Build Your Intelligent Knowledge Base with Simple RAG
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Effortlessly create a sophisticated retrieval-augmented generation (RAG) system. Upload your documents, and let this workflow transform them into an intelligent knowledge base powered by AI.
About This Workflow
The Simple RAG workflow empowers you to build custom AI-powered knowledge bases from your own documents. It seamlessly integrates document ingestion, vectorization, and intelligent querying. Begin by uploading your PDF files via a simple form. These documents are then processed, split into manageable chunks, and embedded using OpenAI's powerful models. The embeddings are stored and indexed in Pinecone, creating a searchable vector database. When a chat message is received, the workflow leverages a sophisticated AI agent, powered by GPT-4.1 and enhanced with memory and reranking capabilities, to retrieve relevant information from your knowledge base and provide accurate, context-aware answers. This makes it ideal for building chatbots, Q&A systems, or internal documentation search tools.
Key Features
- Document Ingestion: Easily upload PDF documents to populate your knowledge base.
- AI-Powered Embeddings: Utilizes OpenAI for creating rich vector representations of your text.
- Scalable Vector Storage: Leverages Pinecone for efficient storage and retrieval of embeddings.
- Intelligent AI Agent: Employs a state-of-the-art AI agent for dynamic question answering.
- Contextual Retrieval: Retrieves relevant information to provide accurate and nuanced responses.
How To Use
- Configure the Form Trigger: Set up the 'On form submission' node to define your PDF upload form (title, fields, accepted file types).
- Connect Pinecone: Ensure your Pinecone API credentials are set up and linked to the 'Pinecone Vector Store' node for data ingestion.
- Set up OpenAI Credentials: Provide your OpenAI API credentials for the 'Embeddings OpenAI' and 'OpenAI Chat Model' nodes.
- Define Text Splitting: Configure the 'Default Data Loader' and 'Recursive Character Text Splitter' nodes to control how your documents are chunked.
- Configure the AI Agent: Set up the 'AI Agent' node with your desired system message and ensure the 'OpenAI Chat Model' and 'Simple Memory' nodes are connected.
- Link VectorDB for Retrieval: Connect the 'VectorDB' node (configured for
retrieve-as-tool) to the 'AI Agent' to enable knowledge base querying. Ensure the 'Reranker Cohere' node is connected for optimized retrieval. - Activate Chat Trigger: Set up the 'When chat message received' node to initiate interactions with your AI agent.
Apps Used
Workflow JSON
{
"id": "5a6c723f-d14a-44ad-812b-3d9eec3e4e84",
"name": "Build Your Intelligent Knowledge Base with Simple RAG",
"nodes": 20,
"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.
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ID: 5a6c723f-d14a...
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