This n8n workflow automates the process of generating, storing, and logging achievement suggestions using AI and language models. It is designed to receive input via a webhook, process the data through text splitting, embedding, and similarity search, and finally generate meaningful achievement suggestions. The workflow also logs the interactions for review and analysis.
**Step-by-step Explanation:**
1. The workflow begins with a Webhook node that receives data, triggering the process.
2. The received content is passed to a Sticky Note node for annotation or documentation purposes.
3. A Splitter node divides the input text into manageable chunks based on characters, facilitating better processing.
4. Each chunk is processed through HuggingFace’s language model to generate embeddings — vector representations of the text.
5. These embeddings are stored in a Supabase vector store for efficient similarity searches.
6. When a query is initiated, the stored embeddings are retrieved to find relevant achievement suggestions.
7. The retrieved data is processed through an AI Tool node that interacts with the language model, generating tailored achievement suggestions.
8. The AI-generated suggestions are stored in a Supabase database for future reference.
9. A Chat node, powered by the HuggingFace language model, helps to refine or generate responses based on context, maintaining a memory buffer for ongoing interactions.
10. An Agent node helps define tasks or clarify prompts, ensuring relevant and accurate achievement suggestions.
11. Finally, the generated achievement suggestions, along with interaction logs, are saved into a Google Sheet for record-keeping and analysis.
**Use Cases:**
This workflow is ideal for game developers, educational platforms, or corporate training programs seeking automated, AI-driven achievement recommendations based on user activity or content. It streamlines content analysis, suggestion generation, and logging, enhancing user engagement and data tracking.
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