Generating image embeddings by text summarization
Workflow Description
Extract documents from Google Drive, process their content into manageable text chunks, generate semantic embeddings using OpenAI, and store them in an in-memory vector database for intelligent retrieval and semantic search capabilities.
How it works
- 1.Retrieve and load documents from Google Drive storage
- 2.Split text content recursively while maintaining context
- 3.Generate OpenAI embeddings for processed text segments
- 4.Store embeddings in memory-based vector store for fast retrieval
Use cases
- Semantic search across large corporate document libraries
- Retrieve most relevant documents based on natural language queries
- Build intelligent question-answering systems over company documentation
Requirements
- Valid OpenAI API credentials and available quota
- Authorized access to Google Drive account
- Support for multiple document formats (PDF, Word, text)
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (22)
When clicking "Test workflow"
Manual Trigger
Google Drive
Google Drive
Get Color Information
Edit Image
Resize Image
Edit Image
Default Data Loader
Document Default Data Loader
Recursive Character Text Splitter
Text Splitter Recursive Character Text Splitter
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note
Combine Image Analysis
Merge
Document for Embedding
Set
Sticky Note3
Sticky Note
Sticky Note4
Sticky Note
Sticky Note5
Sticky Note
Sticky Note6
Sticky Note
Embeddings OpenAI1
OpenAI
Sticky Note7
Sticky Note
Sticky Note8
Sticky Note
Get Image Keywords
OpenAI
In-Memory Vector Store
Vector Store In Memory
Embeddings OpenAI
OpenAI
Search for Image
Vector Store In Memory