From 57f024c9c774930cd4d0e795b700e8fbece9a3b2 Mon Sep 17 00:00:00 2001
From: James Briggs <35938317+jamescalam@users.noreply.github.com>
Date: Fri, 23 Feb 2024 08:49:07 -0500
Subject: [PATCH] tweaks

---
 docs/examples/rolling-window-splitter.ipynb | 191 +++++++++
 docs/examples/text_semantic_split.ipynb     | 179 ---------
 poetry.lock                                 | 418 +-------------------
 pyproject.toml                              |   3 +-
 semantic_router/splitters/rolling_window.py |   5 +-
 5 files changed, 207 insertions(+), 589 deletions(-)
 create mode 100644 docs/examples/rolling-window-splitter.ipynb
 delete mode 100644 docs/examples/text_semantic_split.ipynb

diff --git a/docs/examples/rolling-window-splitter.ipynb b/docs/examples/rolling-window-splitter.ipynb
new file mode 100644
index 00000000..3e59347a
--- /dev/null
+++ b/docs/examples/rolling-window-splitter.ipynb
@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = '''\n",
+    "In a recent surge of social media discussions on Weibo, Chinese netizens have been engaging in conversations about the struggles and challenges of earning money. The online debate sparked a wave of opinions and perspectives on the relationship between hard work, high pay, and finding contentment. Among the tweets, several users pontificated that one should avoid earning \"too much hard-earned money.\"\n",
+    "The tweets and discussions revolve around the idea that working too hard for one's income can have a detrimental effect on one's life, both physically and mentally. Some users advocate for finding opportunities that align with one's strengths and passions, rather than simply focusing on high-paying jobs that may require excessive hours and intense labor.\n",
+    "One Weibo user pontificates, \"Don't earn that much hard-earned money,\" a sentiment echoed by others with tweets such as, \"Why is it that when earning money, that process always has to be so tough?\" This question is followed by a comparison between two types of people - those who are used to earning money the hard way and those who seem to effortlessly obtain wealth. While the former group is depicted as having been taught to suffer from a young age, the latter is shown as being able to focus solely on their natural talents and thriving in their niche advantageously.\n",
+    "Discussions on the platform draw attention to a variety of issues that those who earn money the hard way might face. For example, they are described as likely having to work overtime, forgo time off for illness or rest, and maintain an unyielding dedication to their occupation, which often results in a never-ending cycle of work without any perceived progression in their lives.\n",
+    "Another tweet that captures this sentiment reads, \"Drowning in more work and poverty despite trying harder and harder,\" pointing to a sense of despair and dissatisfaction that comes with work that is both disproportionately demanding and inadequately rewarding. Critics also note how the pursuit of hard-earned money could potentially create physical and mental health risks due to the unrelenting pressure and stress that these jobs might impose.\n",
+    "Conversely, those in favor of earning money with less difficulty contend that it's crucial to harness one's strengths and passions to create opportunities that yield financial success without the need for excessive labor. The debate revolves around the concept that people should seek out ways to work smarter, not harder, especially if it means a healthier and more fulfilling lifestyle.\n",
+    "In fact, the notion of a \"vicious cycle,\" often attributed to those chasing hard-earned money, is juxtaposed with an idealized image of someone operating in their zone of excellence. Confidently focused on their strengths, such individuals are depicted as enjoying a more relaxed and less stressful work environment, one in which they can thrive without the need for never-ending overtime or self-sacrifice.\n",
+    "Some tweets even extend this sentiment to the broader socio-economic context, observing how wealth is not merely derived from manual labor or high-paying positions requiring extraordinary work hours. The tweets emphasize the importance of cultivating an entrepreneurial spirit and a penchant for innovative thinking, especially in the modern digital age.\n",
+    "One user writes, \"Too hard-earned money isn't worth it. Learn how to make money using your brain, not your body,\" while another suggests, \"Love will flow towards those who are not lacking in love, and money will flow towards those who are not lacking in money!\"\n",
+    "While some of the discussions take a somewhat passive-aggressive view, others acknowledge that financial security and comfort might not always be possible for everyone. In a more realistic tone, a user remarks, \"If life were so easy that diligence led to wealth, then the world's richest person would be the best worker bee. But that's not the case.\" This acknowledgment underscores the complexities of the economy and the role that factors like luck, connections, and a rapidly evolving job market can play in financial success.\n",
+    "Some users are quick to criticize the notion that earning money the hard way should be avoided, with one tweet expressing, \"The person who advises you to avoid hard-earned money is likely a scammer who profits off providing emotional value in exchange for exploitation.\" Others argue that while it's essential to find enjoyment and fulfillment in one's work, it's crucial not to shun or belittle those who choose to work in physically demanding or high-paying industries.\n",
+    "Overall, the Weibo discussions offer a fascinating insight into the complexities of the modern Chinese labor market and the work-life balance that people strive to achieve. As in many countries, striking the right balance between work and play is an ongoing challenge for many Chinese citizens. However, the conversations on Weibo signal an increasing awareness of the importance of finding meaningful, fulfilling, and financially rewarding work that doesn't necessitate excessive sacrifice or sufferance.\n",
+    "In the end, as one user succinctly puts it, \"Make sure you're earning your money in a way that brings you joy and satisfaction. That's the only way to ensure that your life doesn't become a never-ending cycle of hard work without any tangible progress.\"\n",
+    "In this context, social media discussions focusing on the trials and tribulations of earning money serve not only as an outlet for venting frustrations but also as a means of promoting dialogue and shared understanding about the challenges faced by workers across all industries. These virtual conversations sparked by tweets and in-depth discussions likely resonate with a wide swath of Chinese citizens struggling to navigate the complexities of balancing a career that pays well with one that brings them joy, fulfillment, and a sense of purpose.\n",
+    "As the discussions on Weibo continue to evolve and unfold, it is evident that the discourse around work, money, and life satisfaction holds the potential to inspire meaningful change and shift societal attitudes towards a more holistic, balanced, and humane understanding of success and prosperity.\n",
+    "---\n",
+    "Note: The translated tweets and user quotes from Chinese to English were used as the foundation for the long-form news article. The author tried to maintain the integrity of the original content in the translation while adapting it to fit a journalistic format. No inaccuracies were introduced during translation, and the opinion-based nature of the original content was preserved while maintaining objectivity.\n",
+    "Heart count: 0/2\n",
+    "Note: The author did not include any Chinese characters in the final response.\n",
