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from typing import Dict, Any, List, Optional, BinaryIO
from ...core.base import LatticeComponent, LatticeError
from pydantic import BaseModel
import fitz # PyMuPDF
from docx import Document as DocxDocument
import pandas as pd
import hashlib
from pathlib import Path
import magic
import logging
from datetime import datetime
class DocumentConfig(BaseModel):
"""Document processing configuration"""
extract_text: bool = True
extract_metadata: bool = True
extract_images: bool = False
chunk_size: int = 500
chunk_overlap: int = 50
encoding: str = 'utf-8'
ocr_enabled: bool = False
class ProcessedChunk(BaseModel):
"""Processed document chunk"""
content: str
start_index: int
end_index: int
metadata: Dict[str, Any]
class ProcessedDocument(BaseModel):
"""Processed document result"""
doc_id: str
content: str
chunks: List[ProcessedChunk]
metadata: Dict[str, Any]
file_type: str
timestamp: datetime
class DocumentProcessor(LatticeComponent):
"""Main document processor"""
SUPPORTED_TYPES = {
'pdf': ['application/pdf'],
'docx': ['application/vnd.openxmlformats-officedocument.wordprocessingml.document'],
'txt': ['text/plain'],
'csv': ['text/csv', 'application/csv']
}
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.doc_config = DocumentConfig(**(config or {}))
async def initialize(self) -> None:
"""Initialize document processor"""
try:
# Initialize OCR if enabled
if self.doc_config.ocr_enabled:
import pytesseract
self.ocr = pytesseract
self._initialized = True
except Exception as e:
raise LatticeError(f"Failed to initialize document processor: {str(e)}")
async def validate_config(self) -> bool:
"""Validate configuration"""
try:
DocumentConfig(**(self.config or {}))
return True
except Exception as e:
self.logger.error(f"Invalid configuration: {str(e)}")
return False
def get_file_type(self, file: BinaryIO) -> str:
"""Determine file type using magic numbers"""
mime = magic.from_buffer(file.read(2048), mime=True)
file.seek(0)
for file_type, mime_types in self.SUPPORTED_TYPES.items():
if mime in mime_types:
return file_type
raise LatticeError(f"Unsupported file type: {mime}")
async def process_document(
self,
file: BinaryIO,
file_type: Optional[str] = None
) -> ProcessedDocument:
"""Process document"""
self.ensure_initialized()
try:
# Determine file type if not provided
if not file_type:
file_type = self.get_file_type(file)
# Generate document ID
doc_id = self._generate_doc_id(file)
# Extract content and metadata
if file_type == 'pdf':
content, metadata = self._process_pdf(file)
elif file_type == 'docx':
content, metadata = self._process_docx(file)
elif file_type == 'txt':
content, metadata = self._process_text(file)
elif file_type == 'csv':
content, metadata = self._process_csv(file)
else:
raise LatticeError(f"Unsupported file type: {file_type}")
# Create chunks
chunks = self._create_chunks(content)
return ProcessedDocument(
doc_id=doc_id,
content=content,
chunks=chunks,
metadata=metadata,
file_type=file_type,
timestamp=datetime.now()
)
except Exception as e:
self.logger.error(f"Error processing document: {str(e)}")
raise LatticeError(f"Document processing failed: {str(e)}")
def _generate_doc_id(self, file: BinaryIO) -> str:
"""Generate unique document ID"""
file_hash = hashlib.sha256()
for chunk in iter(lambda: file.read(4096), b""):
file_hash.update(chunk)
file.seek(0)
return file_hash.hexdigest()[:16]
def _process_pdf(self, file: BinaryIO) -> tuple[str, Dict[str, Any]]:
"""Process PDF document"""
pdf = fitz.open(stream=file.read())
# Extract text
text = ""
if self.doc_config.extract_text:
for page in pdf:
text += page.get_text()
# Extract metadata
metadata = {}
if self.doc_config.extract_metadata:
metadata = {
'title': pdf.metadata.get('title'),
'author': pdf.metadata.get('author'),
'subject': pdf.metadata.get('subject'),
'keywords': pdf.metadata.get('keywords'),
'page_count': len(pdf),
'file_size': file.tell()
}
return text, metadata
def _process_docx(self, file: BinaryIO) -> tuple[str, Dict[str, Any]]:
"""Process DOCX document"""
doc = DocxDocument(file)
# Extract text
text = ""
if self.doc_config.extract_text:
for para in doc.paragraphs:
text += para.text + "\n"
# Extract metadata
metadata = {}
if self.doc_config.extract_metadata:
core_props = doc.core_properties
metadata = {
'title': core_props.title,
'author': core_props.author,
'created': core_props.created.isoformat() if core_props.created else None,
'modified': core_props.modified.isoformat() if core_props.modified else None,
'paragraph_count': len(doc.paragraphs),
'file_size': file.tell()
}
return text, metadata
def _process_text(self, file: BinaryIO) -> tuple[str, Dict[str, Any]]:
"""Process text document"""
content = file.read().decode(self.doc_config.encoding)
metadata = {
'file_size': file.tell(),
'encoding': self.doc_config.encoding,
'line_count': content.count('\n') + 1
}
return content, metadata
def _process_csv(self, file: BinaryIO) -> tuple[str, Dict[str, Any]]:
"""Process CSV document"""
df = pd.read_csv(file)
# Convert to string representation
content = df.to_string()
metadata = {
'file_size': file.tell(),
'row_count': len(df),
'column_count': len(df.columns),
'columns': df.columns.tolist()
}
return content, metadata
def _create_chunks(self, content: str) -> List[ProcessedChunk]:
"""Create document chunks"""
chunks = []
start = 0
while start < len(content):
end = start + self.doc_config.chunk_size
# Adjust end to prevent cutting words
if end < len(content):
end = content.rfind(' ', start, end) + 1
chunk_content = content[start:end]
chunks.append(
ProcessedChunk(
content=chunk_content,
start_index=start,
end_index=end,
metadata={
'chunk_size': len(chunk_content),
'position': len(chunks)
}
)
)
start = end - self.doc_config.chunk_overlap
return chunks |