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Update rss_processor.py
Browse files- rss_processor.py +2 -10
rss_processor.py
CHANGED
@@ -30,8 +30,6 @@ hf_api = HfApi()
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embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
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vector_db = None
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def clean_text(text):
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if not text or not isinstance(text, str):
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return ""
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@@ -161,11 +159,7 @@ def categorize_feed(url):
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logger.warning(f"No matching category found for URL: {url}")
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return "Uncategorized"
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def process_and_store_articles(articles):
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if not vector_db:
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logger.error("Vector database is not initialized. Cannot process articles.")
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return
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documents = []
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doc_ids = []
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@@ -252,8 +246,6 @@ def upload_to_hf_hub():
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logger.error(f"Error uploading to Hugging Face Hub: {e}")
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def run_update_pipeline():
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global vector_db
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download_from_hf_hub()
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vector_db = Chroma(
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@@ -264,7 +256,7 @@ def run_update_pipeline():
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articles = fetch_rss_feeds()
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if articles:
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process_and_store_articles(articles)
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upload_to_hf_hub()
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logger.info("Update pipeline finished.")
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embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
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def clean_text(text):
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if not text or not isinstance(text, str):
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return ""
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logger.warning(f"No matching category found for URL: {url}")
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return "Uncategorized"
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def process_and_store_articles(articles, vector_db):
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documents = []
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doc_ids = []
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logger.error(f"Error uploading to Hugging Face Hub: {e}")
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def run_update_pipeline():
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download_from_hf_hub()
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vector_db = Chroma(
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articles = fetch_rss_feeds()
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if articles:
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process_and_store_articles(articles, vector_db)
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upload_to_hf_hub()
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logger.info("Update pipeline finished.")
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