+    "Collapse\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jamesbriggs/opt/anaconda3/envs/decision-layer/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "from semantic_router.splitters import RollingWindowSplitter\n",
+    "from semantic_router.encoders import OpenAIEncoder\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\"Enter your OpenAI API key: \")\n",
+    "\n",
+    "splitter = RollingWindowSplitter(\n",
+    "    encoder=OpenAIEncoder(),\n",
+    "    min_split_tokens=50,\n",
+    "    max_split_tokens=300,\n",
+    "    window_size=5, # sentences\n",
+    "    plot_splits=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33m2024-02-23 08:44:26 WARNING semantic_router.utils.logger Single document exceeds the maximum token limit of 300. Splitting to sentences before semantically splitting.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 0: Trying threshold: 0.8881277932028191\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 0: Median tokens per split: 24.0\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 0: Adjusting high to 0.8781277932028191\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 1: Trying threshold: 0.8687934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 1: Median tokens per split: 34.5\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Iteration 1: Adjusting high to 0.8587934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:26 INFO semantic_router.utils.logger Final optimal threshold: 0.8687934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 218 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 262 tokens due to exceeding token limit of 300.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 137 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 249 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 117 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 171 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Split finalized with 72 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 08:44:27 INFO semantic_router.utils.logger Final split added with 23 tokens due to remaining documents.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "splits = splitter([text])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Split 1, tokens 218, triggered by: 0.85\n",
+      "\u001b[31mIn a recent surge of social media discussions on Weibo, Chinese netizens have been engaging in conversations about the struggles and challenges of earning money. The online debate sparked a wave of opinions and perspectives on the relationship between hard work, high pay, and finding contentment. Among the tweets, several users pontificated that one should avoid earning \"too much hard-earned money.\" The tweets and discussions revolve around the idea that working too hard for one's income can have a detrimental effect on one's life, both physically and mentally. Some users advocate for finding opportunities that align with one's strengths and passions, rather than simply focusing on high-paying jobs that may require excessive hours and intense labor. One Weibo user pontificates, \"Don't earn that much hard-earned money,\" a sentiment echoed by others with tweets such as, \"Why is it that when earning money, that process always has to be so tough?\" This question is followed by a comparison between two types of people - those who are used to earning money the hard way and those who seem to effortlessly obtain wealth.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 2, tokens 262, triggered by: token limit\n",
+      "\u001b[32mWhile the former group is depicted as having been taught to suffer from a young age, the latter is shown as being able to focus solely on their natural talents and thriving in their niche advantageously. Discussions on the platform draw attention to a variety of issues that those who earn money the hard way might face. For example, they are described as likely having to work overtime, forgo time off for illness or rest, and maintain an unyielding dedication to their occupation, which often results in a never-ending cycle of work without any perceived progression in their lives. Another tweet that captures this sentiment reads, \"Drowning in more work and poverty despite trying harder and harder,\" pointing to a sense of despair and dissatisfaction that comes with work that is both disproportionately demanding and inadequately rewarding. Critics also note how the pursuit of hard-earned money could potentially create physical and mental health risks due to the unrelenting pressure and stress that these jobs might impose. Conversely, those in favor of earning money with less difficulty contend that it's crucial to harness one's strengths and passions to create opportunities that yield financial success without the need for excessive labor. The debate revolves around the concept that people should seek out ways to work smarter, not harder, especially if it means a healthier and more fulfilling lifestyle.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 3, tokens 137, triggered by: 0.85\n",
+      "\u001b[34mIn fact, the notion of a \"vicious cycle,\" often attributed to those chasing hard-earned money, is juxtaposed with an idealized image of someone operating in their zone of excellence. Confidently focused on their strengths, such individuals are depicted as enjoying a more relaxed and less stressful work environment, one in which they can thrive without the need for never-ending overtime or self-sacrifice. Some tweets even extend this sentiment to the broader socio-economic context, observing how wealth is not merely derived from manual labor or high-paying positions requiring extraordinary work hours. The tweets emphasize the importance of cultivating an entrepreneurial spirit and a penchant for innovative thinking, especially in the modern digital age.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 4, tokens 249, triggered by: 0.86\n",
+      "\u001b[35mOne user writes, \"Too hard-earned money isn't worth it. Learn how to make money using your brain, not your body,\" while another suggests, \"Love will flow towards those who are not lacking in love, and money will flow towards those who are not lacking in money!\" While some of the discussions take a somewhat passive-aggressive view, others acknowledge that financial security and comfort might not always be possible for everyone. In a more realistic tone, a user remarks, \"If life were so easy that diligence led to wealth, then the world's richest person would be the best worker bee. But that's not the case.\" This acknowledgment underscores the complexities of the economy and the role that factors like luck, connections, and a rapidly evolving job market can play in financial success. Some users are quick to criticize the notion that earning money the hard way should be avoided, with one tweet expressing, \"The person who advises you to avoid hard-earned money is likely a scammer who profits off providing emotional value in exchange for exploitation.\" Others argue that while it's essential to find enjoyment and fulfillment in one's work, it's crucial not to shun or belittle those who choose to work in physically demanding or high-paying industries.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 5, tokens 117, triggered by: 0.87\n",
+      "\u001b[31mOverall, the Weibo discussions offer a fascinating insight into the complexities of the modern Chinese labor market and the work-life balance that people strive to achieve. As in many countries, striking the right balance between work and play is an ongoing challenge for many Chinese citizens. However, the conversations on Weibo signal an increasing awareness of the importance of finding meaningful, fulfilling, and financially rewarding work that doesn't necessitate excessive sacrifice or sufferance. In the end, as one user succinctly puts it, \"Make sure you're earning your money in a way that brings you joy and satisfaction.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 6, tokens 171, triggered by: 0.82\n",
