Introduction and System Overview
Building an intelligent document generation system requires careful consideration of multiple components working in harmony. The system we will explore combines local Large Language Models with speech recognition capabilities to create a seamless experience for generating business documents, personal letters, and reports. The core principle behind this implementation is maintaining user privacy while providing enterprise-grade functionality through local deployment.
The system architecture revolves around a chatbot interface that accepts both text and voice inputs from users. When a user requests document generation, they specify the type of document, its purpose, and any specific requirements. The system then processes this request through a local LLM, generates appropriate content, and creates a Microsoft Word document. The interactive nature allows users to refine and modify the generated content through additional voice or text commands.
Local deployment offers significant advantages over cloud-based solutions, particularly in terms of data privacy, response latency, and operational costs. However, it also presents challenges in terms of computational requirements and model selection. We will address these considerations throughout our implementation.
Architecture Components and Design Decisions
The system consists of several interconnected components that work together to provide the complete functionality. The primary components include a voice recognition module, a text processing engine, a local LLM inference server, a document generation service, and a user interface layer.
The voice recognition component captures audio input from users and converts it to text. This component must handle various accents, speaking speeds, and background noise conditions. For our implementation, we will use OpenAI's Whisper model, which can be deployed locally and supports multiple languages with high accuracy.
The text processing engine handles both direct text input and converted speech-to-text output. This component performs language detection, intent classification, and parameter extraction from user requests. It also manages the conversation context and maintains the state of document editing sessions.
The LLM inference server hosts our chosen local language model and handles text generation requests. This component must balance model capability with computational efficiency. We will implement this using the Ollama framework, which provides excellent local LLM deployment capabilities with optimized inference performance.
The document generation service creates and modifies Microsoft Word documents based on the LLM output. This component handles document formatting, template application, and incremental updates when users request modifications.
Local LLM Selection and Setup
Selecting an appropriate local LLM requires balancing several factors including model size, capability, language support, and computational requirements. For our implementation, we will use Llama 2 13B as our primary choice due to its excellent performance in text generation tasks while maintaining reasonable computational requirements.
Llama 2 13B provides strong performance across various document types and supports multiple languages effectively. The model requires approximately 26GB of RAM for optimal performance, making it suitable for modern workstations and servers. Alternative options include Mistral 7B for lower resource requirements or Code Llama for technical document generation.
The Ollama framework simplifies local LLM deployment by handling model quantization, memory management, and API endpoints automatically. Installing Ollama involves downloading the framework and pulling the desired model. The following code example demonstrates the basic setup process.
This code example shows how to initialize the Ollama client and load our chosen model. The client configuration includes memory optimization settings and response formatting parameters. The model loading process may take several minutes depending on the model size and available system resources.
import ollama
import json
from typing import Dict, Any
class LocalLLMManager:
def __init__(self, model_name: str = "llama2:13b"):
self.model_name = model_name
self.client = ollama.Client()
self.conversation_history = []
def initialize_model(self):
"""Initialize and warm up the local LLM model"""
try:
# Pull the model if not already available
self.client.pull(self.model_name)
# Warm up the model with a simple prompt
warmup_response = self.client.generate(
model=self.model_name,
prompt="Hello, please respond briefly.",
options={
'temperature': 0.7,
'max_tokens': 50,
'top_p': 0.9
}
)
print(f"Model {self.model_name} initialized successfully")
return True
except Exception as e:
print(f"Error initializing model: {e}")
return False
def generate_document_content(self, user_request: str, document_type: str) -> str:
"""Generate document content based on user request"""
system_prompt = f"""You are an expert document writer. Create a professional {document_type}
based on the user's request. Ensure the content is well-structured, appropriate for the
specified purpose, and maintains a professional tone. Format the output as plain text
that can be easily inserted into a Word document."""
full_prompt = f"{system_prompt}\n\nUser Request: {user_request}"
response = self.client.generate(
model=self.model_name,
prompt=full_prompt,
options={
'temperature': 0.8,
'max_tokens': 1000,
'top_p': 0.95
}
)
return response['response']
This implementation provides a robust foundation for local LLM management. The LocalLLMManager class encapsulates model initialization, conversation management, and content generation functionality. The generate_document_content method accepts user requests and document types, then constructs appropriate prompts for the LLM to generate relevant content.
Voice Recognition Implementation
Implementing voice recognition requires careful consideration of accuracy, latency, and language support. OpenAI's Whisper model provides excellent performance for local deployment and supports automatic language detection. The model comes in several sizes, with the medium model offering the best balance between accuracy and computational requirements for most applications.
The voice recognition component must handle real-time audio capture, noise reduction, and speech-to-text conversion. We implement this using the whisper library combined with pyaudio for audio capture. The system supports continuous listening with voice activity detection to automatically start and stop recording based on speech presence.
The following code example demonstrates the implementation of a voice recognition system that integrates seamlessly with our document generation pipeline. This implementation includes audio preprocessing, speech detection, and language identification capabilities.
import whisper
import pyaudio
import wave
import numpy as np
import threading
import queue
from typing import Optional
class VoiceRecognitionManager:
def __init__(self, model_size: str = "medium"):
self.model = whisper.load_model(model_size)
self.audio_queue = queue.Queue()
self.is_listening = False
self.audio_format = pyaudio.paInt16
self.channels = 1
self.rate = 16000
self.chunk = 1024
self.silence_threshold = 500
self.silence_duration = 2.0
def start_listening(self):
"""Start continuous voice recognition"""
self.is_listening = True
audio_thread = threading.Thread(target=self._audio_capture_loop)
audio_thread.daemon = True
audio_thread.start()
def stop_listening(self):
"""Stop voice recognition"""
self.is_listening = False
def _audio_capture_loop(self):
"""Continuous audio capture with voice activity detection"""
p = pyaudio.PyAudio()
stream = p.open(
format=self.audio_format,
channels=self.channels,
rate=self.rate,
input=True,
frames_per_buffer=self.chunk
)
frames = []
silence_frames = 0
recording = False
while self.is_listening:
data = stream.read(self.chunk)
audio_data = np.frombuffer(data, dtype=np.int16)
volume = np.sqrt(np.mean(audio_data**2))
if volume > self.silence_threshold:
if not recording:
recording = True
frames = []
frames.append(data)
silence_frames = 0
else:
if recording:
silence_frames += 1
frames.append(data)
if silence_frames > (self.silence_duration * self.rate / self.chunk):
# Process the recorded audio
audio_bytes = b''.join(frames)
self._process_audio(audio_bytes)
recording = False
frames = []
silence_frames = 0
stream.stop_stream()
stream.close()
p.terminate()
def _process_audio(self, audio_bytes: bytes) -> Optional[str]:
"""Process captured audio and convert to text"""
try:
# Convert bytes to numpy array
audio_data = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32768.0
# Use Whisper for speech recognition
result = self.model.transcribe(audio_data, language=None)
transcribed_text = result["text"].strip()
detected_language = result["language"]
if transcribed_text:
self.audio_queue.put({
'text': transcribed_text,
'language': detected_language,
'confidence': 1.0 # Whisper doesn't provide confidence scores
})
return transcribed_text
except Exception as e:
print(f"Error processing audio: {e}")
return None
def get_transcribed_text(self) -> Optional[Dict]:
"""Get the next transcribed text from the queue"""
try:
return self.audio_queue.get_nowait()
except queue.Empty:
return None
This voice recognition implementation provides robust audio capture and speech-to-text conversion capabilities. The system uses voice activity detection to automatically start and stop recording based on speech presence, reducing computational overhead and improving user experience. The _process_audio method handles the conversion from raw audio to text using the Whisper model, while also detecting the spoken language for multilingual support.
