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import logging
from typing import List, Dict, Any, Optional
import asyncio
from services.llm_service import LLMService
from mcp_tools.search_tool import SearchTool
from core.models import SearchResult
logger = logging.getLogger(__name__)
class GenerativeTool:
def __init__(self, llm_service: LLMService, search_tool: Optional[SearchTool] = None):
self.llm_service = llm_service
self.search_tool = search_tool
async def summarize(self, content: str, style: str = "concise", max_length: Optional[int] = None) -> str:
"""Generate a summary of the given content"""
try:
if not content.strip():
return "No content provided for summarization."
logger.info(f"Generating {style} summary for content of length {len(content)}")
summary = await self.llm_service.summarize(content, style, max_length)
logger.info(f"Generated summary of length {len(summary)}")
return summary
except Exception as e:
logger.error(f"Error generating summary: {str(e)}")
return f"Error generating summary: {str(e)}"
async def generate_tags(self, content: str, max_tags: int = 5) -> List[str]:
"""Generate relevant tags for the given content"""
try:
if not content.strip():
return []
logger.info(f"Generating up to {max_tags} tags for content")
tags = await self.llm_service.generate_tags(content, max_tags)
logger.info(f"Generated {len(tags)} tags")
return tags
except Exception as e:
logger.error(f"Error generating tags: {str(e)}")
return []
async def categorize(self, content: str, categories: List[str]) -> str:
"""Categorize content into one of the provided categories"""
try:
if not content.strip():
return "Uncategorized"
if not categories:
categories = ["Technology", "Business", "Science", "Education", "Entertainment", "News", "Research", "Other"]
logger.info(f"Categorizing content into one of {len(categories)} categories")
category = await self.llm_service.categorize(content, categories)
logger.info(f"Categorized as: {category}")
return category
except Exception as e:
logger.error(f"Error categorizing content: {str(e)}")
return "Uncategorized"
async def answer_question(self, question: str, context_results: List[SearchResult] = None) -> str:
"""Answer a question using the provided context or RAG"""
try:
if not question.strip():
return "No question provided."
logger.info(f"Answering question: {question[:100]}...")
# If no context provided and search tool is available, search for relevant context
if not context_results and self.search_tool:
logger.info("No context provided, searching for relevant information")
context_results = await self.search_tool.search(question, top_k=5)
# Prepare context from search results
if context_results:
context_texts = []
for result in context_results:
context_texts.append(f"Source: {result.document_id}\nContent: {result.content}\n")
context = "\n---\n".join(context_texts)
logger.info(f"Using context from {len(context_results)} sources")
else:
context = ""
logger.info("No context available for answering question")
# Generate answer
answer = await self.llm_service.answer_question(question, context)
logger.info(f"Generated answer of length {len(answer)}")
return answer
except Exception as e:
logger.error(f"Error answering question: {str(e)}")
return f"I encountered an error while trying to answer your question: {str(e)}"
async def generate_outline(self, topic: str, num_sections: int = 5, detail_level: str = "medium") -> str:
"""Generate an outline for the given topic"""
try:
if not topic.strip():
return "No topic provided."
detail_descriptions = {
"brief": "brief bullet points",
"medium": "detailed bullet points with descriptions",
"detailed": "comprehensive outline with sub-sections and explanations"
}
detail_desc = detail_descriptions.get(detail_level, "detailed bullet points")
prompt = f"""Create a {detail_desc} outline for the topic: "{topic}"
The outline should have {num_sections} main sections and be well-structured and informative.
Format the outline clearly with proper numbering and indentation.
Topic: {topic}
Outline:"""
outline = await self.llm_service.generate_text(prompt, max_tokens=800, temperature=0.7)
logger.info(f"Generated outline for topic: {topic}")
return outline
except Exception as e:
logger.error(f"Error generating outline: {str(e)}")
return f"Error generating outline: {str(e)}"
async def explain_concept(self, concept: str, audience: str = "general", length: str = "medium") -> str:
"""Explain a concept for a specific audience"""
try:
if not concept.strip():
return "No concept provided."
audience_styles = {
"general": "a general audience using simple, clear language",
"technical": "a technical audience with appropriate jargon and detail",
"beginner": "beginners with no prior knowledge, using analogies and examples",
"expert": "experts in the field with advanced terminology and depth"
}
length_guidance = {
"brief": "Keep the explanation concise and to the point (2-3 paragraphs).",
"medium": "Provide a comprehensive explanation (4-6 paragraphs).",
"detailed": "Give a thorough, in-depth explanation with examples."
}
audience_desc = audience_styles.get(audience, "a general audience")
length_desc = length_guidance.get(length, "Provide a comprehensive explanation.")
prompt = f"""Explain the concept of "{concept}" for {audience_desc}.
