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  "path": "/t/completely-inaccurate-results-from-file-read/176825#post_2",
  "publishedAt": "2026-06-15T21:53:39.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "digitally-born, scanned, and OCRed PDFs",
    "PyMuPDF OCR recipes",
    "LM Studio — Chat with Documents",
    "How to force LM Studio RAG to use full document in context?",
    "lmstudio-ai/lmstudio-bug-tracker#438",
    "lmstudio-ai/lmstudio-bug-tracker#789",
    "lmstudio-ai/lmstudio-bug-tracker#1572",
    "Prompt Template"
  ],
  "textContent": "Hmm… PDFs are often tricky because their internal structure can vary a lot, but there are also several known document/RAG-related failure modes around LM Studio, so this could be a compound issue:\n\n* * *\n\n## Short answer\n\nI would not debug this first as “which model is best?” I would debug the **document pipeline** first.\n\nChanging from DeepSeek to Qwen to Llama may help later, but only after you know that the model is actually receiving useful evidence from the file. If the PDF text is not extracted correctly, if OCR is bad, if LM Studio retrieves the wrong chunks, if the loaded context window is too small, or if the RAG/template path is failing, then changing the final chat model may not fix the root cause.\n\nA useful mental model is:\n\n\n    PDF/file\n    → text extraction or OCR\n    → chunking\n    → embeddings / indexing\n    → retrieval / citations\n    → context injection\n    → prompt/chat template\n    → final model answer\n\n\nA failure anywhere early in that chain can look, from the chat UI, like “the model ignored my PDF” or “the model hallucinated the book.”\n\nI am not saying all of these are happening in your case. The point is that several different failures can produce the same surface symptom: the model appears to ignore the file or invent details.\n\n* * *\n\n## 1. First check what kind of PDF it is\n\nA PDF is not always “a text file with pages.” Different PDFs behave very differently in document-chat/RAG tools.\n\nPDF type | What it contains | What LM Studio / RAG may see | Typical failure mode\n---|---|---|---\n**Digitally-born / text-based PDF** | Real text objects produced from Word, Google Docs, LaTeX, ebook tools, etc. | A PDF parser can often extract the text directly. | Text may extract, but paragraph order, page order, headers/footers, footnotes, tables, or chapter structure can still be messy.\n**Scanned / image-only PDF** | Images of pages, with no real text layer. | A text extractor may see little or no text unless OCR is run. | The model appears to “ignore the PDF” because there may be no usable extracted text.\n**OCRed / hybrid PDF** | Page images plus a hidden OCR text layer. | The tool may index the hidden OCR text, not what your eyes see on the page image. | Text may be selectable but still wrong: bad names, broken punctuation, wrong reading order, missing lines, bad hyphenation, or mixed-up pages.\n\nFor background, pypdf has a good explanation of why PDF text extraction is hard and why it helps to distinguish digitally-born, scanned, and OCRed PDFs. PyMuPDF also has a practical OCR note: PyMuPDF OCR recipes.\n\nSo the first diagnostic should be simple but important:\n\n  1. Open the PDF.\n  2. Try selecting/copying text from several pages.\n  3. Extract the text to `.txt` or `.md` outside LM Studio if possible.\n  4. Inspect the beginning, middle, and end of the extracted text.\n  5. Check whether character names, chapter headings, paragraph order, and page order survived.\n\n\n\nBeing able to visually read the PDF is not enough. The real question is whether LM Studio is receiving clean text.\n\n* * *\n\n## 2. LM Studio may not be putting the whole document into the model\n\nLM Studio’s own documentation says you can attach `.docx`, `.pdf`, and `.txt` files to chats, but it also distinguishes between short documents and long documents. Short documents may go fully into context; longer documents may use RAG, where LM Studio retrieves relevant parts of the document instead of sending the whole file to the model. See: LM Studio — Chat with Documents.\n\nThat distinction is important:\n\n**Attaching a file does not always mean the whole file is inside the model’s context.**\n\nIf LM Studio uses RAG, the model is probably seeing retrieved snippets, not the whole novel. That can work well for targeted questions, but it can be weak for whole-book tasks such as:\n\n  * “Create a discussion worksheet.”\n  * “Write a blurb for the novel.”\n  * “Summarize the whole plot.”\n  * “Analyze the main character arc.”\n  * “Find the main themes.”\n\n\n\nThose are global synthesis tasks. Basic RAG is often better at local lookup than whole-document understanding.\n\nFor a novel-length PDF, “make a worksheet” may not retrieve the right passages. The query is too broad. RAG has to decide which chunks are relevant, and it may miss the setup, turning points, ending, and character development.