Build a ServiceNow Knowledge Chatbot with OpenAI and Qdrant RAG

### **1. Data Ingestion Workflow (Left Panel - Pink Section)** This part collects data from the ServiceNow Knowledge Article table, processes it into embeddings, and stores it in Qdrant. #### **Steps:** 1. **Trigger: When clicking "Execute workflow"** - The workflow starts manually when you click *Execute workflow* in n8n. 2. **Get Many Table Records** - Fetches multiple records from the ServiceNow Knowledge Article table. Each record typically contains knowledge article content that needs to be indexed. 3. **Default Data Loader** - Takes the fetched data and structures it into a format suitable for text splitting and embedding generation. 4. **Recursive Character Text Splitter** - Splits large text (e.g., long knowledge articles) into smaller, manageable chunks for embeddings. This step ensures that each text chunk can be properly processed by the embedding model. 5. **Embeddings OpenAI** - Uses OpenAI's Embeddings API to convert each text chunk into a high-dimensional vector representation. These embeddings are essential for semantic search in the vector database. 6. **Qdrant Vector Store** - Stores the generated embeddings along with metadata (e.g., article ID, title) in the Qdrant vector database. This database will later be used for similarity searches during chatbot interactions. --- ### **2. RAG Chatbot Workflow (Right Panel - Green Section)** This section powers the Retrieval-Augmented Generation (RAG) chatbot that retrieves relevant information from Qdrant and responds intelligently. #### **Steps:** 1. **Trigger: When chat message received** - Starts when a user sends a chat message to the system. 2. **AI Agent** - Acts as the orchestrator, combining memory, tools, and LLM reasoning. Connects to the OpenAI Chat Model and Qdrant Vector Store. 3. **OpenAI Chat Model** - Processes user messages and generates responses, enriched with context retrieved from Qdrant. 4. **Simple Memory** - Stores conversational history or context to ensure continuity in multi-turn conversations. 5. **Qdrant Vector Store** - Performs a similarity search on stored embeddings using the user's query. Retrieves the most relevant knowledge article chunks for the chatbot. 6. **Embeddings OpenAI** - Converts user query into embeddings for vector search in Qdrant.

Platform

n8n

Category

AI

Price

Free

Creator

Tushar Mishra

• serviceNow
• stickyNote
• manualTrigger
• agent
• chatTrigger
• lmChatOpenAi
• embeddingsOpenAi
• vectorStoreQdrant
• memoryBufferWindow
• documentDefaultDataLoader