Unlock Your Gmail Data: AI-Powered Insights with Vector Embeddings
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Transform your Gmail inbox into a knowledge base. This workflow automatically converts your emails into vector embeddings using Ollama, making them searchable and analyzable with AI. Store and query these embeddings in PGVector for powerful insights and intelligent automation.
About This Workflow
Are you drowning in emails and struggling to extract valuable information? This n8n workflow revolutionizes how you interact with your Gmail data. By leveraging the power of AI, it takes your incoming emails, converts their content into sophisticated vector embeddings using Ollama, and prepares them for storage and querying in a PGVector database. This process unlocks the ability to perform advanced semantic searches, identify trends, and build intelligent applications directly from your email communications. Imagine effortlessly finding related discussions, summarizing long threads, or even training custom AI models on your personal or business correspondence.
Key Features
- Automated Email to Embedding Conversion: Seamlessly process emails from your Gmail inbox.
- Powerful Text Splitting: Efficiently handles large email bodies with recursive character splitting.
- AI-Powered Embeddings: Utilizes Ollama and the
nomic-embed-textmodel for rich vector representations. - Metadata Enrichment: Attaches crucial email metadata (ID, thread ID) to embeddings for contextual search.
- Flexible Scheduling: Trigger the workflow manually for bulk imports or set it to run every minute for real-time processing.
How To Use
- Set up Gmail Trigger: Configure the
Gmail Triggernode with your Gmail account credentials to fetch new emails. - Define Date Range (Optional): Use the
Set before and after datesnode to specify a date range for initial bulk import. For ongoing processing, the Gmail trigger will handle new emails. - Extract Email Fields: Utilize the
Extract email fieldsnode to parse and select relevant data from each email, such as the text content, sender, recipient, date, and ID. - Create Document from Data: The
Default Data Loadernode takes the extracted email text and associated metadata to create structured documents. - Split Text into Chunks: Employ the
Recursive Character Text Splitterto divide longer email content into manageable chunks for embedding. - Generate Vector Embeddings: Connect the
Embeddings Ollamanode to process the text chunks and generate vector embeddings using your chosen Ollama model. - Store and Query Embeddings: (External step, not shown in snippet) Connect the output of the embedding node to your PGVector database to store the generated embeddings. You can then build a separate query mechanism to search and analyze this data.
Apps Used
Workflow JSON
{
"id": "495ebe87-4cb4-460c-8d35-64cd1462d610",
"name": "Unlock Your Gmail Data: AI-Powered Insights with Vector Embeddings",
"nodes": 8,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 495ebe87-4cb4...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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