How do we integrate semantic search into our application?

How It Works:

Index documents with embedding vectors, deploy a similarity search engine (e.g., Faiss, Pinecone), and query the index with user embeddings to get ranked results.

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Key Benefits:

• Scalable performance: Handles millions of vectors.
• Continuous improvement: Re-index as content updates.
• Rich analytics: Track query-result similarity trends.

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Real-World Use Cases:

• Enterprise IA: Find experts or documents across large corpora.
• Media archives: Retrieve relevant clips by theme or topic.