928c2f20aa
- Fix text selection menu not showing: wire contentRef via inputRef on multiline TextField - Fix blog title not truncating: add min-w-0 for flex item overflow - Fix outline generation 500: escape curly braces in f-string prompt template - Fix content generation 'NoneType not callable': replace SessionLocal() with get_session_for_user(), add db param to MediumBlogGenerator, fix signature mismatch in database_task_manager - Fix writing assistant suggest 500: add auth + user_id to API endpoint and service, replace sync requests with httpx.AsyncClient - Fix hallucination detector 404: explicitly include router in main.py and app.py - Fix missing error_data in task failure responses - Hide CopilotKit web inspector button - Remove hardcoded fallback suggestions from SmartTypingAssist - Fix stale closure refs in SmartTypingAssist handleTypingChange - Add two-column editor layout, stats bar, section hover menu - Various subscription, billing, and research module improvements
101 lines
4.3 KiB
Python
101 lines
4.3 KiB
Python
"""
|
|
Keyword Analyzer - AI-powered keyword analysis for research content.
|
|
|
|
Extracts and analyzes keywords from research content using structured AI responses.
|
|
"""
|
|
|
|
from typing import Dict, Any, List
|
|
from loguru import logger
|
|
import json
|
|
|
|
|
|
class KeywordAnalyzer:
|
|
"""Analyzes keywords from research content using AI-powered extraction."""
|
|
|
|
def analyze(self, content: str, original_keywords: List[str], user_id: str = None) -> Dict[str, Any]:
|
|
"""Parse comprehensive keyword analysis from the research content using AI."""
|
|
# Use AI to extract and analyze keywords from the rich research content
|
|
keyword_prompt = f"""
|
|
Analyze the following research content and extract comprehensive keyword insights for: {', '.join(original_keywords)}
|
|
|
|
Research Content:
|
|
{content[:3000]} # Limit to avoid token limits
|
|
|
|
Extract and analyze:
|
|
1. Primary keywords (main topic terms)
|
|
2. Secondary keywords (related terms, synonyms)
|
|
3. Long-tail opportunities (specific phrases people search for)
|
|
4. Search intent (informational, commercial, navigational, transactional)
|
|
5. Keyword difficulty assessment (1-10 scale)
|
|
6. Content gaps (what competitors are missing)
|
|
7. Semantic keywords (related concepts)
|
|
8. Trending terms (emerging keywords)
|
|
|
|
Respond with JSON:
|
|
{{
|
|
"primary": ["keyword1", "keyword2"],
|
|
"secondary": ["related1", "related2"],
|
|
"long_tail": ["specific phrase 1", "specific phrase 2"],
|
|
"search_intent": "informational|commercial|navigational|transactional",
|
|
"difficulty": 7,
|
|
"content_gaps": ["gap1", "gap2"],
|
|
"semantic_keywords": ["concept1", "concept2"],
|
|
"trending_terms": ["trend1", "trend2"],
|
|
"analysis_insights": "Brief analysis of keyword landscape"
|
|
}}
|
|
"""
|
|
|
|
from services.llm_providers.main_text_generation import llm_text_gen
|
|
|
|
keyword_schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"primary": {"type": "array", "items": {"type": "string"}},
|
|
"secondary": {"type": "array", "items": {"type": "string"}},
|
|
"long_tail": {"type": "array", "items": {"type": "string"}},
|
|
"search_intent": {"type": "string"},
|
|
"difficulty": {"type": "integer"},
|
|
"content_gaps": {"type": "array", "items": {"type": "string"}},
|
|
"semantic_keywords": {"type": "array", "items": {"type": "string"}},
|
|
"trending_terms": {"type": "array", "items": {"type": "string"}},
|
|
"analysis_insights": {"type": "string"}
|
|
},
|
|
"required": ["primary", "secondary", "long_tail", "search_intent", "difficulty", "content_gaps", "semantic_keywords", "trending_terms", "analysis_insights"]
|
|
}
|
|
|
|
raw = llm_text_gen(
|
|
prompt=keyword_prompt,
|
|
user_id=user_id
|
|
)
|
|
|
|
# Parse JSON from LLM response (works with both string and dict return types)
|
|
import re
|
|
if isinstance(raw, str):
|
|
cleaned = raw.strip()
|
|
if cleaned.startswith('```json'):
|
|
cleaned = cleaned[7:]
|
|
if cleaned.startswith('```'):
|
|
cleaned = cleaned[3:]
|
|
if cleaned.endswith('```'):
|
|
cleaned = cleaned[:-3]
|
|
cleaned = cleaned.strip()
|
|
try:
|
|
keyword_analysis = json.loads(cleaned)
|
|
except json.JSONDecodeError:
|
|
json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
|
|
if json_match:
|
|
keyword_analysis = json.loads(json_match.group(0))
|
|
else:
|
|
raise ValueError(f"Keyword analysis returned non-JSON string: {cleaned[:200]}")
|
|
elif isinstance(raw, dict):
|
|
keyword_analysis = raw
|
|
else:
|
|
raise ValueError(f"Unexpected LLM response type: {type(raw)}")
|
|
|
|
if 'error' in keyword_analysis:
|
|
raise ValueError(f"Keyword analysis failed: {keyword_analysis.get('error', 'Unknown error')}")
|
|
|
|
logger.info("✅ AI keyword analysis completed successfully")
|
|
return keyword_analysis
|
|
|