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Qdrant Vector Store
Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations from the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get the top-3 recommendations.
How it works
- Upload the IMDB-1000 dataset to the Qdrant Vector Store, embedding movie descriptions with OpenAI.
- Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat.
- Create a workflow that calls Qdrant's Recommendation API to retrieve the top-3 movie recommendations based on your positive and negative examples.
Set Up Steps
- You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials.
- You'll need OpenAI credentials.
- You'll need GitHub credentials & upload the IMDB Kaggle dataset to your GitHub.
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$299/m
Per $1K Tokens or 1 TB added, custom integration (per month)
Everything in Professional, and:
Host up to around 20+ Applications
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Introducing an integration-software suite that's automated, creative, & collaborative. Open-sourced. On-Premise. On-device.
