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adds initial tutorial contents
After replicating the local model embeddings. I am able to provide a high level solution of what the tutorial entails. Signed-off-by: Brian Flores <[email protected]>
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# Topic
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This tutorial shows how to generate embeddings using a local asymmetric embedding model in OpenSearch implemented in a Docker container .
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Note: Replace the placeholders that start with `your_` with your own values.
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# Steps
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## 1. Spin up a docker OpenSearch Cluster
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### a. Use a docker compose file
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## 2. Prepare the model for OpenSearch
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### a. Clone the model
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### b. Zip the contents
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### c. Calculate hash
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### d. service the zip file using a python server
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- can cancel the server now
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## 3. Register a model group
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## 4. Register the model
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## 5. Deploy The model
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## 6. Run Inference
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## Next steps
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- Create an ingest pipeline for your documents with assymetric embeddings
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- Run a query using KNN with your asymmetric model
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# References
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Wang, Liang, et al. (2024). *Multilingual E5 Text Embeddings: A Technical Report*. arXiv preprint arXiv:2402.05672. [Link](https://arxiv.org/abs/2402.05672)

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