AutomationFlows › Ollama Embeddings

n8n workflows for Ollama Embeddings.

All n8n workflows that use the Ollama Embeddings integration. Each is integration-tagged, privacy-stripped, and importable into your n8n instance in one click.

Most-used Ollama Embeddings workflows

  1. My Workflow (output Parser Structured) (82 nodes)
  2. Search Worflow Docker Complete — n8n Ollama Embeddings workflow (71 nodes)
  3. Crawl4 AI (65 nodes)
  4. Search Worflow Docker — n8n Ollama Embeddings workflow (65 nodes)
  5. Automate Web Research with Gpt-4, Claude & Apify for Content Analysis and Insights (42 nodes)
  6. V3 Local Agentic RAG AI Agent — n8n Ollama Embeddings workflow (41 nodes)
  7. Local Document Question Answering with Ollama Ai, Agentic RAG & Pgvector (41 nodes)
  8. Homerag — n8n Ollama Embeddings workflow (41 nodes)
  9. Create an AI Knowledge Base Assistant Using Ollama, Pgvector and Telegram (40 nodes)
  10. Local_rag — n8n Ollama Embeddings workflow (39 nodes)
AI & RAG
My Workflow (output Parser Structured)

My Workflow. Uses outputParserStructured, httpRequest, lmChatGoogleGemini, chainLlm. Scheduled trigger; 82 nodes.

Output Parser Structured, HTTP Request, Google Gemini Chat +15
AI & RAG
Search Worflow Docker Complete

Search Worflow Docker Complete. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 71 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +14
AI & RAG
Crawl4 AI

crawl4 ai. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12
AI & RAG
Search Worflow Docker

Search Worflow Docker. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12
AI & RAG
Automate Web Research with Gpt-4, Claude & Apify for Content Analysis and Insights

This n8n template demonstrates how to automate comprehensive web research using multiple AI models to find, analyze, and extract insights from authoritative sources.

HTTP Request, Execute Workflow Trigger, Output Parser Structured +7
AI & RAG
V3 Local Agentic RAG AI Agent

V3 Local Agentic RAG AI Agent. Uses documentDefaultDataLoader, memoryPostgresChat, chatTrigger, agent. Webhook trigger; 41 nodes.

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +9
AI & RAG
Local Document Question Answering with Ollama Ai, Agentic RAG & Pgvector

Author: Jadai kongolo

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +9
AI & RAG
Homerag

Homerag. Uses documentDefaultDataLoader, memoryPostgresChat, chatTrigger, agent. Webhook trigger; 41 nodes.

Document Default Data Loader, Memory Postgres Chat, Chat Trigger +9
AI & RAG
Create an AI Knowledge Base Assistant Using Ollama, Pgvector and Telegram

This workflow automatically converts uploaded documents and text into an AI-powered searchable knowledge base using semantic vector embeddings and Retrieval-Augmented Generation (RAG). Users can uploa

Form Trigger, Vector Store Pgvector, Ollama Embeddings +7
AI & RAG
Local_rag

local_RAG. Uses documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, chatTrigger, memoryBufferWindow. Event-driven trigger; 39 nodes.

Document Default Data Loader, Text Splitter Recursive Character Text Splitter, Chat Trigger +9
AI & RAG
Rssフィードから海外のテック記事を収集し、aiで選定・翻訳・要約する

RSSフィードから海外のテック記事を収集し、AIで選定・翻訳・要約する. Uses rssFeedRead, n8n-nodes-qdrant, vectorStoreQdrant, documentDefaultDataLoader. Webhook trigger; 39 nodes.

RSS Feed Read, N8N Nodes Qdrant, Qdrant Vector Store +9
AI & RAG
Indexation

Indexation. Uses formTrigger, embeddingsOllama, textSplitterRecursiveCharacterTextSplitter, modelSelector. Event-driven trigger; 36 nodes.

Form Trigger, Ollama Embeddings, Text Splitter Recursive Character Text Splitter +10
AI & RAG
Bitrix24 Open Chanel RAG Chatbot Application Workflow Example with Webhook Integration

Bitrix24 Open Chanel RAG Chatbot Application Workflow example with Webhook Integration. Uses httpRequest, noOp, respondToWebhook, documentDefaultDataLoader. Webhook trigger; 34 nodes.

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6
AI & RAG
Bitrix24 Ai-powered RAG Chatbot for Open Line Channels

Transform your Bitrix24 Open Line channels with an intelligent chatbot that leverages Retrieval-Augmented Generation (RAG) technology to provide accurate, document-based responses to customer inquirie

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +6
AI & RAG
Build Comprehensive Entity Profiles with Gpt-4, Wikipedia & Vector Db for Content

This n8n template demonstrates how to build an intelligent entity research system that automatically discovers, researches, and creates comprehensive profiles for business entities, concepts, and term

Execute Workflow Trigger, OpenAI Chat, Tool Wikipedia +8
AI & RAG
Build a Local RAG Chatbot with Ollama, Qwen, Bge-m3 and Postgres Pgvector

Build a fully local RAG chatbot using Ollama that works without tool calling — ideal for smaller open-source models like Qwen that don't support native function calls. This template lets you run a pri

Memory Postgres Chat, Agent, Ollama Chat +3
AI & RAG
Ejemplo RAG Vs Crag

ejemplo RAG vs CRAG. Uses googleDrive, vectorStoreQdrant, embeddingsOllama, agent. Event-driven trigger; 30 nodes.

Google Drive, Qdrant Vector Store, Ollama Embeddings +9
AI & RAG
Chat with Internal Documents Using Ollama, Supabase Vector Db & Google Drive

Answers should given only within provided text. Chat interface powered by LLM (Ollama) Retrieval-Augmented Generation (RAG) using Supabase Vector DB Multi-format file support (PDF, Excel, Google Docs,

Document Default Data Loader, Google Drive, Google Drive Trigger +10
AI & RAG
RAG Agent Integration Hub Mit Knowledge Management

RAG Agent Integration Hub mit Knowledge Management. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 27 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +8
AI & RAG
Telegram Bot

Telegram bot. Uses telegramTrigger, embeddingsOllama, lmChatOllama, toolVectorStore. Event-driven trigger; 26 nodes.

Telegram Trigger, Ollama Embeddings, Ollama Chat +7
AI & RAG
Agente De Procesamiento De Documentos

Agente de Procesamiento de Documentos. Uses httpRequest, vectorStoreSupabase, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Scheduled trigger; 25 nodes.

HTTP Request, Supabase Vector Store, Document Default Data Loader +6
AI & RAG
Indexation (form Trigger)

Indexation. Uses formTrigger, embeddingsOllama, textSplitterRecursiveCharacterTextSplitter, modelSelector. Event-driven trigger; 25 nodes.

Form Trigger, Ollama Embeddings, Text Splitter Recursive Character Text Splitter +9
AI & RAG
Local RAG AI Agent

Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +9
AI & RAG
V1 Ocal RAG AI Agent

V1 ocal RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +9
AI & RAG
V1 Local RAG AI Agent

V1 Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +9
AI & RAG
V1 Local RAG AI Agent (memory Postgres Chat)

V1 Local RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +9
AI & RAG
Agent: Local AI Rag: Ollama & Qdrant

Agent: Local AI RAG: Ollama & Qdrant. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Event-driven trigger; 24 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +9
AI & RAG
V2 Supabase RAG AI Agent

V2 Supabase RAG AI Agent. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 23 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +10
AI & RAG
Build an On-premises AI Kaggle Competition Assistant with Qdrant RAG and Ollama

An on-premises, domain-specific AI assistant for Kaggle (tested on binary disaster-tweet classification), combining LLM, an n8n workflow engine, and Qdrant-backed Retrieval-Augmented Generation (RAG).

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +10
AI & RAG
Agent: Local AI Rag: Ollama & Supabase Vector

Agent: Local AI RAG: Ollama & Supabase Vector. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 23 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +10
AI & RAG
Build Hybrid RAG Search Over Pdfs with Qdrant and Ollama

This workflow ingests a local PDF into Qdrant with Ollama embeddings, then supports hybrid retrieval by querying Qdrant with both dense vectors and BM25 sparse vectors from an n8n chat trigger. Starts

Read Write File, N8N Nodes Qdrant, Ollama Embeddings +5
AI & RAG
Local RAG AI Agent with Knowledge Management

Local RAG AI Agent with Knowledge Management. Uses memoryPostgresChat, lmChatOllama, lmOllama, toolVectorStore. Chat trigger; 22 nodes.

Memory Postgres Chat, Ollama Chat, Lm Ollama +8
AI & RAG
Automated Document Compliance Validation with AI and Vector Database

This workflow automates compliance validation between a policy/procedure and a corresponding uploaded document. It leverages an AI agent to determine whether the content of the document aligns with th

HTTP Request, Ollama Embeddings, Qdrant Vector Store +5
AI & RAG
AI Mail Assistant

AI Mail Assistant. Uses emailReadImap, lmChatOllama, n8n-nodes-imap, agent. Manual trigger; 21 nodes.

Email Read Imap, Ollama Chat, N8N Nodes Imap +7
AI & RAG
AI Agent Flow

ai agent flow. Uses httpRequest, agent, lmChatOllama, executeCommand. Webhook trigger; 21 nodes.

HTTP Request, Agent, Ollama Chat +5
AI & RAG
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, splitInBatches, chatTrigger, vectorStorePGVector. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Gmail to Vector Embeddings with Pgvector and Ollama

Gmail to Vector Embeddings with PGVector and Ollama. Uses embeddingsOllama, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, gmailTrigger. Event-driven trigger; 20 nodes.

Ollama Embeddings, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +4
AI & RAG
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector (telegram Trigger)

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, chatTrigger, vectorStorePGVector, toolWorkflow. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Google Drive Knowledge Sync

Google Drive Knowledge Sync. Uses googleDriveTrigger, googleDrive, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Event-driven trigger; 20 nodes.

Google Drive Trigger, Google Drive, Text Splitter Recursive Character Text Splitter +5
AI & RAG
Chat with Your Email History Using Telegram, Mistral and Pgvector for RAG

Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?".

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Create a Private Document Q&a System with Llama3, Postgres, Qdrant and Google Drive

⚠️ Note: This system only works for self-hosted n8n instances. It will not function on n8n.cloud or other remote setups. LocalRAG.AI is a private, on-prem AI assistant that uses your own documents to

Chat Trigger, Agent, Ollama Chat +9
AI & RAG
N8n Ollama Pgvector

n8n_ollama_pgvector. Uses chatTrigger, vectorStorePGVector, embeddingsGoogleGemini, documentDefaultDataLoader. Chat trigger; 20 nodes.

Chat Trigger, Vector Store Pgvector, Google Gemini Embeddings +8
AI & RAG
E-mail Chatbot with Both Semantic and Structured Rag, Using Telegram and Pgvector (telegram Trigger) (telegram Trigger)

e-mail Chatbot with both semantic and structured RAG, using Telegram and Pgvector. Uses telegramTrigger, chatTrigger, vectorStorePGVector, toolWorkflow. Event-driven trigger; 20 nodes.

Telegram Trigger, Chat Trigger, Vector Store Pgvector +6
AI & RAG
Affine Content Sync to Vector Store

Affine Content Sync to Vector Store. Uses httpRequest, postgres, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader. Scheduled trigger; 19 nodes.

HTTP Request, Postgres, Text Splitter Recursive Character Text Splitter +3
AI & RAG
Insightslm - Chat (lm Chat Ollama)

InsightsLM - Chat. Uses lmChatOllama, outputParserStructured, chainLlm, vectorStoreSupabase. Webhook trigger; 19 nodes.

Ollama Chat, Output Parser Structured, Chain Llm +3
AI & RAG
Appflowy Direct Query Tool

AppFlowy Direct Query Tool. Uses embeddingsOllama, n8n-nodes-appflowy, postgres. Webhook trigger; 17 nodes.

Ollama Embeddings, N8N Nodes Appflowy, Postgres
AI & RAG
Rag-strapi

Rag-Strapi. Uses lmChatOllama, embeddingsOllama, chatTrigger, httpRequest. Chat trigger; 17 nodes.

Ollama Chat, Ollama Embeddings, Chat Trigger +7
AI & RAG
Larry Llama

Larry Llama. Uses agent, lmChatOllama, memoryPostgresChat, embeddingsOllama. Webhook trigger; 17 nodes.

Agent, Ollama Chat, Memory Postgres Chat +6
AI & RAG
Sitemap to Supabase

Sitemap To Supabase. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 16 nodes.

