Embed your Vector Store
Configured with Agent, Chat Trigger, Document Default Data Loader, Embeddings Open Ai, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Transform
- Files / media
- Data store
- Routing
- Integration
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| AI Agent | AI & models | Agent | See node parameters |
| OpenAI Chat Model | AI & models | OpenAI Chat Model | See node parameters |
| Split Out | Transform | Split Out | See node parameters |
| Embeddings OpenAI1 | AI & models | Embeddings Open Ai | See node parameters |
| Default Data Loader | Files / media | Document Default Data Loader | See node parameters |
| Token Splitter1 | Transform | Text Splitter Token Splitter | See node parameters |
| Window Buffer Memory | Data store | Memory Buffer Window | See node parameters |
| Embeddings OpenAI2 | AI & models | Embeddings Open Ai | See node parameters |
| Extract from File | Transform | Extract From File | fromJson |
| When clicking ‘Test workflow’ | Trigger | Manual Trigger | See node parameters |
| AI Agent1 | AI & models | Agent | See node parameters |
| OpenAI Chat Model1 | AI & models | OpenAI Chat Model | See node parameters |
| Embeddings OpenAI | AI & models | Embeddings Open Ai | See node parameters |
| Loop Over Items | Routing | Loop Over Items | See node parameters |
| Structured Output Parser | Transform | Output Parser Structured | See node parameters |
| Pull Mitre Data From Gdrive | Files / media | Google Drive | download |
| Embed JSON in Qdrant Collection | Data store | Vector Store Qdrant | See node parameters |
| Query Qdrant Vector Store | Data store | Vector Store Qdrant | See node parameters |
| Qdrant Vector Store query | Data store | Vector Store Qdrant | See node parameters |
| Get all Zendesk Tickets | Integration | Zendesk | getAll |
| Update Zendesk with Mitre Data | Integration | Zendesk | update |
| Move on to next ticket | Routing | No Op | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
googleDriveOAuth2ApiopenAiApiqdrantApizendeskApi
Workflow notes
Embed your Vector Store
Embed your Vector Store
To provide data for your Vector store, you need to pass it in as JSON, and ensure it's setup correctly. This flow pulls the JSON file from Google Drive and extracts the JSON data and then passes it into the qdrant collection.
Talk to your Vector Store
Talk to your Vector Store
Now that your vector store has been updated with the embedded data, you can use the n8n chat interface to talk to your data using OpenAI, Ollama, or any of our supported LLMs.
Deploy your Vector Store
Deploy your Vector Store
This flow adds contextual information to your tickets using the Mitre Attack framework to help contextualize the ticket data.
Embed your Vector Store
Embed your Vector Store
Purpose and structure
This folder contains the Embed your Vector Store workflow. Its executable nodes use agent, chatTrigger, documentDefaultDataLoader, embeddingsOpenAi, extractFromFile, googleDrive, lmChatOpenAi, manualTrigger, memoryBufferWindow, noOp, outputParserStructured, splitInBatches, splitOut, textSplitterTokenSplitter, vectorStoreQdrant, zendesk. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received, When clicking ‘Test workflow’
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Splitout/1332_Splitout_Zendesk_Send_Triggered.json MIT; preserve source notices.