Text Generation Pipeline
The text generation pipeline orchestrates the entire process from user input to final document content. This component integrates voice recognition, language detection, intent parsing, and LLM inference to produce coherent and contextually appropriate document content.
The pipeline begins by processing user input, whether from voice or text. It then analyzes the request to determine the document type, purpose, and specific requirements. This analysis involves natural language understanding techniques to extract structured information from unstructured user requests.
Context management plays a crucial role in maintaining conversation continuity and document coherence. The system maintains a conversation history and document state to enable iterative refinement and modification of generated content. This allows users to make incremental changes without losing the overall document structure and context.
The following code example demonstrates a comprehensive text generation pipeline that integrates all components and manages the complete workflow from user input to document generation.
import re
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
class DocumentType(Enum):
BUSINESS_LETTER = "business_letter"
PERSONAL_LETTER = "personal_letter"
EMAIL = "email"
REPORT = "report"
MEMO = "memo"
PROPOSAL = "proposal"
@dataclass
class DocumentRequest:
document_type: DocumentType
purpose: str
recipient: Optional[str]
sender: Optional[str]
subject: Optional[str]
key_points: List[str]
tone: str
language: str
additional_requirements: List[str]
class TextGenerationPipeline:
def __init__(self, llm_manager: LocalLLMManager, voice_manager: VoiceRecognitionManager):
self.llm_manager = llm_manager
self.voice_manager = voice_manager
self.current_document = None
self.conversation_context = []
self.supported_languages = ['en', 'de', 'fr', 'es', 'it', 'pt', 'nl']
def parse_user_request(self, user_input: str, detected_language: str = 'en') -> DocumentRequest:
"""Parse user input to extract document requirements"""
# Document type detection patterns
type_patterns = {
DocumentType.BUSINESS_LETTER: ['business letter', 'formal letter', 'official letter'],
DocumentType.PERSONAL_LETTER: ['personal letter', 'informal letter', 'private letter'],
DocumentType.EMAIL: ['email', 'e-mail', 'electronic mail'],
DocumentType.REPORT: ['report', 'analysis', 'summary'],
DocumentType.MEMO: ['memo', 'memorandum', 'internal note'],
DocumentType.PROPOSAL: ['proposal', 'suggestion', 'recommendation']
}
# Detect document type
document_type = DocumentType.BUSINESS_LETTER # default
for doc_type, patterns in type_patterns.items():
if any(pattern in user_input.lower() for pattern in patterns):
document_type = doc_type
break
# Extract key information using regex patterns
recipient_match = re.search(r'(?:to|for|recipient|address(?:ed)? to)\s+([A-Za-z\s]+)', user_input, re.IGNORECASE)
sender_match = re.search(r'(?:from|sender|signed by)\s+([A-Za-z\s]+)', user_input, re.IGNORECASE)
subject_match = re.search(r'(?:subject|about|regarding|concerning)\s+([^.!?]+)', user_input, re.IGNORECASE)
# Extract tone indicators
tone = 'professional'
if any(word in user_input.lower() for word in ['friendly', 'casual', 'informal']):
tone = 'friendly'
elif any(word in user_input.lower() for word in ['formal', 'official', 'strict']):
tone = 'formal'
elif any(word in user_input.lower() for word in ['urgent', 'important', 'critical']):
tone = 'urgent'
return DocumentRequest(
document_type=document_type,
purpose=user_input,
recipient=recipient_match.group(1).strip() if recipient_match else None,
sender=sender_match.group(1).strip() if sender_match else None,
subject=subject_match.group(1).strip() if subject_match else None,
key_points=[],
tone=tone,
language=detected_language,
additional_requirements=[]
)
def generate_document_content(self, request: DocumentRequest) -> str:
"""Generate document content based on parsed request"""
# Construct detailed prompt based on document type and requirements
prompt_template = self._get_prompt_template(request.document_type, request.language)
context_info = {
'document_type': request.document_type.value,
'purpose': request.purpose,
'recipient': request.recipient or 'the recipient',
'sender': request.sender or 'the sender',
'subject': request.subject or 'the specified matter',
'tone': request.tone,
'language': request.language
}
formatted_prompt = prompt_template.format(**context_info)
# Add conversation context if available
if self.conversation_context:
context_summary = "\n".join(self.conversation_context[-3:]) # Last 3 interactions
formatted_prompt += f"\n\nPrevious context: {context_summary}"
# Generate content using LLM
generated_content = self.llm_manager.generate_document_content(
formatted_prompt,
request.document_type.value
)
# Store in conversation context
self.conversation_context.append(f"Generated {request.document_type.value}: {request.purpose}")
return generated_content
def _get_prompt_template(self, doc_type: DocumentType, language: str) -> str:
"""Get appropriate prompt template based on document type and language"""
base_templates = {
DocumentType.BUSINESS_LETTER: """
Create a professional business letter with the following specifications:
- Document type: {document_type}
- Purpose: {purpose}
- Recipient: {recipient}
- Sender: {sender}
- Subject: {subject}
- Tone: {tone}
- Language: {language}
Include proper business letter formatting with date, addresses, salutation, body paragraphs,
and professional closing. Ensure the content is clear, concise, and appropriate for business communication.
""",
DocumentType.EMAIL: """
Compose a professional email with these requirements:
- Purpose: {purpose}
- Recipient: {recipient}
- Subject: {subject}
- Tone: {tone}
- Language: {language}
Structure the email with a clear subject line, appropriate greeting, well-organized body content,
and professional signature. Keep the content concise and actionable.
