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
+253
-24
@@ -1,13 +1,16 @@
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"""FastAPI backend for DS Task AI News"""
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from fastapi import FastAPI, HTTPException, Query
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from fastapi import FastAPI, HTTPException, Query, Request
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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import uvicorn
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import time
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from collections import defaultdict
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from config import settings
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from news_fetcher import NewsFetcher
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from recommender import NewsRecommender
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from ai_analyzer import AIAnalyzer
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# Groq integration
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try:
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@@ -42,6 +45,30 @@ app.add_middleware(
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# Initialize components
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news_fetcher = NewsFetcher()
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recommender = NewsRecommender()
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ai_analyzer = AIAnalyzer()
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# Simple rate limiter
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rate_limit_storage = defaultdict(list)
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RATE_LIMIT_REQUESTS = 100 # requests per minute
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RATE_LIMIT_WINDOW = 60 # seconds
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def check_rate_limit(client_ip: str) -> bool:
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"""Check if client has exceeded rate limit"""
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current_time = time.time()
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# Clean old requests
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rate_limit_storage[client_ip] = [
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req_time for req_time in rate_limit_storage[client_ip]
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if current_time - req_time < RATE_LIMIT_WINDOW
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]
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# Check if limit exceeded
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if len(rate_limit_storage[client_ip]) >= RATE_LIMIT_REQUESTS:
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return False
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# Add current request
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rate_limit_storage[client_ip].append(current_time)
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return True
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# Pydantic models
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class NewsQuery(BaseModel):
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@@ -55,7 +82,17 @@ class InterestsQuery(BaseModel):
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class SearchQuery(BaseModel):
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query: str
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source: Optional[str] = None
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category: Optional[str] = None
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date_from: Optional[str] = None
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date_to: Optional[str] = None
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top_k: int = 10
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include_content: bool = False
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class AnalyzeRequest(BaseModel):
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article_id: str
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class InsightsRequest(BaseModel):
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article_count: int = 5
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# API Endpoints
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@@ -179,44 +216,174 @@ async def get_trending_news(top_k: int = Query(10, description="Number of trendi
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@app.get("/articles")
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async def get_all_articles(
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source: Optional[str] = Query(None, description="Filter by news source"),
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limit: int = Query(50, description="Maximum number of articles to return")
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limit: int = Query(50, description="Maximum number of articles to return"),
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offset: int = Query(0, description="Number of articles to skip for pagination"),
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category: Optional[str] = Query(None, description="Filter by article category"),
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date_from: Optional[str] = Query(None, description="Filter articles from this date (YYYY-MM-DD)"),
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date_to: Optional[str] = Query(None, description="Filter articles to this date (YYYY-MM-DD)")
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):
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"""Get all articles with optional filtering"""
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"""Get all articles with pagination and advanced filtering"""
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try:
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# Get all articles first
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all_articles = recommender.vector_store.get_all_articles()
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# Apply filters
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filtered_articles = all_articles
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# Filter by source
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if source:
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articles = recommender.get_articles_by_source(source, limit)
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else:
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all_articles = recommender.vector_store.get_all_articles()
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articles = sorted(all_articles, key=lambda x: x.get('published_date', ''), reverse=True)[:limit]
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filtered_articles = [a for a in filtered_articles if a.get('source', '').lower() == source.lower()]
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# Filter by category (if articles have categories)
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if category:
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filtered_articles = [a for a in filtered_articles
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if category.lower() in [cat.lower() for cat in a.get('categories', [])]]
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# Filter by date range
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if date_from or date_to:
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from datetime import datetime
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def parse_date(date_str):
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try:
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return datetime.fromisoformat(date_str.replace('Z', '+00:00'))
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except:
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try:
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return datetime.strptime(date_str, '%Y-%m-%d')
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except:
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return None
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if date_from:
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from_date = parse_date(date_from)
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if from_date:
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filtered_articles = [a for a in filtered_articles
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if parse_date(a.get('published_date', '')) and
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parse_date(a.get('published_date', '')) >= from_date]
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if date_to:
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to_date = parse_date(date_to)
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if to_date:
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filtered_articles = [a for a in filtered_articles
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if parse_date(a.get('published_date', '')) and
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parse_date(a.get('published_date', '')) <= to_date]
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# Sort by published date (newest first)
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filtered_articles = sorted(filtered_articles,
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key=lambda x: x.get('published_date', ''),
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reverse=True)
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# Calculate pagination
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total_count = len(filtered_articles)
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start_idx = offset
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end_idx = offset + limit
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paginated_articles = filtered_articles[start_idx:end_idx]
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# Calculate pagination metadata
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has_next = end_idx < total_count
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has_prev = offset > 0
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total_pages = (total_count + limit - 1) // limit # Ceiling division
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current_page = (offset // limit) + 1
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return {
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"success": True,
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"articles": articles,
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"count": len(articles),
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"source_filter": source
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"articles": paginated_articles,
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"pagination": {
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"total_count": total_count,
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"count": len(paginated_articles),
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"limit": limit,
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"offset": offset,
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"current_page": current_page,
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"total_pages": total_pages,
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"has_next": has_next,
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"has_prev": has_prev,
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"next_offset": end_idx if has_next else None,
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"prev_offset": max(0, offset - limit) if has_prev else None
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},
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"filters": {
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"source": source,
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"category": category,
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"date_from": date_from,
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"date_to": date_to
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}
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting articles: {str(e)}")
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@app.post("/search")
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async def search_articles(search_data: SearchQuery):
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"""Advanced search with filters"""
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async def search_articles(search_data: SearchQuery, request: Request):
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"""Advanced search with multiple filters and semantic similarity"""
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try:
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filters = {}
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# Rate limiting
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client_ip = request.client.host
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if not check_rate_limit(client_ip):
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raise HTTPException(status_code=429, detail="Rate limit exceeded. Please try again later.")
