This n8n workflow automates the process of generating and logging TV rating trend reports using advanced language models and vector search technology. Starting with a webhook trigger, the workflow receives input data related to TV ratings, which can be processed, stored, and analyzed dynamically.
The process begins when an external source POSTs data to the specified webhook endpoint. This data is then split into manageable chunks by a text splitter node. These chunks are embedded into high-dimensional vector representations using Hugging Face’s embeddings service, enabling semantic similarity searches.
The embeddings are inserted into a Redis-based vector store, which facilitates efficient querying of related content later on. When a query is made, it retrieves the most relevant information from the vector store, supporting contextual understanding. An AI language model, such as GPT, interacts with the user or system prompts and generates insights or summaries.
Additionally, the workflow includes memory buffers to maintain context across interactions and leverages an agent node to define further prompts or actions. Finally, the generated report or logs are saved automatically into a Google Sheets document for record-keeping and future analysis.
This workflow is ideal for media analysts, content managers, or anyone needing automated, data-driven TV trend reports—saving time and improving accuracy through AI-powered automation.
Reviews
There are no reviews yet.