OpenAI Authenticator
Validates OpenAI API keys by sending a request to the OpenAI models endpoint without consuming tokens. Learn more
Collection
KNIME for Generative AI
Explore this collection of ready to use workflows to get started with using Large Language Models (LLMs). Browse workflows for connecting to and interacting with LLMs, building chat apps, performing retrieval-augmented generation (RAG), detecting vulnerabilities in LLMs and RAG-based systems and more.
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Connect to AI providers
In this section, we introduce the nodes needed to authenticate with different AI providers and select the preferred LLM. The KNIME AI extension provides built-in connectivity to different AI providers, such as OpenAI (and Azure OpenAI), Anthropic, Gemini via Google AI Studio and Vertex AI, DeepSeek, Databricks AI, IBM watsonx.ai, Hugging Face and GPT4All.
OpenAI
OpenAI is most known for its application ChatGPT and offers some of the most state-of-the-art general-purpose LLMs, such as GPT-4o and beyond. These models are designed to handle a wide range of natural language processing tasks with strong performance and versatility. OpenAI also supports vision models with image generation and editing capabilities, allowing users to create and modify visuals from natural language prompts.
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OpenAI
Demonstrates prompt-based LLM calls in KNIME using the OpenAI integration: send prompts, receive responses, and manage authentica…
AI Extension Guide1. Prompting LLMProvidersOpenAI
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Image generation and editing with GenAI
Uses KNIME’s OpenAI Image Generator node to generate or edit images via DALL·E‑3 or GPT Image 1—supports loops, batch output, and…
AI Extension Example Workflows5) Use CasesImage generation and editing with GenAI
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Anthropic
Anthropic develops the Claude series of LLMs, focused on safety, reliability, and helpfulness in a wide range of conversational and reasoning tasks.
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Anthropic
Demonstrates integration of Anthropic models into KNIME workflows using dedicated Anthropic‑node authentication and prompt execut…
AI Extension Guide1. Prompting LLMProvidersAnthropic
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Google Gemini
Gemini is a family of multimodal LLMs developed by Google DeepMind, designed to handle text, code, and image inputs for a wide range of tasks, available from Google AI Studio or via Google Cloud’s Vertex AI platform.
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Gemini
Demonstrates use of Google AI Studio and Gemini LLM/embedding nodes in KNIME workflows, including model selection and prompt inte…
AI Extension Guide1. Prompting LLMProvidersGemini
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Vertex AI
Connects KNIME to Google Cloud Vertex AI, enabling use of Gemini chat and embedding models via the Vertex AI Connector node.
AI Extension Guide1. Prompting LLMProvidersVertex AI
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Databricks AI
Databricks AI offers a suite of artificial intelligence capabilities and features integrated within the Databricks Data Intelligence Platform for managing and deploying generative AI models, including proprietary models like DBRX and support for open-source and third-party foundation models.
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Databricks AI
This workflow shows how to use Databricks nodes in KNIME. It is part of the KNIME AI Extension Guide.
AI Extension Guide1. Prompting LLMProvidersDatabricks AI
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GPT4All
GPT4All is an open-source AI platform by Nomic AI that enables running language models locally without internet access. It supports lightweight models optimized for privacy and offline use.
Prompt LLMs
In this section, we introduce the nodes that can be used to craft text instructions, and the prompting nodes that send the instructions to the selected LLM.
Prompt engineering
In KNIME, it’s possible to craft prompts as a simple string of text or combining text with images.
LLM Prompters
The KNIME AI Extension supports sending prompts to an LLM in two fashions: one-shot prompts for each input row without retaining any conversation history, or chat-style prompts where the conversation history is input to generate context-aware responses.
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LLM Prompter
Part of the AI Extension Guide; demonstrates use of the LLM Prompter node for prompt‑based LLM interaction in KNIME, including au…
AI Extension Guide1. Prompting LLMLLM Prompter
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LLM Chat Prompter
Demonstrates chat-based prompting using the Chat LLM Prompter node—it supports multi-turn conversation history and evaluation wor…
AI Extension Guide1. Prompting LLMLLM Chat Prompter
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Build vector stores and RAG systems
KNIME makes it easy to enhance AI applications with custom data. The KNIME AI Extension supports creating and managing vector stores, which are commonly used in retrieval-augmented generation (RAG) systems. RAG systems leverage user-curated, custom knowledge bases to help LLMs generate domain-specific responses while reducing the risk of hallucinations.
Vector store creation and management
Vector stores are specialized databases that store and manage objects—such as documents, code, or dictionaries—as vectors in a multidimensional space, also known as embeddings. The KNIME AI Extension includes nodes to preprocess documents, generate embeddings using various AI providers, and create and manage vector stores.
