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| import os | |
| import feedparser | |
| from langchain.vectorstores import Chroma | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.docstore.document import Document | |
| import logging | |
| from huggingface_hub import HfApi, login, snapshot_download | |
| import shutil | |
| import rss_feeds | |
| from datetime import datetime | |
| import dateutil.parser | |
| import hashlib | |
| import re | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| LOCAL_DB_DIR = "chroma_db" | |
| RSS_FEEDS = rss_feeds.RSS_FEEDS | |
| COLLECTION_NAME = "news_articles" | |
| HF_API_TOKEN = os.getenv("HF_TOKEN") | |
| REPO_ID = "broadfield-dev/news-rag-db" | |
| login(token=HF_API_TOKEN) | |
| hf_api = HfApi() | |
| def get_embedding_model(): | |
| if not hasattr(get_embedding_model, "model"): | |
| get_embedding_model.model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| return get_embedding_model.model | |
| def clean_text(text): | |
| if not text or not isinstance(text, str): | |
| return "" | |
| text = re.sub(r'<.*?>', '', text) | |
| text = ' '.join(text.split()) | |
| return text.strip().lower() | |
| def fetch_rss_feeds(): | |
| articles = [] | |
| seen_keys = set() | |
| for feed_url in RSS_FEEDS: | |
| try: | |
| logger.info(f"Fetching {feed_url}") | |
| feed = feedparser.parse(feed_url) | |
| if feed.bozo: | |
| logger.warning(f"Parse error for {feed_url}: {feed.bozo_exception}") | |
| continue | |
| article_count = 0 | |
| for entry in feed.entries: | |
| if article_count >= 10: | |
| break | |
| title = entry.get("title", "No Title") | |
| link = entry.get("link", "") | |
| description = entry.get("summary", entry.get("description", "")) | |
| cleaned_title = clean_text(title) | |
| cleaned_link = clean_text(link) | |
| published = "Unknown Date" | |
| for date_field in ["published", "updated", "created", "pubDate"]: | |
| if date_field in entry: | |
| try: | |
| parsed_date = dateutil.parser.parse(entry[date_field]) | |
| published = parsed_date.strftime("%Y-%m-%d %H:%M:%S") | |
| break | |
| except (ValueError, TypeError): | |
| continue | |
| key = f"{cleaned_title}|{cleaned_link}|{published}" | |
| if key not in seen_keys: | |
| seen_keys.add(key) | |
| image = "svg" | |
| for img_source in [ | |
| lambda e: clean_text(e.get("media_content", [{}])[0].get("url")) if e.get("media_content") else "", | |
| lambda e: clean_text(e.get("media_thumbnail", [{}])[0].get("url")) if e.get("media_thumbnail") else "", | |
| ]: | |
| try: | |
| img = img_source(entry) | |
| if img and img.strip(): | |
| image = img | |
| break | |
| except (IndexError, AttributeError, TypeError): | |
| continue | |
| articles.append({ | |
| "title": title, | |
| "link": link, | |
| "description": description, | |
| "published": published, | |
| "category": categorize_feed(feed_url), | |
| "image": image, | |
| }) | |
| article_count += 1 | |
| except Exception as e: | |
| logger.error(f"Error fetching {feed_url}: {e}") | |
| logger.info(f"Total articles fetched: {len(articles)}") | |
| return articles | |
| def categorize_feed(url): | |
| if not url or not isinstance(url, str): | |
| return "Uncategorized" | |
| url = url.lower().strip() | |
| if any(keyword in url for keyword in ["nature", "science.org", "arxiv.org", "plos.org", "jneurosci.org", "nejm.org", "lancet.com"]): return "Academic Papers" | |
| if any(keyword in url for keyword in ["ft.com", "marketwatch.com", "cnbc.com", "wsj.com", "economist.com"]): return "Business" | |
| if any(keyword in url for keyword in ["investing.com", "fool.co.uk", "seekingalpha.com", "yahoofinance.com"]): return "Stocks & Markets" | |
