receive local file events and read write file and default data loader
Configured with Chain Retrieval Qa, Chat Trigger, Document Default Data Loader, Embeddings Mistral Cloud, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- Transform
- Files / media
- AI & models
- Web / API
- Routing
- Data store
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 |
|---|---|---|---|
| Local File Trigger | Trigger | Local File Trigger | See node parameters |
| When clicking "Test workflow" | Trigger | Manual Trigger | See node parameters |
| Set Variables | Transform | Edit Fields | See node parameters |
| Read File | Files / media | Read Write File | See node parameters |
| Embeddings Mistral Cloud | AI & models | Embeddings Mistral Cloud | See node parameters |
| Default Data Loader | Files / media | Document Default Data Loader | See node parameters |
| Recursive Character Text Splitter | Transform | Text Splitter Recursive Character Text Splitter | See node parameters |
| Prepare Embedding Document | Transform | Edit Fields | See node parameters |
| Chat Trigger | Trigger | Chat Trigger | See node parameters |
| Question and Answer Chain | AI & models | Chain Retrieval Qa | See node parameters |
| Mistral Cloud Chat Model | AI & models | LM Chat Mistral Cloud | See node parameters |
| Vector Store Retriever | AI & models | Retriever Vector Store | See node parameters |
| Embeddings Mistral Cloud1 | AI & models | Embeddings Mistral Cloud | See node parameters |
| Remap for File_Added Flow | Transform | Edit Fields | See node parameters |
| Search For Existing Point | Web / API | HTTP Request | POST |
| Has Existing Point? | Routing | If | See node parameters |
| Delete Existing Point | Web / API | HTTP Request | POST |
| Search For Existing Point1 | Web / API | HTTP Request | POST |
| Has Existing Point?1 | Routing | If | See node parameters |
| Delete Existing Point1 | Web / API | HTTP Request | POST |
| Handle File Event | Routing | Switch | See node parameters |
| Qdrant Vector Store | Data store | Vector Store Qdrant | See node parameters |
| Qdrant Vector Store1 | Data store | Vector Store Qdrant | 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
mistralCloudApiqdrantApi
Workflow notes
Step 1. Select the target folder
Step 1. Select the target folder
Read more about local file trigger
In this workflow, we'll monitor a specific folder on disk that n8n has access to. Since we're using docker, we can either use the n8n volume or mount a folder from the host machine.
The local file trigger is useful to execute the workflow whenever changes are made to our target folder.
Try It Out!
Try It Out!
This workflow does the following:
- Monitors a target folder for changes using the local file trigger
- Synchronises files in the target folder with their vectors in Qdrant
- Mistral AI is used to create a Q&A AI agent on all files in the target folder
Need Help?
Join the Discord or ask in the Forum!
Happy Hacking!
Step 2. When files are removed, the vector point is cleared.
Step 2. When files are removed, the vector point is cleared.
Learn how to delete points using the Qdrant API
To keep our vectorstore relevant, we'll implement a simple synchronisation system whereby documents deleted from the local file folder are also purged from Qdrant. This can be simply achieved using Qdrant APIs.
Step 3. When files are updated, the vector point is updated.
Step 3. When files are updated, the vector point is updated.
Learn how to delete points using the Qdrant API
Similarly to the files deleted branch, when we encounter a change in a file we'll update the matching vector point in Qdrant to ensure our vector store stays relevant. Here, we can achieve this my deleting the existing vector point and creating it anew with the updated bank statement.
Step 4. When new files are added, add them to Qdrant Vectorstore.
Step 4. When new files are added, add them to Qdrant Vectorstore.
Read more about the Qdrant node
Using Qdrant, we'll able to create a simple yet powerful RAG based application for our bank statements. One of Qdrant's most powerful features is its filtering system, we'll use it to manage the synchronisation of our local file system and Qdrant.
Step 5. Create AI Agent expert on historic bank statements
Step 5. Create AI Agent expert on historic bank statements
Read more about the Question & Answer Chain
Finally, let's use a Question & Answer AI node to combine the Mistral AI model and Qdrant as the vector store retriever to create a local expert for all our bank statements questions.
receive local file events and read write file and default data loader
receive local file events and read write file and default data loader
Purpose and structure
This folder contains the receive local file events and read write file and default data loader workflow. Its executable nodes use chainRetrievalQa, chatTrigger, documentDefaultDataLoader, embeddingsMistralCloud, httpRequest, if, lmChatMistralCloud, localFileTrigger, manualTrigger, readWriteFile, retrieverVectorStore, set, switch, textSplitterRecursiveCharacterTextSplitter, vectorStoreQdrant. 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
Local File Trigger, When clicking "Test workflow", Chat Trigger
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/Localfile/0535_Localfile_Manual_Create_Webhook.json MIT; preserve source notices.