File size: 6,822 Bytes
44c2f50 4e6fd2a 44c2f50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | """Load documents (PDF / PNG / JPG) into text + page images for extraction.
Strategy:
1. For PDFs, try text extraction first via pdfplumber. If the doc has meaningful
text, we use it directly (fast, cheap).
2. Always also render page images via PyMuPDF — GPT-5 nano vision handles layout
information that pure text misses (tables, spatial context on receipts).
3. Standalone images (PNG/JPG) skip text extraction and go straight to vision.
The extractor decides how to use text vs images based on `source_type`.
"""
from __future__ import annotations
import base64
import io
from dataclasses import dataclass, field
from pathlib import Path
import fitz # PyMuPDF
import pdfplumber
from PIL import Image
from src.utils.logging import logger
# --- Config knobs ------------------------------------------------------------
# If extracted text is shorter than this, treat the PDF as scanned/image-based.
_MIN_TEXT_CHARS_FOR_TEXT_PDF = 100
# Max pages we render as images (guards against absurdly long PDFs pre-v2).
_MAX_PAGES_TO_RENDER = 5
# DPI for rendering — higher = clearer OCR but slower + bigger payload.
# 200 DPI is a good sweet spot for receipts + invoices.
_RENDER_DPI = 200
# Max side length before we downscale — GPT-5 nano vision handles up to 2048.
_MAX_IMAGE_SIDE = 2048
@dataclass
class LoadedDocument:
"""The output of the loader — text and/or images ready for the LLM."""
text: str = ""
images_b64: list[str] = field(default_factory=list)
source_type: str = "unknown" # "text_pdf" | "image_pdf" | "image" | "empty"
page_count: int = 0
filename: str = ""
# --- Helpers ---------------------------------------------------------------
def _image_to_b64(img: Image.Image) -> str:
"""PIL Image -> base64-encoded PNG string suitable for OpenAI vision."""
# Downscale if needed
if max(img.size) > _MAX_IMAGE_SIDE:
ratio = _MAX_IMAGE_SIDE / max(img.size)
new_size = (int(img.size[0] * ratio), int(img.size[1] * ratio))
img = img.resize(new_size, Image.LANCZOS)
buf = io.BytesIO()
img.convert("RGB").save(buf, format="PNG", optimize=True)
return base64.b64encode(buf.getvalue()).decode("ascii")
def _render_pdf_pages(pdf_bytes: bytes, max_pages: int = _MAX_PAGES_TO_RENDER) -> list[str]:
"""Render each PDF page to a base64 PNG using PyMuPDF."""
images_b64: list[str] = []
with fitz.open(stream=pdf_bytes, filetype="pdf") as doc:
for page_num in range(min(len(doc), max_pages)):
page = doc[page_num]
pix = page.get_pixmap(dpi=_RENDER_DPI)
img = Image.frombytes("RGB", (pix.width, pix.height), pix.samples)
images_b64.append(_image_to_b64(img))
return images_b64
def _extract_pdf_text(pdf_bytes: bytes) -> str:
"""Extract text from all pages via pdfplumber. Empty string on failure."""
try:
with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
parts: list[str] = []
for page in pdf.pages[:_MAX_PAGES_TO_RENDER]:
text = page.extract_text() or ""
if text.strip():
parts.append(text)
return "\n\n".join(parts).strip()
except Exception as e:
logger.warning(f"pdfplumber text extraction failed: {e}")
return ""
# --- Public API ------------------------------------------------------------
def load_document(
file_bytes: bytes,
filename: str = "document",
*,
render_images: bool = True,
) -> LoadedDocument:
"""Turn raw file bytes into a LoadedDocument.
- `render_images=False` skips image rendering for cost savings on text-heavy PDFs.
The extractor can decide based on doc size / cost budget.
"""
ext = Path(filename).suffix.lower()
# --- Standalone images -> straight to vision -----------------------------
if ext in {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".tiff", ".tif"}:
try:
img = Image.open(io.BytesIO(file_bytes))
return LoadedDocument(
text="",
images_b64=[_image_to_b64(img)],
source_type="image",
page_count=1,
filename=filename,
)
except Exception as e:
logger.error(f"Failed to load image {filename}: {e}")
return LoadedDocument(source_type="empty", filename=filename)
# --- PDFs ---------------------------------------------------------------
if ext == ".pdf" or file_bytes[:4] == b"%PDF":
text = _extract_pdf_text(file_bytes)
has_text = len(text) >= _MIN_TEXT_CHARS_FOR_TEXT_PDF
images_b64: list[str] = []
if render_images or not has_text:
try:
images_b64 = _render_pdf_pages(file_bytes)
except Exception as e:
logger.warning(f"PDF image rendering failed: {e}")
source_type = "text_pdf" if has_text else "image_pdf"
# Get page count for logging
try:
with fitz.open(stream=file_bytes, filetype="pdf") as doc:
page_count = len(doc)
except Exception:
page_count = len(images_b64)
logger.info(
f"Loaded {filename}: source_type={source_type}, pages={page_count}, "
f"text_chars={len(text)}, images={len(images_b64)}"
)
return LoadedDocument(
text=text,
images_b64=images_b64,
source_type=source_type,
page_count=page_count,
filename=filename,
)
# --- Plain text ---------------------------------------------------------
# `.txt` (and other text-like extensions) come from the eval CLI's inline
# `text` field, and from OCR outputs. No images to render — the LLM works
# on the decoded string directly. Fall back to lossy UTF-8 decoding so a
# stray non-UTF byte doesn't kill the whole record.
if ext in {".txt", ".text", ".md", ".log"}:
try:
text = file_bytes.decode("utf-8", errors="replace").strip()
except Exception as e:
logger.error(f"Failed to decode text file {filename}: {e}")
return LoadedDocument(source_type="empty", filename=filename)
source_type = "text" if text else "empty"
logger.info(f"Loaded {filename}: source_type={source_type}, text_chars={len(text)}")
return LoadedDocument(
text=text,
images_b64=[],
source_type=source_type,
page_count=1 if text else 0,
filename=filename,
)
# --- Unknown format -----------------------------------------------------
logger.warning(f"Unknown file extension {ext!r} for {filename}. Treating as empty.")
return LoadedDocument(source_type="empty", filename=filename)
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