Intelligent Document Chunking for Enhanced Search Retrieval
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This n8n workflow revolutionizes how you handle documents by intelligently chunking them with context-aware AI. It processes files from Google Drive, leverages Gemini and OpenRouter for smart context generation, and stores vectorized data in Pinecone for superior search retrieval.
About This Workflow
Unlock the full potential of your documents with this powerful n8n workflow designed for advanced Retrieval Augmented Generation (RAG). It seamlessly ingests documents from Google Drive, transforming raw text into semantically rich, contextually aware chunks. The workflow utilizes the AI Agent - Prepare Context node, powered by OpenRouter and Gemini, to generate succinct contextual summaries for each text chunk. This contextual information is then concatenated with the original chunk and processed through a Recursive Character Text Splitter for optimal vectorization. Finally, the Pinecone Vector Store node efficiently stores these vectorized embeddings, creating a highly searchable knowledge base.
This solution is perfect for building sophisticated chatbots, intelligent search engines, and AI-powered knowledge management systems where understanding the nuance and context of your documents is paramount.
Key Features
- Context-Aware Chunking: Generates contextually relevant summaries for each document chunk using AI, improving search accuracy.
- Seamless Google Drive Integration: Effortlessly pulls documents directly from your Google Drive.
- Powerful AI Models: Leverages OpenRouter and Google Gemini for advanced text processing and embedding.
- Efficient Vectorization: Utilizes Langchain's text splitters and embeddings for optimal data preparation.
- Scalable Vector Storage: Integrates with Pinecone for robust and scalable vector database management.
How To Use
- Trigger Workflow: Initiate the workflow by clicking 'Test workflow' or through an external trigger.
- Download Document: The 'Get Document From Google Drive' node fetches your specified document.
- Extract Text: 'Extract Text Data From Google Document' converts the file content into plain text.
- Split into Sections: The 'Split Document Text Into Sections' code node divides the text into logical sections based on defined separators.
- Prepare for Looping: 'Prepare Sections For Looping' prepares these sections for individual processing.
- Generate Context: The 'AI Agent - Prepare Context' node (using OpenRouter and Gemini) creates a succinct context for each section.
- Concatenate Text: 'Concatenate the context and section text' combines the generated context with the original text chunk.
- Embed and Vectorize: The 'Embeddings Google Gemini' and 'Recursive Character Text Splitter' nodes prepare the text for vectorization.
- Load to Vector Store: 'Pinecone Vector Store' inserts the vectorized data into your Pinecone index. Ensure your Pinecone index is set up with the name 'context-rag-test'.
Apps Used
Workflow JSON
{
"id": "27755bd2-ed6b-4418-b4a1-7ffcbcc51378",
"name": "Intelligent Document Chunking for Enhanced Search Retrieval",
"nodes": 25,
"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: 27755bd2-ed6b...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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