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Qdrant Vector Store
This rantir template is part 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 Hacker News (HN) comments can be quickly grouped by similarity, and an AI agent can generate insights based on those groupings.
With this workflow, researchers or HN users can quickly break down community consensus on a particular topic and identify frequently mentioned positives and negatives.
How it works
- HN comments are imported via the Hacknews API node.
- Comments are then inserted into a Qdrant collection, carefully tagged with the Hackernews API metadata.
- The comments are fetched and processed through a clustering algorithm using the Python Code node. The Qdrant points are returned in clustered groups.
- Each group is looped to fetch the payloads of the points and feed them to the AI agent for summarization and insight generation.
- The resulting insights and raw responses are saved to a Google Spreadsheet for further analysis by the researcher or HN user.
Requirements
- Works best with a large number of comments!
- Qdrant Vectorstore for storing embeddings.
- OpenAI account for embeddings and LLM usage.
Customizing the Template
- Adjust clustering parameters to suit your data.
- Adjust sentimentality settings if comments are predominantly negative at times.
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