You are building a search engine for a large document repository using generative AI. The goal is to implement a system that can understand the context of queries and return the most relevant documents. Which combination of tools would be most effective for implementing a context-aware search engine?

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TensorFlow for training, NumPy for embeddings, and a NoSQL database

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spaCy for parsing, Keras for embeddings, and a traditional relational database

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spaCy for text processing, Hugging Face transformers for embeddings, and a vector database

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NumPy for text embeddings, pandas for data storage, and Matplotlib for visualization

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