diff --git a/docs/examples/text_semantic_split.ipynb b/docs/examples/text_semantic_split.ipynb
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+{
+ "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": 5,
+   "metadata": {},
+   "outputs": [],
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33m2024-02-23 09:50:04 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 09:50:05 INFO semantic_router.utils.logger Iteration 0: Trying threshold: 0.8881277932028191\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Iteration 0: Median tokens per split: 24.0\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Iteration 0: Adjusting high to 0.8781277932028191\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Iteration 1: Trying threshold: 0.8687934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Iteration 1: Median tokens per split: 34.5\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Iteration 1: Adjusting high to 0.8587934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Final optimal threshold: 0.8687934834140205\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 218 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 262 tokens due to exceeding token limit of 300.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 137 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 249 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 117 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 171 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 INFO semantic_router.utils.logger Split finalized with 72 tokens due to threshold 0.8687934834140205.\u001b[0m\n",
+      "\u001b[32m2024-02-23 09:50:05 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": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Split 1, tokens 218:\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:\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:\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:\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:\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:\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:\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:\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": [
+    "from semantic_router.splitters.utils import plot_splits\n",
+    "\n",
+    "plot_splits(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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index ef8400e58e93c9e031b60f0a9ab86eb5311a2664..c728f42ffb889390a808651838bebe730c875c37 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -213,6 +213,27 @@ files = [
     {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
 ]
 
+[[package]]
+name = "beautifulsoup4"
+version = "4.12.3"
+description = "Screen-scraping library"
+optional = true
+python-versions = ">=3.6.0"
+files = [
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
+]
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
 [[package]]
 name = "black"
 version = "23.12.1"
@@ -334,6 +355,17 @@ files = [
 [package.dependencies]
 pycparser = "*"
 
+[[package]]
+name = "chardet"
+version = "5.2.0"
+description = "Universal encoding detector for Python 3"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
+    {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
+]
+
 [[package]]
 name = "charset-normalizer"
 version = "3.3.2"
@@ -449,13 +481,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "4.45"
+version = "4.47"
 description = "Python SDK for the Cohere API"
 optional = false
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "cohere-4.45-py3-none-any.whl", hash = "sha256:bdaa2e5e1c64cf3b1d55caf9d483a33fa8eafed731a999fb0934ae12c0638b75"},
-    {file = "cohere-4.45.tar.gz", hash = "sha256:63b21b2dc3abd718b18cae726a69d1b096a34eb59f3331c20469fd0df1672816"},
+    {file = "cohere-4.47-py3-none-any.whl", hash = "sha256:8b96a1ab57713eceffb4ffd58bf647055676fb741a4e5153c14296f9814c77fa"},
+    {file = "cohere-4.47.tar.gz", hash = "sha256:d689212079c1a545bd497b143cff7aa5632831fa6cf41bf7acc93b4a6effb839"},
 ]
 
