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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 Survey Insights scenario, where survey participant responses are quickly grouped by similarity, and an AI agent generates insights based on those groupings.
With this workflow, researchers can save days or even weeks of work by breaking down cohorts of participants and identifying frequently mentioned positives and negatives.
How it works
- Survey questions and responses are imported from a Google Sheet.
- Responses are inserted into a Qdrant collection, carefully tagged with question and survey metadata.
- For each question, all relevant responses are processed using a clustering algorithm via the Python Code node. The Qdrant points are returned as clustered groups.
- Each group is then looped, and the payloads of the points are fed to the AI agent to summarize and generate insights.
- The resulting insights and raw responses are saved to the Google Spreadsheet for further analysis by the researcher.
Requirements
- Survey data formatted as shown in the provided Google Sheet.
- Qdrant Vectorstore for storing embeddings.
- OpenAI account for embeddings and LLM.
Customizing the Template
- Adjust the clustering parameters to suit your data. For open-ended questions, add more clusters, and for multiple-choice responses, use fewer clusters.
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