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Release version 0.3.8 (#257)
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docs/overview/cli.ipynb

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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m13:59:20\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m RedisVL version 0.3.7\n"
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"\u001b[32m14:08:09\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m RedisVL version 0.3.8\n"
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docs/user_guide/getting_started_01.ipynb

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"## Creating `VectorQuery` Objects\n",
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"\n",
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"Next we will create a vector query object for our newly populated index. This example will use a simple vector to demonstrate how vector similarity works. Vectors in production will likely be much larger than 3 floats and often require Machine Learning models (i.e. Huggingface sentence transformers) or an embeddings API (Cohere, OpenAI). `redisvl` provides a set of [Vectorizers](https://www.redisvl.com/user_guide/vectorizers_04.html#openai) to assist in vector creation."
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"Next we will create a vector query object for our newly populated index. This example will use a simple vector to demonstrate how vector similarity works. Vectors in production will likely be much larger than 3 floats and often require Machine Learning models (i.e. Huggingface sentence transformers) or an embeddings API (Cohere, OpenAI). `redisvl` provides a set of [Vectorizers](https://docs.redisvl.com/en/latest/user_guide/vectorizers_04.html#openai) to assist in vector creation."
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pyproject.toml

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[tool.poetry]
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name = "redisvl"
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version = "0.3.7"
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version = "0.3.8"
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description = "Python client library and CLI for using Redis as a vector database"
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authors = ["Redis Inc. <[email protected]>"]
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license = "MIT"

redisvl/version.py

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__version__ = "0.3.7"
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__version__ = "0.3.8"

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