Release version 0.3.8 (#257)

docs/overview/cli.ipynb

@@ -24,7 +24,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[32m13:59:20\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   RedisVL version 0.3.7\n"
+      "\u001b[32m14:08:09\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m   RedisVL version 0.3.8\n"
      ]
     }
    ],

docs/user_guide/getting_started_01.ipynb

@@ -413,7 +413,7 @@
    "source": [
     "## Creating `VectorQuery` Objects\n",
     "\n",
-    "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."
+    "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."
    ]
   },
   {

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "redisvl"
-version = "0.3.7"
+version = "0.3.8"
 description = "Python client library and CLI for using Redis as a vector database"
 authors = ["Redis Inc. <[email protected]>"]
 license = "MIT"

redisvl/version.py

@@ -1 +1 @@
-__version__ = "0.3.7"
+__version__ = "0.3.8"