Community insights using Qdrant, Python and Information Extractor

by Jimleuk ✓ · 71,035 views · created 2024-08-05 · Market Research, AI Summarization

What this workflow does

This AI workflow has 23 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Hacker News, Qdrant Vector Store, Embeddings OpenAI and OpenAI Chat Model and 1 more. It also calls qdrant:6333 over HTTP.

How it runs

Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).

1. Set Variables (Edit Fields (Set)): sets 1 field: story_id
2. Set Variables (Edit Fields (Set)): sets 1 field: story_id
3. Clear Existing Comments (HTTP Request): calls qdrant:6333 (POST)
4. Find Comments (HTTP Request): calls an HTTP API (POST)
5. Hacker News: uses Hacker News
6. Apply K-means Clustering Algorithm (Code): runs custom Python code
7. Get Comments (Edit Fields (Set)): sets 1 field: data
8. Clusters To List (Split Out): splits a list into separate items
9. Split Out: splits a list into separate items
10. Only Clusters With 3+ points (Filter): keeps only the items that match a condition

…plus 7 more steps. See the full node list below.

Services it uses

Hacker NewsQdrant Vector StoreEmbeddings OpenAIOpenAI Chat ModelGoogle Sheets

HTTP calls to: qdrant:6333

Before you upgrade to n8n 3.0

Nodes used (16)

• Manual Triggern8n-nodes-base.manualTrigger
• Hacker Newsn8n-nodes-base.hackerNews
• Split Outn8n-nodes-base.splitOut
• Edit Fields (Set)n8n-nodes-base.set
• Qdrant Vector Store@n8n/n8n-nodes-langchain.vectorStoreQdrant
• Embeddings OpenAI@n8n/n8n-nodes-langchain.embeddingsOpenAi
• Default Data Loader@n8n/n8n-nodes-langchain.documentDefaultDataLoader
• Recursive Character Text Splitter@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter
• HTTP Requestn8n-nodes-base.httpRequest
• OpenAI Chat Model@n8n/n8n-nodes-langchain.lmChatOpenAi
• Filtern8n-nodes-base.filter
• Google Sheetsn8n-nodes-base.googleSheets
• Execute Workflow Triggern8n-nodes-base.executeWorkflowTrigger
• Execute Sub-workflown8n-nodes-base.executeWorkflow
• Coden8n-nodes-base.code
• Information Extractor@n8n/n8n-nodes-langchain.informationExtractor

From the author

This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights This template demonstrates the Community Insights scenario where HN commments can be quickly grouped by…

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