 [package.dependencies]
@@ -528,6 +560,69 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "contourpy"
+version = "1.2.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0274c1cb63625972c0c007ab14dd9ba9e199c36ae1a231ce45d725cbcbfd10a8"},
+    {file = "contourpy-1.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ab459a1cbbf18e8698399c595a01f6dcc5c138220ca3ea9e7e6126232d102bb4"},
+    {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fdd887f17c2f4572ce548461e4f96396681212d858cae7bd52ba3310bc6f00f"},
+    {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5d16edfc3fc09968e09ddffada434b3bf989bf4911535e04eada58469873e28e"},
+    {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1c203f617abc0dde5792beb586f827021069fb6d403d7f4d5c2b543d87edceb9"},
+    {file = "contourpy-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b69303ceb2e4d4f146bf82fda78891ef7bcd80c41bf16bfca3d0d7eb545448aa"},
+    {file = "contourpy-1.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:884c3f9d42d7218304bc74a8a7693d172685c84bd7ab2bab1ee567b769696df9"},
+    {file = "contourpy-1.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4a1b1208102be6e851f20066bf0e7a96b7d48a07c9b0cfe6d0d4545c2f6cadab"},
+    {file = "contourpy-1.2.0-cp310-cp310-win32.whl", hash = "sha256:34b9071c040d6fe45d9826cbbe3727d20d83f1b6110d219b83eb0e2a01d79488"},
+    {file = "contourpy-1.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:bd2f1ae63998da104f16a8b788f685e55d65760cd1929518fd94cd682bf03e41"},
+    {file = "contourpy-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:dd10c26b4eadae44783c45ad6655220426f971c61d9b239e6f7b16d5cdaaa727"},
+    {file = "contourpy-1.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5c6b28956b7b232ae801406e529ad7b350d3f09a4fde958dfdf3c0520cdde0dd"},
+    {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ebeac59e9e1eb4b84940d076d9f9a6cec0064e241818bcb6e32124cc5c3e377a"},
+    {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:139d8d2e1c1dd52d78682f505e980f592ba53c9f73bd6be102233e358b401063"},
+    {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1e9dc350fb4c58adc64df3e0703ab076f60aac06e67d48b3848c23647ae4310e"},
+    {file = "contourpy-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18fc2b4ed8e4a8fe849d18dce4bd3c7ea637758c6343a1f2bae1e9bd4c9f4686"},
+    {file = "contourpy-1.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:16a7380e943a6d52472096cb7ad5264ecee36ed60888e2a3d3814991a0107286"},
+    {file = "contourpy-1.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8d8faf05be5ec8e02a4d86f616fc2a0322ff4a4ce26c0f09d9f7fb5330a35c95"},
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+    {file = "contourpy-1.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:99ad97258985328b4f207a5e777c1b44a83bfe7cf1f87b99f9c11d4ee477c4de"},
+    {file = "contourpy-1.2.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:575bcaf957a25d1194903a10bc9f316c136c19f24e0985a2b9b5608bdf5dbfe0"},
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+    {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:464b423bc2a009088f19bdf1f232299e8b6917963e2b7e1d277da5041f33a779"},
+    {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:68ce4788b7d93e47f84edd3f1f95acdcd142ae60bc0e5493bfd120683d2d4316"},
+    {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3d7d1f8871998cdff5d2ff6a087e5e1780139abe2838e85b0b46b7ae6cc25399"},
+    {file = "contourpy-1.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6e739530c662a8d6d42c37c2ed52a6f0932c2d4a3e8c1f90692ad0ce1274abe0"},
+    {file = "contourpy-1.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:247b9d16535acaa766d03037d8e8fb20866d054d3c7fbf6fd1f993f11fc60ca0"},
+    {file = "contourpy-1.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:461e3ae84cd90b30f8d533f07d87c00379644205b1d33a5ea03381edc4b69431"},
+    {file = "contourpy-1.2.0-cp312-cp312-win32.whl", hash = "sha256:1c2559d6cffc94890b0529ea7eeecc20d6fadc1539273aa27faf503eb4656d8f"},
+    {file = "contourpy-1.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:491b1917afdd8638a05b611a56d46587d5a632cabead889a5440f7c638bc6ed9"},
+    {file = "contourpy-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5fd1810973a375ca0e097dee059c407913ba35723b111df75671a1976efa04bc"},
+    {file = "contourpy-1.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:999c71939aad2780f003979b25ac5b8f2df651dac7b38fb8ce6c46ba5abe6ae9"},
+    {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7caf9b241464c404613512d5594a6e2ff0cc9cb5615c9475cc1d9b514218ae8"},
+    {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:266270c6f6608340f6c9836a0fb9b367be61dde0c9a9a18d5ece97774105ff3e"},
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+    {file = "contourpy-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11f8d2554e52f459918f7b8e6aa20ec2a3bce35ce95c1f0ef4ba36fbda306df5"},
+    {file = "contourpy-1.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ce96dd400486e80ac7d195b2d800b03e3e6a787e2a522bfb83755938465a819e"},
+    {file = "contourpy-1.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6d3364b999c62f539cd403f8123ae426da946e142312a514162adb2addd8d808"},
+    {file = "contourpy-1.2.0-cp39-cp39-win32.whl", hash = "sha256:1c88dfb9e0c77612febebb6ac69d44a8d81e3dc60f993215425b62c1161353f4"},
+    {file = "contourpy-1.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:78e6ad33cf2e2e80c5dfaaa0beec3d61face0fb650557100ee36db808bfa6843"},
+    {file = "contourpy-1.2.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:be16975d94c320432657ad2402f6760990cb640c161ae6da1363051805fa8108"},
+    {file = "contourpy-1.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b95a225d4948b26a28c08307a60ac00fb8671b14f2047fc5476613252a129776"},
+    {file = "contourpy-1.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:0d7e03c0f9a4f90dc18d4e77e9ef4ec7b7bbb437f7f675be8e530d65ae6ef956"},
+    {file = "contourpy-1.2.0.tar.gz", hash = "sha256:171f311cb758de7da13fc53af221ae47a5877be5a0843a9fe150818c51ed276a"},
+]
+
+[package.dependencies]
+numpy = ">=1.20,<2.0"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+
 [[package]]
 name = "coverage"
 version = "7.4.1"
@@ -595,31 +690,134 @@ 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"},
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 typing = ["typing-extensions (>=4.8)"]
 
+[[package]]
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+
 [[package]]
 name = "flatbuffers"
 version = "23.5.26"
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+
+[package.extras]
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+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["munkres", "pycairo", "scipy"]
+lxml = ["lxml (>=4.0)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=15.1.0)"]
+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
@@ -923,13 +1222,13 @@ files = [
 
 [[package]]
 name = "httpcore"
-version = "1.0.2"
+version = "1.0.3"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
-    {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
+    {file = "httpcore-1.0.3-py3-none-any.whl", hash = "sha256:9a6a501c3099307d9fd76ac244e08503427679b1e81ceb1d922485e2f2462ad2"},
+    {file = "httpcore-1.0.3.tar.gz", hash = "sha256:5c0f9546ad17dac4d0772b0808856eb616eb8b48ce94f49ed819fd6982a8a544"},
 ]
 
 [package.dependencies]
@@ -940,7 +1239,7 @@ h11 = ">=0.13,<0.15"
 asyncio = ["anyio (>=4.0,<5.0)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
-trio = ["trio (>=0.22.0,<0.23.0)"]
+trio = ["trio (>=0.22.0,<0.24.0)"]
 
 [[package]]
 name = "httpx"
@@ -1043,6 +1342,24 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
+[[package]]
+name = "importlib-resources"
+version = "6.1.1"
+description = "Read resources from Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
+    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
+]
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[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-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -1124,6 +1441,24 @@ 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"},
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+]
+
+[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"
@@ -1143,6 +1478,21 @@ 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 = [
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+    {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"
@@ -1171,6 +1521,17 @@ files = [
     {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
 ]
 
