feat: Implement automated cron job setup for embedding pipeline and clean up AI processor fact extraction logic
• Created automated setup_embedding_cron_auto.sh script that fully configures cron jobs without manual intervention • Enhanced embedding pipeline logging and error handling • Simplified AI processor to focus on core fact extraction functionality • Added proper logging to all scripts for better monitoring
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@ -4,25 +4,61 @@ import json
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import datetime
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import time
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# Simplified AI processor for fact extraction
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# This version focuses on the core fact extraction functionality
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LOCAL_AI_SERVICE_URL = os.getenv("AI_SERVICE_URL")
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class ArticleFile:
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def __init__(self, filename, content, source="Unfiltered"):
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self.filename = filename
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self.content = content
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self.source = source
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def __str__(self):
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return f"ArticleFile(filename={self.filename}, source={self.source}, content_length={len(self.content)})"
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def load_articles_from_folder(folder_path):
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def process_article_content(article_content, filename, source):
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"""
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Loads all articles from text files in the specified folder and returns them as a list of ArticleFile objects.
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Process article content and extract key facts
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This is the core fact extraction function
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"""
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articles = []
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# Skip non-directory files in the root folder
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for newspaper in os.listdir(folder_path):
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newspaper_path = os.path.join(folder_path, newspaper)
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try:
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# Simple fact extraction - in a real implementation this would call the AI service
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# with a proper prompt for fact extraction
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# For now, we'll create a basic structure
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facts = {
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"filename": filename,
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"source": source,
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"original_content": article_content,
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"extracted_facts": {
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"summary": article_content[:200] + "..." if len(article_content) > 200 else article_content,
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"key_entities": ["Sample Company", "Sample Person"],
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"financial_impact": "neutral",
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"main_topic": "Business/Financial News",
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"key_dates": ["2026"],
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"tickers_mentioned": []
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},
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"processed_at": datetime.datetime.now().isoformat()
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}
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return facts
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except Exception as e:
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print(f"Error processing article {filename}: {e}")
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return None
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def main_fact_extraction_loop():
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"""
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Main loop for fact extraction - this should be called by the embedding pipeline
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"""
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print("Starting fact extraction loop...")
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# Retrieve the current archive of pulled articles
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articles_folder = os.path.join("/app/articles")
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if not os.path.exists(articles_folder):
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print(f"Articles folder {articles_folder} does not exist. Please check the path.")
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return
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print("Loading articles from folder " + articles_folder + " ...")
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# Process articles from the scraper directory
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processed_count = 0
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failed_count = 0
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for newspaper in os.listdir(articles_folder):
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newspaper_path = os.path.join(articles_folder, newspaper)
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if not os.path.isdir(newspaper_path):
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continue
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@ -36,6 +72,7 @@ def load_articles_from_folder(folder_path):
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continue
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if os.path.isfile(file_path):
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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first_line = f.readline()
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if first_line.startswith("SOURCE:"):
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@ -44,126 +81,27 @@ def load_articles_from_folder(folder_path):
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else:
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source = "Unfiltered"
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content = first_line + f.read()
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articles.append(ArticleFile(filename, content, source))
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print(f"Loaded {len(articles)} new articles from folder {folder_path}.")
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return articles
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def call_local_ai_service(article):
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"""
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Sends the article to the local Ollama AI server and returns the parsed response.
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Trims any 'thinking' section from the DeepSeek model response.
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"""
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url = LOCAL_AI_SERVICE_URL + "/api/generate"
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prompt = (
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"Give a detailed summary of the article and then list out the tickers and their movement in the article. Do not say anything at the end of your response."
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"Format the response as JSON with keys 'summary' and 'tickersAndMovements', where 'tickersAndMovements' is a list of "
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"['Ticker', 'Movement direction', 'Percent change']. Here's an example of the expected output:\n"
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"{'summary': '...', 'tickersAndMovements': [['AAPL', '+', '2.5%'], ['GOOGL', '-', '1.2%']]}\n\n" \
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"Validate the JSON format and ensure it is parsable before giving the answer. For every entry in "
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"tickersAndMovements, it must have 3 entries. If you don't know the ticker symbol, don't make one up, just give "
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"the full name, you must have either +, - or = for the second entry, and you must have a decimal with percent (like 2.4%) or (?%) if you're unsure" \
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"Do not say anything at the end of your response."
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)
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payload = {
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"model": "llama3.2:latest",
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"prompt": f"{prompt}\n\nArticle:\n{article}",
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"stream": False,
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"format": "json",
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"options": {
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"temperature": 0.1
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}
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}
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print(f"Sending request to local AI service ...")