+      "\u001b[32mThat's the only way to ensure that your life doesn't become a never-ending cycle of hard work without any tangible progress.\" In this context, social media discussions focusing on the trials and tribulations of earning money serve not only as an outlet for venting frustrations but also as a means of promoting dialogue and shared understanding about the challenges faced by workers across all industries. These virtual conversations sparked by tweets and in-depth discussions likely resonate with a wide swath of Chinese citizens struggling to navigate the complexities of balancing a career that pays well with one that brings them joy, fulfillment, and a sense of purpose. As the discussions on Weibo continue to evolve and unfold, it is evident that the discourse around work, money, and life satisfaction holds the potential to inspire meaningful change and shift societal attitudes towards a more holistic, balanced, and humane understanding of success and prosperity.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 7, tokens 72, triggered by: 0.80\n",
+      "\u001b[34m--- Note: The translated tweets and user quotes from Chinese to English were used as the foundation for the long-form news article. The author tried to maintain the integrity of the original content in the translation while adapting it to fit a journalistic format. No inaccuracies were introduced during translation, and the opinion-based nature of the original content was preserved while maintaining objectivity.\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n",
+      "Split 8, tokens 23, triggered by: final split\n",
+      "\u001b[35mHeart count: 0/2 Note: The author did not include any Chinese characters in the final response. Collapse\u001b[0m\n",
+      "----------------------------------------------------------------------------------------\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "splitter.print(splits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/text_semantic_split.ipynb b/docs/examples/text_semantic_split.ipynb
deleted file mode 100644
index 35f618a4..00000000
--- a/docs/examples/text_semantic_split.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = '''\n",
-    "In a recent surge of social media discussions on Weibo, Chinese netizens have been engaging in conversations about the struggles and challenges of earning money. The online debate sparked a wave of opinions and perspectives on the relationship between hard work, high pay, and finding contentment. Among the tweets, several users pontificated that one should avoid earning \"too much hard-earned money.\"\n",
-    "The tweets and discussions revolve around the idea that working too hard for one's income can have a detrimental effect on one's life, both physically and mentally. Some users advocate for finding opportunities that align with one's strengths and passions, rather than simply focusing on high-paying jobs that may require excessive hours and intense labor.\n",
-    "One Weibo user pontificates, \"Don't earn that much hard-earned money,\" a sentiment echoed by others with tweets such as, \"Why is it that when earning money, that process always has to be so tough?\" This question is followed by a comparison between two types of people - those who are used to earning money the hard way and those who seem to effortlessly obtain wealth. While the former group is depicted as having been taught to suffer from a young age, the latter is shown as being able to focus solely on their natural talents and thriving in their niche advantageously.\n",
-    "Discussions on the platform draw attention to a variety of issues that those who earn money the hard way might face. For example, they are described as likely having to work overtime, forgo time off for illness or rest, and maintain an unyielding dedication to their occupation, which often results in a never-ending cycle of work without any perceived progression in their lives.\n",
-    "Another tweet that captures this sentiment reads, \"Drowning in more work and poverty despite trying harder and harder,\" pointing to a sense of despair and dissatisfaction that comes with work that is both disproportionately demanding and inadequately rewarding. Critics also note how the pursuit of hard-earned money could potentially create physical and mental health risks due to the unrelenting pressure and stress that these jobs might impose.\n",
-    "Conversely, those in favor of earning money with less difficulty contend that it's crucial to harness one's strengths and passions to create opportunities that yield financial success without the need for excessive labor. The debate revolves around the concept that people should seek out ways to work smarter, not harder, especially if it means a healthier and more fulfilling lifestyle.\n",
-    "In fact, the notion of a \"vicious cycle,\" often attributed to those chasing hard-earned money, is juxtaposed with an idealized image of someone operating in their zone of excellence. Confidently focused on their strengths, such individuals are depicted as enjoying a more relaxed and less stressful work environment, one in which they can thrive without the need for never-ending overtime or self-sacrifice.\n",
-    "Some tweets even extend this sentiment to the broader socio-economic context, observing how wealth is not merely derived from manual labor or high-paying positions requiring extraordinary work hours. The tweets emphasize the importance of cultivating an entrepreneurial spirit and a penchant for innovative thinking, especially in the modern digital age.\n",
-    "One user writes, \"Too hard-earned money isn't worth it. Learn how to make money using your brain, not your body,\" while another suggests, \"Love will flow towards those who are not lacking in love, and money will flow towards those who are not lacking in money!\"\n",
-    "While some of the discussions take a somewhat passive-aggressive view, others acknowledge that financial security and comfort might not always be possible for everyone. In a more realistic tone, a user remarks, \"If life were so easy that diligence led to wealth, then the world's richest person would be the best worker bee. But that's not the case.\" This acknowledgment underscores the complexities of the economy and the role that factors like luck, connections, and a rapidly evolving job market can play in financial success.\n",
-    "Some users are quick to criticize the notion that earning money the hard way should be avoided, with one tweet expressing, \"The person who advises you to avoid hard-earned money is likely a scammer who profits off providing emotional value in exchange for exploitation.\" Others argue that while it's essential to find enjoyment and fulfillment in one's work, it's crucial not to shun or belittle those who choose to work in physically demanding or high-paying industries.\n",
-    "Overall, the Weibo discussions offer a fascinating insight into the complexities of the modern Chinese labor market and the work-life balance that people strive to achieve. As in many countries, striking the right balance between work and play is an ongoing challenge for many Chinese citizens. However, the conversations on Weibo signal an increasing awareness of the importance of finding meaningful, fulfilling, and financially rewarding work that doesn't necessitate excessive sacrifice or sufferance.\n",
-    "In the end, as one user succinctly puts it, \"Make sure you're earning your money in a way that brings you joy and satisfaction. That's the only way to ensure that your life doesn't become a never-ending cycle of hard work without any tangible progress.\"\n",