+[[package]]
+name = "jsonpath-python"
+version = "1.0.6"
+description = "A more powerful JSONPath implementation in modern python"
+optional = true
+python-versions = ">=3.6"
+files = [
+    {file = "jsonpath-python-1.0.6.tar.gz", hash = "sha256:dd5be4a72d8a2995c3f583cf82bf3cd1a9544cfdabf2d22595b67aff07349666"},
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+]
+
 [[package]]
 name = "jupyter-client"
 version = "8.6.0"
@@ -1214,14 +1575,164 @@ 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 = [
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+]
+
+[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
+python-versions = ">=3.7"
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+
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+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
+kiwisolver = ">=1.3.1"
+numpy = ">=1.21,<2"
+packaging = ">=20.0"
+pillow = ">=8"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.6"
@@ -1319,6 +2031,17 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "mistralai"
 version = "0.0.12"
@@ -1379,6 +2102,17 @@ files = [
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+version = "10.2.0"
+description = "More routines for operating on iterables, beyond itertools"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
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 version = "1.3.0"
@@ -1582,6 +2316,31 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
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 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 = [
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 [[package]]
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 version = "3.8.1"
@@ -1878,13 +2637,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.11.1"
+version = "1.12.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
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 [package.dependencies]
@@ -1901,61 +2660,61 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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-version = "3.9.13"
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 [[package]]
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 [package.dependencies]
 ptyprocess = ">=0.5"
 
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+fpx = ["olefile"]
+mic = ["olefile"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
+
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 name = "pinecone-client"
-version = "3.0.2"
+version = "3.0.3"
 description = "Pinecone client and SDK"
 optional = true
 python-versions = ">=3.8,<3.13"
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@@ -2057,6 +2901,20 @@ 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 = [
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+]
+
+[package.extras]
+testing = ["pytest", "pytest-cov"]
+
 [[package]]
 name = "platformdirs"
 version = "4.2.0"
@@ -2103,22 +2961,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "4.25.2"
+version = "4.25.3"
 description = ""
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -2310,6 +3168,38 @@ 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 = [
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+]
+
+[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
+python-versions = ">=3.6.8"
+files = [
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+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pyreadline3"
 version = "3.4.1"
@@ -2412,6 +3302,31 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+name = "python-iso639"
+version = "2024.2.7"
+description = "Look-up utilities for ISO 639 language codes and names"
+optional = true
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+dev = ["black (==24.1.1)", "build (==1.0.3)", "flake8 (==7.0.0)", "pytest (==8.0.0)", "twine (==4.0.2)"]
+
+[[package]]
+name = "python-magic"
+version = "0.4.27"
+description = "File type identification using libmagic"
+optional = true
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+]
+
 [[package]]
 name = "pywin32"
 version = "306"
@@ -2435,6 +3350,17 @@ files = [
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 ]
 
+[[package]]
+name = "pywin32-ctypes"
+version = "0.2.2"
+description = "A (partial) reimplementation of pywin32 using ctypes/cffi"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.1"
@@ -2600,11 +3526,132 @@ files = [
 [package.dependencies]
 cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
+[[package]]
+name = "rapidfuzz"
+version = "3.6.1"
+description = "rapid fuzzy string matching"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+
 [[package]]
 name = "regex"
 version = "2023.12.25"
 description = "Alternative regular expression module, to replace re."
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 python-versions = ">=3.7"
 files = [
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 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+
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+version = "2.0.0"
+description = "Validating URI References per RFC 3986"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.extras]
+idna2008 = ["idna"]
+
+[[package]]
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+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 = [
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+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
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 version = "0.1.15"
@@ -2881,6 +3974,31 @@ 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)"]
 
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+description = "Python bindings to FreeDesktop.org Secret Service API"
+optional = false
+python-versions = ">=3.6"
+files = [
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+
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+jeepney = ">=0.6"
+
+[[package]]
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+version = "0.1.2"
+description = "A Simple Websocket Server written in Python"
+optional = false
+python-versions = "*"
+files = [
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+
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 version = "1.16.0"
@@ -2903,6 +4021,17 @@ files = [
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 ]
 
+[[package]]
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+optional = true
+python-versions = ">=3.8"
+files = [
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+
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@@ -2922,6 +4051,17 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "style"
+version = "1.1.0"
+description = "🌈 Terminal string styling"
+optional = false
+python-versions = "*"
+files = [
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+]
+
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 name = "sympy"
 version = "1.12"
@@ -2936,6 +4076,20 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+description = "Pretty-print tabular data"
+optional = true
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.extras]
+widechars = ["wcwidth"]
+
 [[package]]
 name = "termcolor"
 version = "2.4.0"
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 [package.extras]
 tests = ["pytest", "pytest-cov"]
 
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+    {file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
 ]
 
 [package.dependencies]
@@ -3168,13 +4374,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.1"
+version = "4.66.2"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
 ]
 
 [package.dependencies]
@@ -3292,6 +4498,28 @@ build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
 tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
+[[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"
@@ -3317,6 +4545,17 @@ 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"
@@ -3328,6 +4567,154 @@ files = [
     {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
 ]
 
