Automate Document Embedding and Q&A with Voyage-Context-3 and MongoDB Atlas
This workflow automates the process of embedding research papers using Voyage-Context-3 and storing them in MongoDB Atlas. It also sets up a Q&A system to evaluate the effectiveness of the embeddings.
This workflow is divided into two main parts: the first part imports a research document, chunks it, and embeds it into a vector store using Voyage-Context-3. The second part creates a RAG-based Q&A agent to test the retrieval capabilities of the vector store. The workflow is designed to handle large documents efficiently by using subworkflows and batch processing, ensuring stability and performance. It integrates with MongoDB Atlas for vector storage and uses OpenAI's GPT-4.1-mini for the Q&A a
- Platform
- n8n
- Category
- AI & Machine Learning
- Price
- $24.99
- Creator
- Nina Petrova
- set
- code
- noOp
- wait
- merge
- mongoDb
- splitOut
- aggregate
- stickyNote
- httpRequest
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.
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