Automated Text Embedding and Storage in Qdrant

This n8n workflow automates the process of downloading JSON files from an FTP server, splitting the text content into manageable chunks, generating embeddings using OpenAI’s API, and storing these embeddings in a Qdrant vector database. The goal is to enable semantic search and retrieval of the stored text data based on its meaning rather than keywords.

Workflow Steps:

1. It begins with a manual trigger for testing and development.

2. The workflow lists all relevant JSON files in a specified FTP directory.

3. It iterates over each file, downloading them individually in binary format.

4. Each JSON document is parsed and, if necessary, split into smaller chunks based on specific delimiters (such as “chunk_id”) to optimize embedding processing.

5. The text chunks are then embedded using OpenAI’s embedding API.

6. The resulting vectors, along with metadata, are batch-inserted into a Qdrant collection, facilitating efficient semantic search.

This workflow is useful for building a semantic search engine from large text datasets, such as documents, articles, or transcripts, stored in JSON format. It can be adapted for various AI-powered search and retrieval applications, making it ideal for knowledge management, content indexing, or digital assistants.

The integration of FTP, OpenAI, and Qdrant demonstrates a seamless pipeline from raw data to intelligent storage, enabling scalable and automated AI-driven insights.