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Create a vector store
Uses OpenAI embeddings (FAISS) to build a vector store from KNIME table data and enables similarity search via Vector Store Retri…
AI Extension Example Workflows3) Vector Stores3.1 - Create a Vector Store
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Update a vector store
Demonstrates updating an existing vector store using local embedding updates or adding new documents (incremental storage).
EducationGetting Started CoursesAI Chatbots, RAG & Governance with Data Workflows - Getting Started03 Update a vector store
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Context-aware responses with RAG
After building vector stores with custom knowledge bases, they can be queried to retrieve semantically relevant documents (Retrieval). This retrieved information enriches the user prompt with contextual details (Augmented), enabling the LLM to generate more accurate, relevant or domain-specific responses (Generation).
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Retrieval‑Augmented Generation (RAG)
Demonstrates a visual RAG implementation in KNIME: creating vector stores, retrieving relevant context, and augmenting prompts fo…
EducationGetting Started CoursesAI Chatbots, RAG & Governance with Data Workflows - Getting Started02 Retrieval-Augmented Generation (RAG)
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Product FAQ using RAG
Uses Retrieval‑Augmented Generation on a FAQ dataset: embedding product Q&A, creating vector store, retrieving relevant entries, …
AI Extension Guide2. RAGProduct FAQ
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Build AI chatbots
KNIME simplifies the creation of AI-driven chatbots that incorporate conversation history and an interactive chat view.
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AI Chatbots
Demonstrates how to build custom chatbots using KNIME: from single Q&A bots to full interactive chat components with conversation…
EducationGetting Started CoursesAI Chatbots, RAG & Governance with Data Workflows - Getting Started01 AI Chatbots
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AI Governance
The KNIME AI Extension and KNIME Software offer dedicated nodes and features to ensure the safe and responsible development and deployment of GenAI technologies. This includes a suite of governance features and safeguarding mechanisms to evaluate LLMs and RAG-based systems, centrally manage and control access to AI models, and automates the end-to-end deployment of AI solutions safely into production.
Evaluate vulnerabilities in LLMs and RAG-based systems
AI vulnerabilities are weaknesses and risks in LLMs or RAG-based systems that can be exploited to produce incorrect, biased, or harmful outputs, or pose a severe security threat for sensitive information. The KNIME AI Extension supports dedicated nodes that integrate Giskard’s capabilities for testing, monitoring, and evaluating AI models.
Centralized model access and management
For enterprise users interested in centralized model governance and access control, KNIME Business Hub includes GenAI Gateway. This feature allows admins to configure and manage LLMs and embedding models centrally across the organization.
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Meeting transcript generation and summarization with KNIME GenAI Gateway
Connects to GenAI Gateway to generate fictional meeting transcripts, then uses a chat‑model (e.g. GPT‑4o) to extract summaries, a…
AI Extension Example Workflows5) Use CasesMeeting transcript generation and summarization with KNIME GenAI Gateway
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CV summarization with KNIME GenAI Gateway and local LLMs
Summarizes applicants’ CVs using GenAI Gateway and local LLMs, with PII anonymization and interactive dashboard for HR screening.
AI Extension Example Workflows5) Use CasesGenAI Summarizer of job applicants' CVs
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Continuous Deployment for Data Science (CDDS) for Business Hub
The Continuous Deployment for Data Science (CDDS) extension for KNIME Business Hub automates the end-to-end process of deploying data science and AI solutions safely into production. It offers users an intuitive UI for deployment, validation, monitoring, and retraining, while administrators can oversee the entire deployment flow.
Use cases
AI can be applied in various industries and for different tasks, such as marketing campaigns and financial services. Here are some common use cases that utilize KNIME AI capabilities.
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City and neighborhood insights with Geo-GenAI
Explores Zurich city data with maps, charts, and LLM-generated insights using GenAI.
AI Extension Example Workflows5) Use CasesCity and neighborhood insights with Geo-GenAI
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Feature Engineering with GenAI for Classification
Generates ML-ready features from text using GenAI for loan approval classification.
AI Extension Example Workflows5) Use CasesFeature Engineering with GenAI for Classification
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Research Paper Summaries Generation using Generative AI
Summarizes long medical papers using LLM chunking and prompt-based generation.
AI Extension Example Workflows5) Use CasesResearch Paper Summaries Generation using Generative AI
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Google Ads Assets Creation with Generative AI
Creates ad headlines by combining keyword insights with GenAI prompting.
AI Extension Example Workflows5) Use CasesGoogle Ads Assets Creation with Generative AI
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Explain KPI report with multimodal LLMs
Analyzes KPI charts with local vision-language models to generate narrative explanations.
AI Extension Example Workflows5) Use CasesExplain KPI report with multimodal LLMs
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Learning Resources
Learn how to integrate Generative AI and data workflows, build AI chatbots and custom RAG systems, and apply AI governance best practices using KNIME. Take these free, self-paced courses and earn a microcredential on completion.
Additional Material
This section includes additional resources and materials to complement the main content.