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"""
Nomination Assistant for Award Identification
AI-assisted preparation of award nominations and grant applications.
FEATURES (Planned):
------------------
1. DOCUMENT GENERATION:
- Executive summaries
- Project descriptions
- Budget justifications
- Team CVs and bios
2. TEMPLATE MATCHING:
- Match to funder templates
- Format compliance checking
- Character/word limit validation
3. QUALITY ASSURANCE:
- CriticAgent validation
- Reviewer simulation
- Gap identification
4. COLLABORATION:
- Multi-author editing
- Comment and review workflows
- Version control
HUMAN-IN-THE-LOOP:
-----------------
Document preparation requires extensive human input:
- Initial content drafting
- Review and revision cycles
- Final approval before submission
This assistant accelerates the process but doesn't replace
human expertise in grant writing.
Author: SPARKNET Team
Project: VISTA/Horizon EU
Status: Placeholder - In Development
"""
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from loguru import logger
class DocumentTemplate(str, Enum):
"""Standard document templates."""
HORIZON_PROPOSAL = "horizon_proposal"
ERC_APPLICATION = "erc_application"
NATIONAL_GRANT = "national_grant"
AWARD_NOMINATION = "award_nomination"
LETTER_OF_INTENT = "letter_of_intent"
BUDGET_TEMPLATE = "budget_template"
CV_EUROPASS = "cv_europass"
CUSTOM = "custom"
class ReviewStatus(str, Enum):
"""Document review status."""
DRAFT = "draft"
INTERNAL_REVIEW = "internal_review"
REVISION_NEEDED = "revision_needed"
APPROVED = "approved"
SUBMITTED = "submitted"
@dataclass
class DocumentSection:
"""
Section of a nomination document.
Represents a structured section with content and metadata.
"""
section_id: str
title: str
content: str
word_limit: Optional[int] = None
current_words: int = 0
status: str = "draft"
ai_generated: bool = False
human_reviewed: bool = False
reviewer_comments: List[str] = field(default_factory=list)
suggestions: List[str] = field(default_factory=list)
@dataclass
class DocumentReview:
"""
Review of a nomination document.
Contains feedback from AI and human reviewers.
"""
review_id: str
document_id: str
reviewer_type: str # "ai", "human", "external"
reviewer_name: Optional[str] = None
overall_score: Optional[float] = None
section_scores: Dict[str, float] = field(default_factory=dict)
strengths: List[str] = field(default_factory=list)
weaknesses: List[str] = field(default_factory=list)
suggestions: List[str] = field(default_factory=list)
decision: str = "pending" # approve, revise, reject
created_at: datetime = field(default_factory=datetime.now)
class NominationAssistant:
"""
AI assistant for preparing nominations and applications.
This component:
- Generates document sections
- Checks format compliance
- Simulates reviewer feedback
- Manages revision workflows
INTEGRATION WITH CRITICAGENT:
-----------------------------
Uses CriticAgent for:
- Document quality validation
- Format compliance checking
- Reviewer perspective simulation
- Gap and weakness identification
CONFIDENCE SCORING:
------------------
All AI-generated content includes:
- Confidence score (0.0-1.0)
- Source references where applicable
- Suggestions for improvement
- Flag for human review
Generated content with low confidence scores
is automatically flagged for human review.
"""
def __init__(
self,
llm_client: Optional[Any] = None,
critic_agent: Optional[Any] = None,
template_library: Optional[Dict[str, Any]] = None,
):
"""
Initialize Nomination Assistant.
Args:
llm_client: LangChain LLM client for content generation
critic_agent: CriticAgent for validation
template_library: Library of document templates
"""
self.llm_client = llm_client
self.critic_agent = critic_agent
self.template_library = template_library or {}
self.name = "NominationAssistant"
# Threshold for requiring human review
self.confidence_threshold = 0.7
logger.info(f"Initialized {self.name} (placeholder)")
async def generate_section(
self,
document_id: str,
section_type: str,
context: Dict[str, Any],
word_limit: Optional[int] = None,
) -> DocumentSection:
"""
Generate a document section using AI.