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+description = "Runtime inspection utilities for typing module."
+optional = true
+python-versions = "*"
+files = [
+    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
+    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=0.3.0"
+typing-extensions = ">=3.7.4"
+
+[[package]]
+name = "unstructured"
+version = "0.12.4"
+description = "A library that prepares raw documents for downstream ML tasks."
+optional = true
+python-versions = ">=3.9.0,<3.12"
+files = [
+    {file = "unstructured-0.12.4-py3-none-any.whl", hash = "sha256:f1aa046297a3afba3aa16895e513aca6a93802ef73b7a18080656435c4deb217"},
+    {file = "unstructured-0.12.4.tar.gz", hash = "sha256:019cf52e9e2bfa286e61ffa0d7d336e1645280f9a0f165e697583143fcfe708a"},
+]
+
+[package.dependencies]
+backoff = "*"
+beautifulsoup4 = "*"
+chardet = "*"
+dataclasses-json = "*"
+emoji = "*"
+filetype = "*"
+langdetect = "*"
+lxml = "*"
+nltk = "*"
+numpy = "*"
+python-iso639 = "*"
+python-magic = "*"
+rapidfuzz = "*"
+requests = "*"
+tabulate = "*"
+typing-extensions = "*"
+unstructured-client = ">=0.15.1"
+wrapt = "*"
+
+[package.extras]
+airtable = ["pyairtable"]
+all-docs = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
+azure = ["adlfs", "fsspec"]
+azure-cognitive-search = ["azure-search-documents"]
+bedrock = ["boto3", "langchain-community"]
+biomed = ["bs4"]
+box = ["boxfs", "fsspec"]
+chroma = ["chromadb"]
+confluence = ["atlassian-python-api"]
+csv = ["pandas"]
+databricks-volumes = ["databricks-sdk"]
+delta-table = ["deltalake", "fsspec"]
+discord = ["discord-py"]
+doc = ["python-docx"]
+docx = ["python-docx"]
+dropbox = ["dropboxdrivefs", "fsspec"]
+elasticsearch = ["elasticsearch"]
+embed-huggingface = ["huggingface", "langchain-community", "sentence-transformers"]
+epub = ["pypandoc"]
+gcs = ["bs4", "fsspec", "gcsfs"]
+github = ["pygithub (>1.58.0)"]
+gitlab = ["python-gitlab"]
+google-drive = ["google-api-python-client"]
+hubspot = ["hubspot-api-client", "urllib3"]
+huggingface = ["langdetect", "sacremoses", "sentencepiece", "torch", "transformers"]
+image = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
+jira = ["atlassian-python-api"]
+local-inference = ["markdown", "msg-parser", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypandoc", "pypdf", "python-docx", "python-pptx (<=0.6.23)", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
+md = ["markdown"]
+mongodb = ["pymongo"]
+msg = ["msg-parser"]
+notion = ["htmlBuilder", "notion-client"]
+odt = ["pypandoc", "python-docx"]
+onedrive = ["Office365-REST-Python-Client", "bs4", "msal"]
+openai = ["langchain-community", "openai", "tiktoken"]
+opensearch = ["opensearch-py"]
+org = ["pypandoc"]
+outlook = ["Office365-REST-Python-Client", "msal"]
+paddleocr = ["unstructured.paddleocr (==2.6.1.3)"]
+pdf = ["onnx", "pdf2image", "pdfminer.six", "pikepdf", "pillow-heif", "pypdf", "unstructured-inference (==0.7.23)", "unstructured.pytesseract (>=0.3.12)"]
+pinecone = ["pinecone-client (==2.2.4)"]
+postgres = ["psycopg2-binary"]
+ppt = ["python-pptx (<=0.6.23)"]
+pptx = ["python-pptx (<=0.6.23)"]
+qdrant = ["qdrant-client"]
+reddit = ["praw"]
+rst = ["pypandoc"]
+rtf = ["pypandoc"]
+s3 = ["fsspec", "s3fs"]
+salesforce = ["simple-salesforce"]
+sftp = ["fsspec", "paramiko"]
+sharepoint = ["Office365-REST-Python-Client", "msal"]
+slack = ["slack-sdk"]
+tsv = ["pandas"]
+weaviate = ["weaviate-client"]
+wikipedia = ["wikipedia"]
+xlsx = ["networkx", "openpyxl", "pandas", "xlrd"]
+
+[[package]]
+name = "unstructured-client"
+version = "0.18.0"
+description = "Python Client SDK for Unstructured API"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "unstructured-client-0.18.0.tar.gz", hash = "sha256:b5f1866b6a48d2e28645e37e86c9d58b1ee7df2d88e79adf873572338c027aa8"},
+    {file = "unstructured_client-0.18.0-py3-none-any.whl", hash = "sha256:36d8c5cb01b97a87e271e11d4d5a063d1c5b85fc5fd7f07819c35a9bef74821f"},
+]
+
+[package.dependencies]
+certifi = ">=2023.7.22"
+charset-normalizer = ">=3.2.0"
+dataclasses-json-speakeasy = ">=0.5.11"
+idna = ">=3.4"
+jsonpath-python = ">=1.0.6"
+marshmallow = ">=3.19.0"
+mypy-extensions = ">=1.0.0"
+packaging = ">=23.1"
+python-dateutil = ">=2.8.2"
+requests = ">=2.31.0"
+six = ">=1.16.0"
+typing-extensions = ">=4.7.1"
+typing-inspect = ">=0.9.0"
+urllib3 = ">=1.26.18"
+
+[package.extras]
+dev = ["pylint (==2.16.2)"]
+
+[[package]]
+name = "update"
+version = "0.0.1"
+description = ""
+optional = false
+python-versions = "*"
+files = [
+    {file = "update-0.0.1-py2.py3-none-any.whl", hash = "sha256:a25522b4bf60e3e3c1a3ff3ca3a4f5a328ac4b8ff400fdc9614483147e313323"},
+]
+
+[package.dependencies]
+style = "1.1.0"
+
 [[package]]
 name = "urllib3"
 version = "2.2.0"
@@ -3345,6 +4732,16 @@ 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"
@@ -3356,6 +4753,22 @@ 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"
@@ -3366,6 +4779,85 @@ files = [
     {file = "wget-3.2.zip", hash = "sha256:35e630eca2aa50ce998b9b1a127bb26b30dfee573702782aa982f875e3f16061"},
 ]
 
