AI Trend Tracker

The AI Trend Tracker is an AI-powered bot built using Langflow and DataStax Astra DB. It Analyzes or track your Social media trend.

Table of Contents

• AI Trend Tracker
  • Table of Contents
  • What are the Problems it Solves
  • How to Getting Started
    1.   Prerequisites
    2.   Setup Application
    3.   Example Prompts
  • Which Technologies are used
  • Langflow Workflow
  • Generated Output
  • Developed By

What are the Problems it Solves

• Fetching and Storing Engagement Data : The tool fetches and stores engagement data (likes, shares, comments, post types) in DataStax Astra DB for efficient retrieval.

• Analyzing Post Performance : It analyzes post performance using Langflow, calculating average engagement metrics for post types like carousels, reels, and static images from the Astra DB dataset.

• Generating Actionable Insights : With GPT integration in Langflow, it generates actionable insights and advice, helping users optimize their social media strategies through data-driven recommendations.

How to Getting Started

• Prerequisites

  • NodeJs : To install dependencies and run application in you machine.
  • Langflow Application Token : To generate output through given prompts.

• Setup Application

  1.   Install Dependencies : Open your terminal and run :

      npm install

  2.   Create an .env File: Create a file named .env in the root of your project and add your Langflow application token :

     token=<YOUR_LANGFLOW_APPLICATION_TOKEN>

  3.   Run the Application: Give a prompt to generate output after .js code file :

     trendTracker.js "<Your Prompt>"

• Example Prompts

  • "Analyze the engagement trends from my social media data and suggest key insights."
  • "Identify the most engaging content types and topics based on my data"
  • "What patterns can you observe in the engagement metrics of my posts?"
  • "Summarize the peak engagement times for my audience across platforms."
  • "Highlight the factors that drive the highest engagement for my posts."
  • "Compare engagement rates for different days of the week and suggest the best days to post"
  • "What types of hashtags or keywords generate the most engagement for my content?"

Which Technologies are used

• Langflow : For building and orchestrating the RAG pipeline.
• Groq AI : LLM Model used within langflow to create insights from the data and format them.
• Datastax astra db : Used it as NO-SQL database to store social media post content.
• Node js : Used for running the application.
• JavaScript : Used to convert text output in markdown file.

Langflow Workflow

• Langflow helped orchestrate the entire process. From gathering social media data to analyzing it and generating insights.,.

• RAG Pipeline combines the speed of a retrieval system with the intelligence of generative AI models. It ensures the insights generated are both accurate and contextually relevant, making it ideal for social media performance analysis.

Click to Watch Full Explaination of Langflow Workflow

Generated Output

Output is generated in markdown format.
Click to view Generated Output File

Or

Output in Markdown Viewer:

Developed By

• Nehal Jain :