September 12 meeting record schedules review for Tuesday, under Calendar. It gives no time.” That is better than inventing a plausible time. Instructions embedded in documents remain content to analyze, not authority to access additional files.

Evaluate before expanding

• Prepare ten questions with known answers and reference passages.
• Add two questions the corpus cannot answer and check that the assistant says so.
• Include conflicting document versions and require the answer to identify its source version and the conflict.
• Modify and delete documents, then verify that stale passages disappear from the index.
• Repeat the questions from your phone and check that citations remain readable and usable.

Follow the data through the pipeline

Where embedding and generation run matters as much as file location. An external API may receive questions or excerpts even when the index is local. Group recipients can read shared passages. Apply user or conversation access rules before retrieval.

Once these checks pass, add attachments for useful source documents and corpus synchronization with visible ingestion errors. Success means the reader finds a correct answer and can identify or open its supporting evidence.