+[[package]]
+name = "wrapt"
+version = "1.16.0"
+description = "Module for decorators, wrappers and monkey patching."
+optional = true
+python-versions = ">=3.6"
+files = [
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+    {file = "wrapt-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:eb1b046be06b0fce7249f1d025cd359b4b80fc1c3e24ad9eca33e0dcdb2e4a35"},
+    {file = "wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1"},
+    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.9.4"
@@ -3489,8 +4981,9 @@ fastembed = ["fastembed"]
 hybrid = ["pinecone-text"]
 local = ["llama-cpp-python", "torch", "transformers"]
 pinecone = ["pinecone-client"]
+unstructured = ["unstructured"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "894b9692ad914e11c364d588bca8d7a99ef862cdd5b2137c00e4d5cac77a35c9"
+content-hash = "0dd678c1d3f1136dd0244295ba1bc9b1accbcd6c3da959b70027e1187996d08c"
diff --git a/pyproject.toml b/pyproject.toml
index 67851a3c34a39cff5b9fae0aee856696193e8c34..b490ad3cc3721f16aea4a5fd57ef5ed2a36c309c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,10 @@ llama-cpp-python = {version = "^0.2.28", optional = true}
 black = "^23.12.1"
 colorama = "^0.4.6"
 pinecone-client = {version="^3.0.0", optional = true}
+update = "^0.0.1"
+tiktoken = "^0.6.0"
+regex = "^2023.12.25"
+
 [tool.poetry.extras]
 hybrid = ["pinecone-text"]
 fastembed = ["fastembed"]
@@ -48,6 +52,7 @@ mypy = "^1.7.1"
 types-pyyaml = "^6.0.12.12"
 types-requests = "^2.31.0"
 termcolor = "^2.4.0"
+matplot = "^0.1.9"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 6a5a0637a1190652b39f8d779eb23138e53ebfe3..066aa6911774de4f141ef8caff6d0802aeeb152e 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -77,6 +77,12 @@ class Message(BaseModel):
 
 
 class DocumentSplit(BaseModel):
-    docs: List[str]
+    docs: list[str]
     is_triggered: bool = False
-    triggered_score: Optional[float] = None
+    triggered_score: float | None = None
+    token_count: int | None = None
+    metadata: dict | None = None
+
+    @property
+    def content(self) -> str:
+        return " ".join(self.docs)
diff --git a/semantic_router/splitters/__init__.py b/semantic_router/splitters/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..44acb9e39ef60f0db98b2b07ddafee69cffeb756 100644
--- a/semantic_router/splitters/__init__.py
+++ b/semantic_router/splitters/__init__.py
@@ -0,0 +1,11 @@
+from semantic_router.splitters.base import BaseSplitter
+from semantic_router.splitters.consecutive_sim import ConsecutiveSimSplitter
+from semantic_router.splitters.cumulative_sim import CumulativeSimSplitter
+from semantic_router.splitters.rolling_window import RollingWindowSplitter
+
+__all__ = [
+    "BaseSplitter",
+    "ConsecutiveSimSplitter",
+    "CumulativeSimSplitter",
+    "RollingWindowSplitter",
+]
diff --git a/semantic_router/splitters/base.py b/semantic_router/splitters/base.py
index c64d384c0726e7595333c61007f4d9d5d7bc82d5..87daedd127bbe39afe6f9fa44694a3ec22c9a54d 100644
--- a/semantic_router/splitters/base.py
+++ b/semantic_router/splitters/base.py
@@ -1,5 +1,5 @@
 from itertools import cycle
-from typing import List
+from typing import List, Optional
 
 from pydantic.v1 import BaseModel
 from termcolor import colored
@@ -12,6 +12,8 @@ class BaseSplitter(BaseModel):
     name: str
     encoder: BaseEncoder
     score_threshold: float
+    min_split_tokens: Optional[int] = None
+    max_split_tokens: Optional[int] = None
 
     def __call__(self, docs: List[str]) -> List[DocumentSplit]:
         raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/splitters/rolling_window.py b/semantic_router/splitters/rolling_window.py
index ad5c568e4ebf38572caeb2f36a2a37488744ef6f..030719de6f49ce719da14849acf213c8bfd36723 100644
--- a/semantic_router/splitters/rolling_window.py
+++ b/semantic_router/splitters/rolling_window.py
@@ -1,88 +1,38 @@
-from typing import List
-
 import numpy as np
-from matplotlib import pyplot as plt
-from nltk.tokenize import word_tokenize
-from semantic_router.encoders.base import BaseEncoder
 
+from semantic_router.encoders.base import BaseEncoder
 from semantic_router.schema import DocumentSplit
-from semantic_router.splitters.base import BaseSplitter
+from semantic_router.splitters.utils import split_to_sentences, tiktoken_length
 from semantic_router.utils.logger import logger
 
 
-class RollingWindowSplitter(BaseSplitter):
-    """
-    A splitter that divides documents into segments based on semantic similarity
-    using a rolling window approach.
-    It adjusts the similarity threshold dynamically.
-    Splitting is based:
-    - On the similarity threshold
-    - On the maximum token limit for a split
-
-    Attributes:
-        encoder (Callable): A function to encode documents into semantic vectors.
-        score_threshold (float): Initial threshold for similarity scores to decide
-        splits.
-        window_size (int): Size of the rolling window to calculate document context.
-        plot_splits (bool): Whether to plot the similarity scores and splits for
-        visualization.
-        min_split_tokens (int): Minimum number of tokens for a valid document split.
-        max_split_tokens (int): Maximum number of tokens a split can contain.
-        split_tokens_tolerance (int): Tolerance in token count to still consider a split
-        valid.
-        threshold_step_size (float): Step size to adjust the similarity threshold during
-        optimization.
-    """
-
+class RollingWindowSplitter:
     def __init__(
         self,
         encoder: BaseEncoder,
-        score_threshold=0.3,
+        threshold_adjustment: float = 0.01,
         window_size=5,
-        plot_splits=False,
         min_split_tokens=100,
         max_split_tokens=300,
         split_tokens_tolerance=10,
-        threshold_step_size=0.01,
+        plot_splits=False,
     ):
+        self.calculated_threshold: float
         self.encoder = encoder
-        self.score_threshold = score_threshold
+        self.threshold_adjustment = threshold_adjustment
         self.window_size = window_size
         self.plot_splits = plot_splits
         self.min_split_tokens = min_split_tokens
         self.max_split_tokens = max_split_tokens
         self.split_tokens_tolerance = split_tokens_tolerance
-        self.threshold_step_size = threshold_step_size
 