Args:
document_id: Parent document ID
section_type: Type of section to generate
context: Context information for generation
word_limit: Optional word limit
Returns:
Generated section with confidence score
TODO: Implement actual LLM generation
"""
logger.info(f"Generating {section_type} section for document: {document_id}")
# Placeholder response
return DocumentSection(
section_id=f"sec_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
title=section_type.replace("_", " ").title(),
content="[AI-generated content placeholder]",
word_limit=word_limit,
current_words=0,
status="draft",
ai_generated=True,
human_reviewed=False,
suggestions=["Complete implementation with actual LLM generation"],
)
async def check_format_compliance(
self,
document_id: str,
template: DocumentTemplate,
) -> Dict[str, Any]:
"""
Check document compliance with template requirements.
Args:
document_id: Document to check
template: Template to check against
Returns:
Compliance report with issues
TODO: Implement actual compliance checking
"""
logger.info(f"Checking format compliance for document: {document_id}")
# Placeholder response
return {
"document_id": document_id,
"template": template.value,
"compliant": False,
"issues": [
{
"type": "placeholder",
"message": "Compliance checking not yet implemented",
"severity": "info",
}
],
"word_counts": {},
"missing_sections": [],
}
async def simulate_review(
self,
document_id: str,
reviewer_perspective: str = "general",
) -> DocumentReview:
"""
Simulate reviewer feedback using AI.
Generates feedback from the perspective of a grant
reviewer to identify potential weaknesses.
Args:
document_id: Document to review
reviewer_perspective: Type of reviewer to simulate
Returns:
Simulated review with scores and feedback
TODO: Implement actual review simulation
"""
logger.info(f"Simulating {reviewer_perspective} review for document: {document_id}")
# Placeholder response
return DocumentReview(
review_id=f"rev_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
document_id=document_id,
reviewer_type="ai",
reviewer_name=f"AI ({reviewer_perspective})",
overall_score=0.0,
strengths=["Review simulation not yet implemented"],
weaknesses=["Cannot assess without implementation"],
suggestions=["Complete the AI review simulation feature"],
decision="pending",
)
async def suggest_improvements(
self,
section: DocumentSection,
) -> List[str]:
"""
Suggest improvements for a document section.
Uses CriticAgent to analyze section and generate
actionable improvement suggestions.
Args:
section: Section to analyze
Returns:
List of improvement suggestions
TODO: Implement CriticAgent integration
"""
logger.info(f"Generating improvement suggestions for section: {section.section_id}")
# Placeholder response
return [
"Improvement suggestions not yet implemented",
"Will integrate with CriticAgent for validation",
]
async def validate_with_critic(
self,
document_id: str,
) -> Dict[str, Any]:
"""
Validate document using CriticAgent.
Performs comprehensive validation including:
- Content quality assessment
- Format compliance
- Logical consistency
- Citation verification
Args:
document_id: Document to validate
Returns:
Validation result with scores and issues
TODO: Implement CriticAgent integration
"""
logger.info(f"Validating document with CriticAgent: {document_id}")
# Placeholder response
return {
"document_id": document_id,
"valid": False,
"overall_score": 0.0,
"dimension_scores": {
"completeness": 0.0,
"clarity": 0.0,
"accuracy": 0.0,
"compliance": 0.0,
},
"issues": ["CriticAgent validation not yet implemented"],
"suggestions": ["Complete CriticAgent integration"],
"human_review_required": True,
"confidence": 0.0,
}
def requires_human_review(
self,
confidence_score: float,
section_type: str,
) -> bool:
"""
Determine if content requires human review.
Human review is required when:
- Confidence is below threshold
- Section is critical (executive summary, budget)
- Content makes claims about capabilities
Args:
confidence_score: AI confidence score
section_type: Type of section
Returns:
True if human review required
"""
# Low confidence always requires review
if confidence_score < self.confidence_threshold:
return True
# Critical sections always require review
critical_sections = [
"executive_summary",
"budget",
"team_qualifications",
"methodology",
]
if section_type.lower() in critical_sections:
return True
return False
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