""",
DocumentType.REPORT: """
Generate a comprehensive report with the following parameters:
- Purpose: {purpose}
- Tone: {tone}
- Language: {language}
Structure the report with an executive summary, main sections with clear headings,
supporting details, and conclusions. Ensure factual accuracy and logical flow.
"""
}
return base_templates.get(doc_type, base_templates[DocumentType.BUSINESS_LETTER])
def process_modification_request(self, modification_text: str, current_content: str) -> str:
"""Process user request to modify existing document content"""
modification_prompt = f"""
The user wants to modify the following document content:
CURRENT CONTENT:
{current_content}
MODIFICATION REQUEST:
{modification_text}
Please apply the requested modifications while maintaining the document's overall structure,
tone, and professional quality. Return the complete modified document.
"""
modified_content = self.llm_manager.generate_document_content(
modification_prompt,
"document_modification"
)
# Update conversation context
self.conversation_context.append(f"Modified document: {modification_text}")
return modified_content
This text generation pipeline provides comprehensive functionality for processing user requests and generating appropriate document content. The parse_user_request method extracts structured information from natural language input, while the generate_document_content method creates contextually appropriate content using the local LLM. The system maintains conversation context to enable iterative refinement and modification of generated documents.
Microsoft Word Document Creation
Creating and manipulating Microsoft Word documents programmatically requires the python-docx library, which provides comprehensive functionality for document creation, formatting, and modification. The document creation component handles template application, content insertion, and formatting based on document type and user preferences.
The system supports various document templates and formatting styles appropriate for different document types. Business letters receive formal formatting with proper letterhead spacing, while reports include structured headings and professional styling. The implementation allows for dynamic content insertion and real-time document updates as users request modifications.
Document versioning and backup functionality ensure that users can revert changes if needed. The system maintains document history and provides rollback capabilities for iterative editing sessions.
The following code example demonstrates a comprehensive document creation and management system that integrates with our text generation pipeline.
from docx import Document
from docx.shared import Inches, Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.style import WD_STYLE_TYPE
from datetime import datetime
import os
from typing import Optional
class WordDocumentManager:
def __init__(self, output_directory: str = "generated_documents"):
self.output_directory = output_directory
self.document_templates = {}
self.current_document = None
self.document_history = []
# Create output directory if it doesn't exist
os.makedirs(output_directory, exist_ok=True)
# Initialize document templates
self._initialize_templates()
def _initialize_templates(self):
"""Initialize document templates for different document types"""
# Business letter template configuration
self.document_templates[DocumentType.BUSINESS_LETTER] = {
'margins': {'top': 1.0, 'bottom': 1.0, 'left': 1.25, 'right': 1.0},
'font_name': 'Times New Roman',
'font_size': 12,
'line_spacing': 1.15,
'include_date': True,
'include_addresses': True
}
# Email template configuration
self.document_templates[DocumentType.EMAIL] = {
'margins': {'top': 1.0, 'bottom': 1.0, 'left': 1.0, 'right': 1.0},
'font_name': 'Calibri',
'font_size': 11,
'line_spacing': 1.0,
'include_date': False,
'include_addresses': False
}
# Report template configuration
self.document_templates[DocumentType.REPORT] = {
'margins': {'top': 1.0, 'bottom': 1.0, 'left': 1.0, 'right': 1.0},
'font_name': 'Arial',
'font_size': 11,
'line_spacing': 1.5,
'include_date': True,
'include_addresses': False
}
def create_document(self, content: str, request: DocumentRequest) -> str:
"""Create a new Word document with the specified content and formatting"""
# Create new document
doc = Document()
template_config = self.document_templates.get(
request.document_type,
self.document_templates[DocumentType.BUSINESS_LETTER]
)
# Set document margins
sections = doc.sections
for section in sections:
section.top_margin = Inches(template_config['margins']['top'])
section.bottom_margin = Inches(template_config['margins']['bottom'])
section.left_margin = Inches(template_config['margins']['left'])
section.right_margin = Inches(template_config['margins']['right'])
# Apply document-specific formatting
if request.document_type == DocumentType.BUSINESS_LETTER:
self._format_business_letter(doc, content, request, template_config)
elif request.document_type == DocumentType.EMAIL:
self._format_email(doc, content, request, template_config)
elif request.document_type == DocumentType.REPORT:
self._format_report(doc, content, request, template_config)
else:
self._format_generic_document(doc, content, request, template_config)
# Generate filename and save document
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"{request.document_type.value}_{timestamp}.docx"
filepath = os.path.join(self.output_directory, filename)
doc.save(filepath)
# Store document reference and history
self.current_document = {
'filepath': filepath,
'request': request,
'content': content,
'created_at': datetime.now()
}
self.document_history.append(self.current_document.copy())
return filepath
def _format_business_letter(self, doc: Document, content: str, request: DocumentRequest, config: dict):
"""Apply business letter formatting"""
# Add date
if config['include_date']:
date_paragraph = doc.add_paragraph()
date_paragraph.alignment = WD_ALIGN_PARAGRAPH.RIGHT
date_run = date_paragraph.add_run(datetime.now().strftime("%B %d, %Y"))
date_run.font.name = config['font_name']
date_run.font.size = Pt(config['font_size'])
doc.add_paragraph() # Add spacing
# Add recipient address if provided
if request.recipient and config['include_addresses']:
recipient_paragraph = doc.add_paragraph()
recipient_run = recipient_paragraph.add_run(request.recipient)
recipient_run.font.name = config['font_name']
recipient_run.font.size = Pt(config['font_size'])
doc.add_paragraph() # Add spacing
# Add salutation
salutation = f"Dear {request.recipient or 'Sir/Madam'},"
salutation_paragraph = doc.add_paragraph()
salutation_run = salutation_paragraph.add_run(salutation)
salutation_run.font.name = config['font_name']
salutation_run.font.size = Pt(config['font_size'])
doc.add_paragraph() # Add spacing
# Add main content
self._add_formatted_content(doc, content, config)
# Add closing
doc.add_paragraph()
closing_paragraph = doc.add_paragraph()
closing_run = closing_paragraph.add_run("Sincerely,")
closing_run.font.name = config['font_name']
closing_run.font.size = Pt(config['font_size'])
# Add signature space
doc.add_paragraph()
doc.add_paragraph()
if request.sender:
signature_paragraph = doc.add_paragraph()