     def encode_documents(self, docs: list[str]) -> np.ndarray:
-        return np.array(self.encoder(docs))
-
-    def find_optimal_threshold(self, docs: list[str], encoded_docs: np.ndarray):
-        logger.info(f"Number of documents for finding optimal threshold: {len(docs)}")
-        token_counts = [len(word_tokenize(doc)) for doc in docs]
-        low, high = 0, 1
-        while low <= high:
-            self.score_threshold = (low + high) / 2
-            similarity_scores = self.calculate_similarity_scores(encoded_docs)
-            split_indices = self.find_split_indices(similarity_scores)
-            average_tokens = np.mean(
-                [
-                    sum(token_counts[start:end])
-                    for start, end in zip(
-                        [0] + split_indices, split_indices + [len(token_counts)]
-                    )
-                ]
-            )
-            if (
-                self.min_split_tokens - self.split_tokens_tolerance
-                <= average_tokens
-                <= self.max_split_tokens + self.split_tokens_tolerance
-            ):
-                break
-            elif average_tokens < self.min_split_tokens:
-                high = self.score_threshold - self.threshold_step_size
-            else:
-                low = self.score_threshold + self.threshold_step_size
+        try:
+            embeddings = self.encoder(docs)
+            return np.array(embeddings)
+        except Exception as e:
+            logger.error(f"Error encoding documents {docs}: {e}")
+            raise
 
     def calculate_similarity_scores(self, encoded_docs: np.ndarray) -> list[float]:
         raw_similarities = []
@@ -97,11 +47,68 @@ class RollingWindowSplitter(BaseSplitter):
         return raw_similarities
 
     def find_split_indices(self, similarities: list[float]) -> list[int]:
-        return [
-            idx + 1
-            for idx, sim in enumerate(similarities)
-            if sim < self.score_threshold
-        ]
+        split_indices = []
+        for idx in range(1, len(similarities)):
+            if similarities[idx] < self.calculated_threshold:
+                split_indices.append(idx + 1)
+        return split_indices
+
+    def find_optimal_threshold(self, docs: list[str], encoded_docs: np.ndarray):
+        token_counts = [tiktoken_length(doc) for doc in docs]
+        cumulative_token_counts = np.cumsum([0] + token_counts)
+        similarity_scores = self.calculate_similarity_scores(encoded_docs)
+
+        # Analyze the distribution of similarity scores to set initial bounds
+        median_score = np.median(similarity_scores)
+        std_dev = np.std(similarity_scores)
+
+        # Set initial bounds based on median and standard deviation
+        low = max(0.0, float(median_score - std_dev))
+        high = min(1.0, float(median_score + std_dev))
+
+        iteration = 0
+        while low <= high:
+            self.calculated_threshold = (low + high) / 2
+            logger.info(
+                f"Iteration {iteration}: Trying threshold: {self.calculated_threshold}"
+            )
+            split_indices = self.find_split_indices(similarity_scores)
+
+            # Calculate the token counts for each split using the cumulative sums
+            split_token_counts = [
+                cumulative_token_counts[end] - cumulative_token_counts[start]
+                for start, end in zip(
+                    [0] + split_indices, split_indices + [len(token_counts)]
+                )
+            ]
+
+            # Calculate the median token count for the splits
+            median_tokens = np.median(split_token_counts)
+            logger.info(
+                f"Iteration {iteration}: Median tokens per split: {median_tokens}"
+            )
+            if (
+                self.min_split_tokens - self.split_tokens_tolerance
+                <= median_tokens
+                <= self.max_split_tokens + self.split_tokens_tolerance
+            ):
+                logger.info(
+                    f"Iteration {iteration}: "
+                    f"Optimal threshold {self.calculated_threshold} found "
+                    f"with median tokens ({median_tokens}) in target range "
+                    f" {self.min_split_tokens}-{self.max_split_tokens}."
+                )
+                break
+            elif median_tokens < self.min_split_tokens:
+                high = self.calculated_threshold - self.threshold_adjustment
+                logger.info(f"Iteration {iteration}: Adjusting high to {high}")
+            else:
+                low = self.calculated_threshold + self.threshold_adjustment
+                logger.info(f"Iteration {iteration}: Adjusting low to {low}")
+            iteration += 1
+
+        logger.info(f"Final optimal threshold: {self.calculated_threshold}")
+        return self.calculated_threshold
 
     def split_documents(
         self, docs: list[str], split_indices: list[int], similarities: list[float]
@@ -114,93 +121,98 @@ class RollingWindowSplitter(BaseSplitter):
         or when a split point is reached and the minimum token requirement is met,
         the current split is finalized and added to the list of splits.
         """
-        token_counts = [len(word_tokenize(doc)) for doc in docs]
-        splits: List[DocumentSplit] = []
-        current_split: List[str] = []
+        token_counts = [tiktoken_length(doc) for doc in docs]
+        splits, current_split = [], []
         current_tokens_count = 0
 
         for doc_idx, doc in enumerate(docs):
             doc_token_count = token_counts[doc_idx]
-            # Check if current document causes token count to exceed max limit
-            if (
-                current_tokens_count + doc_token_count > self.max_split_tokens
-                and current_tokens_count >= self.min_split_tokens
-            ):
-                splits.append(
-                    DocumentSplit(docs=current_split.copy(), is_triggered=True)
-                )
-                logger.info(
-                    f"Split finalized with {current_tokens_count} tokens due to "
-                    f"exceeding token limit of {self.max_split_tokens}."
-                )
-                current_split, current_tokens_count = [], 0
-
-            current_split.append(doc)
-            current_tokens_count += doc_token_count
 
