Automated Knowledge Base Agent for Internal Support
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Streamline your internal support operations with an intelligent AI agent that leverages your product documentation. This workflow automatically indexes your knowledge base and answers employee questions accurately and efficiently, freeing up your support team.
About This Workflow
This workflow empowers your internal support team by creating an AI-powered knowledge base agent. It automates the process of ingesting and indexing your official product documentation (currently from Google Docs) into a searchable MongoDB vector database. When an employee asks a question, the agent uses retrieval-augmented generation (RAG) to consult this curated documentation, providing clear, concise, and context-aware answers. The agent is designed to respond in the detected language of the query, maintaining a professional and direct tone, and strictly adhering to the provided documentation without fabricating information or sharing direct links. This significantly reduces the burden on your support staff and ensures employees get the information they need, when they need it.
Key Features
- Automated Documentation Indexing: Seamlessly imports and indexes your product documentation into a vector database for rapid retrieval.
- Intelligent Q&A with RAG: Utilizes Retrieval-Augmented Generation to provide accurate answers directly from your knowledge base.
- Multi-language Support: Automatically detects and responds in the language of the user's query.
- Strict Information Adherence: Guarantees answers are based solely on provided documentation, preventing misinformation.
- Efficient Support Operations: Reduces ticket volume and frees up valuable support team resources.
How To Use
- Connect Your Services: Ensure your OpenAI API key and MongoDB Atlas credentials are configured within n8n.
- Import Documentation: Configure the 'Google Docs Importer' node with the URL of your product documentation. This workflow will automatically chunk and embed this data.
- Index to Vector Store: The 'MongoDB Vector Store Inserter' and 'OpenAI Embeddings Generator' nodes will process and store the document chunks with vector embeddings in your MongoDB Atlas instance.
- Configure Agent Behavior: Customize the 'Knowledge Base Agent' node's
systemMessageto define the AI's persona, behavior rules, and available tools (e.g.,productDocs). - Set Up Trigger: Utilize the 'When chat message received' node to trigger the workflow when a new chat message arrives.
- Define Chat Input: Connect the trigger to the 'Knowledge Base Agent' node, ensuring the user's chat input is passed to the
textparameter. - Manage Conversation History: Integrate the 'Simple Memory' node to allow the agent to recall previous parts of the conversation, enhancing context.
- Execute and Test: Manually run the workflow to test the indexing process and then send chat messages to interact with your new knowledge base agent.
Apps Used
Workflow JSON
{
"id": "546f4eb3-b1a8-41a0-8dc9-aa51ff6102b8",
"name": "Automated Knowledge Base Agent for Internal Support",
"nodes": 11,
"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: 546f4eb3-b1a8...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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