signature_run = signature_paragraph.add_run(request.sender)
signature_run.font.name = config['font_name']
signature_run.font.size = Pt(config['font_size'])
def _format_email(self, doc: Document, content: str, request: DocumentRequest, config: dict):
"""Apply email formatting"""
# Add email headers
if request.recipient:
to_paragraph = doc.add_paragraph()
to_run = to_paragraph.add_run(f"To: {request.recipient}")
to_run.font.name = config['font_name']
to_run.font.size = Pt(config['font_size'])
to_run.bold = True
if request.sender:
from_paragraph = doc.add_paragraph()
from_run = from_paragraph.add_run(f"From: {request.sender}")
from_run.font.name = config['font_name']
from_run.font.size = Pt(config['font_size'])
from_run.bold = True
if request.subject:
subject_paragraph = doc.add_paragraph()
subject_run = subject_paragraph.add_run(f"Subject: {request.subject}")
subject_run.font.name = config['font_name']
subject_run.font.size = Pt(config['font_size'])
subject_run.bold = True
doc.add_paragraph() # Add spacing
# Add main content
self._add_formatted_content(doc, content, config)
def _format_report(self, doc: Document, content: str, request: DocumentRequest, config: dict):
"""Apply report formatting with structured headings"""
# Add title
if request.subject:
title_paragraph = doc.add_paragraph()
title_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER
title_run = title_paragraph.add_run(request.subject.upper())
title_run.font.name = config['font_name']
title_run.font.size = Pt(config['font_size'] + 4)
title_run.bold = True
doc.add_paragraph()
# Add date
if config['include_date']:
date_paragraph = doc.add_paragraph()
date_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER
date_run = date_paragraph.add_run(datetime.now().strftime("%B %d, %Y"))
date_run.font.name = config['font_name']
date_run.font.size = Pt(config['font_size'])
doc.add_paragraph()
# Add main content with structured formatting
self._add_formatted_content(doc, content, config, is_report=True)
def _add_formatted_content(self, doc: Document, content: str, config: dict, is_report: bool = False):
"""Add formatted content to the document"""
paragraphs = content.split('\n\n')
for paragraph_text in paragraphs:
if paragraph_text.strip():
paragraph = doc.add_paragraph()
# Check if this is a heading (for reports)
if is_report and (paragraph_text.strip().endswith(':') or paragraph_text.isupper()):
run = paragraph.add_run(paragraph_text.strip())
run.font.name = config['font_name']
run.font.size = Pt(config['font_size'] + 1)
run.bold = True
else:
run = paragraph.add_run(paragraph_text.strip())
run.font.name = config['font_name']
run.font.size = Pt(config['font_size'])
# Set line spacing
paragraph_format = paragraph.paragraph_format
paragraph_format.line_spacing = config['line_spacing']
def modify_document(self, new_content: str) -> str:
"""Modify the current document with new content"""
if not self.current_document:
raise ValueError("No current document to modify")
# Create backup of current version
backup_info = self.current_document.copy()
self.document_history.append(backup_info)
# Create new document with modified content
modified_filepath = self.create_document(new_content, self.current_document['request'])
return modified_filepath
def get_document_history(self) -> List[Dict]:
"""Get the history of document modifications"""
return self.document_history.copy()
This Word document management system provides comprehensive functionality for creating, formatting, and modifying documents based on the generated content. The system supports multiple document types with appropriate formatting templates, maintains document history for version control, and provides seamless integration with the text generation pipeline.
Interactive Editing and Refinement
The interactive editing component enables users to refine and modify generated documents through natural language commands. This functionality allows for iterative improvement of document content without requiring users to manually edit the Word document. The system processes modification requests and applies changes while maintaining document structure and formatting consistency.
The editing system supports various types of modifications including content addition, deletion, restructuring, tone adjustment, and formatting changes. Users can specify modifications through voice commands or text input, making the system accessible and user-friendly for different interaction preferences.
Context awareness plays a crucial role in the editing process. The system maintains understanding of the current document state, previous modifications, and user preferences to provide intelligent suggestions and accurate modifications. This contextual understanding enables more natural and effective human-computer interaction.
The following code example demonstrates a comprehensive interactive editing system that integrates with all previous components to provide seamless document refinement capabilities.
import re
from typing import List, Dict, Tuple
from enum import Enum
class ModificationType(Enum):
ADD_CONTENT = "add_content"
REMOVE_CONTENT = "remove_content"
REPLACE_CONTENT = "replace_content"
CHANGE_TONE = "change_tone"
RESTRUCTURE = "restructure"
FORMAT_CHANGE = "format_change"
class InteractiveEditor:
def __init__(self, pipeline: TextGenerationPipeline, doc_manager: WordDocumentManager):
self.pipeline = pipeline
self.doc_manager = doc_manager
self.editing_session = None
self.modification_patterns = self._initialize_modification_patterns()
def _initialize_modification_patterns(self) -> Dict[ModificationType, List[str]]:
"""Initialize patterns for detecting modification types"""
return {
ModificationType.ADD_CONTENT: [
r'add\s+(.+)',
r'include\s+(.+)',
r'insert\s+(.+)',
r'append\s+(.+)',
r'also mention\s+(.+)'
],
ModificationType.REMOVE_CONTENT: [
r'remove\s+(.+)',
r'delete\s+(.+)',
r'take out\s+(.+)',
r'eliminate\s+(.+)'
],
ModificationType.REPLACE_CONTENT: [
r'replace\s+(.+)\s+with\s+(.+)',
r'change\s+(.+)\s+to\s+(.+)',
r'substitute\s+(.+)\s+with\s+(.+)'
],
ModificationType.CHANGE_TONE: [
r'make it more\s+(\w+)',
r'change tone to\s+(\w+)',
r'sound more\s+(\w+)',
r'be more\s+(\w+)'
],
ModificationType.RESTRUCTURE: [
r'reorganize\s+(.+)',
r'restructure\s+(.+)',
r'reorder\s+(.+)',
r'rearrange\s+(.+)'
]
}
def start_editing_session(self, document_content: str, document_request: DocumentRequest):
"""Start a new interactive editing session"""
self.editing_session = {
'original_content': document_content,
'current_content': document_content,
'request': document_request,
'modifications': [],
'session_start': datetime.now()
}
print("Interactive editing session started. You can now make modifications using voice or text.")
print("Available commands:")
print("- Add content: 'Add a paragraph about...'")
print("- Remove content: 'Remove the section about...'")
print("- Change tone: 'Make it more formal'")
print("- Replace content: 'Replace the introduction with...'")
print("- Finish editing: 'Finish' or 'Done'")
def process_modification_command(self, command: str, language: str = 'en') -> Tuple[bool, str]:
"""Process a modification command and apply changes"""
if not self.editing_session:
return False, "No active editing session. Please start a session first."
