Ayomide
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
from .embeddings import get_embeddings, get_query_embedding, rerank_results
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from .vector_store import VectorDB
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import groq
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from .config import Config
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# Initialize Groq client
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groq_client = groq.Groq(api_key=Config.GROQ_API_KEY)
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# Vector database instance
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vector_db = VectorDB()
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def process_articles_for_vector_db(articles):
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"""Process articles and add to vector database"""
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if not articles:
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return
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# Extract text content for embedding
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texts = []
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for article in articles:
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text = f"{article['title']} {article['content']}"
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texts.append(text)
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# Generate embeddings
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embeddings = get_embeddings(texts)
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if embeddings:
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vector_db.add_vectors(embeddings, articles)
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def recommend_similar(article_id, top_n=3):
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"""Recommend similar articles based on article ID"""
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article = vector_db.get_article_by_id(article_id)
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if not article:
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return []
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# Get embedding for the article
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article_text = f"{article['title']} {article['content']}"
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query_embedding = get_query_embedding(article_text)
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if not query_embedding:
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return []
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# Search for similar articles
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similar_articles = vector_db.search(query_embedding, k=top_n + 1)
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# Filter out the original article
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recommendations = [art for art in similar_articles if art.get('slug') != article_id]
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return recommendations[:top_n]
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def analyze_article_with_groq(article_text):
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"""Analyze article using Groq LLM"""
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try:
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response = groq_client.chat.completions.create(
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model=Config.GROQ_MODEL,
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messages=[
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{
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"role": "system",
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"content": "You are an AI news analyst. Provide insights, key points, and sentiment analysis for the given article."
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},
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{
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"role": "user",
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"content": f"Analyze this news article: {article_text}"
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}
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],
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max_tokens=500,
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temperature=0.3
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)
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return response.choices[0].message.content
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except Exception as e:
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print(f"Error analyzing article with Groq: {str(e)}")
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return "Analysis unavailable"
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def get_personalized_recommendations(user_interests, top_n=5):
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"""Get personalized recommendations based on user interests"""
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query_embedding = get_query_embedding(user_interests)
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if not query_embedding:
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return []
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recommendations = vector_db.search(query_embedding, k=top_n)
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# Re-rank results for better relevance
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if recommendations:
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documents = [f"{art['title']} {art['content']}" for art in recommendations]
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reranked = rerank_results(user_interests, documents)
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if reranked:
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# Reorder recommendations based on reranking
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reordered = []
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for result in reranked:
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if result.index < len(recommendations):
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reordered.append(recommendations[result.index])
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return reordered
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return recommendations
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