             # Check if current index is a split point based on similarity
-            if doc_idx + 1 in split_indices or doc_idx == len(docs) - 1:
-                if current_tokens_count >= self.min_split_tokens:
-                    if doc_idx < len(similarities):
-                        triggered_score = similarities[doc_idx]
-                        splits.append(
-                            DocumentSplit(
-                                docs=current_split.copy(),
-                                is_triggered=True,
-                                triggered_score=triggered_score,
-                            )
-                        )
-                        logger.info(
-                            f"Split finalized with {current_tokens_count} tokens due to"
-                            f" similarity score {triggered_score:.2f}."
-                        )
-                    else:
-                        # This case handles the end of the document list
-                        # where there's no similarity score
-                        splits.append(
-                            DocumentSplit(docs=current_split.copy(), is_triggered=False)
+            if doc_idx + 1 in split_indices:
+                if current_tokens_count + doc_token_count >= self.min_split_tokens:
+                    # Include the current document before splitting
+                    # if it doesn't exceed the max limit
+                    current_split.append(doc)
+                    current_tokens_count += doc_token_count
+
+                    triggered_score = (
+                        similarities[doc_idx] if doc_idx < len(similarities) else None
+                    )
+                    splits.append(
+                        DocumentSplit(
+                            docs=current_split.copy(),
+                            is_triggered=True,
+                            triggered_score=triggered_score,
+                            token_count=current_tokens_count,
                         )
-                        logger.info(
-                            f"Split finalized with {current_tokens_count} tokens "
-                            "at the end of the document list."
+                    )
+                    logger.info(
+                        f"Split finalized with {current_tokens_count} tokens due to "
+                        f"threshold {self.calculated_threshold}."
+                    )
+                    current_split, current_tokens_count = [], 0
+                    continue  # Move to the next document after splitting
+
+            # Check if adding the current document exceeds the max token limit
+            if current_tokens_count + doc_token_count > self.max_split_tokens:
+                if current_tokens_count >= self.min_split_tokens:
+                    splits.append(
+                        DocumentSplit(
+                            docs=current_split.copy(),
+                            is_triggered=False,
+                            triggered_score=None,
+                            token_count=current_tokens_count,
                         )
+                    )
+                    logger.info(
+                        f"Split finalized with {current_tokens_count} tokens due to "
+                        f"exceeding token limit of {self.max_split_tokens}."
+                    )
                     current_split, current_tokens_count = [], 0
 
-        # Ensure any remaining documents are included in the final token count
+            current_split.append(doc)
+            current_tokens_count += doc_token_count
+
+        # Handle the last split
         if current_split:
-            splits.append(DocumentSplit(docs=current_split.copy(), is_triggered=False))
+            splits.append(
+                DocumentSplit(
+                    docs=current_split.copy(),
+                    is_triggered=False,
+                    triggered_score=None,
+                    token_count=current_tokens_count,
+                )
+            )
             logger.info(
-                f"Final split added with {current_tokens_count} tokens "
-                "due to remaining documents."
+                f"Final split added with {current_tokens_count} "
+                "tokens due to remaining documents."
             )
 
-        # Validation
+        # Validation to ensure no tokens are lost during the split
         original_token_count = sum(token_counts)
         split_token_count = sum(
-            [len(word_tokenize(doc)) for split in splits for doc in split.docs]
+            [tiktoken_length(doc) for split in splits for doc in split.docs]
         )
-        logger.debug(
-            f"Original Token Count: {original_token_count}, "
-            f"Split Token Count: {split_token_count}"
-        )
-
         if original_token_count != split_token_count:
             logger.error(
                 f"Token count mismatch: {original_token_count} != {split_token_count}"
             )
-            for i, split in enumerate(splits):
-                split_token_count = sum([len(word_tokenize(doc)) for doc in split.docs])
-                logger.error(f"Split {i} Token Count: {split_token_count}")
             raise ValueError(
                 f"Token count mismatch: {original_token_count} != {split_token_count}"
             )
 
         return splits
 
-    # TODO: fix to plot split based on token count and final split
     def plot_similarity_scores(
         self, similarities: list[float], split_indices: list[int]
     ):
+        try:
+            from matplotlib import pyplot as plt
+        except ImportError:
+            logger.warning("Plotting is disabled. Please `pip install matplotlib`.")
+            return
+
         if not self.plot_splits:
             return
         plt.figure(figsize=(12, 6))
@@ -213,24 +225,97 @@ class RollingWindowSplitter(BaseSplitter):
                 label="Split" if split_index == split_indices[0] else "",
             )
         plt.axhline(
-            y=self.score_threshold,
+            y=self.calculated_threshold,
             color="g",
             linestyle="-.",
             label="Threshold Similarity Score",
         )
+
+        # Annotating each similarity score
+        for i, score in enumerate(similarities):
+            plt.annotate(
+                f"{score:.2f}",  # Formatting to two decimal places
+                (i, score),
+                textcoords="offset points",
+                xytext=(0, 10),  # Positioning the text above the point
+                ha="center",
+            )  # Center-align the text
+
         plt.xlabel("Document Segment Index")
         plt.ylabel("Similarity Score")
-        plt.title(f"Threshold: {self.score_threshold}", loc="right", fontsize=10)
+        plt.title(
+            f"Threshold: {self.calculated_threshold} |"
+            " Window Size: {self.window_size}",
+            loc="right",
+            fontsize=10,
+        )
         plt.suptitle("Document Similarity Scores", fontsize=14)
         plt.legend()
         plt.show()
 