# Check for session termination commands
if command.lower().strip() in ['finish', 'done', 'complete', 'save']:
return self._finalize_editing_session()
# Detect modification type and extract parameters
modification_type, parameters = self._analyze_modification_command(command)
if modification_type is None:
return False, "Could not understand the modification request. Please try rephrasing."
# Apply the modification
success, result = self._apply_modification(modification_type, parameters, command)
if success:
# Update document
new_filepath = self.doc_manager.modify_document(self.editing_session['current_content'])
# Record modification
self.editing_session['modifications'].append({
'command': command,
'type': modification_type,
'parameters': parameters,
'timestamp': datetime.now(),
'result_filepath': new_filepath
})
return True, f"Modification applied successfully. Document saved as: {new_filepath}"
else:
return False, result
def _analyze_modification_command(self, command: str) -> Tuple[Optional[ModificationType], Dict]:
"""Analyze modification command to determine type and extract parameters"""
command_lower = command.lower().strip()
for mod_type, patterns in self.modification_patterns.items():
for pattern in patterns:
match = re.search(pattern, command_lower)
if match:
if mod_type == ModificationType.REPLACE_CONTENT:
return mod_type, {
'original': match.group(1),
'replacement': match.group(2)
}
elif mod_type == ModificationType.CHANGE_TONE:
return mod_type, {
'new_tone': match.group(1)
}
else:
return mod_type, {
'content': match.group(1)
}
return None, {}
def _apply_modification(self, mod_type: ModificationType, parameters: Dict, original_command: str) -> Tuple[bool, str]:
"""Apply the specified modification to the current document content"""
current_content = self.editing_session['current_content']
try:
if mod_type == ModificationType.ADD_CONTENT:
modified_content = self._add_content(current_content, parameters['content'], original_command)
elif mod_type == ModificationType.REMOVE_CONTENT:
modified_content = self._remove_content(current_content, parameters['content'])
elif mod_type == ModificationType.REPLACE_CONTENT:
modified_content = self._replace_content(
current_content,
parameters['original'],
parameters['replacement']
)
elif mod_type == ModificationType.CHANGE_TONE:
modified_content = self._change_tone(current_content, parameters['new_tone'])
elif mod_type == ModificationType.RESTRUCTURE:
modified_content = self._restructure_content(current_content, parameters['content'])
else:
return False, f"Modification type {mod_type} not implemented"
self.editing_session['current_content'] = modified_content
return True, "Modification applied successfully"
except Exception as e:
return False, f"Error applying modification: {str(e)}"
def _add_content(self, current_content: str, content_to_add: str, original_command: str) -> str:
"""Add content to the document using LLM assistance"""
modification_prompt = f"""
Current document content:
{current_content}
User wants to add the following content: {content_to_add}
Original command: {original_command}
Please integrate this new content appropriately into the existing document while maintaining
coherence, flow, and the original document structure. Return the complete modified document.
"""
return self.pipeline.llm_manager.generate_document_content(
modification_prompt,
"content_addition"
)
def _remove_content(self, current_content: str, content_to_remove: str) -> str:
"""Remove specified content from the document"""
modification_prompt = f"""
Current document content:
{current_content}
User wants to remove content related to: {content_to_remove}
Please remove the specified content while maintaining document coherence and flow.
Ensure smooth transitions between remaining sections. Return the complete modified document.
"""
return self.pipeline.llm_manager.generate_document_content(
modification_prompt,
"content_removal"
)
def _replace_content(self, current_content: str, original_content: str, replacement_content: str) -> str:
"""Replace specified content with new content"""
modification_prompt = f"""
Current document content:
{current_content}
Replace this content: {original_content}
With this content: {replacement_content}
Please make the replacement while maintaining document coherence, appropriate transitions,
and consistent tone. Return the complete modified document.
"""
return self.pipeline.llm_manager.generate_document_content(
modification_prompt,
"content_replacement"
)
def _change_tone(self, current_content: str, new_tone: str) -> str:
"""Change the tone of the document"""
modification_prompt = f"""
Current document content:
{current_content}
Please rewrite this document with a {new_tone} tone while maintaining all the key information,
structure, and purpose. Ensure the new tone is consistent throughout the document.
"""
return self.pipeline.llm_manager.generate_document_content(
modification_prompt,
"tone_modification"
)
def _restructure_content(self, current_content: str, restructure_instruction: str) -> str:
"""Restructure the document content based on user instructions"""
modification_prompt = f"""
Current document content:
{current_content}
Restructuring instruction: {restructure_instruction}
Please reorganize the document content according to the instruction while maintaining
all important information and ensuring logical flow. Return the complete restructured document.
"""
return self.pipeline.llm_manager.generate_document_content(
modification_prompt,
"content_restructuring"
)
def _finalize_editing_session(self) -> Tuple[bool, str]:
"""Finalize the current editing session"""
if not self.editing_session:
return False, "No active editing session to finalize"
# Save final document
final_filepath = self.doc_manager.modify_document(self.editing_session['current_content'])
# Generate session summary
num_modifications = len(self.editing_session['modifications'])
session_duration = datetime.now() - self.editing_session['session_start']
summary = f"""
Editing session completed successfully!
Final document: {final_filepath}
Number of modifications: {num_modifications}
Session duration: {session_duration}
Modification history:
"""
for i, mod in enumerate(self.editing_session['modifications'], 1):
summary += f"\n{i}. {mod['command']} ({mod['type'].value})"
# Clear session
self.editing_session = None
return True, summary
def get_current_content(self) -> Optional[str]:
"""Get the current document content"""
if self.editing_session:
return self.editing_session['current_content']
return None
def undo_last_modification(self) -> Tuple[bool, str]:
"""Undo the last modification"""
if not self.editing_session or not self.editing_session['modifications']:
return False, "No modifications to undo"
# Remove last modification
last_modification = self.editing_session['modifications'].pop()
# Revert to previous state or original if no modifications remain
if self.editing_session['modifications']:
# Reapply all modifications except the last one
self.editing_session['current_content'] = self.editing_session['original_content']
for mod in self.editing_session['modifications']:
# Reapply modification (simplified - in production, store intermediate states)
pass
else:
self.editing_session['current_content'] = self.editing_session['original_content']
return True, f"Undid modification: {last_modification['command']}"
This interactive editing system provides comprehensive functionality for document refinement through natural language commands. The system analyzes user modification requests, applies appropriate changes using the LLM, and maintains session state for undo functionality and modification tracking.
Language Detection and Multilingual Support
Implementing robust multilingual support requires careful consideration of language detection, content generation in multiple languages, and cultural adaptation of document formats. The system must accurately detect the user's language preference and generate content that is not only linguistically correct but also culturally appropriate for the target language and region.
