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update readme w screenshot, next steps
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emoji: π
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sdk: docker
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title: Movie recommender
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## Movie recommender (RAG and Chainlit demo)
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This movie recommender is a demo of RAG using Chainlit.
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Run the demo: https://huggingface.co/spaces/key2xanadu/chainlit-movie-rag
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Demo screenshot:
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Code files: https://huggingface.co/spaces/key2xanadu/chainlit-movie-rag/tree/main
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Next steps / ideas:
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* Use more caching. For example, load the vector store instead of creating a new one.
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(try downloading the files created in co-lab session)
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* Download the files from your notebook and store them in your Chainlit repo so you won't be recreating them every time you run the notebook (which will be a lot faster).
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* Get FAISS working on local files [see reference](https://python.langchain.com/v0.1/docs/integrations/vectorstores/faiss/#saving-and-loading)
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