doc-reader

name doc-reader description Read any common document/data file — PDF, Word (.docx), Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR), CSV/TSV, plain text, JSON/YAML/TOML, HTML/XML, and most source-code files. Use the `read_document` tool. category tool Universal Document Reader Purpose Return extracted text from any supported file in a single unified JSON envelope. The tool dispatches by file extension — you always call the same tool regardless of format. Supported formats Category Extensions Notes PDF .pdf Text pages extracted in ms; scanned/image pages fall back to OCR Word .docx Paragraphs + table cells Excel .xlsx , .xls All sheets, first 100 rows per sheet as preview PowerPoint .pptx Slide text content Images .png/.jpg/.jpeg/.gif/.bmp/.webp/.tiff OCR only CSV / TSV .csv , .tsv Raw text with encoding fallback Plain text .txt/.md/.log/.rst Encoding fallback Config .json/.yaml/.yml/.toml/.ini/.cfg/.env Raw text Markup .html/.htm/.xml Raw text (no HTML stripping) Source code .py/.js/.ts/.tsx/.go/.rs/.java/.cpp/.c/.sql/.sh/... Raw text Unknown extension anything else Best-effort read as UTF-8/GBK text Blocked (rejected at /upload ): executables ( .exe/.dll/.so/... ) and archives ( .zip/.tar/... ). Ask the user to unpack archives locally first. Usage Always call the tool directly — do not run Python from bash. read_document(file_path="uploads/paper.pdf") read_document(file_path="uploads/annual_report.pdf", pages="1-10") read_document(file_path="uploads/contract.docx") read_document(file_path="uploads/sales.xlsx") read_document(file_path="uploads/deck.pptx") read_document(file_path="uploads/chart.png") # image → OCR read_document(file_path="uploads/config.yaml") read_document(file_path="uploads/notes.md") The pages parameter only applies to PDF; other formats ignore it. Return envelope All formats share this shape: { "status" : "ok" , "file" : "paper.pdf" , "format" : "pdf" , "char_count" : 52000 , "truncated" : true , "text" : "..." } Format-specific extra fields: Format Extra keys pdf total_pages , pages_read , ocr_pages , ocr_engine , ocr_quality , skipped_pages docx paragraphs , tables excel sheets (array of {name, rows, cols} ) pptx slides text encoding , size Content longer than 15000 chars is truncated; for PDFs use the pages parameter to read slices. Workflows Paper / report summary 1. read_document(file_path="paper.pdf") → full text 2. Extract abstract, methodology, conclusion → summarize Contract review 1. read_document(file_path="contract.docx") → paragraphs + tables 2. Flag key clauses (termination, liability, payment, IP) Spreadsheet quick-look 1. read_document(file_path="sales.xlsx") → all sheet previews 2. If user wants trade journal analysis specifically, pivot to `analyze_trade_journal` tool instead (see trade-journal skill). Chart / screenshot / scanned PDF 1. read_document(file_path="scan.png") → OCR text 2. If OCR returns empty, tell the user; don't fabricate. OCR Configuration The read_document tool automatically uses OCR for PDF pages with insufficient extractable text. OCR Threshold Use min_text_per_page to control when OCR is triggered (default: 50 characters): read_document( "scanned_report.pdf" , min_text_per_page= 10 ) # More aggressive OCR read_document( "mixed_pdf.pdf" , min_text_per_page= 100 ) # Less aggressive OCR OCR Engine Configuration Two OCR engines are built in — no extra packages needed beyond the engine SDK: Engine Type Requires Install rapid Local (offline) rapidocr_onnxruntime pip install rapidocr_onnxruntime llm-vision Cloud A vision-capable LLM model + API key No extra install — uses your existing LLM provider config The llm-vision engine works with any OpenAI-compatible vision model (GPT-4o, Qwen-VL, Gemini, Claude, GLM-4V, etc.). It reuses your existing LANGCHAIN_PROVIDER / LANGCHAIN_MODEL_NAME / API key configuration — no separate provider mapping needed. If you explicitly set VIBE_TRADING_OCR_ENGINE=llm-vision , your model choice is trusted; a real API error from the provider is clearer feedback than a heuristic guess. To override the model used for OCR (without changing your agent's main model): VIBE_TRADING_OCR_LLM_MODEL=qwen3.7-plus Set VIBE_TRADING_OCR_ENGINE to select the engine: auto (default): use local engines only, never cloud (privacy: document pages never leave the machine) rapid : force RapidOCR (local, ONNX) llm-vision : force LLM vision OCR (cloud — pages are sent to your configured LLM provider) none : disable OCR entirely Response Fields PDF responses include OCR metadata: ocr_engine : name of the OCR engine used (e.g. "rapid", "llm-vision") or null ocr_pages : number of pages processed via OCR skipped_pages : number of pages skipped (no OCR engine available) ocr_quality : object with quality_flag ( good / degraded / no_ocr_engine / no_ocr_needed ), ocr_pages , and text_density (chars per page) Notes Encoding fallback order for text: utf-8 → utf-8-sig → gbk → gb2312 → big5 → latin-1. OCR uses the configured engine (RapidOCR for local, or LLM vision for cloud). If no engine is available, image/scanned files return empty text with a note field — tell the user to install rapidocr-onnxruntime or set VIBE_TRADING_OCR_ENGINE=llm-vision with a vision-capable model. Excel previews are limited to 100 rows per sheet to stay in budget. If the user needs full data (e.g. trade journals), call analyze_trade_journal instead. Source-code files are returned raw; do not re-format or re-indent.