\n\n* * *\n\n## 3. Similar symptoms can come from LM Studio/RAG, not only from PDFs\n\nI would not treat this as only a PDF problem. PDF extraction/OCR is one possible failure layer, especially for scanned PDFs, but similar symptoms can happen with plain text files or code files too.\n\nA few relevant examples:\n\nSymptom | Why it matters\n---|---\nA user asked how to force LM Studio RAG to use the full document/context instead of only some files/chunks. | This is close to the “whole novel” problem: the user wants holistic analysis, while RAG may retrieve only a subset. See: How to force LM Studio RAG to use full document in context?\nLM Studio issue where retrieval strategy is chosen, but retrieving relevant citations fails. | This shows that “file attached” and “useful retrieved context reached the model” are not the same thing. See: lmstudio-ai/lmstudio-bug-tracker#438.\nLM Studio issue where RAG generated citations but still answered a whole-file counting question incorrectly. | This is a useful reminder that citations appearing does not prove whole-document understanding. See: lmstudio-ai/lmstudio-bug-tracker#789.\nQwen + rag-v1 / prompt-template issue report. | Not necessarily your exact issue, but since you mentioned Qwen, it is worth knowing that prompt-template/RAG interactions can fail. See: lmstudio-ai/lmstudio-bug-tracker#1572.\n\nThe important point is not “LM Studio is broken.” The important point is that “the model gave inaccurate results from an attached file” is a surface symptom, not a root cause.\n\n* * *\n\n## 4. Practical tests I would run\n\nI would test from the earliest failure layer upward.\n\n### A. Test whether the PDF has usable text\n\nAsk:\n\n  * Can I select/copy text from the PDF?\n  * Does copied text preserve names, chapter headings, and paragraph order?\n  * If I extract the PDF to `.txt` or `.md`, does the output look like the novel?\n  * Are the beginning, middle, and end all present?\n  * Is the book scanned, OCRed, or digitally generated?\n\n\n\nIf the extracted text is empty, garbled, out of order, or missing chapters, fix that first. Use OCR or a better document parser before testing models.\n\n### B. Test whether LM Studio can quote exact local evidence\n\nBefore asking for a worksheet or blurb, ask narrow verification questions:\n\n\n    Using only the attached file, quote the first paragraph of Chapter 1.\n\n\n\n    Using only the attached file, what is the first sentence of Chapter 3?\n\n\n\n    Find the exact phrase \"<unique phrase from the book>\" and quote the surrounding paragraph.\n\n\n\n    Using only the attached file, list the chapter titles you can find.\n\n\nIf it cannot do these, do not trust a worksheet or blurb yet.\n\n### C. Check whether LM Studio is using full context or RAG\n\nIf LM Studio shows citations, inspect them.\n\n  * Are the citations from the right part of the book?\n  * Are they from only one tiny section?\n  * Are they irrelevant?\n  * Are citations missing?\n  * Does LM Studio show retrieval/citation errors?\n\n\n\nIf the citations are wrong or absent, the problem is probably retrieval/context, not simply model quality.\n\n### D. Check the actually loaded context length\n\nDo not rely only on the model’s advertised maximum context length. Check what context length LM Studio actually loaded.\n\nIf the model is actually loaded with a small context window, a clean text file can still fail because the full document does not fit. LM Studio may then use retrieval, truncation, or some other overflow behavior instead of giving the model the whole book.\n\n### E. Check the embedding/indexing layer\n\nRAG is not only the final LLM. It also depends on an embedding model and an index.\n\nSo if you change DeepSeek → Qwen → Llama but the retrieval/indexing layer is broken, the answers may stay bad. If you changed embedding models, RAG plugins, or file indexing settings, rebuild/reindex if the workflow supports it.\n\n### F. Check the prompt/chat template if one model family behaves especially oddly\n\nLM Studio usually detects the prompt template from model metadata, and in most cases you should not need to touch it. But template problems do exist, especially with newer or less-tested model/template combinations. LM Studio has prompt-template docs here: Prompt Template.\n\nThis is not the first thing I would blame, but if one model family fails in a different way from the others, try a known-good LM Studio community quant/template or another model family.\n\n* * *\n\n## 5. A better workflow for a whole novel\n\nFor a novel-length document, I would not start with:\n\n\n    Make a discussion worksheet for this novel.\n\n\nThat asks the system to solve many hidden subtasks at once:\n\n  * read the whole book\n  * preserve chapter order\n  * identify plot structure\n  * identify characters\n  * identify themes\n  * distinguish major/minor events\n  * synthesize discussion questions\n  * avoid inventing details\n\n\n\nA safer workflow is staged:\n\nStep | Goal\n---|---\n1. Extract/OCR the PDF to clean text or Markdown. | Make sure the book exists as usable text.