mirror of
https://github.com/crewAIInc/crewAI.git
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bccb27ab2e
- Restructure pyproject.toml to move non-essential dependencies to optional extras - Add graceful handling for missing optional dependencies in core modules - Create memory, knowledge, telemetry, visualization, auth, and llm-integrations extras - Implement helpful ImportError messages directing users to install specific extras - Add comprehensive test suite for lite installation scenarios - Maintain backward compatibility with existing installations - Support minimal core installation with Agent/Crew/Task functionality Addresses GitHub issue #3026 for lightweight crewAI installation Co-Authored-By: João <joao@crewai.com>
59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
from pathlib import Path
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from typing import Dict, List
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try:
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import pdfplumber
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PDFPLUMBER_AVAILABLE = True
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except ImportError:
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PDFPLUMBER_AVAILABLE = False
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pdfplumber = None
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from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
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class PDFKnowledgeSource(BaseFileKnowledgeSource):
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"""A knowledge source that stores and queries PDF file content using embeddings."""
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def load_content(self) -> Dict[Path, str]:
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"""Load and preprocess PDF file content."""
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pdfplumber = self._import_pdfplumber()
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content = {}
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for path in self.safe_file_paths:
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text = ""
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path = self.convert_to_path(path)
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with pdfplumber.open(path) as pdf:
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for page in pdf.pages:
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page_text = page.extract_text()
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if page_text:
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text += page_text + "\n"
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content[path] = text
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return content
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def _import_pdfplumber(self):
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"""Dynamically import pdfplumber."""
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if not PDFPLUMBER_AVAILABLE:
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raise ImportError(
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"pdfplumber is required for PDF knowledge sources. "
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"Please install it with: pip install 'crewai[knowledge]'"
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)
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return pdfplumber
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def add(self) -> None:
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"""
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Add PDF file content to the knowledge source, chunk it, compute embeddings,
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and save the embeddings.
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"""
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for _, text in self.content.items():
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new_chunks = self._chunk_text(text)
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self.chunks.extend(new_chunks)
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self._save_documents()
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def _chunk_text(self, text: str) -> List[str]:
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"""Utility method to split text into chunks."""
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return [
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text[i : i + self.chunk_size]
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for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
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]
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