Automated News Categorization with LangChain

Who Should Use This Workflow

• Tech Enthusiasts: Individuals interested in tracking the evolution of technology news over the years.
• Researchers: Professionals conducting studies on trends in technology and news media.
• Developers: Programmers looking to integrate automated systems for news aggregation and analysis.
• Content Creators: Writers and bloggers who want to leverage historical data for content creation.
• Marketers: Marketing professionals analyzing historical headlines for insights into consumer behavior and tech trends.

What Problem Does This Workflow Solve

• Information Overload: It helps users sift through a large amount of data from Hacker News by summarizing key headlines from multiple years into a concise format.
• Historical Context: Provides a way to analyze how technology news has changed over time, offering insights into trends and shifts in the tech landscape.
• Automation: Automates the process of fetching, analyzing, and categorizing news headlines, saving users time and effort.

Detailed Explanation of the Workflow Process

1. Schedule Trigger: The workflow is initiated on a set schedule (e.g., daily at 21:00).
2. Create Years List: Generates a list of dates to fetch headlines from, spanning multiple years starting from 2007.
3. Clean Up Year List: Prepares the list of dates for processing.
4. Split Out Year List: Divides the list of dates into individual entries for fetching.
5. Get Front Page: Makes an HTTP request to Hacker News to retrieve the front page headlines for each date.
6. Extract Details: Parses the HTML response to extract the headlines and corresponding dates.
7. Get Headlines: Assigns the extracted headlines to a variable for further processing.
8. Get Date: Assigns the date of the headlines for reference.
9. Merge Headlines and Date: Combines the headlines with their respective dates.
10. Single JSON: Aggregates all the data into a single JSON format.
11. Basic LLM Chain: Processes the aggregated data using a language model to categorize and format it into a Markdown output.
12. Telegram: Sends the final output to a specified Telegram chat, providing users with a neatly formatted summary of the headlines.