{length_desc}
Make sure to:
- Use appropriate language for the audience
- Include relevant examples or analogies
- Structure the explanation logically
- Ensure clarity and accuracy
Concept to explain: {concept}
Explanation:"""
explanation = await self.llm_service.generate_text(prompt, max_tokens=600, temperature=0.5)
logger.info(f"Generated explanation for concept: {concept}")
return explanation
except Exception as e:
logger.error(f"Error explaining concept: {str(e)}")
return f"Error explaining concept: {str(e)}"
async def compare_concepts(self, concept1: str, concept2: str, aspects: List[str] = None) -> str:
"""Compare two concepts across specified aspects"""
try:
if not concept1.strip() or not concept2.strip():
return "Both concepts must be provided for comparison."
if not aspects:
aspects = ["definition", "key features", "advantages", "disadvantages", "use cases"]
aspects_str = ", ".join(aspects)
prompt = f"""Compare and contrast "{concept1}" and "{concept2}" across the following aspects: {aspects_str}.
Structure your comparison clearly, addressing each aspect for both concepts.
Format:
## Comparison: {concept1} vs {concept2}
For each aspect, provide:
- **{concept1}**: [description]
- **{concept2}**: [description]
- **Key Difference**: [summary]
Concepts to compare:
1. {concept1}
2. {concept2}
Comparison:"""
comparison = await self.llm_service.generate_text(prompt, max_tokens=800, temperature=0.6)
logger.info(f"Generated comparison between {concept1} and {concept2}")
return comparison
except Exception as e:
logger.error(f"Error comparing concepts: {str(e)}")
return f"Error comparing concepts: {str(e)}"
async def generate_questions(self, content: str, question_type: str = "comprehension", num_questions: int = 5) -> List[str]:
"""Generate questions based on the provided content"""
try:
if not content.strip():
return []
question_types = {
"comprehension": "comprehension questions that test understanding of key concepts",
"analysis": "analytical questions that require deeper thinking and evaluation",
"application": "application questions that ask how to use the concepts in practice",
"creative": "creative questions that encourage original thinking and exploration",
"factual": "factual questions about specific details and information"
}
question_desc = question_types.get(question_type, "comprehension questions")
prompt = f"""Based on the following content, generate {num_questions} {question_desc}.
The questions should be:
- Clear and well-formulated
- Relevant to the content
- Appropriate for the specified type
- Engaging and thought-provoking
Content:
{content[:2000]} # Limit content length
Questions:"""
response = await self.llm_service.generate_text(prompt, max_tokens=400, temperature=0.7)
# Parse questions from response
questions = []
lines = response.split('\n')
for line in lines:
line = line.strip()
if line and ('?' in line or line.startswith(('1.', '2.', '3.', '4.', '5.', '-', '*'))):
# Clean up the question
question = line.lstrip('0123456789.-* ').strip()
if question and '?' in question:
questions.append(question)
logger.info(f"Generated {len(questions)} {question_type} questions")
return questions[:num_questions]
except Exception as e:
logger.error(f"Error generating questions: {str(e)}")
return []
async def paraphrase_text(self, text: str, style: str = "formal", preserve_meaning: bool = True) -> str:
"""Paraphrase text in a different style while preserving meaning"""
try:
if not text.strip():
return "No text provided for paraphrasing."
style_instructions = {
"formal": "formal, professional language",
"casual": "casual, conversational language",
"academic": "academic, scholarly language",
"simple": "simple, easy-to-understand language",
"technical": "technical, precise language"
}
style_desc = style_instructions.get(style, "clear, appropriate language")
meaning_instruction = "while preserving the exact meaning and key information" if preserve_meaning else "while maintaining the general intent"
prompt = f"""Paraphrase the following text using {style_desc} {meaning_instruction}.
Original text:
{text}
Paraphrased text:"""
paraphrase = await self.llm_service.generate_text(prompt, max_tokens=len(text.split()) * 2, temperature=0.6)
logger.info(f"Paraphrased text in {style} style")
return paraphrase.strip()
except Exception as e:
logger.error(f"Error paraphrasing text: {str(e)}")
return f"Error paraphrasing text: {str(e)}"
async def extract_key_insights(self, content: str, num_insights: int = 5) -> List[str]:
"""Extract key insights from the provided content"""
try:
if not content.strip():
return []
prompt = f"""Analyze the following content and extract {num_insights} key insights or takeaways.
Each insight should be:
- A clear, concise statement
- Significant and meaningful
- Based on the content provided
- Actionable or thought-provoking when possible
Content:
{content[:3000]} # Limit content length
Key Insights:"""
response = await self.llm_service.generate_text(prompt, max_tokens=400, temperature=0.6)
# Parse insights from response
insights = []
lines = response.split('\n')
for line in lines:
line = line.strip()
if line and (line.startswith(('1.', '2.', '3.', '4.', '5.', '-', '*')) or len(insights) == 0):
# Clean up the insight
insight = line.lstrip('0123456789.-* ').strip()
if insight and len(insight) > 10: # Minimum insight length
insights.append(insight)
logger.info(f"Extracted {len(insights)} key insights")
return insights[:num_insights]
except Exception as e:
logger.error(f"Error extracting insights: {str(e)}")
return [] |