Language detection occurs at multiple levels within the system. The voice recognition component automatically detects spoken language using Whisper's built-in language detection capabilities. For text input, we implement additional language detection using specialized libraries to ensure accurate identification even for short text snippets.
Cultural adaptation extends beyond simple translation to include appropriate document formatting, greeting styles, closing formalities, and business communication norms specific to different cultures and languages. The system maintains language-specific templates and formatting rules to ensure generated documents meet local expectations and professional standards.
The following code example demonstrates a comprehensive multilingual support system that integrates language detection, culturally-aware content generation, and localized document formatting.
import langdetect
from langdetect import detect, LangDetectError
from typing import Dict, List, Optional
import json
class MultilingualManager:
def __init__(self):
self.supported_languages = {
'en': 'English',
'de': 'German',
'fr': 'French',
'es': 'Spanish',
'it': 'Italian',
'pt': 'Portuguese',
'nl': 'Dutch',
'ru': 'Russian',
'zh': 'Chinese',
'ja': 'Japanese'
}
self.cultural_adaptations = self._initialize_cultural_adaptations()
self.language_specific_prompts = self._initialize_language_prompts()
def _initialize_cultural_adaptations(self) -> Dict[str, Dict]:
"""Initialize cultural adaptations for different languages"""
return {
'en': {
'formal_greeting': 'Dear',
'informal_greeting': 'Hello',
'formal_closing': 'Sincerely',
'informal_closing': 'Best regards',
'date_format': '%B %d, %Y',
'address_format': 'recipient_first',
'business_tone': 'professional_direct'
},
'de': {
'formal_greeting': 'Sehr geehrte/r',
'informal_greeting': 'Liebe/r',
'formal_closing': 'Mit freundlichen Grüßen',
'informal_closing': 'Beste Grüße',
'date_format': '%d. %B %Y',
'address_format': 'sender_first',
'business_tone': 'formal_respectful'
},
'fr': {
'formal_greeting': 'Madame, Monsieur',
'informal_greeting': 'Cher/Chère',
'formal_closing': 'Veuillez agréer mes salutations distinguées',
'informal_closing': 'Cordialement',
'date_format': '%d %B %Y',
'address_format': 'recipient_first',
'business_tone': 'formal_elaborate'
},
'es': {
'formal_greeting': 'Estimado/a',
'informal_greeting': 'Querido/a',
'formal_closing': 'Atentamente',
'informal_closing': 'Saludos cordiales',
'date_format': '%d de %B de %Y',
'address_format': 'recipient_first',
'business_tone': 'warm_professional'
},
'it': {
'formal_greeting': 'Egregio/a',
'informal_greeting': 'Caro/a',
'formal_closing': 'Distinti saluti',
'informal_closing': 'Cordiali saluti',
'date_format': '%d %B %Y',
'address_format': 'recipient_first',
'business_tone': 'elegant_formal'
}
}
def _initialize_language_prompts(self) -> Dict[str, Dict]:
"""Initialize language-specific prompt templates"""
return {
'en': {
'business_letter': "Create a professional business letter in English with proper formatting and business etiquette.",
'email': "Compose a professional email in English with clear structure and appropriate tone.",
'report': "Generate a comprehensive report in English with logical structure and professional language."
},
'de': {
'business_letter': "Erstellen Sie einen professionellen Geschäftsbrief auf Deutsch mit korrekter Formatierung und Geschäftsetikette.",
'email': "Verfassen Sie eine professionelle E-Mail auf Deutsch mit klarer Struktur und angemessenem Ton.",
'report': "Erstellen Sie einen umfassenden Bericht auf Deutsch mit logischer Struktur und professioneller Sprache."
},
'fr': {
'business_letter': "Créez une lettre commerciale professionnelle en français avec un formatage approprié et l'étiquette commerciale.",
'email': "Rédigez un email professionnel en français avec une structure claire et un ton approprié.",
'report': "Générez un rapport complet en français avec une structure logique et un langage professionnel."
},
'es': {
'business_letter': "Cree una carta comercial profesional en español con formato apropiado y etiqueta comercial.",
'email': "Redacte un correo electrónico profesional en español con estructura clara y tono apropiado.",
'report': "Genere un informe completo en español con estructura lógica y lenguaje profesional."
},
'it': {
'business_letter': "Crea una lettera commerciale professionale in italiano con formattazione appropriata ed etichetta commerciale.",
'email': "Componi un'email professionale in italiano con struttura chiara e tono appropriato.",
'report': "Genera un rapporto completo in italiano con struttura logica e linguaggio professionale."
}
}
def detect_language(self, text: str, fallback_language: str = 'en') -> str:
"""Detect language of input text with fallback"""
try:
# Clean text for better detection
cleaned_text = self._clean_text_for_detection(text)
if len(cleaned_text.strip()) < 10:
# Text too short for reliable detection
return fallback_language
detected_lang = detect(cleaned_text)
# Verify detected language is supported
if detected_lang in self.supported_languages:
return detected_lang
else:
# Map similar languages
language_mapping = {
'ca': 'es', # Catalan -> Spanish
'gl': 'es', # Galician -> Spanish
'eu': 'es', # Basque -> Spanish
'no': 'en', # Norwegian -> English
'da': 'en', # Danish -> English
'sv': 'en', # Swedish -> English
}
return language_mapping.get(detected_lang, fallback_language)
except LangDetectError:
return fallback_language
def _clean_text_for_detection(self, text: str) -> str:
"""Clean text to improve language detection accuracy"""
# Remove common English command words that might skew detection
command_words = ['add', 'remove', 'change', 'create', 'generate', 'write', 'draft']
words = text.split()
cleaned_words = [word for word in words if word.lower() not in command_words]
return ' '.join(cleaned_words)
def get_cultural_adaptation(self, language: str, document_type: DocumentType, tone: str = 'formal') -> Dict:
"""Get cultural adaptation settings for specific language and document type"""
base_adaptation = self.cultural_adaptations.get(language, self.cultural_adaptations['en'])
# Adapt based on document type and tone
adaptation = base_adaptation.copy()
if tone == 'informal' or document_type == DocumentType.EMAIL:
adaptation['greeting'] = adaptation['informal_greeting']
adaptation['closing'] = adaptation['informal_closing']
else:
adaptation['greeting'] = adaptation['formal_greeting']
adaptation['closing'] = adaptation['formal_closing']
return adaptation
def generate_localized_prompt(self, language: str, document_type: DocumentType, request: DocumentRequest) -> str:
"""Generate culturally-appropriate prompt for the specified language"""
# Get base prompt template
lang_prompts = self.language_specific_prompts.get(language, self.language_specific_prompts['en'])
base_prompt = lang_prompts.get(document_type.value, lang_prompts['business_letter'])
# Get cultural adaptations
cultural_settings = self.get_cultural_adaptation(language, document_type, request.tone)
# Construct detailed localized prompt
localized_prompt = f"""
{base_prompt}
Language: {self.supported_languages[language]}
Document Type: {document_type.value}
Purpose: {request.purpose}
Tone: {request.tone}
Cultural Guidelines:
- Use appropriate greeting: {cultural_settings['greeting']}
- Use appropriate closing: {cultural_settings['closing']}
- Follow {language} business communication conventions
- Maintain {cultural_settings['business_tone']} tone throughout
- Format dates according to local conventions
Content Requirements:
- Recipient: {request.recipient or 'appropriate recipient'}
- Sender: {request.sender or 'the sender'}
- Subject: {request.subject or 'the specified matter'}
Please ensure the content is culturally appropriate, linguistically correct,
and follows local business etiquette for {self.supported_languages[language]} speakers.