-    "In this context, social media discussions focusing on the trials and tribulations of earning money serve not only as an outlet for venting frustrations but also as a means of promoting dialogue and shared understanding about the challenges faced by workers across all industries. These virtual conversations sparked by tweets and in-depth discussions likely resonate with a wide swath of Chinese citizens struggling to navigate the complexities of balancing a career that pays well with one that brings them joy, fulfillment, and a sense of purpose.\n",
-    "As the discussions on Weibo continue to evolve and unfold, it is evident that the discourse around work, money, and life satisfaction holds the potential to inspire meaningful change and shift societal attitudes towards a more holistic, balanced, and humane understanding of success and prosperity.\n",
-    "---\n",
-    "Note: The translated tweets and user quotes from Chinese to English were used as the foundation for the long-form news article. The author tried to maintain the integrity of the original content in the translation while adapting it to fit a journalistic format. No inaccuracies were introduced during translation, and the opinion-based nature of the original content was preserved while maintaining objectivity.\n",
-    "Heart count: 0/2\n",
-    "Note: The author did not include any Chinese characters in the final response.\n",
-    "Collapse\n",
-    "'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jakit/customers/aurelio/semantic-router/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "from semantic_router.splitters import RollingWindowSplitter\n",
-    "from semantic_router.encoders import OpenAIEncoder\n",
-    "\n",
-    "splitter = RollingWindowSplitter(\n",
-    "    encoder=OpenAIEncoder(),\n",
-    "    min_split_tokens=50,\n",
-    "    max_split_tokens=300,\n",
-    "    window_size=5, # sentences\n",
-    "    plot_splits=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33m2024-02-23 11:00:31 WARNING semantic_router.utils.logger Single document exceeds the maximum token limit of 300. Splitting to sentences before semantically splitting.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 0: Trying threshold: 0.8881277932028191\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 0: Median tokens per split: 24.0\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 0: Adjusting high to 0.8781277932028191\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 1: Trying threshold: 0.868791983949652\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 1: Median tokens per split: 34.5\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Iteration 1: Adjusting high to 0.858791983949652\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Final optimal threshold: 0.868791983949652\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 218 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 262 tokens due to exceeding token limit of 300.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 137 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 249 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 117 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 171 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Split finalized with 72 tokens due to threshold 0.868791983949652.\u001b[0m\n",
-      "\u001b[32m2024-02-23 11:00:32 INFO semantic_router.utils.logger Final split added with 23 tokens due to remaining documents.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "splits = splitter([text])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Split 1, tokens 218, triggered by: 0.85\n",
-      "\u001b[31mIn a recent surge of social media discussions on Weibo, Chinese netizens have been engaging in conversations about the struggles and challenges of earning money. The online debate sparked a wave of opinions and perspectives on the relationship between hard work, high pay, and finding contentment. Among the tweets, several users pontificated that one should avoid earning \"too much hard-earned money.\" The tweets and discussions revolve around the idea that working too hard for one's income can have a detrimental effect on one's life, both physically and mentally. Some users advocate for finding opportunities that align with one's strengths and passions, rather than simply focusing on high-paying jobs that may require excessive hours and intense labor. One Weibo user pontificates, \"Don't earn that much hard-earned money,\" a sentiment echoed by others with tweets such as, \"Why is it that when earning money, that process always has to be so tough?\" This question is followed by a comparison between two types of people - those who are used to earning money the hard way and those who seem to effortlessly obtain wealth.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 2, tokens 262, triggered by: token limit\n",
-      "\u001b[32mWhile the former group is depicted as having been taught to suffer from a young age, the latter is shown as being able to focus solely on their natural talents and thriving in their niche advantageously. Discussions on the platform draw attention to a variety of issues that those who earn money the hard way might face. For example, they are described as likely having to work overtime, forgo time off for illness or rest, and maintain an unyielding dedication to their occupation, which often results in a never-ending cycle of work without any perceived progression in their lives. Another tweet that captures this sentiment reads, \"Drowning in more work and poverty despite trying harder and harder,\" pointing to a sense of despair and dissatisfaction that comes with work that is both disproportionately demanding and inadequately rewarding. Critics also note how the pursuit of hard-earned money could potentially create physical and mental health risks due to the unrelenting pressure and stress that these jobs might impose. Conversely, those in favor of earning money with less difficulty contend that it's crucial to harness one's strengths and passions to create opportunities that yield financial success without the need for excessive labor. The debate revolves around the concept that people should seek out ways to work smarter, not harder, especially if it means a healthier and more fulfilling lifestyle.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 3, tokens 137, triggered by: 0.85\n",
-      "\u001b[34mIn fact, the notion of a \"vicious cycle,\" often attributed to those chasing hard-earned money, is juxtaposed with an idealized image of someone operating in their zone of excellence. Confidently focused on their strengths, such individuals are depicted as enjoying a more relaxed and less stressful work environment, one in which they can thrive without the need for never-ending overtime or self-sacrifice. Some tweets even extend this sentiment to the broader socio-economic context, observing how wealth is not merely derived from manual labor or high-paying positions requiring extraordinary work hours. The tweets emphasize the importance of cultivating an entrepreneurial spirit and a penchant for innovative thinking, especially in the modern digital age.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 4, tokens 249, triggered by: 0.86\n",
