Automate Document Contextual Summarization and Storage with Kimi-K2, Gemini Embeddings, and Qdrant - n8n Workflow | Neura Market
Automate Document Contextual Summarization and Storage with Kimi-K2, Gemini Embeddings, and Qdrant
This workflow automates the process of generating contextual summaries from large documents, converting them into embeddings, and storing them in a Qdrant vector store for efficient retrieval.
The workflow begins by downloading a large document and extracting its text. Each page is processed to generate a contextual summary using the Kimi-K2 model via Featherless.ai. These summaries are then converted into embeddings using the Gemini-embedding-001 model and stored in a Qdrant collection. This setup is ideal for creating a retrieval-augmented generation (RAG) system that can efficiently handle large volumes of text data.
Platform
n8n
Category
AI
Price
Free
Creator
Zainab Ali
set
noOp
splitOut
qdrant
stickyNote
httpRequest
manualTrigger
splitInBatches
agent
executeWorkflow
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.