d90d441019
- Blog writer enhancements and bug fixes - Wix integration improvements - Frontend UI updates - GSC dashboard docs cleanup - Image studio assets - LinkedIn requirements file - Various dependency updates
101 lines
4.2 KiB
Python
101 lines
4.2 KiB
Python
"""
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Keyword Analyzer - AI-powered keyword analysis for research content.
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Extracts and analyzes keywords from research content using structured AI responses.
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"""
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from typing import Dict, Any, List
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from loguru import logger
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import json
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class KeywordAnalyzer:
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"""Analyzes keywords from research content using AI-powered extraction."""
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def analyze(self, content: str, original_keywords: List[str], user_id: str = None) -> Dict[str, Any]:
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"""Parse comprehensive keyword analysis from the research content using AI."""
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# Use AI to extract and analyze keywords from the rich research content
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keyword_prompt = f"""
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Analyze the following research content and extract comprehensive keyword insights for: {', '.join(original_keywords)}
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Research Content:
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{content[:8000]}
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Extract and analyze:
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1. Primary keywords (main topic terms)
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2. Secondary keywords (related terms, synonyms)
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3. Long-tail opportunities (specific phrases people search for)
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4. Search intent (informational, commercial, navigational, transactional)
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5. Keyword difficulty assessment (1-10 scale)
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6. Content gaps (what competitors are missing)
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7. Semantic keywords (related concepts)
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8. Trending terms (emerging keywords)
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Respond with JSON:
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{{
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"primary": ["keyword1", "keyword2"],
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"secondary": ["related1", "related2"],
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"long_tail": ["specific phrase 1", "specific phrase 2"],
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"search_intent": "informational|commercial|navigational|transactional",
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"difficulty": 7,
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"content_gaps": ["gap1", "gap2"],
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"semantic_keywords": ["concept1", "concept2"],
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"trending_terms": ["trend1", "trend2"],
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"analysis_insights": "Brief analysis of keyword landscape"
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}}
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"""
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from services.llm_providers.main_text_generation import llm_text_gen
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keyword_schema = {
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"type": "object",
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"properties": {
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"primary": {"type": "array", "items": {"type": "string"}},
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"secondary": {"type": "array", "items": {"type": "string"}},
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"long_tail": {"type": "array", "items": {"type": "string"}},
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"search_intent": {"type": "string"},
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"difficulty": {"type": "integer"},
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"content_gaps": {"type": "array", "items": {"type": "string"}},
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"semantic_keywords": {"type": "array", "items": {"type": "string"}},
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"trending_terms": {"type": "array", "items": {"type": "string"}},
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"analysis_insights": {"type": "string"}
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},
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"required": ["primary", "secondary", "long_tail", "search_intent", "difficulty", "content_gaps", "semantic_keywords", "trending_terms", "analysis_insights"]
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}
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raw = llm_text_gen(
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prompt=keyword_prompt,
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user_id=user_id
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)
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# Parse JSON from LLM response (works with both string and dict return types)
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import re
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if isinstance(raw, str):
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cleaned = raw.strip()
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if cleaned.startswith('```json'):
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cleaned = cleaned[7:]
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if cleaned.startswith('```'):
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cleaned = cleaned[3:]
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if cleaned.endswith('```'):
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cleaned = cleaned[:-3]
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cleaned = cleaned.strip()
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try:
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keyword_analysis = json.loads(cleaned)
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except json.JSONDecodeError:
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json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
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if json_match:
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keyword_analysis = json.loads(json_match.group(0))
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else:
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raise ValueError(f"Keyword analysis returned non-JSON string: {cleaned[:200]}")
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elif isinstance(raw, dict):
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keyword_analysis = raw
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else:
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raise ValueError(f"Unexpected LLM response type: {type(raw)}")
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if 'error' in keyword_analysis:
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raise ValueError(f"Keyword analysis failed: {keyword_analysis.get('error', 'Unknown error')}")
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logger.info("✅ AI keyword analysis completed successfully")
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return keyword_analysis
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