This n8n workflow automates the process of forecasting solar energy output by integrating data collection, natural language processing, and database indexing. It starts with an HTTP webhook trigger that receives input, which is then split into manageable chunks for analysis. The workflow employs Hugging Face embeddings to convert text data into vectors and stores these in a Supabase vector database. A query retrieves relevant data, which is then processed using AI tools for contextual understanding and planning. The results are logged into a Google Sheet for record-keeping. This automation is useful for renewable energy companies seeking to predict and optimize solar power generation.

Key Steps:

1. Receive input via Webhook.

2. Split the input text into chunks suitable for analysis.

3. Generate embeddings using Hugging Face models.

4. Store embeddings in a Supabase vector database.

5. Query the database for relevant data.

6. Use AI tools for analysis and contextual processing.

7. Log output into Google Sheets for reporting.

This workflow streamlines data processing and forecasting in solar energy projects, enabling better planning and decision-making.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.embeddingsHuggingFace, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter, @n8n/n8n-nodes-langchain.toolVectorStore, @n8n/n8n-nodes-langchain.vectorStoreSupabase, googleSheets, stickyNote, webhook