- Integrate language models with existing data storage solutions
- Enhance AI capabilities for data analysis and retrieval
- Streamline text processing and information extraction
- Utilize embeddings for improved machine learning applications
Trigger the workflow manually to start the process Split input text into tokens for processing Store processed data in Xata for easy access Parse output for structured results Retrieve additional information from Wikipedia Generate embeddings using Hugging Face for advanced analysis Store vectors in Supabase for efficient data management
- 1Import the workflow template into n8n
- 2Configure the Xata credentials for memory storage
- 3Set up the Supabase vector store parameters
- 4Adjust any necessary parameters for the token splitter and output parser
- 5Execute the workflow to test the integration