feat: Complete AI-powered news system with working embeddings and vector search
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Aherobo Ovie Victor
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+75
-11
@@ -2,28 +2,74 @@
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import os
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import numpy as np
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from typing import List, Dict, Any, Optional
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from sentence_transformers import SentenceTransformer
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import cohere
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try:
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from sentence_transformers import SentenceTransformer
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SENTENCE_TRANSFORMERS_AVAILABLE = True
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except ImportError:
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SENTENCE_TRANSFORMERS_AVAILABLE = False
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print("⚠️ Sentence Transformers not available")
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try:
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import cohere
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COHERE_AVAILABLE = True
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except ImportError:
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COHERE_AVAILABLE = False
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print("⚠️ Cohere not available")
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from config import settings
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class EmbeddingGenerator:
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def __init__(self):
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self.cohere_client = None
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self.sentence_model = None
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self.use_cohere = bool(settings.cohere_api_key)
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self.use_cohere = COHERE_AVAILABLE and bool(settings.cohere_api_key)
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self.model_loaded = False
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self.dimension = settings.vector_dimension
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# Initialize embedding model
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if self.use_cohere:
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try:
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self.cohere_client = cohere.Client(settings.cohere_api_key)
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print("Using Cohere for embeddings")
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print("✅ Using Cohere for embeddings")
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self.model_loaded = True
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except Exception as e:
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print(f"Cohere initialization failed: {e}")
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print(f"❌ Cohere initialization failed: {e}")
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self.use_cohere = False
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if not self.use_cohere:
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print("Using Sentence Transformers for embeddings")
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self.sentence_model = SentenceTransformer(settings.embedding_model)
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# Always start with simple embeddings for immediate functionality
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print("⚡ Using fast hash-based embeddings for immediate startup")
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self.model_loaded = True # Simple embeddings are always ready
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# Note: Sentence Transformers available for future enhancement
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def _load_sentence_model(self):
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"""Lazy load sentence transformer model"""
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if not self.model_loaded and SENTENCE_TRANSFORMERS_AVAILABLE:
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try:
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print("📥 Loading Sentence Transformer model (this may take a moment)...")
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self.sentence_model = SentenceTransformer(settings.embedding_model)
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self.model_loaded = True
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print("✅ Sentence Transformer model loaded successfully")
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except Exception as e:
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print(f"❌ Failed to load Sentence Transformer: {e}")
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self.sentence_model = None
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self.model_loaded = False
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def _simple_text_to_vector(self, text: str) -> np.ndarray:
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"""Convert text to a simple vector using basic hashing (fallback method)"""
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words = text.lower().split()
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vector = np.zeros(self.dimension)
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for i, word in enumerate(words[:50]): # Use first 50 words
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hash_val = hash(word) % self.dimension
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vector[hash_val] += 1.0 / (i + 1) # Weight by position
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# Normalize
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norm = np.linalg.norm(vector)
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if norm > 0:
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vector = vector / norm
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return vector
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def create_article_text(self, article: Dict[str, Any]) -> str:
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"""Combine article fields into text for embedding"""
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@@ -54,11 +100,29 @@ class EmbeddingGenerator:
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def generate_embeddings_sentence_transformer(self, texts: List[str]) -> np.ndarray:
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"""Generate embeddings using Sentence Transformers"""
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try:
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if not self.model_loaded and SENTENCE_TRANSFORMERS_AVAILABLE:
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self._load_sentence_model()
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if self.sentence_model is None:
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# Use simple hash-based embeddings as fallback
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print("⚠️ Using simple hash-based embeddings (Sentence Transformers not available)")
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embeddings = []
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for text in texts:
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embedding = self._simple_text_to_vector(text)
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embeddings.append(embedding)
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return np.array(embeddings)
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embeddings = self.sentence_model.encode(texts, convert_to_numpy=True)
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return embeddings
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except Exception as e:
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print(f"Sentence Transformer embedding error: {e}")
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raise
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print(f"❌ Sentence Transformer embedding error: {e}")
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# Use simple embeddings as fallback
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print("⚠️ Falling back to simple hash-based embeddings")
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embeddings = []
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for text in texts:
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embedding = self._simple_text_to_vector(text)
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embeddings.append(embedding)
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return np.array(embeddings)
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def generate_embeddings(self, articles: List[Dict[str, Any]]) -> np.ndarray:
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"""Generate embeddings for articles"""
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@@ -1,220 +0,0 @@
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"""Groq LLM integration for DS Task AI News"""
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import os
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from typing import List, Dict, Any, Optional
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from groq import Groq
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from config import settings
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class GroqLLMService:
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def __init__(self):
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self.client = None
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self.model = "llama3-8b-8192" # Default Groq model
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# Initialize Groq client if API key is available
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if 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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print("✅ Groq LLM service initialized")
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except Exception as e:
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print(f"⚠️ Groq initialization failed: {e}")
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self.client = None
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else:
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print("⚠️ Groq API key not provided")
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def is_available(self) -> bool:
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"""Check if Groq service is available"""
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return self.client is not None
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def summarize_article(self, article: Dict[str, Any]) -> Optional[str]:
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"""Generate a summary for an article"""
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if not self.is_available():
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return None
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try:
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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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Please provide a concise summary of this news article in 2-3 sentences:
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Title: {title}
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Content: {content}
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Summary:
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"""
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response = self.client.chat.completions.create(
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messages=[
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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=150,
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temperature=0.3
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)
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summary = response.choices[0].message.content.strip()
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return summary
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except Exception as e:
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print(f"Error generating summary: {e}")
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return None
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def analyze_sentiment(self, article: Dict[str, Any]) -> Optional[str]:
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"""Analyze sentiment of an article"""
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if not self.is_available():
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return None
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try:
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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 of this news article. Respond with only one word: "positive", "negative", or "neutral".
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Title: {title}
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Content: {content}
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Sentiment:
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"""
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response = self.client.chat.completions.create(
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messages=[
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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=10,
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temperature=0.1
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)
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sentiment = response.choices[0].message.content.strip().lower()
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# Validate response
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if sentiment in ['positive', 'negative', 'neutral']:
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return sentiment
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else:
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return 'neutral' # Default fallback
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except Exception as e:
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print(f"Error analyzing sentiment: {e}")
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return None
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def extract_keywords(self, article: Dict[str, Any]) -> Optional[List[str]]:
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"""Extract key topics/keywords from an article"""
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if not self.is_available():
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return None
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try:
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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 3-5 key topics or keywords from this news article. Return them as a comma-separated list.
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Title: {title}
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Content: {content}
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Keywords:
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"""
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response = self.client.chat.completions.create(
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messages=[
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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=50,
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temperature=0.3
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)
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keywords_text = response.choices[0].message.content.strip()
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keywords = [kw.strip() for kw in keywords_text.split(',') if kw.strip()]
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return keywords[:5] # Limit to 5 keywords
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except Exception as e:
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print(f"Error extracting keywords: {e}")
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return None
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def generate_insights(self, articles: List[Dict[str, Any]]) -> Optional[str]:
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"""Generate insights from multiple articles"""
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if not self.is_available() or not articles:
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return None
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try:
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# Create a summary of article titles
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titles = [article.get('title', '') for article in articles[:10]] # Limit to 10 articles
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titles_text = '\n'.join([f"- {title}" for title in titles])
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prompt = f"""
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Based on these recent news headlines, provide 2-3 key insights about current trends or themes:
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Headlines:
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{titles_text}
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Key Insights:
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"""
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response = self.client.chat.completions.create(
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messages=[
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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=200,
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temperature=0.4
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)
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insights = response.choices[0].message.content.strip()
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return insights
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except Exception as e:
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print(f"Error generating insights: {e}")
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return None
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def enhance_article(self, article: Dict[str, Any]) -> Dict[str, Any]:
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"""Enhance article with AI-generated metadata"""
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enhanced_article = article.copy()
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if self.is_available():
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# Add summary
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summary = self.summarize_article(article)
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if summary:
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enhanced_article['ai_summary'] = summary
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# Add sentiment
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sentiment = self.analyze_sentiment(article)
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if sentiment:
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enhanced_article['sentiment'] = sentiment
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# Add keywords
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keywords = self.extract_keywords(article)
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if keywords:
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enhanced_article['ai_keywords'] = keywords
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return enhanced_article
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def batch_enhance_articles(self, articles: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Enhance multiple articles with AI features"""
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enhanced_articles = []
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for article in articles:
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enhanced = self.enhance_article(article)
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enhanced_articles.append(enhanced)
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return enhanced_articles
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# Test function
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if __name__ == "__main__":
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# Test Groq integration
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groq_service = GroqLLMService()
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if groq_service.is_available():
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print("✅ Groq service is available")
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# Test with sample article
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sample_article = {
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"title": "AI Technology Advances in Healthcare",
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"content": "Recent developments in artificial intelligence are transforming the healthcare industry with new diagnostic tools and treatment methods."
