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Build a ServiceNow knowledge chatbot with OpenAI and Qdrant RAG

Tushar MishraTushar Mishra
928 views
2/3/2026
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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 Store1

    • 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.

ServiceNow Knowledge Chatbot with OpenAI and Qdrant RAG

This n8n workflow demonstrates how to build a powerful knowledge chatbot that integrates with ServiceNow, leveraging OpenAI for conversational AI and Qdrant for efficient Retrieval Augmented Generation (RAG). It allows users to ask questions and receive relevant answers based on a knowledge base stored in Qdrant, with the ability to create ServiceNow records if needed.

What it does

This workflow orchestrates the following steps:

  1. Listens for Chat Messages: It acts as a chatbot, waiting for incoming messages or queries from a user.
  2. Processes User Input with AI Agent: The incoming chat message is fed into an AI Agent (LangChain) which uses a conversational chat model (OpenAI) and a simple memory to maintain context.
  3. Retrieves Relevant Information from Qdrant: The AI Agent is equipped with a Qdrant Vector Store tool. This tool allows the agent to search a Qdrant instance for relevant documents or knowledge articles based on the user's query, using OpenAI embeddings for vector similarity search.
  4. Generates Responses: Based on the retrieved information and the conversational context, the OpenAI Chat Model generates a coherent and helpful response.
  5. Optionally Creates ServiceNow Records: The AI Agent is also equipped with a ServiceNow tool. If the AI determines that the user's query requires an action in ServiceNow (e.g., creating an incident, a knowledge article, or a request), it can interact with the ServiceNow API to perform that action.
  6. Provides a Default Data Loader: Includes a default data loader for initial data ingestion into the vector store, if required.
  7. Splits Text for Embedding: Utilizes a Recursive Character Text Splitter to prepare text data for embedding, ensuring optimal chunking for vector storage and retrieval.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the OpenAI Chat Model and Embeddings.
  • Qdrant Instance: Access to a Qdrant vector database.
  • ServiceNow Account: Credentials for your ServiceNow instance.
  • LangChain Nodes: Ensure you have the @n8n/n8n-nodes-langchain package installed in your n8n instance.

Setup/Usage

  1. Import the Workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • OpenAI Chat Model: Configure your OpenAI API Key credential.
    • Embeddings OpenAI: Configure your OpenAI API Key credential.
    • Qdrant Vector Store: Configure your Qdrant API key and host URL.
    • ServiceNow: Configure your ServiceNow instance URL, username, and password.
  3. Initialize Qdrant (if not already done): Use the "Default Data Loader" and "Recursive Character Text Splitter" nodes in conjunction with the "Embeddings OpenAI" and "Qdrant Vector Store" nodes to ingest your knowledge base documents into Qdrant. This step might require a separate execution or a temporary modification of the workflow to run the data loading process.
  4. Activate the Workflow: Once all credentials are set and Qdrant is populated, activate the "When chat message received" trigger node.
  5. Start Chatting: Interact with the chatbot through the configured chat interface (e.g., n8n's built-in chat UI or an external integration that triggers the "Chat Trigger" node).

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