| if any(keyword in url for keyword in ["nasa", "spaceweatherlive", "space.com", "universetoday.com", "esa.int"]): return "Space" | |
| if any(keyword in url for keyword in ["sciencedaily", "quantamagazine", "scientificamerican", "newscientist", "livescience"]): return "Science" | |
| if any(keyword in url for keyword in ["wired", "techcrunch", "arstechnica", "gizmodo", "theverge"]): return "Tech" | |
| if any(keyword in url for keyword in ["horoscope", "astrostyle"]): return "Astrology" | |
| if any(keyword in url for keyword in ["bbci.co.uk/news/politics", "politico.com", "thehill.com"]): return "Politics" | |
| if any(keyword in url for keyword in ["weather.com", "weather.gov", "swpc.noaa.gov", "foxweather"]): return "Earth Weather" | |
| if "phys.org" in url or "aps.org" in url: return "Physics" | |
| return "Uncategorized" | |
| def process_and_store_articles(articles): | |
| vector_db = Chroma( | |
| persist_directory=LOCAL_DB_DIR, | |
| embedding_function=get_embedding_model(), | |
| collection_name=COLLECTION_NAME | |
| ) | |
| try: | |
| existing_ids = set(vector_db.get(include=[])["ids"]) | |
| logger.info(f"Loaded {len(existing_ids)} existing document IDs from {LOCAL_DB_DIR}.") | |
| except Exception: | |
| existing_ids = set() | |
| logger.info("No existing DB found or it is empty. Starting fresh.") | |
| docs_to_add = [] | |
| ids_to_add = [] | |
| for article in articles: | |
| cleaned_title = clean_text(article["title"]) | |
| cleaned_link = clean_text(article["link"]) | |
| doc_id = f"{cleaned_title}|{cleaned_link}|{article['published']}" | |
| if doc_id in existing_ids: | |
| continue | |
| metadata = { | |
| "title": article["title"], | |
| "link": article["link"], | |
| "original_description": article["description"], | |
| "published": article["published"], | |
| "category": article["category"], | |
| "image": article["image"], | |
| } | |
| doc = Document(page_content=clean_text(article["description"]), metadata=metadata) | |
| docs_to_add.append(doc) | |
| ids_to_add.append(doc_id) | |
| existing_ids.add(doc_id) | |
| if docs_to_add: | |
| try: | |
| vector_db.add_documents(documents=docs_to_add, ids=ids_to_add) | |
| vector_db.persist() | |
| logger.info(f"Added {len(docs_to_add)} new articles to DB. Total in DB: {vector_db._collection.count()}") | |
| except Exception as e: | |
| logger.error(f"Error storing articles: {e}") | |
| def download_from_hf_hub(): | |
| if not os.path.exists(LOCAL_DB_DIR): | |
| try: | |
| logger.info(f"Downloading Chroma DB from {REPO_ID} to {LOCAL_DB_DIR}...") | |
| snapshot_download( | |
| repo_id=REPO_ID, | |
| repo_type="dataset", | |
| local_dir=".", | |
| local_dir_use_symlinks=False, | |
| allow_patterns=f"{LOCAL_DB_DIR}/**", | |
| token=HF_API_TOKEN | |
| ) | |
| logger.info("Finished downloading DB.") | |
| except Exception as e: | |
| logger.warning(f"Could not download from Hugging Face Hub (this is normal on first run): {e}") | |
| else: | |
| logger.info("Local Chroma DB exists, loading existing data.") | |
| def upload_to_hf_hub(): | |
| if os.path.exists(LOCAL_DB_DIR): | |
| try: | |
| logger.info(f"Uploading updated Chroma DB '{LOCAL_DB_DIR}' to {REPO_ID}...") | |
| hf_api.upload_folder( | |
| folder_path=LOCAL_DB_DIR, | |
| path_in_repo=LOCAL_DB_DIR, | |
| repo_id=REPO_ID, | |
| repo_type="dataset", | |
| token=HF_API_TOKEN, | |
| commit_message="Update RSS news database" | |
| ) | |
| logger.info(f"Database folder '{LOCAL_DB_DIR}' uploaded to: {REPO_ID}") | |
| except Exception as e: | |
| logger.error(f"Error uploading to Hugging Face Hub: {e}") | |
| if __name__ == "__main__": | |
| download_from_hf_hub() | |
| articles = fetch_rss_feeds() | |
| process_and_store_articles(articles) | |
| upload_to_hf_hub() |