\n2. Split by chapter. | Avoid relying on one huge retrieval step.\n3. Summarize each chapter separately. | Preserve local details and sequence.\n4. Build a “story bible.” | Characters, setting, timeline, conflicts, themes, ending.\n5. Generate the worksheet/blurb from the story bible. | Now the model has a compact whole-book representation.\n6. Verify against source passages. | Prevent invented plot points or wrong character claims.\n\nFor example:\n\n\n    First, summarize Chapter 1 only. Include:\n    - setting\n    - characters introduced\n    - key events\n    - conflicts\n    - important quotes\n    - unresolved questions\n\n    Use only the attached chapter text.\n\n\nThen after all chapters:\n\n\n    Using the chapter summaries below, create:\n    1. a spoiler-free blurb\n    2. 10 discussion questions\n    3. 5 theme questions\n    4. 5 character-arc questions\n    5. an answer key with evidence references\n\n    Do not add details that are not supported by the summaries.\n\n\nThis is slower, but it is much more reliable than asking a local RAG setup to infer the whole novel in one pass.\n\n* * *\n\n## 6. Prompting RAG more effectively\n\nIf you use RAG, make the retrieval target explicit.\n\nWeak prompt:\n\n\n    Make a discussion worksheet for this novel.\n\n\nBetter prompt:\n\n\n    Using only the attached novel, first retrieve and quote passages about:\n    - the protagonist's goal\n    - the main conflict\n    - major turning points\n    - the ending\n    - recurring themes\n    - important relationships\n\n    After quoting the evidence, create a discussion worksheet based only on that evidence.\n    If you cannot find enough evidence, say so instead of guessing.\n\n\nEven better, ask for evidence before synthesis:\n\n\n    Before making the worksheet, find 8-12 short passages that establish the plot, main characters, conflict, themes, and ending. Quote those passages first. Then use only those passages to draft the worksheet.\n\n\nThis helps because retrieval systems work better when the query contains the terms and ideas they should search for. LM Studio’s RAG docs also recommend giving the query enough context and expected terminology: LM Studio — Chat with Documents.\n\n* * *\n\n## 7. Model choice still matters, but later\n\nOnce the document pipeline is verified, then model choice matters.\n\nA larger or better model may improve:\n\n  * summarization quality\n  * reasoning over chapter summaries\n  * worksheet quality\n  * tone and style\n  * avoiding shallow questions\n  * following instructions\n\n\n\nBut model choice will not fix:\n\n  * an image-only PDF with no OCR\n  * a broken OCR text layer\n  * bad reading order\n  * missing chapters\n  * irrelevant retrieved chunks\n  * a too-small context window\n  * a broken embedding/indexing layer\n  * a prompt-template/RAG formatting issue\n\n\n\nSo I would debug in this order:\n\n  1. **PDF/text extraction**\n  2. **OCR/layout quality**\n  3. **LM Studio file indexing**\n  4. **RAG/citations**\n  5. **actual loaded context length**\n  6. **embedding/index settings**\n  7. **prompt/chat template**\n  8. **model choice**\n\n\n\n* * *\n\n## 8. What I would include if asking for more help\n\nIf you want others to debug this efficiently, I would include:\n\nDetail | Why it helps\n---|---\nLM Studio version | Document/RAG behavior can change across versions.\nOS and hardware | Local processing, memory, and model loading can matter.\nExact model file / quant | “Qwen” or “Llama” is not specific enough.\nActual loaded context length | Advertised context is not always the loaded context.\nPDF type | Digitally-born, scanned, or OCRed/hybrid.\nWhether extracted text looks correct | Separates PDF/OCR problems from RAG/model problems.\nWhether citations appear | Shows whether retrieval is happening.\nWhether citations are relevant | Shows whether retrieval is useful.\nA tiny reproducible test | Example: one page or one chapter that should be easy to quote.\n\nA very useful minimal test is:\n\n\n    Here is a 1-page excerpt copied as plain text. Can the model answer correctly from this?\n\n\nIf it works from plain text but fails from the PDF, the problem is likely PDF extraction/OCR/RAG ingestion.\n\nIf it fails even from plain text, then look at context length, prompt template, model settings, or the model itself.\n\n* * *\n\n## Bottom line\n\nThis may become a model-quality issue eventually, but I would not start there.\n\nFor this kind of task, the most likely broad categories are:\n\n  1. the PDF did not become clean text,\n  2. LM Studio used RAG and retrieved the wrong or incomplete chunks,\n  3. the task requires whole-book understanding rather than local lookup,\n  4. context/embedding/template settings are interfering,\n  5. only after that, the chosen local model may be too small or weak for the synthesis task.\n\n\n\nSo the best first move is not “try another model.” The best first move is to prove that LM Studio is receiving the right text and the right evidence.",
  "title": "Completely inaccurate results from file read"
}