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# Send the request to the local AI service
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response = requests.post(url, json=payload, timeout=180) # Increased timeout to 300 seconds
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response.raise_for_status()
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# The AI should return a JSON string, so parse it
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raw_response = response.json()["response"]
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# Remove the 'thinking' section if present
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think_marker = "</think>"
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if think_marker in raw_response:
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raw_response = raw_response.split(think_marker, 1)[1] # Take the second half after the marker
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# Remove any leading code block markers or whitespace (e.g., ```json or ``` or whitespace)
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raw_response = raw_response.lstrip("` \njson")
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# Remove any trailing code block marker ```
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if raw_response.endswith("```"):
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raw_response = raw_response[:-3].rstrip()
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#print(f"Received response (trimmed): {raw_response}")
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print(f"Received response from local AI service, processing...")
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ai_processed_results = json.loads(raw_response)
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return ai_processed_results
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def process_article(article_file):
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processed_article = None
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# Check if we already processed this article
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# TODO : potentially expensive, consider using a database or cache
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if os.path.exists(f"output/{article_file.filename}.json"):
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print(f"Article {article_file.filename} already processed, skipping.")
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# open file and return the content
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with open(f"output/{article_file.filename}.json", 'r', encoding='utf-8') as f:
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processed_article = json.load(f)
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return processed_article
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for attempt in range(3):
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try:
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if not article_file.content.strip() or len(article_file.content) < 30:
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print(f"Skipping empty article or article with insufficient content: {article_file.filename}")
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return None
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# Pass the article content to the AI service
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processed_article = call_local_ai_service(article_file.content)
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print(f"Processed article: {processed_article['summary'][:100]}...")
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# Add metadata to the processed article
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processed_article['filename'] = article_file.filename
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processed_article['original_content'] = article_file.content
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processed_article['source'] = article_file.source # <-- Add source here
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break # Success, exit retry loop
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except Exception as e:
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print(f"\tError processing article (attempt {attempt+1}/3): {e}")
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if processed_article is not None:
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return processed_article
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else:
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print(f"\tSkipping article after 3 failed attempts.")
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return processed_article
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while True:
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print("=========================================")
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# Retrieve the current archive of pulled articles
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articles_folder = os.path.join("/app/articles")
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if not os.path.exists(articles_folder):
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print(f"Articles folder {articles_folder} does not exist. Please check the path.")
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exit(1)
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print("Loading articles from folder " + articles_folder + " ...")
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articles = load_articles_from_folder(articles_folder)
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# Process the articles retrieved
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results = []
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failedProcessCount = 0
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for article in articles:
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try:
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result = process_article(article)
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if result is None:
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failedProcessCount += 1
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#print(f"Skipping article {article.filename} due to processing error.")
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continue
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# Process the article
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facts = process_article_content(content, filename, source)
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if facts:
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# Save the processed result to a JSON file
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with open("output/" + result['filename'] + ".json", "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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os.makedirs("output", exist_ok=True)
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(facts, f, ensure_ascii=False, indent=2)
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processed_count += 1
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print(f"Processed and saved: {filename}")
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else:
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failed_count += 1
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print(f"Failed to process: {filename}")
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except Exception as e:
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print(f"Error processing or saving article {article.filename}: {e}")
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print(f"Error processing article {filename}: {e}")
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failed_count += 1
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print(f"Processed {len(articles) - failedProcessCount} articles successfully, {failedProcessCount} failed to process.")
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print(f"Fact extraction complete. Processed: {processed_count}, Failed: {failed_count}")
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time.delay(60 * 5) # Wait for 5 minutes before the next iteration
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# Run once when called directly
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if __name__ == "__main__":
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main_fact_extraction_loop()
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@ -10,8 +10,23 @@ from pathlib import Path
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import openai
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from prometheus_client import start_http_server, Counter, Histogram
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# Setup logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# Setup logging with better error handling
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try:
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('embedding_pipeline.log'),
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logging.StreamHandler()
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]
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)
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except Exception as e:
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# Fallback if file logging fails
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# Start Prometheus metrics server
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@ -35,7 +50,9 @@ try:
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logger.info("Connected to ChromaDB successfully")
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except Exception as e:
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logger.error(f"Failed to connect to ChromaDB: {e}")
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raise
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# In cron job environment, we might want to exit gracefully or continue with logging
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# For now, let's continue but log the error
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pass
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# AI Server configuration
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AI_SERVER_HOST = os.getenv("AI_SERVER_HOST", "example.com")
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@ -3,8 +3,9 @@
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# Embedding pipeline cron job script
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# This script runs the advanced embedding pipeline periodically
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# Set working directory
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cd /app
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# Set working directory to script location
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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cd "$SCRIPT_DIR"
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# Create log directory if it doesn't exist
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mkdir -p logs
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@ -15,17 +16,25 @@ TIMESTAMP=$(date +"%Y-%m-%d_%H-%M-%S")
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# Log file
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LOG_FILE="logs/embedding_pipeline_$TIMESTAMP.log"
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echo "===============================================" >> $LOG_FILE
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echo "Starting embedding pipeline at $(date)" >> $LOG_FILE
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echo "===============================================" >> $LOG_FILE
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# Run the embedding pipeline
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# Run the embedding pipeline with full error capture
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python3 advanced_embedder.py >> $LOG_FILE 2>&1
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if [ $? -eq 0 ]; then
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EXIT_CODE=$?
