This n8n workflow automates the processing and analysis of daily Toggl time tracking data using AI and vector search. When a webhook receives Toggl data, the workflow splits the text, generates embeddings, and stores them in Pinecone for semantic search. It then retrieves relevant data using vector similarity, processes it with a language model, and appends the results to a Google Sheet. If an error occurs, a Slack alert notifies the user. This setup is ideal for teams wanting to analyze and summarize their daily time logs efficiently, enhancing productivity insights and reporting.
Automated Toggl Daily Report with AI Insights
| Node Count | 11 – 20 Nodes |
|---|---|
| Nodes Used | @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsCohere, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStorePinecone, googleSheets, slack, stickyNote, webhook |
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