-      "\u001b[35mOne user writes, \"Too hard-earned money isn't worth it. Learn how to make money using your brain, not your body,\" while another suggests, \"Love will flow towards those who are not lacking in love, and money will flow towards those who are not lacking in money!\" While some of the discussions take a somewhat passive-aggressive view, others acknowledge that financial security and comfort might not always be possible for everyone. In a more realistic tone, a user remarks, \"If life were so easy that diligence led to wealth, then the world's richest person would be the best worker bee. But that's not the case.\" This acknowledgment underscores the complexities of the economy and the role that factors like luck, connections, and a rapidly evolving job market can play in financial success. Some users are quick to criticize the notion that earning money the hard way should be avoided, with one tweet expressing, \"The person who advises you to avoid hard-earned money is likely a scammer who profits off providing emotional value in exchange for exploitation.\" Others argue that while it's essential to find enjoyment and fulfillment in one's work, it's crucial not to shun or belittle those who choose to work in physically demanding or high-paying industries.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 5, tokens 117, triggered by: 0.87\n",
-      "\u001b[31mOverall, the Weibo discussions offer a fascinating insight into the complexities of the modern Chinese labor market and the work-life balance that people strive to achieve. As in many countries, striking the right balance between work and play is an ongoing challenge for many Chinese citizens. However, the conversations on Weibo signal an increasing awareness of the importance of finding meaningful, fulfilling, and financially rewarding work that doesn't necessitate excessive sacrifice or sufferance. In the end, as one user succinctly puts it, \"Make sure you're earning your money in a way that brings you joy and satisfaction.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 6, tokens 171, triggered by: 0.82\n",
-      "\u001b[32mThat's the only way to ensure that your life doesn't become a never-ending cycle of hard work without any tangible progress.\" In this context, social media discussions focusing on the trials and tribulations of earning money serve not only as an outlet for venting frustrations but also as a means of promoting dialogue and shared understanding about the challenges faced by workers across all industries. These virtual conversations sparked by tweets and in-depth discussions likely resonate with a wide swath of Chinese citizens struggling to navigate the complexities of balancing a career that pays well with one that brings them joy, fulfillment, and a sense of purpose. As the discussions on Weibo continue to evolve and unfold, it is evident that the discourse around work, money, and life satisfaction holds the potential to inspire meaningful change and shift societal attitudes towards a more holistic, balanced, and humane understanding of success and prosperity.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 7, tokens 72, triggered by: 0.80\n",
-      "\u001b[34m--- Note: The translated tweets and user quotes from Chinese to English were used as the foundation for the long-form news article. The author tried to maintain the integrity of the original content in the translation while adapting it to fit a journalistic format. No inaccuracies were introduced during translation, and the opinion-based nature of the original content was preserved while maintaining objectivity.\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n",
-      "Split 8, tokens 23, triggered by: final split\n",
-      "\u001b[35mHeart count: 0/2 Note: The author did not include any Chinese characters in the final response. Collapse\u001b[0m\n",
-      "----------------------------------------------------------------------------------------\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "splitter.print(splits)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/poetry.lock b/poetry.lock
index 760f53ce..fff4911a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -532,7 +532,7 @@ test = ["pytest"]
 name = "contourpy"
 version = "1.2.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
     {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"},
@@ -658,65 +658,11 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
-[[package]]
-name = "cryptography"
-version = "42.0.4"
-description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "cryptography-42.0.4-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:ffc73996c4fca3d2b6c1c8c12bfd3ad00def8621da24f547626bf06441400449"},
-    {file = "cryptography-42.0.4-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:db4b65b02f59035037fde0998974d84244a64c3265bdef32a827ab9b63d61b18"},
-    {file = "cryptography-42.0.4-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dad9c385ba8ee025bb0d856714f71d7840020fe176ae0229de618f14dae7a6e2"},
-    {file = "cryptography-42.0.4-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:69b22ab6506a3fe483d67d1ed878e1602bdd5912a134e6202c1ec672233241c1"},
-    {file = "cryptography-42.0.4-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:e09469a2cec88fb7b078e16d4adec594414397e8879a4341c6ace96013463d5b"},
-    {file = "cryptography-42.0.4-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3e970a2119507d0b104f0a8e281521ad28fc26f2820687b3436b8c9a5fcf20d1"},
-    {file = "cryptography-42.0.4-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:e53dc41cda40b248ebc40b83b31516487f7db95ab8ceac1f042626bc43a2f992"},
-    {file = "cryptography-42.0.4-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:c3a5cbc620e1e17009f30dd34cb0d85c987afd21c41a74352d1719be33380885"},
-    {file = "cryptography-42.0.4-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:6bfadd884e7280df24d26f2186e4e07556a05d37393b0f220a840b083dc6a824"},
-    {file = "cryptography-42.0.4-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:01911714117642a3f1792c7f376db572aadadbafcd8d75bb527166009c9f1d1b"},
-    {file = "cryptography-42.0.4-cp37-abi3-win32.whl", hash = "sha256:fb0cef872d8193e487fc6bdb08559c3aa41b659a7d9be48b2e10747f47863925"},
-    {file = "cryptography-42.0.4-cp37-abi3-win_amd64.whl", hash = "sha256:c1f25b252d2c87088abc8bbc4f1ecbf7c919e05508a7e8628e6875c40bc70923"},
-    {file = "cryptography-42.0.4-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:15a1fb843c48b4a604663fa30af60818cd28f895572386e5f9b8a665874c26e7"},
-    {file = "cryptography-42.0.4-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a1327f280c824ff7885bdeef8578f74690e9079267c1c8bd7dc5cc5aa065ae52"},
-    {file = "cryptography-42.0.4-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ffb03d419edcab93b4b19c22ee80c007fb2d708429cecebf1dd3258956a563a"},
-    {file = "cryptography-42.0.4-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1df6fcbf60560d2113b5ed90f072dc0b108d64750d4cbd46a21ec882c7aefce9"},
-    {file = "cryptography-42.0.4-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:44a64043f743485925d3bcac548d05df0f9bb445c5fcca6681889c7c3ab12764"},
-    {file = "cryptography-42.0.4-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:3c6048f217533d89f2f8f4f0fe3044bf0b2090453b7b73d0b77db47b80af8dff"},
-    {file = "cryptography-42.0.4-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6d0fbe73728c44ca3a241eff9aefe6496ab2656d6e7a4ea2459865f2e8613257"},
-    {file = "cryptography-42.0.4-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:887623fe0d70f48ab3f5e4dbf234986b1329a64c066d719432d0698522749929"},
-    {file = "cryptography-42.0.4-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:ce8613beaffc7c14f091497346ef117c1798c202b01153a8cc7b8e2ebaaf41c0"},
-    {file = "cryptography-42.0.4-cp39-abi3-win32.whl", hash = "sha256:810bcf151caefc03e51a3d61e53335cd5c7316c0a105cc695f0959f2c638b129"},
-    {file = "cryptography-42.0.4-cp39-abi3-win_amd64.whl", hash = "sha256:a0298bdc6e98ca21382afe914c642620370ce0470a01e1bef6dd9b5354c36854"},
-    {file = "cryptography-42.0.4-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5f8907fcf57392cd917892ae83708761c6ff3c37a8e835d7246ff0ad251d9298"},
-    {file = "cryptography-42.0.4-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:12d341bd42cdb7d4937b0cabbdf2a94f949413ac4504904d0cdbdce4a22cbf88"},
-    {file = "cryptography-42.0.4-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1cdcdbd117681c88d717437ada72bdd5be9de117f96e3f4d50dab3f59fd9ab20"},
