Youtube RAG search with Frontend using Apify, Qdrant and AI — n8n Workflow

Hoch Komplexität Auslöser49 Knoten AI👁 3,475 Aufrufevon Jimleuk

Übersicht

Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant and your LLM of choice to quickly and efficiently browse over many videos for research.

I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel.

Check out a demo here: https://jimleuk.app.n8n.c

Verwendete Knoten

HTTP RequestRedisHTMLBasic LLM ChainEmbeddings OpenAIOpenAI Chat ModelRecursive Character Text SplitterDefault Data LoaderQdrant Vector StoreInformation Extractor

Workflow-Vorschau

1. Fetch Latest Videos with Apify.com
Learn more about Apify.com - [Youtube Scraper](https://
2. Get Video Transcript with Apify.com
Learn more about subworkflows
3. Populate Qdrant Vector Store to Build a Se
[Learn more about Qdrant Vector Store](https://docs.n8n
4. Search API with Rate Limiting
Learn more about Redis
Webhooks are a great feature to build simple APIs with
5. Qdrant Advanced Search - Point Groups
Learn more about Qdrant's Search Groups API
Our goal is to return videos and timestamps
6. Contextually Understanding Transcripts wit
[Read more about the Information Extractor](https://doc
7. Generate Results HTML Template
Learn more about the Edit Fields node
Once we have our extracted transcript parts, we ju
8. Summarise Results to Generate Answer
Read more about the Basic LLM node
This is a nice extra bi
9. Return Answer & Search Results
Learn more about the webhook node
Finally, we combine the AI answer and re
10. N8N Video Search Frontend using Web UI
Learn more about the HTML node
Building and deploying simple webpages using n
Fig 1. N8N Video Search Frontend
!screenshot of web frontend
Create Qdrant Collection
You may need to create the qdrant collection manually.
```
PUT collections/n8n_videos
{
"vectors": {
Try It Out!
Ever wanted to build your own RAG search over Youtube v
embedmodelmodeldoc
W
When clicking ‘Test work…
Get Video Subtitles
C
Chunk Subtitles
Qdrant Vector Store
Default Data Loader
Embeddings
Text Splitter
F
For Each Video
V
Video Ref
F
For Each Chunk
W
Wait
C
Clean Up Output
S
Sort By Video ID
R
Respond to Webhook
Extract Results
S
SEARCH API
G
Get Query
G
Generate Template
Answer Query
H
Has Results?
G
Generate Empty Response
M
Map Fields
Incr Rate Limit
1
10req/min
V
Vectorise Subworkflow
V
Vectorise Subworkflow1
W
WEB UI
Qdrant Groups Search
Get Embeddings
F
For Each Group
G
Group Ref
C
Combine Results
T
Transcripts to Items
R
Respond to Webhook2
R
Respond to Webhook3
S
Schedule Trigger
I
Ignore Already Seen
Get Latest Youtube Videos
C
Chunks to Items
4
429 Response
R
Render Page
Generate Webpage
M
Markdown
H
Has Results?1
G
Generate Empty Response1
R
Respond to Webhook4
G
Groups to Items1
OpenAI Chat Model
OpenAI Chat Model1
49 nodes48 edges

So funktioniert es

  1. 1

    Auslöser

    Der Workflow startet mit einem auslöser-Auslöser.

  2. 2

    Verarbeitung

    Die Daten fließen durch 49 Knoten, connecting chainllm, documentdefaultdataloader, embeddingsopenai.

  3. 3

    Ausgabe

    Der Workflow schließt seine Automatisierung ab und liefert das Ergebnis an das konfigurierte Ziel.

Knotendetails (49)

HT

HTTP Request

httpRequest

#1
RE

Redis

redis

#2
HT

HTML

html

#3
BA

Basic LLM Chain

n8n-nodes-langchain.chainLlm

#4
EM

Embeddings OpenAI

n8n-nodes-langchain.embeddingsOpenAi

#5
OP

OpenAI Chat Model

n8n-nodes-langchain.lmChatOpenAi

#6
RE

Recursive Character Text Splitter

n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter

#7
DE

Default Data Loader

n8n-nodes-langchain.documentDefaultDataLoader

#8
QD

Qdrant Vector Store

n8n-nodes-langchain.vectorStoreQdrant

#9
IN

Information Extractor

n8n-nodes-langchain.informationExtractor

#10

So importieren Sie diesen Workflow

  1. 1Klicken Sie rechts auf die Schaltfläche JSON herunterladen, um die Workflow-Datei zu speichern.
  2. 2Öffnen Sie Ihre n8n-Instanz. Gehen Sie zu Workflows → Neu → Aus Datei importieren.
  3. 3Wählen Sie die heruntergeladene Datei youtube-rag-search-with-frontend-using-apify-qdrant-and-ai und klicken Sie auf Importieren.
  4. 4Richten Sie Anmeldedaten für jeden Dienstknoten ein (API-Schlüssel, OAuth usw.).
  5. 5Klicken Sie auf Workflow testen, um zu überprüfen, ob alles funktioniert, und aktivieren Sie es dann.

Oder direkt in n8n → Aus JSON importieren einfügen:

{ "name": "Youtube RAG search with Frontend using Apify, Qdrant and AI", "nodes": [...], ...}

Integrationen

chainllmdocumentdefaultdataloaderembeddingsopenaiexecuteworkflowexecuteworkflowtriggerhtmlhttprequestifinformationextractorlmchatopenaimanualtriggermarkdownredisremoveduplicatesrespondtowebhookscheduletriggersetsortsplitinbatchessplitout

Diesen Workflow holen

Herunterladen und mit einem Klick importieren

JSON herunterladenAuf n8n.io ansehen
Knoten49
Komplexitäthigh
Auslösertrigger
Aufrufe3,475
KategorieAI

Erstellt von

Jimleuk

Jimleuk

@jimleuk

Tags

chainllmdocumentdefaultdataloaderembeddingsopenaiexecuteworkflowexecuteworkflowtriggerhtmlhttprequestifinformationextractorlmchatopenai

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