AutomationFlows › Recipes › Chainretrievalqa → Retrievervectorstore

Chainretrievalqa → Retrievervectorstore

When you need Chainretrievalqa and Retrievervectorstore talking to each other, here are the 63 n8n workflows in the catalog that already do it. Each is integration-tagged and privacy-stripped — copy the JSON and import.

Workflows that pair Chainretrievalqa with Retrievervectorstore

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
Build a Multi-functional Telegram Bot with Gemini, RAG PDF Search & Google Suite

This comprehensive workflow bundle is designed as a powerful starter kit, enabling you to build a multi-functional AI assistant on Telegram. It seamlessly integrates AI-powered voice interactions, an

Telegram Trigger, Telegram, OpenAI +19
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
Heydinastia

HeyDinastia. Uses executeCommand, httpRequest, youTube, postgres. Webhook trigger; 66 nodes.

Execute Command, HTTP Request, YouTube +15
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
Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant

Breakdown Documents Into Study Notes Using Templating Mistralai And Qdrant. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG
Localfile Wait

Localfile Wait. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG
Workflow 2339

Workflow 2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG
Breakdown Documents Into Study Notes Using Templating Mistralai and Qdrant (local File Trigger)

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +9
AI & RAG
2339

2339. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.

Local File Trigger, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +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
Advanced AI Demo Presented at AI Developers 14 Meetup

Advanced Ai Demo Presented At Ai Developers 14 Meetup. Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
AI & RAG
Advanced AI Demo (presented at AI Developers #14 Meetup)

Advanced Ai Demo (Presented At Ai Developers #14 Meetup). Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
AI & RAG
Workflow 2358

Workflow 2358. Uses slack, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 39 nodes.

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
AI & RAG
2358

2358. Uses slack, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi, documentDefaultDataLoader. Chat trigger; 39 nodes.

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
AI & RAG
Advanced AI Demo (presented at AI Developers #14 Meetup) (slack)

This workflow was presented at the AI Developers meet up in San Fransico on 24 July, 2024. Categorize incoming Gmail emails and assign custom Gmail labels. This example uses the Text Classifier node,

Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
AI & RAG
Generate Exam Questions

Generate Exam Questions. Uses manualTrigger, vectorStoreQdrant, httpRequest, embeddingsOpenAi. Event-driven trigger; 37 nodes.

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12
AI & RAG
Ai-powered Auto-generate Exam Questions and Answers From Google Docs with Gemini

This workflow automates the creation of exam questions (both open-ended and multiple-choice) from educational content stored in Google Docs, using AI-powered analysis and vector database retrieval

Qdrant Vector Store, HTTP Request, OpenAI Embeddings +12
AI & RAG
My Workflow 3

My workflow 3. Uses formTrigger, splitInBatches, lmChatGoogleGemini, httpRequest. Event-driven trigger; 36 nodes.

Form Trigger, Google Gemini Chat, HTTP Request +10
AI & RAG
Upsert Huge Documents in a Vector Store with Supabase and Notion

Upsert Huge Documents In A Vector Store With Supabase And Notion. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

OpenAI Embeddings, Text Splitter Token Splitter, Chain Retrieval Qa +8
AI & RAG
Webhook Schedule

Webhook Schedule. Uses manualTrigger, stickyNote, agent, lmChatOpenAi. Event-driven trigger; 34 nodes.

Agent, OpenAI Chat, Memory Buffer Window +7
AI & RAG
RAG on Living Data

RAG on living data. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

OpenAI Embeddings, Text Splitter Token Splitter, Chain Retrieval Qa +8
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

See more Chainretrievalqa workflows · Retrievervectorstore workflows

FAQ

How do I trigger a Retrievervectorstore action from Chainretrievalqa?

Most workflows in this list use either a Chainretrievalqa webhook trigger (real-time) or a polling trigger (every N minutes). From there, downstream Retrievervectorstore nodes handle the action. Open any workflow's detail page to see the exact node graph.

Do I need both a Chainretrievalqa and a Retrievervectorstore account?

Yes — n8n connects to each integration via your own credentials. AutomationFlows strips credential IDs before publishing, so you'll add your own.

Are these Chainretrievalqa → Retrievervectorstore workflows free?

Yes — every workflow on AutomationFlows is free to browse and copy. Pro adds a multi-signal QualityScore on every workflow plus bulk JSON download.