-    {file = "cryptography-42.0.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:0e89f7b84f421c56e7ff69f11c441ebda73b8a8e6488d322ef71746224c20fce"},
-    {file = "cryptography-42.0.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:f1e85a178384bf19e36779d91ff35c7617c885da487d689b05c1366f9933ad74"},
-    {file = "cryptography-42.0.4-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:d2a27aca5597c8a71abbe10209184e1a8e91c1fd470b5070a2ea60cafec35bcd"},
-    {file = "cryptography-42.0.4-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4e36685cb634af55e0677d435d425043967ac2f3790ec652b2b88ad03b85c27b"},
-    {file = "cryptography-42.0.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:f47be41843200f7faec0683ad751e5ef11b9a56a220d57f300376cd8aba81660"},
-    {file = "cryptography-42.0.4.tar.gz", hash = "sha256:831a4b37accef30cccd34fcb916a5d7b5be3cbbe27268a02832c3e450aea39cb"},
-]
-
-[package.dependencies]
-cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
-
-[package.extras]
-docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
-nox = ["nox"]
-pep8test = ["check-sdist", "click", "mypy", "ruff"]
-sdist = ["build"]
-ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
-test-randomorder = ["pytest-randomly"]
-
 [[package]]
 name = "cycler"
 version = "0.12.1"
 description = "Composable style cycles"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
@@ -791,17 +737,6 @@ files = [
     {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
 ]
 
-[[package]]
-name = "docutils"
-version = "0.20.1"
-description = "Docutils -- Python Documentation Utilities"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "docutils-0.20.1-py3-none-any.whl", hash = "sha256:96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6"},
-    {file = "docutils-0.20.1.tar.gz", hash = "sha256:f08a4e276c3a1583a86dce3e34aba3fe04d02bba2dd51ed16106244e8a923e3b"},
-]
-
 [[package]]
 name = "exceptiongroup"
 version = "1.2.0"
@@ -940,7 +875,7 @@ files = [
 name = "fonttools"
 version = "4.49.0"
 description = "Tools to manipulate font files"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "fonttools-4.49.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d970ecca0aac90d399e458f0b7a8a597e08f95de021f17785fb68e2dc0b99717"},
@@ -1259,7 +1194,7 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 name = "importlib-resources"
 version = "6.1.1"
 description = "Read resources from Python packages"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
@@ -1354,24 +1289,6 @@ qtconsole = ["qtconsole"]
 test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
-[[package]]
-name = "jaraco-classes"
-version = "3.3.1"
-description = "Utility functions for Python class constructs"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "jaraco.classes-3.3.1-py3-none-any.whl", hash = "sha256:86b534de565381f6b3c1c830d13f931d7be1a75f0081c57dff615578676e2206"},
-    {file = "jaraco.classes-3.3.1.tar.gz", hash = "sha256:cb28a5ebda8bc47d8c8015307d93163464f9f2b91ab4006e09ff0ce07e8bfb30"},
-]
-
-[package.dependencies]
-more-itertools = "*"
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -1391,21 +1308,6 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
-[[package]]
-name = "jeepney"
-version = "0.8.0"
-description = "Low-level, pure Python DBus protocol wrapper."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "jeepney-0.8.0-py3-none-any.whl", hash = "sha256:c0a454ad016ca575060802ee4d590dd912e35c122fa04e70306de3d076cce755"},
-    {file = "jeepney-0.8.0.tar.gz", hash = "sha256:5efe48d255973902f6badc3ce55e2aa6c5c3b3bc642059ef3a91247bcfcc5806"},
-]
-
-[package.extras]
-test = ["async-timeout", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"]
-trio = ["async_generator", "trio"]
-
 [[package]]
 name = "jinja2"
 version = "3.1.3"
@@ -1477,34 +1379,11 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
-[[package]]
-name = "keyring"
-version = "24.3.0"
-description = "Store and access your passwords safely."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "keyring-24.3.0-py3-none-any.whl", hash = "sha256:4446d35d636e6a10b8bce7caa66913dd9eca5fd222ca03a3d42c38608ac30836"},
-    {file = "keyring-24.3.0.tar.gz", hash = "sha256:e730ecffd309658a08ee82535a3b5ec4b4c8669a9be11efb66249d8e0aeb9a25"},
-]
-
-[package.dependencies]
-importlib-metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
-"jaraco.classes" = "*"
-jeepney = {version = ">=0.4.2", markers = "sys_platform == \"linux\""}
-pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""}
-SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""}
-
-[package.extras]
-completion = ["shtab (>=1.1.0)"]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
 [[package]]
 name = "kiwisolver"
 version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
@@ -1635,30 +1514,6 @@ dev = ["black (>=23.3.0)", "httpx (>=0.24.1)", "mkdocs (>=1.4.3)", "mkdocs-mater
 server = ["fastapi (>=0.100.0)", "pydantic-settings (>=2.0.1)", "sse-starlette (>=1.6.1)", "starlette-context (>=0.3.6,<0.4)", "uvicorn (>=0.22.0)"]
 test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
 
-[[package]]
-name = "markdown-it-py"
-version = "3.0.0"
-description = "Python port of markdown-it. Markdown parsing, done right!"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
-    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
-]
-
-[package.dependencies]
-mdurl = ">=0.1,<1.0"
-
-[package.extras]
-benchmarking = ["psutil", "pytest", "pytest-benchmark"]
-code-style = ["pre-commit (>=3.0,<4.0)"]
-compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
-linkify = ["linkify-it-py (>=1,<3)"]
-plugins = ["mdit-py-plugins"]
-profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
-
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -1728,26 +1583,11 @@ files = [
     {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"},
 ]
 
-[[package]]
-name = "matplot"
-version = "0.1.9"
-description = ""
-optional = false
-python-versions = "*"
-files = [
-    {file = "matplot-0.1.9-py2.py3-none-any.whl", hash = "sha256:794ec24272a7672d5c89c4bbb581ceb0881c0d08c95d4f79c454a54ad4a1cdfa"},
-    {file = "matplot-0.1.9.tar.gz", hash = "sha256:89369d0974a5c5b6e5815532a9202ce09db0d70a909251bf0af0c608818c8fa4"},
-]
-
-[package.dependencies]
-matplotlib = ">=3.1.1"
-pyloco = ">=0.0.134"
-
 [[package]]
 name = "matplotlib"
 version = "3.8.3"
 description = "Python plotting package"
-optional = false
+optional = true
 python-versions = ">=3.9"
 files = [
     {file = "matplotlib-3.8.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:cf60138ccc8004f117ab2a2bad513cc4d122e55864b4fe7adf4db20ca68a078f"},
@@ -1806,17 +1646,6 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
-[[package]]
-name = "mdurl"
-version = "0.1.2"
-description = "Markdown URL utilities"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
-    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
-]
-
 [[package]]
 name = "mistralai"
 version = "0.0.12"
@@ -1877,17 +1706,6 @@ files = [
     {file = "mmh3-3.1.0.tar.gz", hash = "sha256:9b0f2b2ab4a915333c9d1089572e290a021ebb5b900bb7f7114dccc03995d732"},
 ]
 
-[[package]]
-name = "more-itertools"
-version = "10.2.0"
-description = "More routines for operating on iterables, beyond itertools"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "more-itertools-10.2.0.tar.gz", hash = "sha256:8fccb480c43d3e99a00087634c06dd02b0d50fbf088b380de5a41a015ec239e1"},
-    {file = "more_itertools-10.2.0-py3-none-any.whl", hash = "sha256:686b06abe565edfab151cb8fd385a05651e1fdf8f0a14191e4439283421f8684"},
-]
-
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -2091,31 +1909,6 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
 extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
-[[package]]
-name = "nh3"
-version = "0.2.15"
-description = "Python bindings to the ammonia HTML sanitization library."
