Migrate all repos into monorepo context folders
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
This commit is contained in:
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"""PDF text extraction using pdfplumber.
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Detects scanned pages (no extractable text) and delegates to:
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1. Mistral Vision API (preferred — handles handwriting well)
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2. Tesseract OCR (fallback if no LLM configured)
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"""
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import base64
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import io
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import logging
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from pathlib import Path
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import pdfplumber
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from .base import ExtractionResult
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logger = logging.getLogger(__name__)
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class PDFExtractor:
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"""Extracts text from PDF files, with Vision LLM or OCR fallback for scanned pages."""
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def __init__(
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self,
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ocr_enabled: bool = True,
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ocr_language: str = "deu+eng",
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llm_provider: str = "mistral",
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llm_model: str = "mistral-small-latest",
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mistral_api_key: str = "",
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):
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self.ocr_enabled = ocr_enabled
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self.ocr_language = ocr_language
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self.llm_provider = llm_provider
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self.llm_model = llm_model
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self.mistral_api_key = mistral_api_key
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self._use_vision = bool(mistral_api_key)
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def can_handle(self, file_path: Path) -> bool:
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"""Return True for .pdf files."""
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return file_path.suffix.lower() == ".pdf"
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def extract(self, file_path: Path) -> ExtractionResult:
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"""Extract text from PDF, using Vision LLM or OCR for scanned pages."""
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pages_text: list[str] = []
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has_direct_text = False
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has_vision_text = False
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with pdfplumber.open(file_path) as pdf:
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for page_num, page in enumerate(pdf.pages, start=1):
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text = page.extract_text() or ""
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text = text.strip()
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if text:
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pages_text.append(text)
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has_direct_text = True
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elif self.ocr_enabled:
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# Scanned page — try Vision LLM first, then Tesseract
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logger.debug(
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"Page %d of %s has no text, attempting vision OCR",
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page_num, file_path,
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)
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ocr_text = self._vision_ocr_page(page, page_num, file_path)
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if ocr_text:
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pages_text.append(ocr_text)
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has_vision_text = True
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# Determine extraction method
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if has_direct_text and has_vision_text:
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method = "mixed"
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elif has_vision_text:
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method = "vision-ocr"
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else:
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method = "direct"
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return ExtractionResult(
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text="\n\n".join(pages_text),
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source_file=file_path,
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extraction_method=method,
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metadata={"page_count": len(pages_text)},
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)
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def _vision_ocr_page(self, page, page_num: int, file_path: Path) -> str:
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"""OCR a PDF page using Mistral Vision API, with Tesseract fallback."""
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if self._use_vision:
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try:
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return self._mistral_vision_ocr(page, page_num)
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except Exception as e:
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logger.warning(
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"Vision OCR failed for page %d of %s: %s, trying Tesseract",
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page_num, file_path, e,
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)
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# Fallback to Tesseract
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return self._tesseract_ocr(page, page_num, file_path)
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def _mistral_vision_ocr(self, page, page_num: int) -> str:
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"""Send page image to Mistral Vision for text extraction."""
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import httpx
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# Convert page to PNG image
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image = page.to_image(resolution=200)
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img_buffer = io.BytesIO()
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image.original.save(img_buffer, format="PNG")
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img_bytes = img_buffer.getvalue()
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img_b64 = base64.b64encode(img_bytes).decode("utf-8")
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# Call Mistral Vision API via LiteLLM-compatible format
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response = httpx.post(
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"https://api.mistral.ai/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {self.mistral_api_key}",
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"Content-Type": "application/json",
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},
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json={
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"model": self.llm_model,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Extract ALL text from this image. This is a handwritten note or document. "
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"Transcribe everything you can read, preserving structure (paragraphs, lists, headings). "
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"If text is in German, keep it in German. Return ONLY the extracted text, no commentary.",
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{img_b64}",
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},
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},
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],
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}
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],
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"max_tokens": 4000,
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},
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timeout=60.0,
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)
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response.raise_for_status()
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data = response.json()
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text = data["choices"][0]["message"]["content"]
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logger.info("Vision OCR page %d: extracted %d chars", page_num, len(text))
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return text.strip()
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def _tesseract_ocr(self, page, page_num: int, file_path: Path) -> str:
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"""Fallback: OCR a PDF page using Tesseract."""
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try:
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import pytesseract
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image = page.to_image(resolution=300)
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text = pytesseract.image_to_string(image.original, lang=self.ocr_language)
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return text.strip()
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except Exception as e:
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logger.warning(
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"Tesseract OCR failed for page %d of %s: %s", page_num, file_path, e
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)
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return ""
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