AutomationFlowsAI & RAG › Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant…

Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant…

Original n8n title: Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant (local File Trigger)

ByJimleuk @jimleuk on n8n.io

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.

Event trigger★★★★★ complexityAI-powered42 nodesLocal File TriggerDocument Default Data LoaderText Splitter Recursive Character Text SplitterEmbeddings Mistral CloudLm Chat Mistral CloudOutput Parser Item ListRetriever Vector StoreQdrant Vector Store
AI & RAG Trigger: Event Nodes: 42 Complexity: ★★★★★ AI nodes: yes Added:

This workflow corresponds to n8n.io template #2339 — we link there as the canonical source.

This workflow follows the Chainllm → Chainsummarization recipe pattern — see all workflows that pair these two integrations.

The workflow JSON

Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →

Download .json

  

Credentials you'll need

Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.

Pro

For the full experience including quality scoring and batch install features for each workflow upgrade to Pro

About this workflow

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.

Source: https://n8n.io/workflows/2339/ — original creator credit. Request a take-down →

More AI & RAG workflows → · Browse all categories →

Related workflows

Workflows that share integrations, category, or trigger type with this one. All free to copy and import.

AI & RAG

Breakdown Documents Into Study Notes Using Templating Mistralai And Qdrant. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG

Localfile Wait. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG

Workflow 2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG

Generate Exam Questions. Uses manualTrigger, vectorStoreQdrant, httpRequest, embeddingsOpenAi. Event-driven trigger; 37 nodes.

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12
AI & RAG

This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12