"""
return localized_prompt
def validate_multilingual_content(self, content: str, expected_language: str) -> Tuple[bool, str, float]:
"""Validate that generated content is in the expected language"""
try:
detected_language = self.detect_language(content)
# Calculate confidence based on content length and detection consistency
confidence = min(1.0, len(content) / 500.0) # Higher confidence for longer content
if detected_language == expected_language:
return True, detected_language, confidence
else:
# Check if languages are closely related
related_languages = {
'es': ['ca', 'gl'],
'en': ['no', 'da', 'sv'],
'de': ['nl'],
'fr': ['it']
}
for primary_lang, related_langs in related_languages.items():
if expected_language == primary_lang and detected_language in related_langs:
return True, detected_language, confidence * 0.8
return False, detected_language, confidence
except Exception as e:
return False, "unknown", 0.0
def get_language_specific_formatting(self, language: str) -> Dict:
"""Get language-specific formatting preferences"""
formatting_preferences = {
'en': {
'decimal_separator': '.',
'thousands_separator': ',',
'currency_symbol_position': 'before',
'paragraph_indentation': True,
'quotation_marks': '""'
},
'de': {
'decimal_separator': ',',
'thousands_separator': '.',
'currency_symbol_position': 'after',
'paragraph_indentation': False,
'quotation_marks': '„"'
},
'fr': {
'decimal_separator': ',',
'thousands_separator': ' ',
'currency_symbol_position': 'after',
'paragraph_indentation': True,
'quotation_marks': '« »'
},
'es': {
'decimal_separator': ',',
'thousands_separator': '.',
'currency_symbol_position': 'before',
'paragraph_indentation': True,
'quotation_marks': '""'
}
}
return formatting_preferences.get(language, formatting_preferences['en'])
This multilingual management system provides comprehensive support for multiple languages with cultural adaptations. The system detects user language preferences, generates culturally-appropriate content, and applies language-specific formatting conventions to ensure professional and culturally-sensitive document generation.
Complete Implementation Example
The following comprehensive example demonstrates how all components work together to create a fully functional document generation system. This implementation integrates voice recognition, text processing, local LLM inference, document creation, and interactive editing into a cohesive application.
import asyncio
import threading
import time
from typing import Optional
import signal
import sys
class DocumentGenerationSystem:
def __init__(self):
# Initialize all components
self.llm_manager = LocalLLMManager("llama2:13b")
self.voice_manager = VoiceRecognitionManager("medium")
self.multilingual_manager = MultilingualManager()
self.pipeline = TextGenerationPipeline(self.llm_manager, self.voice_manager)
self.doc_manager = WordDocumentManager("generated_documents")
self.editor = InteractiveEditor(self.pipeline, self.doc_manager)
self.is_running = False
self.current_session = None
def initialize_system(self) -> bool:
"""Initialize all system components"""
print("Initializing Document Generation System...")
# Initialize LLM
print("Loading local LLM model...")
if not self.llm_manager.initialize_model():
print("Failed to initialize LLM model")
return False
print("LLM model loaded successfully")
# Initialize voice recognition
print("Initializing voice recognition...")
try:
self.voice_manager.start_listening()
print("Voice recognition initialized successfully")
except Exception as e:
print(f"Failed to initialize voice recognition: {e}")
return False
print("System initialization complete!")
return True
def start_interactive_session(self):
"""Start the main interactive session"""
self.is_running = True
print("\n" + "="*60)
print("DOCUMENT GENERATION SYSTEM")
print("="*60)
print("Welcome! You can create documents using voice or text input.")
print("Supported document types: business letters, emails, reports, memos")
print("Supported languages: English, German, French, Spanish, Italian")
print("\nCommands:")
print("- 'help' - Show available commands")
print("- 'voice' - Switch to voice input mode")
print("- 'text' - Switch to text input mode")
print("- 'quit' or 'exit' - Exit the system")
print("="*60)
# Set up signal handler for graceful shutdown
signal.signal(signal.SIGINT, self._signal_handler)
input_mode = 'text' # Default to text input
while self.is_running:
try:
if input_mode == 'voice':
self._handle_voice_input()
else:
self._handle_text_input()
# Check for mode switch commands
user_input = input("\nEnter command or document request (or 'voice'/'text' to switch modes): ").strip()
if user_input.lower() in ['quit', 'exit', 'bye']:
break
elif user_input.lower() == 'voice':
input_mode = 'voice'
print("Switched to voice input mode. Speak your request...")
continue
elif user_input.lower() == 'text':
input_mode = 'text'
print("Switched to text input mode.")
continue
elif user_input.lower() == 'help':
self._show_help()
continue
elif user_input:
self._process_user_request(user_input, 'en')
except KeyboardInterrupt:
break
except Exception as e:
print(f"Error in interactive session: {e}")
self._shutdown_system()
def _handle_voice_input(self):
"""Handle voice input processing"""
print("Listening for voice input... (speak now)")
# Wait for voice input with timeout
timeout = 30 # 30 seconds timeout
start_time = time.time()
while time.time() - start_time < timeout:
transcribed_data = self.voice_manager.get_transcribed_text()
if transcribed_data:
user_text = transcribed_data['text']
detected_language = transcribed_data['language']
print(f"Heard: {user_text}")
print(f"Detected language: {detected_language}")
self._process_user_request(user_text, detected_language)
return
time.sleep(0.1) # Small delay to prevent busy waiting
print("Voice input timeout. No speech detected.")
def _handle_text_input(self):
"""Handle text input processing"""
pass # Text input is handled in the main loop
def _process_user_request(self, user_input: str, detected_language: str):
"""Process user request and generate document"""
# Detect language if not provided
if not detected_language or detected_language == 'unknown':
detected_language = self.multilingual_manager.detect_language(user_input)
print(f"Processing request in {self.multilingual_manager.supported_languages.get(detected_language, 'English')}...")
