thiswillbeyourgithub/wdoc/wdoc-skill

wdoc

Written for wdoc v5.1.0. On a different version, some arguments, defaults, or behaviors may differ.

wdoc is a RAG (Retrieval-Augmented Generation) system for summarizing, searching, and querying documents across 20+ file types. It works as a CLI (via Google Fire) and as a Python library (from wdoc import wdoc), routing every LLM call through LiteLLM (100+ providers).

This SKILL.md is the quick orientation. The deep material lives in two companion files:

• [REFERENCE.md](REFERENCE.md): every CLI argument, filetype, loader option, environment variable, and the full Python API.
• [EXAMPLES.md](EXAMPLES.md): copy-pasteable shell and Python examples.

Quick start

pip install -U wdoc[full]            # full install: all loaders. Plain `wdoc` ships only PDF + URL.
export ANTHROPIC_API_KEY="your_key"  # or whichever provider you use
 
wdoc query     paper.pdf "What are the main findings?"   # ask questions (RAG)
wdoc summarize paper.pdf                                  # detailed markdown summary
wdoc parse     paper.pdf                                  # parse to text, no LLM
wdoc web       "latest on quantum computing"             # DuckDuckGo + query

uvx wdoc[full] ... runs it without installing and sidesteps thinking about extras.

The four tasks

Core mechanics worth knowing

• Shortcuts: wdoc query FILE, wdoc summarize FILE, wdoc parse FILE, and wdoc web "q" expand to longer --task=... forms. Positional args work too: wdoc TASK PATH [QUERY].
• Filetype is auto-detected (--filetype=auto) but can be forced (pdf, youtube, anki, zotero, ...). Recursive filetypes (recursive_paths, zotero, karakeep, ddg, ...) fan one selector out into many documents.
• Two models per run: a strong --model answers, a cheap --query_eval_model filters chunks. Both take LiteLLM provider/model ids.
• kebab or snake case: --query-eval-model and --query_eval_model are equivalent.
• Piped input is auto-detected: cat file.pdf | wdoc parse --filetype=pdf.
• Privacy: --private (or WDOC_PRIVATE_MODE=true) blocks all outbound traffic and redacts API keys; pair it with local models (Ollama) and --llms_api_bases.
• Reuse embeddings: --save_embeds_as=idx.pkl once, then --load_embeds_from=idx.pkl to skip re-indexing.
• Cost guard: --dollar_limit (default 5) stops summaries/embeddings before they get expensive.

Common patterns

# Query every PDF in a tree
wdoc --task=query --path="papers/" --filetype=recursive_paths \
     --pattern="**/*.pdf" --recursed_filetype=pdf --query="..."
 
# Fully local / private
wdoc --private --model="ollama/qwen3:8b" --query_eval_model="ollama/qwen3:8b" \
     --embed_model="ollama/snowflake-arctic-embed2" --task=query --path=secret.pdf
 
# Parse for use elsewhere (text, langchain, langchain_dict, xml, split_text)
wdoc parse document.pdf --format=langchain_dict

from wdoc import wdoc
instance = wdoc(task="query", path="paper.pdf", model="openai/gpt-4o")
answer = instance.query_task("What are the main contributions?")
print(answer["final_answer"])

For anything beyond this page (exact argument types, defaults, every filetype's loader options, all WDOC_* env vars, the full Python API surface, and more examples), read [REFERENCE.md](REFERENCE.md) and [EXAMPLES.md](EXAMPLES.md).