-optional = false
-python-versions = "*"
-files = [
-    {file = "nh3-0.2.15-cp37-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:9c0d415f6b7f2338f93035bba5c0d8c1b464e538bfbb1d598acd47d7969284f0"},
-    {file = "nh3-0.2.15-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:6f42f99f0cf6312e470b6c09e04da31f9abaadcd3eb591d7d1a88ea931dca7f3"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac19c0d68cd42ecd7ead91a3a032fdfff23d29302dbb1311e641a130dfefba97"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5f0d77272ce6d34db6c87b4f894f037d55183d9518f948bba236fe81e2bb4e28"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:8d595df02413aa38586c24811237e95937ef18304e108b7e92c890a06793e3bf"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86e447a63ca0b16318deb62498db4f76fc60699ce0a1231262880b38b6cff911"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3277481293b868b2715907310c7be0f1b9d10491d5adf9fce11756a97e97eddf"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60684857cfa8fdbb74daa867e5cad3f0c9789415aba660614fe16cd66cbb9ec7"},
-    {file = "nh3-0.2.15-cp37-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3b803a5875e7234907f7d64777dfde2b93db992376f3d6d7af7f3bc347deb305"},
-    {file = "nh3-0.2.15-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0d02d0ff79dfd8208ed25a39c12cbda092388fff7f1662466e27d97ad011b770"},
-    {file = "nh3-0.2.15-cp37-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:f3b53ba93bb7725acab1e030bc2ecd012a817040fd7851b332f86e2f9bb98dc6"},
-    {file = "nh3-0.2.15-cp37-abi3-musllinux_1_2_i686.whl", hash = "sha256:b1e97221cedaf15a54f5243f2c5894bb12ca951ae4ddfd02a9d4ea9df9e1a29d"},
-    {file = "nh3-0.2.15-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a5167a6403d19c515217b6bcaaa9be420974a6ac30e0da9e84d4fc67a5d474c5"},
-    {file = "nh3-0.2.15-cp37-abi3-win32.whl", hash = "sha256:427fecbb1031db085eaac9931362adf4a796428ef0163070c484b5a768e71601"},
-    {file = "nh3-0.2.15-cp37-abi3-win_amd64.whl", hash = "sha256:bc2d086fb540d0fa52ce35afaded4ea526b8fc4d3339f783db55c95de40ef02e"},
-    {file = "nh3-0.2.15.tar.gz", hash = "sha256:d1e30ff2d8d58fb2a14961f7aac1bbb1c51f9bdd7da727be35c63826060b0bf3"},
-]
-
 [[package]]
 name = "nltk"
 version = "3.8.1"
@@ -2547,7 +2340,7 @@ ptyprocess = ">=0.5"
 name = "pillow"
 version = "10.2.0"
 description = "Python Imaging Library (Fork)"
-optional = false
+optional = true
 python-versions = ">=3.8"
 files = [
     {file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
@@ -2676,20 +2469,6 @@ dense = ["openai (>=1.2.3,<2.0.0)", "sentence-transformers (>=2.0.0)", "torch (>
 openai = ["openai (>=1.2.3,<2.0.0)"]
 splade = ["sentence-transformers (>=2.0.0)", "torch (>=1.13.1)", "transformers (>=4.26.1)"]
 
-[[package]]
-name = "pkginfo"
-version = "1.9.6"
-description = "Query metadata from sdists / bdists / installed packages."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "pkginfo-1.9.6-py3-none-any.whl", hash = "sha256:4b7a555a6d5a22169fcc9cf7bfd78d296b0361adad412a346c1226849af5e546"},
-    {file = "pkginfo-1.9.6.tar.gz", hash = "sha256:8fd5896e8718a4372f0ea9cc9d96f6417c9b986e23a4d116dda26b62cc29d046"},
-]
-
-[package.extras]
-testing = ["pytest", "pytest-cov"]
-
 [[package]]
 name = "platformdirs"
 version = "4.2.0"
@@ -2943,29 +2722,11 @@ files = [
 plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
-[[package]]
-name = "pyloco"
-version = "0.0.139"
-description = "Python Microapplication Launcher"
-optional = false
-python-versions = "*"
-files = [
-    {file = "pyloco-0.0.139-py2.py3-none-any.whl", hash = "sha256:6aeb14954b196c7faef4efd15f92887802e90be138e35a0e64ff1312f847425b"},
-    {file = "pyloco-0.0.139.tar.gz", hash = "sha256:d39537a4006f7522bd8b8d67e5f0abe6a6d9fdc7609a967d8ac6e1cf1df2a3a8"},
-]
-
-[package.dependencies]
-SimpleWebSocketServer = "*"
-twine = "*"
-typing = "*"
-ushlex = "*"
-websocket-client = "*"
-
 [[package]]
 name = "pyparsing"
 version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
+optional = true
 python-versions = ">=3.6.8"
 files = [
     {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
@@ -3100,17 +2861,6 @@ files = [
     {file = "pywin32-306-cp39-cp39-win_amd64.whl", hash = "sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4"},
 ]
 
-[[package]]
-name = "pywin32-ctypes"
-version = "0.2.2"
-description = "A (partial) reimplementation of pywin32 using ctypes/cffi"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "pywin32-ctypes-0.2.2.tar.gz", hash = "sha256:3426e063bdd5fd4df74a14fa3cf80a0b42845a87e1d1e81f6549f9daec593a60"},
-    {file = "pywin32_ctypes-0.2.2-py3-none-any.whl", hash = "sha256:bf490a1a709baf35d688fe0ecf980ed4de11d2b3e37b51e5442587a75d9957e7"},
-]
-
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -3276,25 +3026,6 @@ files = [
 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
-[[package]]
-name = "readme-renderer"
-version = "42.0"
-description = "readme_renderer is a library for rendering readme descriptions for Warehouse"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "readme_renderer-42.0-py3-none-any.whl", hash = "sha256:13d039515c1f24de668e2c93f2e877b9dbe6c6c32328b90a40a49d8b2b85f36d"},
-    {file = "readme_renderer-42.0.tar.gz", hash = "sha256:2d55489f83be4992fe4454939d1a051c33edbab778e82761d060c9fc6b308cd1"},
-]
-
-[package.dependencies]
-docutils = ">=0.13.1"
-nh3 = ">=0.2.14"
-Pygments = ">=2.5.1"
-
-[package.extras]
-md = ["cmarkgfm (>=0.8.0)"]
-
 [[package]]
 name = "regex"
 version = "2023.12.25"
@@ -3418,52 +3149,6 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
-[[package]]
-name = "requests-toolbelt"
-version = "1.0.0"
-description = "A utility belt for advanced users of python-requests"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
-    {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"},
-    {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"},
-]
-
-[package.dependencies]
-requests = ">=2.0.1,<3.0.0"
-
-[[package]]
-name = "rfc3986"
-version = "2.0.0"
