Context-Aware Chunking for Enhanced AI Search
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Leverage the power of context-aware chunking to revolutionize your AI search capabilities. This workflow intelligently processes documents from Google Drive, preparing them for efficient vector embedding and storage in Pinecone.
About This Workflow
This n8n workflow, "RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini," is designed to dramatically improve the accuracy and relevance of your AI-powered search and retrieval systems. It begins by fetching a document from Google Drive and then intelligently splits it into contextually relevant sections using custom code. Each section is then enriched with a succinct contextual summary generated by an AI agent (OpenRouter & Gemini), creating a more meaningful representation of the data. This enriched data is then embedded using Google Gemini and stored in Pinecone, enabling sophisticated retrieval augmented generation (RAG) applications. The workflow is built for efficiency and scalability, making complex data preparation for AI accessible.
Key Features
- Intelligent Document Segmentation: Automatically divides documents into meaningful sections based on custom delimiters.
- Contextual Enrichment: Utilizes AI to generate concise summaries for each text chunk, enhancing semantic understanding.
- Seamless Cloud Integration: Effortlessly fetches documents directly from Google Drive.
- Powerful Vector Storage: Leverages Pinecone for efficient storage and retrieval of vector embeddings.
- Advanced AI Models: Integrates with OpenRouter and Google Gemini for robust language processing and embedding.
How To Use
- Connect Your Accounts: Authenticate your Google Drive, OpenRouter, and Pinecone accounts within n8n.
- Configure Google Drive Node: Specify the
fileIdof the Google Drive document you wish to process. - Customize Text Splitter: Adjust the
split_textvariable in the 'Split Document Text Into Sections' node if your document uses different section delimiters. - Define AI Agent Prompt: Modify the
textparameter in the 'AI Agent - Prepare Context' node to fine-tune the context generation for your specific needs. - Set Up Embeddings and Vector Store: Ensure your Pinecone index name (
context-rag-test) is correct and that the Google Gemini embeddings model (models/text-embedding-004) is configured as desired. - Test and Deploy: Utilize the 'Test workflow' trigger to run the automation and verify that data is correctly processed and stored in Pinecone.
Apps Used
Workflow JSON
{
"id": "6d17f944-73a2-46bc-8795-a29e5cb9c589",
"name": "Context-Aware Chunking for Enhanced AI Search",
"nodes": 10,
"category": "Marketing",
"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: 6d17f944-73a2...
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