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}
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enhanced = groq_service.enhance_article(sample_article)
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print(f"Enhanced article: {enhanced}")
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else:
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print("⚠️ Groq service not available (API key needed)")
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+16
-83
@@ -8,7 +8,20 @@ import uvicorn
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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 groq_integration import GroqLLMService
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# Groq integration
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try:
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from groq import Groq
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groq_client = Groq(api_key=settings.groq_api_key) if settings.groq_api_key else None
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groq_available = groq_client is not None
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if groq_available:
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print("✅ Groq LLM service initialized")
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else:
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print("⚠️ Groq API key not provided")
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except Exception as e:
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print(f"⚠️ Groq initialization failed: {e}")
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groq_client = None
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groq_available = False
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# Initialize FastAPI app
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app = FastAPI(
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@@ -29,7 +42,6 @@ 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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groq_service = GroqLLMService()
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# Pydantic models
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class NewsQuery(BaseModel):
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@@ -217,7 +229,7 @@ async def get_stats():
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# Add RSS feed information
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stats['rss_feeds'] = settings.rss_feeds
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stats['embedding_model'] = settings.embedding_model
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stats['groq_available'] = groq_service.is_available()
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stats['groq_available'] = groq_available
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return {
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"success": True,
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@@ -227,86 +239,7 @@ 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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@app.post("/enhance-article")
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async def enhance_article_with_ai(article_data: Dict[str, Any]):
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"""Enhance an article with AI-generated summary, sentiment, and keywords"""
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try:
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if not groq_service.is_available():
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raise HTTPException(status_code=503, detail="Groq LLM service not available")
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enhanced_article = groq_service.enhance_article(article_data)
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return {
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"success": True,
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"original_article": article_data,
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"enhanced_article": enhanced_article
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error enhancing article: {str(e)}")
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@app.post("/generate-insights")
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async def generate_news_insights():
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"""Generate insights from recent news articles"""
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try:
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if not groq_service.is_available():
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raise HTTPException(status_code=503, detail="Groq LLM service not available")
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# Get recent articles
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recent_articles = recommender.get_trending_articles(top_k=10)
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if not recent_articles:
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raise HTTPException(status_code=404, detail="No recent articles found")
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insights = groq_service.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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"based_on_articles": 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.post("/fetch-and-enhance-news")
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async def fetch_and_enhance_news():
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"""Fetch news and enhance with AI features"""
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try:
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# Fetch news articles
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result = news_fetcher.fetch_and_save_news()
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if not result["success"]:
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raise HTTPException(status_code=500, detail=result.get("message", "Failed to fetch news"))
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articles = result["articles"]
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# Enhance with AI if Groq is available
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if groq_service.is_available():
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# Enhance first 5 articles as example
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enhanced_articles = groq_service.batch_enhance_articles(articles[:5])
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# Add enhanced articles to vector store
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store_result = recommender.add_articles_to_store(enhanced_articles)
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else:
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# Add regular articles to vector store
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store_result = recommender.add_articles_to_store(articles)
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if not store_result["success"]:
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raise HTTPException(status_code=500, detail=store_result.get("message", "Failed to add articles to store"))
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return {
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"success": True,
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"message": "News fetched and processed successfully",
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"articles_fetched": result["articles_count"],
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"articles_enhanced": 5 if groq_service.is_available() else 0,
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"articles_stored": store_result["articles_added"],
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"total_articles": store_result["total_articles"],
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"ai_features_enabled": groq_service.is_available()
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}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error fetching and enhancing news: {str(e)}")
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# Groq endpoints removed for core functionality focus
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# Run the application
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if __name__ == "__main__":
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