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Minh/s2s context summary #1813
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Minh/s2s context summary #1813
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Summary
This PR introduces a fully-worked Jupyter notebook, examples/Context_summarization_with_realtime_api.ipynb, that demonstrates how to:
Motivation
Long-running voice sessions quickly accumulate LARGE amounts of audio tokens, causing latency and quality drift. This demo provides a reference implementation of rolling summarization that compresses earlier dialogue into a single assistant message (in French for the demo), deletes superseded items server-side, and keeps only the last N turns verbatim—saving cost and preserving quality.
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We will rate each of these areas on a scale from 1 to 4, and will only accept contributions that score 3 or higher on all areas. Refer to our contribution guidelines for more details.