HTTP Request, XML, Document Default Data Loader +4
AI & RAG
Airline Web Check-in Data Extraction with Ollama Ai, Google Sheets & Postgres Vector Db

This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in

Google Sheets, Chat Trigger, HTTP Request +6
AI & RAG
Appflowy Content Sync to Vector Store

AppFlowy Content Sync to Vector Store. Uses n8n-nodes-appflowy, textSplitterRecursiveCharacterTextSplitter, documentDefaultDataLoader, embeddingsOllama. Event-driven trigger; 11 nodes.

N8N Nodes Appflowy, Text Splitter Recursive Character Text Splitter, Document Default Data Loader +3
AI & RAG
Gmail Customer Support Auto-responder with Ollama LLM and Pinecone RAG

Built by Setidure Technologies Automate intelligent, friendly replies to customer queries using AI, vector search, and Gmail — all without human effort.

Gmail Trigger, Text Classifier, Agent +6
AI & RAG
RAG Pipeline

RAG Pipeline. Uses formTrigger, vectorStoreQdrant, embeddingsOllama, documentDefaultDataLoader. Event-driven trigger; 13 nodes.

Form Trigger, Qdrant Vector Store, Ollama Embeddings +6
AI & RAG
Local Chatbot with Retrieval Augmented Generation (rag)

Click here to view the YouTube Tutorial

Form Trigger, Qdrant Vector Store, Ollama Embeddings +6
AI & RAG
Demo: RAG in N8n

Demo: RAG in n8n. Uses formTrigger, documentDefaultDataLoader, vectorStoreInMemory, agent. Event-driven trigger; 13 nodes.

Form Trigger, Document Default Data Loader, In-Memory Vector Store +5
AI & RAG
Poc-rag-llm

Poc-Rag-Llm. Uses lmChatOllama, embeddingsOllama, chatTrigger, agent. Chat trigger; 12 nodes.

Ollama Chat, Ollama Embeddings, Chat Trigger +4
AI & RAG
Implement On-prem RAG with Qdrant and Ollama for a Self-hosted Kb

Provides one workflow to maintain the knowledge base and another one to query the knowledge base. Uploaded documents are saved into the Qdrant vector store. When a query is made, the most relevant doc

Document Default Data Loader, Ollama Embeddings, Chat Trigger +5
AI & RAG
Build a Document-based AI Chatbot with Google Drive, Llama 3, and Qdrant RAG

Overview This template allows users to set up an AI-powered chatbot that retrieves and processes knowledge from Google Drive documents using Retrieval-Augmented Generation (RAG). By leveraging Llama 3

Google Drive Trigger, Google Drive, Ollama Embeddings +6
AI & RAG
RAG Agent

RAG Agent. Uses vectorStoreInMemory, documentDefaultDataLoader, agent, lmChatOllama. Webhook trigger; 11 nodes.

In-Memory Vector Store, Document Default Data Loader, Agent +3
AI & RAG
Noc Zabbix AI Triage Agent

noc_zabbix_ai_triage_agent. Uses agent, memoryRedisChat, lmChatOllama, outputParserStructured. Webhook trigger; 11 nodes.

Agent, Memory Redis Chat, Ollama Chat +6
AI & RAG
Build an Mcp Server Which Answers Questions with Retrieval Augmented Generation

Click here to watch the full tutorial on YouTube

Mcp Trigger, Qdrant Vector Store, Ollama Embeddings +2
AI & RAG
Small Dick

small dick. Uses executeWorkflowTrigger, vectorStoreQdrant, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 6 nodes.

Execute Workflow Trigger, Qdrant Vector Store, Document Default Data Loader +2
AI & RAG
Rag_qdrant

RAG_qdrant. Uses formTrigger, embeddingsOllama, vectorStoreQdrant, documentDefaultDataLoader. Event-driven trigger; 5 nodes.

Form Trigger, Ollama Embeddings, Qdrant Vector Store +2

63 of 63 workflows in this view · Browse all →

FAQ

How many n8n Ollama Embeddings workflows are in the catalog?

63 n8n workflows in AutomationFlows currently use the Ollama Embeddings integration — triggers, actions, or both.

How do I connect Ollama Embeddings in n8n?

After importing the workflow JSON, n8n will prompt for Ollama Embeddings credentials on the relevant nodes. AutomationFlows strips credential IDs before publishing — you'll add your own.

Can I combine these with other integrations?

Yes — most Ollama Embeddings workflows pair with adjacent tools (Slack alerts, Google Sheets logging, OpenAI summarisation). Browse the integration tags on each workflow page to discover pairings.