This project demonstrates how Large Language Models (LLMs) can automate retail media analytics workflows — generating insights, campaign summaries, and interactive Q&A from unstructured inputs.
Built with LangChain, OpenAI, and Streamlit, the app simulates how retailers or advertisers can leverage AI for faster decision-making and campaign optimization.
- 🔍 Insight Generation – Automatically produce campaign highlights and optimization suggestions.
- 📝 Summarization – Condense verbose reports into concise, actionable takeaways.
- 💬 Q&A System – Ask natural-language questions about your campaigns and get structured answers.
- 🔗 LangChain SequentialChain – Demonstrates chaining prompts for complex workflows.
- ⚡ Interactive UI – Built with Streamlit for quick experimentation and demos.
🔹 Example: The model takes in raw campaign data (spend, impressions, CTR, conversions) and outputs clear recommendations on ROI and optimizations.
- Python 3.9+
- LangChain – LLM pipelines & orchestration
- OpenAI API – GPT-based language models
- Streamlit – Interactive UI
- [pandas / SQLAlchemy] – Data handling & querying
# Clone the repo
git clone https://github.com/your-username/llm-analytics-automation.git
cd llm-analytics-automation
# Create virtual environment
python -m venv venv
source venv/bin/activate # (or venv\Scripts\activate on Windows)
# Install dependencies
pip install -r requirements.txtSet your OpenAI API Key:
export OPENAI_API_KEY="your_key_here"Run the Streamlit app:
streamlit run main.pyOpen http://localhost:8501 in your browser to interact.
- Retail media performance reporting
- Supplier campaign optimization (CPG, eCommerce)
- Automated BI report generation
- Customer-facing analytics dashboards
MIT License © 2025