# Parse the request
try:
document_request = self.pipeline.parse_user_request(user_input, detected_language)
print(f"Document type: {document_request.document_type.value}")
print(f"Tone: {document_request.tone}")
if document_request.recipient:
print(f"Recipient: {document_request.recipient}")
if document_request.subject:
print(f"Subject: {document_request.subject}")
# Generate localized prompt
localized_prompt = self.multilingual_manager.generate_localized_prompt(
detected_language,
document_request.document_type,
document_request
)
# Generate content
print("Generating document content...")
generated_content = self.llm_manager.generate_document_content(
localized_prompt,
document_request.document_type.value
)
# Validate language
is_valid, detected_content_lang, confidence = self.multilingual_manager.validate_multilingual_content(
generated_content,
detected_language
)
if not is_valid:
print(f"Warning: Generated content language ({detected_content_lang}) doesn't match expected ({detected_language})")
# Create Word document
print("Creating Word document...")
document_path = self.doc_manager.create_document(generated_content, document_request)
print(f"Document created successfully: {document_path}")
# Ask if user wants to edit the document
edit_choice = input("Would you like to edit this document? (y/n): ").strip().lower()
if edit_choice in ['y', 'yes']:
self._start_editing_session(generated_content, document_request)
except Exception as e:
print(f"Error processing request: {e}")
def _start_editing_session(self, content: str, request: DocumentRequest):
"""Start interactive editing session"""
self.editor.start_editing_session(content, request)
while True:
edit_input = input("\nEnter modification command (or 'done' to finish): ").strip()
if not edit_input:
continue
success, message = self.editor.process_modification_command(edit_input)
print(message)
if edit_input.lower() in ['done', 'finish', 'complete']:
break
def _show_help(self):
"""Display help information"""
help_text = """
DOCUMENT GENERATION SYSTEM HELP
Document Types:
- Business Letter: "Create a business letter to [recipient] about [subject]"
- Email: "Draft an email to [recipient] regarding [subject]"
- Report: "Generate a report about [topic]"
- Memo: "Write a memo about [subject]"
Language Support:
- English, German, French, Spanish, Italian, Portuguese, Dutch
- Automatic language detection from your input
Voice Commands:
- Speak naturally: "I need to write a formal letter to my manager about vacation request"
- The system will detect your language and generate appropriate content
Editing Commands:
- "Add a paragraph about [topic]"
- "Remove the section about [topic]"
- "Make it more formal/informal"
- "Replace [old content] with [new content]"
Examples:
- "Create a business letter to John Smith about project proposal"
- "Draft an email to the team regarding the meeting tomorrow"
- "Generate a report about quarterly sales performance"
"""
print(help_text)
def _signal_handler(self, signum, frame):
"""Handle system signals for graceful shutdown"""
print("\nReceived shutdown signal. Cleaning up...")
self.is_running = False
self._shutdown_system()
sys.exit(0)
def _shutdown_system(self):
"""Shutdown system components gracefully"""
print("Shutting down system components...")
if self.voice_manager:
self.voice_manager.stop_listening()
print("Voice recognition stopped")
print("System shutdown complete")
def main():
"""Main application entry point"""
# Create and initialize the document generation system
system = DocumentGenerationSystem()
# Initialize all components
if not system.initialize_system():
print("Failed to initialize system. Exiting.")
return 1
# Start interactive session
try:
system.start_interactive_session()
except Exception as e:
print(f"System error: {e}")
return 1
return 0
# Example usage and testing
def run_example():
"""Run a complete example of the document generation system"""
print("Starting Document Generation System Example...")
# Create system instance
system = DocumentGenerationSystem()
# Initialize components
if not system.initialize_system():
print("Initialization failed")
return
# Example text request
example_request = "Create a business letter to Sarah Johnson about the quarterly budget review meeting scheduled for next week. Make it professional but friendly."
print(f"Processing example request: {example_request}")
system._process_user_request(example_request, 'en')
# Example modification
print("\nTesting document modification...")
if system.editor.editing_session:
system.editor.process_modification_command("Add a paragraph about bringing the financial reports")
system.editor.process_modification_command("Make it more formal")
system.editor.process_modification_command("Done")
if __name__ == "__main__":
# Run the example or main application
import sys
if len(sys.argv) > 1 and sys.argv[1] == "example":
run_example()
else:
exit_code = main()
sys.exit(exit_code)
This complete implementation demonstrates a fully functional document generation system that integrates all previously discussed components. The system provides both voice and text input capabilities, supports multiple languages with cultural adaptations, generates professional documents using local LLMs, and offers interactive editing functionality.
Deployment Considerations and Performance Optimization
Deploying a local LLM-based document generation system requires careful consideration of hardware requirements, performance optimization, and scalability factors. The system demands significant computational resources, particularly for the LLM inference component, which directly impacts response times and user experience.
Hardware requirements vary based on the chosen LLM model size and expected concurrent usage. For optimal performance with Llama 2 13B, the system requires a minimum of 32GB RAM, with 64GB recommended for smooth operation. GPU acceleration using CUDA-compatible graphics cards can significantly improve inference speed, reducing document generation time from minutes to seconds.
Performance optimization strategies include model quantization, caching mechanisms, and request batching. Model quantization reduces memory requirements and inference time while maintaining acceptable output quality. Implementing intelligent caching for common document templates and frequently requested content types can dramatically improve response times for similar requests.
The system architecture should consider scalability requirements for enterprise deployment. This includes implementing load balancing for multiple concurrent users, distributed processing capabilities, and efficient resource management to handle varying workloads throughout the day.
Security considerations are paramount when deploying locally, particularly regarding document storage, user data privacy, and system access controls. The local deployment approach inherently provides better data privacy compared to cloud-based solutions, but proper security measures must still be implemented to protect sensitive business information.
Monitoring and maintenance procedures ensure long-term system reliability and performance. This includes implementing logging mechanisms, performance metrics collection, automated backup procedures, and regular system health checks to identify and resolve issues before they impact user productivity.
The document generation system represents a significant advancement in productivity tools, combining the power of modern AI with practical business needs while maintaining data privacy through local deployment. The comprehensive implementation provides a solid foundation for organizations seeking to enhance their document creation workflows while keeping sensitive information secure within their own infrastructure.
