🔬 Index and query financial documents with Qdrant and Mistral
⚡ 7 views · 🔬 Document Extraction & Analysis
Description
Quick Overview
This workflow ingests local financial documents into a Qdrant vector database using Mistral Cloud embeddings, keeping the index in sync when files are added, changed, or deleted.
How it works
- Runs when you manually start the workflow (for testing) and sets the working folder path and Qdrant collection name.
- Routes the run based on whether a file was added, changed, or deleted.
- For changed or deleted files, searches Qdrant for existing vectors matching the file path and deletes the corresponding points.
- For added files (and changed files after cleanup), reads the file from disk and prepares a text payload that includes file metadata and contents.
- Splits the text into chunks, generates embeddings with Mistral Cloud, and inserts the vectors into the specified Qdrant collection.
Setup
- Create a Qdrant API credential in n8n and ensure your Qdrant instance is reachable at the host/port used in the HTTP requests (for example,
http://qdrant:6333). - Add a Mistral Cloud API key credential for the Mistral embeddings node.
- Update the folder path in the variables (for example,
/home/node/BankStatements) and make sure n8n has filesystem access to that directory. - Create (or update) the Qdrant collection name used by the workflow (for example,
local_file_search) so it matches the collection referenced in the workflow.
🔗 Nodes Used
HTTP Request, Recursive Character Text Splitter, Read/Write Files from Disk, Default Data Loader, Embeddings Mistral Cloud, Qdrant Vector Store
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File