EdIzaguirre/Rosebud
Let's discover films.
# Technical Summary Combines semantic search with dynamic metadata filtering using a self-querying retriever that translates natural language queries into structured Pinecone filters across ~7,400 films (1950–2023). Built on LangChain and OpenAI embeddings, it uses RAGAS for offline evaluation (answer relevancy, context relevancy, faithfulness) and Weights & Biases/Weave for tracking both offline metrics and online user feedback. Prefect orchestrates weekly data refreshes from The Movie Database API, while Streamlit provides the frontend and pytest ensures retrieved documents maintain correct format.
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Python
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Last pushed
Apr 04, 2025
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