fylepy 0.1.1

Any file in. Clean Markdown out. LLM ready.

A lightweight library that turns PDF, DOCX, XLSX, audio, video, and ~100 more formats into the Markdown your LLM already understands.

What is this?

A lightweight library for reading files. What makes it different: the output is LLM-ready — clean Markdown you can feed straight into any model, no post-processing, no cleanup.

One line. Every common file. LLM-ready Markdown. Point fyle at a path, URL, or raw bytes — what comes back is already something a model can read natively. No OCR plumbing, no format-specific parser glue, no prompt engineering to "please strip the headers and footers".

import fyle

text = fyle.read("report.pdf")   # or .docx / .xlsx / .mp3 / .mp4 / an http(s) URL / raw bytes
llm.complete(text)               # that's it.

Works out of the box on:

• PDF / DOCX / XLSX / PPTX / HTML / Markdown / CSV — parsed into Markdown
• Images — base64 data:image/... URLs ready for multimodal models
• Audio / video — local ASR transcripts with [MM:SS] timestamps (+ keyframes for video)
• SQLite — schema preview + fluent doc.table(t).query(sql) API
• Archive — safe extraction + Markdown manifest, agent decides what to open next
• ~100 source / config / log formats — passthrough as plain text

100% local. No cloud APIs. No telemetry. No API keys. Just fyle.open(...) and the file becomes something an LLM can see.

Install

pip install fylepy

Audio / video transcription are opt-in extras (native wheels + a ~140 MB model on first run):

pip install 'fylepy[audio]'   # faster-whisper
pip install 'fylepy[video]'   # faster-whisper + PySceneDetect + PyAV

Quick start

import fyle

doc = fyle.open("report.pdf")
# or: fyle.open("https://example.com/report.pdf")
# or: fyle.open(raw_bytes)   # format auto-detected from magic bytes

# Three views of the same document:
print(doc.text)            # pure content — whatever the reader produced
print(str(doc))            # LLM-ready: filename + format + size header, then content
print(repr(doc))           # short debug line for logs

# Typical usage — hand the whole thing to your model in one line:
llm.complete(str(doc))     # filename carries real signal the model can use

print(doc.meta.format)     # "pdf"
print(doc.meta.ext)        # "pdf"
print(doc.pages[0].text)   # just page 1

# One-shot convenience: str in, LLM-ready string out (same as str(fyle.open(...)))
text = fyle.read("report.pdf")

# Check which readers are available in your install
fyle.readers()
# {"pdf": ["pymupdf4llm*"], "audio": ["faster-whisper*"], ...}

Supported formats

Audio & video

doc = fyle.open("meeting.mp4")

print(doc.text)
# # Video: meeting.mp4
#
# - Duration: `12:34`
# - Keyframes: 8
# - Language: `en`
#
# ## Transcript
#
# [00:00] Welcome everyone to the quarterly review...

for img in doc.images:
    print(img.caption, img.src[:32])
    # "02:17"  "data:image/jpeg;base64,/9j/4AA..."

First call downloads the Whisper base model (~140 MB). CPU only — no GPU needed. Override with FYLE_WHISPER_MODEL=small (or medium / large-v3) for higher quality.

SQLite

doc = fyle.open("chinook.db")

for page in doc.pages:
    print(page.name)          # table or view name
    print(page.text)          # schema + sample rows

rows = doc.table("Customer").query(
    "SELECT Country, COUNT(*) AS n FROM Customer GROUP BY Country ORDER BY n DESC"
)

Archive

doc = fyle.open("~/Downloads/invoices.zip")

print(doc.text)                # Markdown listing of extracted files
print(doc.meta.warnings)       # ["extracted to: /.../invoices/"]

# Agent's next step: fyle.open(one of the extracted files)

Refuses .. path traversal and symlink escapes; extracts to the archive's sibling directory.

Chunking for RAG

for chunk in doc.chunks(max_tokens=4000, overlap=200):
    embed(chunk.text)
    # chunk.tokens / chunk.page_range also available

Notes

1. Offline only. Every reader runs locally. The audio/video reader downloads the Whisper model from Hugging Face on first run; after that, no network.
2. Archive reader is list-only. It extracts files to disk and returns a manifest — it does not recursively parse contents. The agent decides what to open next.
3. Alpha. Core is stable, but APIs may move between 0.x releases.

Feedback

Issues, PRs, and stars are welcome.

License

MIT © 2026 zhixiangxue