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# Get semantic search results first
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semantic_results = recommender.search_articles(search_data.query, {}, search_data.top_k * 2)
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# Apply additional filters
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filtered_results = semantic_results
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# Filter by source
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if search_data.source:
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filters['source'] = search_data.source
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results = recommender.search_articles(search_data.query, filters, search_data.top_k)
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filtered_results = [r for r in filtered_results
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if r.get('source', '').lower() == search_data.source.lower()]
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# Filter by category
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if search_data.category:
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filtered_results = [r for r in filtered_results
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if search_data.category.lower() in [cat.lower() for cat in r.get('categories', [])]]
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# Filter by date range
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if search_data.date_from or search_data.date_to:
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from datetime import datetime
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def parse_date(date_str):
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try:
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return datetime.fromisoformat(date_str.replace('Z', '+00:00'))
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except:
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try:
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return datetime.strptime(date_str, '%Y-%m-%d')
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except:
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return None
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if search_data.date_from:
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from_date = parse_date(search_data.date_from)
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if from_date:
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filtered_results = [r for r in filtered_results
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if parse_date(r.get('published_date', '')) and
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parse_date(r.get('published_date', '')) >= from_date]
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if search_data.date_to:
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to_date = parse_date(search_data.date_to)
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if to_date:
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filtered_results = [r for r in filtered_results
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if parse_date(r.get('published_date', '')) and
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parse_date(r.get('published_date', '')) <= to_date]
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# Limit results to requested amount
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final_results = filtered_results[:search_data.top_k]
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# Optionally include full content
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if not search_data.include_content:
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for result in final_results:
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if 'content' in result and len(result['content']) > 200:
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result['content'] = result['content'][:200] + "..."
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return {
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"success": True,
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"query": search_data.query,
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"filters": filters,
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"results": results,
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"count": len(results)
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"filters": {
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"source": search_data.source,
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"category": search_data.category,
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"date_from": search_data.date_from,
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"date_to": search_data.date_to
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},
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"results": final_results,
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"count": len(final_results),
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"total_semantic_matches": len(semantic_results),
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"filtered_matches": len(filtered_results)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error searching articles: {str(e)}")
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@@ -239,7 +406,69 @@ async def get_stats():
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting stats: {str(e)}")
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# Groq endpoints removed for core functionality focus
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# AI Analysis Endpoints
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@app.post("/analyze-article")
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async def analyze_article(request: AnalyzeRequest):
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"""Analyze a specific article with AI"""
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try:
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# Get article from vector store
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articles = recommender.vector_store.get_all_articles()
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article = next((a for a in articles if a.get('id') == request.article_id), None)
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if not article:
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raise HTTPException(status_code=404, detail="Article not found")
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# Perform AI analysis
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summary = ai_analyzer.summarize_article(article)
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keywords = ai_analyzer.extract_keywords(article)
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sentiment = ai_analyzer.analyze_sentiment(article)
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return {
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"success": True,
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"article_id": request.article_id,
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"analysis": {
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"summary": summary,
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"keywords": keywords,
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"sentiment": sentiment
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}
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error analyzing article: {str(e)}")
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@app.post("/generate-insights")
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async def generate_insights(request: InsightsRequest):
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"""Generate AI insights from recent articles"""
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try:
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# Get recent articles
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recent_articles = recommender.get_trending_articles(request.article_count)
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# Generate insights
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insights = ai_analyzer.generate_insights(recent_articles)
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return {
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"success": True,
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"insights": insights,
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"article_count": len(recent_articles)
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error generating insights: {str(e)}")
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@app.get("/ai-status")
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async def get_ai_status():
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"""Get AI analyzer status and capabilities"""
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try:
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status = ai_analyzer.get_status()
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return {
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"success": True,
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"ai_status": status
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error getting AI status: {str(e)}")
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# Run the application
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if __name__ == "__main__":
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