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.