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Qdrant Vector Store
This rantir template is part of a 3-part series exploring use-cases for clustering vector embedding using qdrant, python & information-extractor
- Survey Insights
- Customer Insights
- Community Insights
This template demonstrates the Customer Insights scenario, where Trustpilot reviews can be quickly grouped by similarity, and an AI agent generates insights on those groupings.
With this workflow, marketers can save days or even weeks of work by breaking down their own or competitor reviews, identifying frequently mentioned positives and negatives.
How it works
- Trustpilot reviews are scraped for a particular company using the HTTP request node.
- The reviews are then inserted into a Qdrant collection, carefully tagged with the question and Trustpilot metadata.
- Reviews 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, and the payloads are sent 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 marketer.
Requirements
- Qdrant Vectorstore for storing embeddings.
- OpenAI account for embeddings and LLM.
Customising the Template
- Adjust clustering parameters to suit your data.
- Consider expanding the date range of reviews for insights over common intervals, such as 3 months, 6 months, and YTD.
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