+    def plot_sentence_similarity_scores(
+        self, docs: list[str], threshold: float, window_size: int
+    ):
+        try:
+            from matplotlib import pyplot as plt
+        except ImportError:
+            logger.warning("Plotting is disabled. Please `pip install matplotlib`.")
+            return
+        """
+        Computes similarity scores between the average of the last
+        'window_size' sentences and the next one,
+        plots a graph of these similarity scores, and prints the first
+        sentence after a similarity score below
+        a specified threshold.
+        """
+        sentences = [sentence for doc in docs for sentence in split_to_sentences(doc)]
+        encoded_sentences = self.encode_documents(sentences)
+        similarity_scores = []
+
+        for i in range(window_size, len(encoded_sentences)):
+            window_avg_encoding = np.mean(
+                encoded_sentences[i - window_size : i], axis=0
+            )
+            sim_score = np.dot(window_avg_encoding, encoded_sentences[i]) / (
+                np.linalg.norm(window_avg_encoding)
+                * np.linalg.norm(encoded_sentences[i])
+                + 1e-10
+            )
+            similarity_scores.append(sim_score)
+
+        plt.figure(figsize=(10, 8))
+        plt.plot(similarity_scores, marker="o", linestyle="-", color="b")
+        plt.title("Sliding Window Sentence Similarity Scores")
+        plt.xlabel("Sentence Index")
+        plt.ylabel("Similarity Score")
+        plt.grid(True)
+        plt.axhline(y=threshold, color="r", linestyle="--", label="Threshold")
+        plt.show()
+
+        for i, score in enumerate(similarity_scores):
+            if score < threshold:
+                print(
+                    f"First sentence after similarity score "
+                    f"below {threshold}: {sentences[i + window_size]}"
+                )
+
     def __call__(self, docs: list[str]) -> list[DocumentSplit]:
+        if not docs:
+            raise ValueError("At least one document is required for splitting.")
+
+        if len(docs) == 1:
+            token_count = tiktoken_length(docs[0])
+            if token_count > self.max_split_tokens:
+                logger.warning(
+                    f"Single document exceeds the maximum token limit "
+                    f"of {self.max_split_tokens}. "
+                    "Splitting to sentences before semantically splitting."
+                )
+            docs = split_to_sentences(docs[0])
         encoded_docs = self.encode_documents(docs)
         self.find_optimal_threshold(docs, encoded_docs)
         similarities = self.calculate_similarity_scores(encoded_docs)
         split_indices = self.find_split_indices(similarities=similarities)
         splits = self.split_documents(docs, split_indices, similarities)
-
         self.plot_similarity_scores(similarities, split_indices)
         return splits
diff --git a/semantic_router/splitters/utils.py b/semantic_router/splitters/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..04e3c8b528d5670a0ae4ffb3e03136491c140d8f
--- /dev/null
+++ b/semantic_router/splitters/utils.py
@@ -0,0 +1,79 @@
+from typing import List
+
+import regex
+import tiktoken
+from colorama import Fore, Style
+
+from semantic_router.schema import DocumentSplit
+
+
+def split_to_sentences(text: str) -> list[str]:
+    """
+    Enhanced regex pattern to split a given text into sentences more accurately.
+
+    The enhanced regex pattern includes handling for:
+    - Direct speech and quotations.
+    - Abbreviations, initials, and acronyms.
+    - Decimal numbers and dates.
+    - Ellipses and other punctuation marks used in informal text.
+    - Removing control characters and format characters.
+
+    Args:
+        text (str): The text to split into sentences.
+
+    Returns:
+        list: A list of sentences extracted from the text.
+    """
+    regex_pattern = r"""
+        # Negative lookbehind for word boundary, word char, dot, word char
+        (?<!\b\w\.\w.)
+        # Negative lookbehind for single uppercase initials like "A."
+        (?<!\b[A-Z][a-z]\.)
+        # Negative lookbehind for abbreviations like "U.S."
+        (?<!\b[A-Z]\.)
+        # Negative lookbehind for abbreviations with uppercase letters and dots
+        (?<!\b\p{Lu}\.\p{Lu}.)
+        # Negative lookbehind for numbers, to avoid splitting decimals
+        (?<!\b\p{N}\.)
+        # Positive lookbehind for punctuation followed by whitespace
+        (?<=\.|\?|!|:|\.\.\.)\s+
+        # Positive lookahead for uppercase letter or opening quote at word boundary
+        (?="?(?=[A-Z])|"\b)
+        # OR
+        |
+        # Splits after punctuation that follows closing punctuation, followed by
+        # whitespace
+        (?<=[\"\'\]\)\}][\.!?])\s+(?=[\"\'\(A-Z])
+        # OR
+        |
+        # Splits after punctuation if not preceded by a period
+        (?<=[^\.][\.!?])\s+(?=[A-Z])
+        # OR
+        |
+        # Handles splitting after ellipses
+        (?<=\.\.\.)\s+(?=[A-Z])
+        # OR
+        |
+        # Matches and removes control characters and format characters
+        [\p{Cc}\p{Cf}]+
+    """
+    sentences = regex.split(regex_pattern, text, flags=regex.VERBOSE)
+    sentences = [sentence.strip() for sentence in sentences if sentence.strip()]
+    return sentences
+
+
+def tiktoken_length(text: str) -> int:
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+    tokens = tokenizer.encode(text, disallowed_special=())
+    return len(tokens)
+
+
+def plot_splits(document_splits: List[DocumentSplit]) -> None:
+    colors = [Fore.RED, Fore.GREEN, Fore.BLUE, Fore.MAGENTA]
+    for i, split in enumerate(document_splits):
+        color = colors[i % len(colors)]
+        colored_content = f"{color}{split.content}{Style.RESET_ALL}"
+        print(f"Split {i + 1}, tokens {split.token_count}:")
+        print(colored_content)
+        print("-" * 88)
+        print("\n")