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You can calculate embeddings based on multi-modal analysis of text, images, and other media, then calculate vector distances between embeddings to recommend matching content.\n\nRead more about [vector search use cases here](https://www.tinybird.co/docs/use-cases/vector-search).\n\n## Deploy it yourself\n\nThis repository mirrors work that Tinybird has done to recommend related blog posts on the official [Tinybird Blog](https://www.tinybird.co/blog).\n\n[Read the guide](https://www.tinybird.co/docs/guides/tutorials/vector-search-recommendation) to deploy this demo application yourself.\n\n## License\n\nThis code is available under the MIT license. See the [LICENSE](./LICENSE.txt) file for more details.\n\n## Need help?\n\n- [Tinybird Slack Community](https://www.tinybird.co/community)\n- [Tinybird Docs](https://www.tinybird.co/docs)\n\n## Authors\n\n- [Jordi Villar](https://github.com/jrdi)\n- [Cameron Archer](https://github.com/tb-peregrine)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftinybirdco%2Fdemo_vector_search_recommendation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftinybirdco%2Fdemo_vector_search_recommendation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftinybirdco%2Fdemo_vector_search_recommendation/lists"}