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Co-authored-by: Ryan McCormick <[email protected]>
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Conceptual_Guide/Part_8-semantic_caching/README.md

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## Sample Reference Implementation
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In this tutorial we provide a reference implementation for a Semantic Cache in
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[semantic_caching.py.](./artifacts/semantic_caching.py) There are 3 key
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[semantic_caching.py](./artifacts/semantic_caching.py). There are 3 key
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dependencies:
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* [SentenceTransformer](https://sbert.net/): a Python framework for computing
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dense vector representations (embeddings) of sentences, paragraphs, and images.
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algorithms.
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- Alternatives include [annoy](https://github.com/spotify/annoy), or
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[cuVS](https://github.com/rapidsai/cuvs). However, note that cuVS already
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has an integration in Faiss, more on this can be found [here.](https://docs.rapids.ai/api/cuvs/nightly/integrations/faiss/)
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has an integration in Faiss, more on this can be found [here](https://docs.rapids.ai/api/cuvs/nightly/integrations/faiss/).
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* [Theine](https://github.com/Yiling-J/theine): High performance in-memory
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cache.
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- We will use it as our exact match cache backend. After the most similar
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let's explore what this patch includes.
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The patch introduces a new script,
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[semantic_caching.py.](./artifacts/semantic_caching.py), which is added to the
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[semantic_caching.py](./artifacts/semantic_caching.py), which is added to the
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appropriate directory. This script implements the core logic for our
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semantic caching functionality.
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Next, the patch integrates semantic caching into the model. Let's walk through
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these changes step-by-step.
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Firstly, it imports the necessary classes from
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[semantic_caching.py.](./artifacts/semantic_caching.py) into the codebase:
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[semantic_caching.py](./artifacts/semantic_caching.py) into the codebase:
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```diff
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...
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We value your input! If you're interested in seeing semantic caching as a
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supported feature in future releases, we invite you to join the ongoing
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[discussion.](https://github.com/triton-inference-server/server/discussions/7742)
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[discussion](https://github.com/triton-inference-server/server/discussions/7742).
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Provide details about why you think semantic caching would
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be valuable for your use case. Your feedback helps shape our product roadmap,
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and we appreciate your contributions to making our software better for everyone.

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