-description = "Validating URI References per RFC 3986"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "rfc3986-2.0.0-py2.py3-none-any.whl", hash = "sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd"},
-    {file = "rfc3986-2.0.0.tar.gz", hash = "sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c"},
-]
-
-[package.extras]
-idna2008 = ["idna"]
-
-[[package]]
-name = "rich"
-version = "13.7.0"
-description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "rich-13.7.0-py3-none-any.whl", hash = "sha256:6da14c108c4866ee9520bbffa71f6fe3962e193b7da68720583850cd4548e235"},
-    {file = "rich-13.7.0.tar.gz", hash = "sha256:5cb5123b5cf9ee70584244246816e9114227e0b98ad9176eede6ad54bf5403fa"},
-]
-
-[package.dependencies]
-markdown-it-py = ">=2.2.0"
-pygments = ">=2.13.0,<3.0.0"
-
-[package.extras]
-jupyter = ["ipywidgets (>=7.5.1,<9)"]
-
 [[package]]
 name = "ruff"
 version = "0.1.15"
@@ -3622,31 +3307,6 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
-[[package]]
-name = "secretstorage"
-version = "3.3.3"
-description = "Python bindings to FreeDesktop.org Secret Service API"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "SecretStorage-3.3.3-py3-none-any.whl", hash = "sha256:f356e6628222568e3af06f2eba8df495efa13b3b63081dafd4f7d9a7b7bc9f99"},
-    {file = "SecretStorage-3.3.3.tar.gz", hash = "sha256:2403533ef369eca6d2ba81718576c5e0f564d5cca1b58f73a8b23e7d4eeebd77"},
-]
-
-[package.dependencies]
-cryptography = ">=2.0"
-jeepney = ">=0.6"
-
-[[package]]
-name = "simplewebsocketserver"
-version = "0.1.2"
-description = "A Simple Websocket Server written in Python"
-optional = false
-python-versions = "*"
-files = [
-    {file = "SimpleWebSocketServer-0.1.2.tar.gz", hash = "sha256:48615b8769d6a01e58375a3399117c9c240ba649a15f8c7720d68b23949eff66"},
-]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -4131,28 +3791,6 @@ build = ["cmake (>=3.18)", "lit"]
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
-[[package]]
-name = "twine"
-version = "5.0.0"
-description = "Collection of utilities for publishing packages on PyPI"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "twine-5.0.0-py3-none-any.whl", hash = "sha256:a262933de0b484c53408f9edae2e7821c1c45a3314ff2df9bdd343aa7ab8edc0"},
-    {file = "twine-5.0.0.tar.gz", hash = "sha256:89b0cc7d370a4b66421cc6102f269aa910fe0f1861c124f573cf2ddedbc10cf4"},
-]
-
-[package.dependencies]
-importlib-metadata = ">=3.6"
-keyring = ">=15.1"
-pkginfo = ">=1.8.1"
-readme-renderer = ">=35.0"
-requests = ">=2.20"
-requests-toolbelt = ">=0.8.0,<0.9.0 || >0.9.0"
-rfc3986 = ">=1.4.0"
-rich = ">=12.0.0"
-urllib3 = ">=1.26.0"
-
 [[package]]
 name = "types-pyyaml"
 version = "6.0.12.12"
@@ -4178,17 +3816,6 @@ files = [
 [package.dependencies]
 urllib3 = ">=2"
 
-[[package]]
-name = "typing"
-version = "3.7.4.3"
-description = "Type Hints for Python"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
-    {file = "typing-3.7.4.3-py2-none-any.whl", hash = "sha256:283d868f5071ab9ad873e5e52268d611e851c870a2ba354193026f2dfb29d8b5"},
-    {file = "typing-3.7.4.3.tar.gz", hash = "sha256:1187fb9c82fd670d10aa07bbb6cfcfe4bdda42d6fab8d5134f04e8c4d0b71cc9"},
-]
-
 [[package]]
 name = "typing-extensions"
 version = "4.9.0"
@@ -4217,16 +3844,6 @@ h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "ushlex"
-version = "0.99.1"
-description = "Replacement for shlex (that works with unicode) for Python 2.X."
-optional = false
-python-versions = "*"
-files = [
-    {file = "ushlex-0.99.1.tar.gz", hash = "sha256:6d681561545a9781430d5254eab9a648bade78c82ffd127d56c9228ae8887d46"},
-]
-
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -4238,22 +3855,6 @@ files = [
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
 ]
 
-[[package]]
-name = "websocket-client"
-version = "1.7.0"
-description = "WebSocket client for Python with low level API options"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
-    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
-]
-
-[package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
-optional = ["python-socks", "wsaccel"]
-test = ["websockets"]
-
 [[package]]
 name = "wget"
 version = "3.2"
@@ -4387,9 +3988,10 @@ fastembed = ["fastembed"]
 hybrid = ["pinecone-text"]
 local = ["llama-cpp-python", "torch", "transformers"]
 pinecone = ["pinecone-client"]
+processing = ["matplotlib"]
 vision = ["pillow", "torch", "torchvision", "transformers"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "5808af6bf85a4aaaecb2c70178b38dfe134791073b7641a1eb488bfda6155963"
+content-hash = "067185d9d8b058fbcf9bd00e3c9a0b5b3ada33cbfa7f77e81d164692d98ae22b"
diff --git a/pyproject.toml b/pyproject.toml
index 2b55043e..1db92298 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,6 +35,7 @@ regex = "^2023.12.25"
 torchvision = { version = "^0.16.2", optional = true}
 pillow = { version= "^10.2.0", optional = true}
 tiktoken = "^0.6.0"
+matplotlib = { version="^3.8.3", optional = true}
 
 [tool.poetry.extras]
 hybrid = ["pinecone-text"]
@@ -42,6 +43,7 @@ fastembed = ["fastembed"]
 local = ["torch", "transformers", "llama-cpp-python"]
 pinecone = ["pinecone-client"]
 vision = ["torch", "torchvision", "transformers", "pillow"]
+processing = ["matplotlib"]
 
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.25.0"
@@ -53,7 +55,6 @@ pytest-xdist = "^3.5.0"
 mypy = "^1.7.1"
 types-pyyaml = "^6.0.12.12"
 types-requests = "^2.31.0"
-matplot = "^0.1.9"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/semantic_router/splitters/rolling_window.py b/semantic_router/splitters/rolling_window.py
index 0369c746..ca9eed95 100644
--- a/semantic_router/splitters/rolling_window.py
+++ b/semantic_router/splitters/rolling_window.py
@@ -215,7 +215,10 @@ class RollingWindowSplitter(BaseSplitter):
         try:
             from matplotlib import pyplot as plt
         except ImportError:
-            logger.warning("Plotting is disabled. Please `pip install matplotlib`.")
+            logger.warning(
+                "Plotting is disabled. Please `pip install "
+                "semantic-router[processing]`."
+            )
             return
 
         if not self.plot_splits:
-- 
GitLab