feat: Complete all 4 major optimization tasks
✅ Network & Model Optimization: - Fixed Sentence Transformers path to use local model - Configured real semantic embeddings (384-dimensional) - Replaced hash-based fallback with AI-powered similarity ✅ Advanced AI Features Integration: - Added ai_analyzer.py with Groq LLM integration - Implemented article summarization, sentiment analysis, keyword extraction - Added AI endpoints: /analyze-article, /generate-insights, /ai-status ✅ API Enhancement & User Experience: - Enhanced articles endpoint with pagination (offset/limit, metadata) - Added advanced filtering (date ranges, source, category) - Improved search with semantic similarity + multi-parameter filters ✅ Production Polish & Performance: - Implemented in-memory caching system in vector_store.py - Added rate limiting (100 req/min per IP) - Enhanced API documentation with deployment guide - Fixed file structure compliance System now production-ready with 1000+ articles indexed and full AI capabilities.
This commit is contained in:
Aherobo Ovie Victor
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"""AI Analysis module for DS Task AI News using Groq LLM"""
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import os
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from typing import Dict, List, Any, Optional
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import json
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from datetime import datetime
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try:
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from groq import Groq
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GROQ_AVAILABLE = True
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except ImportError:
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GROQ_AVAILABLE = False
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print("⚠️ Groq not available - install with: pip install groq")
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from config import settings
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class AIAnalyzer:
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"""AI-powered article analysis using Groq LLM"""
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def __init__(self):
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self.client = None
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self.model = "llama3-8b-8192" # Fast Groq model
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self.available = False
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if GROQ_AVAILABLE and settings.groq_api_key:
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try:
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self.client = Groq(api_key=settings.groq_api_key)
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self.available = True
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print("✅ Groq AI Analyzer initialized successfully")
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except Exception as e:
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print(f"❌ Groq initialization failed: {e}")
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else:
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print("⚠️ Groq AI Analyzer not available (missing API key or library)")
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def _make_groq_request(self, prompt: str, max_tokens: int = 500) -> Optional[str]:
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"""Make a request to Groq API"""
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if not self.available:
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return None
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try:
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response = self.client.chat.completions.create(
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messages=[
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{"role": "system", "content": "You are an expert news analyst. Provide concise, accurate analysis."},
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{"role": "user", "content": prompt}
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],
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model=self.model,
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max_tokens=max_tokens,
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temperature=0.3
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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print(f"❌ Groq API error: {e}")
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return None
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def summarize_article(self, article: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate AI summary of an article"""
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if not self.available:
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return {"summary": "AI analysis not available", "available": False}
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title = article.get('title', '')
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content = article.get('content', '')
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prompt = f"""
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Analyze this news article and provide a concise summary:
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Title: {title}
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Content: {content[:1000]}...
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Provide:
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1. A 2-sentence summary
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2. 3 key points
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3. Main topic category
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Format as JSON:
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{{
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"summary": "Brief 2-sentence summary",
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"key_points": ["point1", "point2", "point3"],
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"category": "Technology/Business/Science/etc"
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}}
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"""
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response = self._make_groq_request(prompt, max_tokens=300)
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if response:
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try:
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analysis = json.loads(response)
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analysis["available"] = True
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analysis["analyzed_at"] = datetime.now().isoformat()
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return analysis
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except json.JSONDecodeError:
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return {
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"summary": response,
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"available": True,
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"analyzed_at": datetime.now().isoformat()
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}
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return {"summary": "Analysis failed", "available": False}
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def extract_keywords(self, article: Dict[str, Any]) -> List[str]:
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"""Extract key terms and entities from article"""
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if not self.available:
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return []
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title = article.get('title', '')
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content = article.get('content', '')
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prompt = f"""
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Extract the most important keywords and entities from this article:
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Title: {title}
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Content: {content[:800]}...
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Return only a JSON array of 5-8 most relevant keywords:
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["keyword1", "keyword2", "keyword3", ...]
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"""
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response = self._make_groq_request(prompt, max_tokens=100)
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if response:
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try:
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keywords = json.loads(response)
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return keywords if isinstance(keywords, list) else []
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except json.JSONDecodeError:
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# Fallback: extract from response text
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words = response.replace('[', '').replace(']', '').replace('"', '').split(',')
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return [word.strip() for word in words[:8]]
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return []
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def analyze_sentiment(self, article: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze sentiment and tone of article"""
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if not self.available:
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return {"sentiment": "neutral", "confidence": 0.0, "available": False}
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title = article.get('title', '')
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content = article.get('content', '')
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prompt = f"""
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Analyze the sentiment and tone of this news article:
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Title: {title}
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Content: {content[:600]}...
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Return JSON with:
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{{
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"sentiment": "positive/negative/neutral",
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"confidence": 0.85,
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"tone": "informative/urgent/optimistic/concerned/etc",
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"reasoning": "Brief explanation"
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}}
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"""
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response = self._make_groq_request(prompt, max_tokens=150)
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if response:
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try:
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sentiment = json.loads(response)
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sentiment["available"] = True
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return sentiment
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except json.JSONDecodeError:
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return {
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"sentiment": "neutral",
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"confidence": 0.5,
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"tone": "informative",
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"reasoning": response,
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"available": True
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}
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return {"sentiment": "neutral", "confidence": 0.0, "available": False}
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def generate_insights(self, articles: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Generate insights from multiple articles"""
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if not self.available or not articles:
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return {"insights": "AI insights not available", "available": False}
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# Prepare article summaries
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article_summaries = []
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for i, article in enumerate(articles[:5]): # Limit to 5 articles
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title = article.get('title', '')
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source = article.get('source', '')
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article_summaries.append(f"{i+1}. {title} (Source: {source})")
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prompt = f"""
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Analyze these recent news articles and provide insights:
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Articles:
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{chr(10).join(article_summaries)}
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Provide:
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1. Main trends or themes
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2. Key developments
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3. Potential implications
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Format as JSON:
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{{
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"trends": ["trend1", "trend2"],
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"key_developments": ["development1", "development2"],
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"implications": "Brief analysis of what this means"
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}}
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"""
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response = self._make_groq_request(prompt, max_tokens=400)
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if response:
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try:
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insights = json.loads(response)
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insights["available"] = True
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insights["analyzed_at"] = datetime.now().isoformat()
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insights["article_count"] = len(articles)
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return insights
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except json.JSONDecodeError:
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return {
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"insights": response,
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"available": True,
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"analyzed_at": datetime.now().isoformat()
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}
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return {"insights": "Analysis failed", "available": False}
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def get_status(self) -> Dict[str, Any]:
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"""Get AI analyzer status"""
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return {
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"available": self.available,
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"model": self.model if self.available else None,
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"features": [
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"Article Summarization",
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"Keyword Extraction",
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"Sentiment Analysis",
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"Trend Insights"
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] if self.available else []
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}
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