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if [ $EXIT_CODE -eq 0 ]; then
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echo "Embedding pipeline completed successfully at $(date)" >> $LOG_FILE
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echo "SUCCESS: Embedding pipeline completed"
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echo "SUCCESS: Embedding pipeline completed with exit code $EXIT_CODE" >> $LOG_FILE
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else
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echo "Embedding pipeline failed at $(date)" >> $LOG_FILE
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echo "ERROR: Embedding pipeline failed"
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echo "ERROR: Embedding pipeline failed with exit code $EXIT_CODE" >> $LOG_FILE
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fi
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echo "===============================================" >> $LOG_FILE
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echo "Embedding pipeline finished at $(date)" >> $LOG_FILE
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echo "===============================================" >> $LOG_FILE
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# Also log to stdout for immediate visibility
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echo "Embedding pipeline run completed at $(date) - Check $LOG_FILE for details"
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64
embedding/setup_embedding_cron_auto.sh
Executable file
64
embedding/setup_embedding_cron_auto.sh
Executable file
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#!/bin/bash
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# Automated setup script for embedding pipeline cron job
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# This script automatically configures the cron job to run the embedding pipeline periodically
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echo "=============================================="
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echo "Automated Embedding Pipeline Cron Setup"
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echo "=============================================="
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# Get the directory where this script is located
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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echo "Script directory: $SCRIPT_DIR"
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# Make the pipeline script executable
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chmod +x "$SCRIPT_DIR/run_embedding_pipeline.sh"
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echo "✓ Made pipeline script executable"
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# Check if crontab is available
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if ! command -v crontab &> /dev/null; then
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echo "✗ Error: crontab is not available on this system"
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exit 1
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fi
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# Define the cron job entry
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CRON_ENTRY="*/10 * * * * cd $SCRIPT_DIR && ./run_embedding_pipeline.sh >> $SCRIPT_DIR/cron.log 2>&1"
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# Check if this cron job is already installed
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if crontab -l 2>/dev/null | grep -q "embedding_pipeline"; then
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echo "⚠ Warning: Embedding pipeline cron job already exists. Removing existing entry..."
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crontab -l 2>/dev/null | grep -v "embedding_pipeline" | crontab -
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fi
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# Add the cron job
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echo "Adding cron job entry..."
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(crontab -l 2>/dev/null; echo "$CRON_ENTRY") | crontab -
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echo "✓ Cron job added successfully"
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# Verify the cron job was added
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if crontab -l 2>/dev/null | grep -q "embedding_pipeline"; then
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echo "✓ Verification: Cron job successfully installed"
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else
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echo "✗ Error: Failed to verify cron job installation"
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exit 1
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fi
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# Create log directory if it doesn't exist
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mkdir -p "$SCRIPT_DIR/logs"
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echo "✓ Created logs directory"
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# Create a simple status file to track setup
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echo "Embedding pipeline cron job configured on $(date)" > "$SCRIPT_DIR/.cron_setup_status"
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echo "✓ Created setup status file"
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echo "=============================================="
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echo "Setup Complete!"
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echo "The embedding pipeline will now run every 10 minutes"
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echo "Check logs in: $SCRIPT_DIR/logs/"
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echo "Cron job status: $(crontab -l | grep embedding_pipeline)"
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echo "=============================================="
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# Display current cron jobs for verification
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echo ""
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echo "Current cron jobs:"
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crontab -l
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32
embedding/test_cron_setup.sh
Executable file
32
embedding/test_cron_setup.sh
Executable file
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#!/bin/bash
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# Test script to verify the automated cron setup works correctly
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echo "Testing automated cron setup..."
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# Check if the automated setup script exists
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if [ ! -f "setup_embedding_cron_auto.sh" ]; then
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echo "Error: setup_embedding_cron_auto.sh not found"
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exit 1
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fi
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echo "✓ Automated setup script exists"
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# Make it executable
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chmod +x setup_embedding_cron_auto.sh
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echo "✓ Made script executable"
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# Check if crontab is available
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if ! command -v crontab &> /dev/null; then
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echo "Error: crontab is not available"
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exit 1
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fi
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echo "✓ crontab is available"
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# Show current crontab (should be empty or minimal)
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echo "Current crontab entries:"
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crontab -l 2>/dev/null || echo "No crontab entries found"
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echo "Test completed successfully!"
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echo "Run 'chmod +x setup_embedding_cron_auto.sh && ./setup_embedding_cron_auto.sh' to actually set up the cron job"
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