update core dependencies and pin ranges more

Author: tylerhutcherson

.github/workflows/run_tests.yml

@@ -98,4 +98,4 @@ jobs:
         AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
         AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
       run: |
-        cd docs/ && poetry run treon -v
+        cd docs/ && poetry run pytest --nbval-lax ./user_guide -vv 

docs/user_guide/vectorizers_04.ipynb

@@ -497,76 +497,76 @@
    ]
   },
   {
-    "cell_type": "markdown",
-    "metadata": {},
-    "source": [
-     "### VoyageAI\n",
-     "\n",
-     "[VoyageAI](https://dash.voyageai.com/) allows you to implement language AI into your product. The `VoyageAITextVectorizer` makes it simple to use RedisVL with the embeddings models at VoyageAI. For this you will need to install `voyageai`.\n",
-     "\n",
-     "```bash\n",
-     "pip install voyageai\n",
-     "```"
-    ]
-   },
-   {
-    "cell_type": "code",
-    "execution_count": 11,
-    "metadata": {},
-    "outputs": [],
-    "source": [
-     "import getpass\n",
-     "# setup the API Key\n",
-     "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")"
-    ]
-   },
-   {
-    "cell_type": "markdown",
-    "metadata": {},
-    "source": [
-     "\n",
-     "Special attention needs to be paid to the `input_type` parameter for each `embed` call. For example, for embedding \n",
-     "queries, you should set `input_type='query'`; for embedding documents, set `input_type='document'`. See\n",
-     "more information [here](https://docs.voyageai.com/docs/embeddings)"
-    ]
-   },
-   {
-    "cell_type": "code",
-    "execution_count": 12,
-    "metadata": {},
-    "outputs": [
-     {
-      "name": "stdout",
-      "output_type": "stream",
-      "text": [
-       "Vector dimensions:  1024\n",
-       "[0.015814896672964096, 0.046988241374492645, -0.00518248463049531, -0.05383478105068207, -0.015586535446345806, -0.0837097093462944, 0.03744547441601753, -0.007797810714691877, 0.00717928446829319, 0.06857716292142868]\n",
-       "Vector dimensions:  1024\n",
-       "[0.006725038401782513, 0.01441393606364727, -0.030212024226784706, -0.06782275438308716, -0.021446991711854935, -0.07667966187000275, 0.01804908737540245, -0.015767497941851616, -0.02152789570391178, 0.049741245806217194]\n"
-      ]
-     }
-    ],
-    "source": [
-     "from redisvl.utils.vectorize import VoyageAITextVectorizer\n",
-     "\n",
-     "# create a vectorizer\n",
-     "vo = VoyageAITextVectorizer(\n",
-     "    model=\"voyage-law-2\",  # Please check the available models at https://docs.voyageai.com/docs/embeddings\n",
-     "    api_config={\"api_key\": api_key},\n",
-     ")\n",
-     "\n",
-     "# embed a search query\n",
-     "test = vo.embed(\"This is a test sentence.\", input_type='query')\n",
-     "print(\"Vector dimensions: \", len(test))\n",
-     "print(test[:10])\n",
-     "\n",
-     "# embed a document\n",
-     "test = vo.embed(\"This is a test sentence.\", input_type='document')\n",
-     "print(\"Vector dimensions: \", len(test))\n",
-     "print(test[:10])"
-    ]
-   },
-   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### VoyageAI\n",
+    "\n",
+    "[VoyageAI](https://dash.voyageai.com/) allows you to implement language AI into your product. The `VoyageAITextVectorizer` makes it simple to use RedisVL with the embeddings models at VoyageAI. For this you will need to install `voyageai`.\n",
+    "\n",
+    "```bash\n",
+    "pip install voyageai\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "# setup the API Key\n",
+    "api_key = os.environ.get(\"VOYAGE_API_KEY\") or getpass.getpass(\"Enter your VoyageAI API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Special attention needs to be paid to the `input_type` parameter for each `embed` call. For example, for embedding \n",
+    "queries, you should set `input_type='query'`; for embedding documents, set `input_type='document'`. See\n",
+    "more information [here](https://docs.voyageai.com/docs/embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector dimensions:  1024\n",
+      "[0.015814896672964096, 0.046988241374492645, -0.00518248463049531, -0.05383478105068207, -0.015586535446345806, -0.0837097093462944, 0.03744547441601753, -0.007797810714691877, 0.00717928446829319, 0.06857716292142868]\n",
+      "Vector dimensions:  1024\n",
+      "[0.006725038401782513, 0.01441393606364727, -0.030212024226784706, -0.06782275438308716, -0.021446991711854935, -0.07667966187000275, 0.01804908737540245, -0.015767497941851616, -0.02152789570391178, 0.049741245806217194]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from redisvl.utils.vectorize import VoyageAITextVectorizer\n",
+    "\n",
+    "# create a vectorizer\n",
+    "vo = VoyageAITextVectorizer(\n",
+    "    model=\"voyage-law-2\",  # Please check the available models at https://docs.voyageai.com/docs/embeddings\n",
+    "    api_config={\"api_key\": api_key},\n",
+    ")\n",
+    "\n",
+    "# embed a search query\n",
+    "test = vo.embed(\"This is a test sentence.\", input_type='query')\n",
+    "print(\"Vector dimensions: \", len(test))\n",
+    "print(test[:10])\n",
+    "\n",
+    "# embed a document\n",
+    "test = vo.embed(\"This is a test sentence.\", input_type='document')\n",
+    "print(\"Vector dimensions: \", len(test))\n",
+    "print(test[:10])"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -831,17 +831,24 @@
     "!rvl index listall"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading data to RedisVL is easy. It expects a list of dictionaries. The vector is stored as bytes."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['doc:17c401b679ce43cb82f3ab2280ad02f2',\n",
-       " 'doc:3fc0502bec434b17a3f06e20824b2e59',\n",
-       " 'doc:199f17b0e5d24dcaa1fd4fb41558150c']"
+       "['doc:529c6d58da9e4be4a29dd0481f59c286',\n",
+       " 'doc:81aa1ef8a9494b299e8593548d0af34a',\n",
+       " 'doc:6ab03d6da8f041ffa3fdb83996d3b297']"
       ]
      },
      "execution_count": 17,
@@ -850,10 +857,10 @@
     }
    ],
    "source": [
-    "# load expects an iterable of dictionaries where\n",
-    "# the vector is stored as a bytes buffer\n",
     "from redisvl.redis.utils import array_to_buffer\n",
     "\n",
+    "embeddings = hf.embed_many(sentences)\n",
+    "\n",
     "data = [{\"text\": t,\n",
     "         \"embedding\": array_to_buffer(v, dtype=\"float32\")}\n",
     "        for t, v in zip(sentences, embeddings)]\n",
@@ -870,9 +877,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "That is a happy dog 0.160862326622\n",
-      "That is a happy person 0.273598492146\n",
-      "Today is a sunny day 0.744559407234\n"
+      "That is a happy dog 0.160862207413\n",
+      "That is a happy dog 0.160862207413\n",
+      "That is a happy person 0.273598313332\n"
      ]
     }
    ],
@@ -905,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +921,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -933,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [