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What does the relevance score represent?
Everything You Need to Know About Topic Similarity API
Understanding document-to-topic alignment is vital for search, curation, and compliance. With this API you can:
- Enhance search by ranking results on topic relevance.
- Automate content tagging and grouping by theme.
- Streamline compliance by filtering topic-specific documents.
- Drive recommendations based on conceptual match.
How the Topic Similarity API Works
- Submit Topic & Documents: Include your topic text and a list of document texts.
- Semantic Encoding: AI models turn each text into a meaning representation.
- Similarity Computation: Scores are calculated by measuring alignment between topic and document representations.
- Retrieve Results: Receive a list of scores you can use to rank, filter, or cluster your documents.
Features of a Good Topic Similarity API
- Deep Semantic Understanding- Beyond keyword overlap.
- High Throughput- For large-scale batch processing.
- Clear, Normalized Scores- For easy thresholding.
- Low Latency- Suitable for interactive applications.
- Robust to Noise- Handling typos and informal text.
- Straightforward Integration- With a simple API interface.
Benefits of Using a Topic Similarity API
- Improve Relevance of search and recommendation systems.
- Save Time by automating manual topic filtering.
- Enhance Insights with transparent, data-driven relevance metrics.
- Scale Easily from dozens to millions of documents.
- Make Better Decisions through clear, actionable scores.
- Integrate Quickly with minimal development effort.
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