Automate Qdrant Vector DB Embedding with OpenAI & FTP Data
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Streamline data embedding into Qdrant from FTP using OpenAI. Automate AI data pipelines for semantic search and RAG applications.
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About This Workflow
How it works
This workflow automates the process of ingesting data from an FTP server, embedding it using OpenAI, and storing it in a Qdrant vector database. It begins by listing all files from a specified FTP path. Each file is then processed individually through a loop. The workflow downloads each file as binary data, loads it using the Default Data Loader, and optionally splits it into smaller chunks using the Character Text Splitter. These chunks are then embedded by OpenAI and finally stored in the Qdrant vector store, ready for semantic retrieval.
Key Features
- Connects to FTP to list and download files.
- Integrates with OpenAI for generating vector embeddings.
- Persists embeddings in Qdrant for efficient semantic search.
- Supports data chunking for optimized embedding.
- Configurable batch processing for embedding and storage.
How To Use
- Configure FTP Credentials: Set up your FTP credentials in n8n.
- Configure OpenAI Credentials: Add your OpenAI API key to n8n.
- Configure Qdrant Credentials: Set up your Qdrant API connection.
- List FTP Files: In the 'List all the files' node, specify the FTP path (e.g.,
Oracle/AI/embedding/svenska) and ensure the operation is set to 'list'. - Loop Over Items: The 'Loop over one item' node will automatically batch the files for processing.
- Download Files: In the 'Downloading item' node, set the
pathto include the dynamic file name (e.g.,Oracle/AI/embedding/svenska/{{ $json.name }}) and specifybinaryPropertyNameasbinary.data. - Load and Split Data: Connect the 'Downloading item' node to the 'Default Data Loader' (set
dataTypetobinary) and then to the 'Character Text Splitter' if chunk normalization is needed. Configure theseparatorfor the text splitter as required (e.g.,"chunk_id"). - Embed Data with OpenAI: Connect the text splitter (or data loader if no splitting is needed) to the 'Embeddings OpenAI' node.
- Store in Qdrant: Connect the 'Embeddings OpenAI' node to the 'Qdrant Vector Store' node. Configure the
qdrantCollectionname (e.g.,sv_lang_data) and ensure themodeis set toinsert. Set theembeddingBatchSize(default is 100). - Qdrant Collection Setup: Ensure your Qdrant collection is created with the correct vector dimensions (e.g., 1536 for
text-embedding-ada-002) and distance metric (e.g., 'Cosine').
Apps Used
Workflow JSON
{
"id": "35e1ae01-a4ae-4675-a4fa-df632c968c25",
"name": "Automate Qdrant Vector DB Embedding with OpenAI & FTP Data",
"nodes": 0,
"category": "AI & LLM",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 35e1ae01-a4ae...
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