diff --git a/src/libs/Cohere/Generated/Cohere.CohereApi.Chat.g.cs b/src/libs/Cohere/Generated/Cohere.CohereApi.Chat.g.cs
index 05188d7..d28327e 100644
--- a/src/libs/Cohere/Generated/Cohere.CohereApi.Chat.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.CohereApi.Chat.g.cs
@@ -561,7 +561,7 @@ partial void ProcessChatResponseContent(
         /// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.<br/>
         /// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.<br/>
         /// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.<br/>
-        /// Compatible Deployments: <br/>
+        /// Compatible Deployments:<br/>
         ///  - AUTO: Cohere Platform Only<br/>
         ///  - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
         /// </param>
diff --git a/src/libs/Cohere/Generated/Cohere.CohereApi.Rerankv2.g.cs b/src/libs/Cohere/Generated/Cohere.CohereApi.Rerankv2.g.cs
index 513cb3d..8777366 100644
--- a/src/libs/Cohere/Generated/Cohere.CohereApi.Rerankv2.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.CohereApi.Rerankv2.g.cs
@@ -526,7 +526,7 @@ partial void ProcessRerankv2ResponseContent(
         /// A list of texts that will be compared to the `query`.<br/>
         /// For optimal performance we recommend against sending more than 1,000 documents in a single request.<br/>
         /// **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.<br/>
-        /// **Note**: structured data should be formatted as YAML strings for best performance.  
+        /// **Note**: structured data should be formatted as YAML strings for best performance.
         /// </param>
         /// <param name="topN">
         /// Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned.
diff --git a/src/libs/Cohere/Generated/Cohere.ICohereApi.Chat.g.cs b/src/libs/Cohere/Generated/Cohere.ICohereApi.Chat.g.cs
index a5b723c..2b1f9ec 100644
--- a/src/libs/Cohere/Generated/Cohere.ICohereApi.Chat.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.ICohereApi.Chat.g.cs
@@ -64,7 +64,7 @@ public partial interface ICohereApi
         /// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.<br/>
         /// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.<br/>
         /// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.<br/>
-        /// Compatible Deployments: <br/>
+        /// Compatible Deployments:<br/>
         ///  - AUTO: Cohere Platform Only<br/>
         ///  - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
         /// </param>
diff --git a/src/libs/Cohere/Generated/Cohere.ICohereApi.Rerankv2.g.cs b/src/libs/Cohere/Generated/Cohere.ICohereApi.Rerankv2.g.cs
index 2bad946..591d255 100644
--- a/src/libs/Cohere/Generated/Cohere.ICohereApi.Rerankv2.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.ICohereApi.Rerankv2.g.cs
@@ -37,7 +37,7 @@ public partial interface ICohereApi
         /// A list of texts that will be compared to the `query`.<br/>
         /// For optimal performance we recommend against sending more than 1,000 documents in a single request.<br/>
         /// **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.<br/>
-        /// **Note**: structured data should be formatted as YAML strings for best performance.  
+        /// **Note**: structured data should be formatted as YAML strings for best performance.
         /// </param>
         /// <param name="topN">
         /// Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned.
diff --git a/src/libs/Cohere/Generated/Cohere.Models.ChatRequest.g.cs b/src/libs/Cohere/Generated/Cohere.Models.ChatRequest.g.cs
index d2cf3d8..36c6ef3 100644
--- a/src/libs/Cohere/Generated/Cohere.Models.ChatRequest.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.Models.ChatRequest.g.cs
@@ -64,7 +64,7 @@ public sealed partial class ChatRequest
         /// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.<br/>
         /// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.<br/>
         /// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.<br/>
-        /// Compatible Deployments: <br/>
+        /// Compatible Deployments:<br/>
         ///  - AUTO: Cohere Platform Only<br/>
         ///  - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
         /// </summary>
@@ -306,7 +306,7 @@ public sealed partial class ChatRequest
         /// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.<br/>
         /// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.<br/>
         /// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.<br/>
-        /// Compatible Deployments: <br/>
+        /// Compatible Deployments:<br/>
         ///  - AUTO: Cohere Platform Only<br/>
         ///  - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
         /// </param>
diff --git a/src/libs/Cohere/Generated/Cohere.Models.ChatRequestPromptTruncation.g.cs b/src/libs/Cohere/Generated/Cohere.Models.ChatRequestPromptTruncation.g.cs
index ccc64a3..9f3d796 100644
--- a/src/libs/Cohere/Generated/Cohere.Models.ChatRequestPromptTruncation.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.Models.ChatRequestPromptTruncation.g.cs
@@ -9,7 +9,7 @@ namespace Cohere
     /// With `prompt_truncation` set to "AUTO", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.<br/>
     /// With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.<br/>
     /// With `prompt_truncation` set to "OFF", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.<br/>
-    /// Compatible Deployments: <br/>
+    /// Compatible Deployments:<br/>
     ///  - AUTO: Cohere Platform Only<br/>
     ///  - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
     /// </summary>
diff --git a/src/libs/Cohere/Generated/Cohere.Models.Rerankv2Request.g.cs b/src/libs/Cohere/Generated/Cohere.Models.Rerankv2Request.g.cs
index a081a71..d35427e 100644
--- a/src/libs/Cohere/Generated/Cohere.Models.Rerankv2Request.g.cs
+++ b/src/libs/Cohere/Generated/Cohere.Models.Rerankv2Request.g.cs
@@ -31,7 +31,7 @@ public sealed partial class Rerankv2Request
         /// A list of texts that will be compared to the `query`.<br/>
         /// For optimal performance we recommend against sending more than 1,000 documents in a single request.<br/>
         /// **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.<br/>
-        /// **Note**: structured data should be formatted as YAML strings for best performance.  
+        /// **Note**: structured data should be formatted as YAML strings for best performance.
         /// </summary>
         [global::System.Text.Json.Serialization.JsonPropertyName("documents")]
         [global::System.Text.Json.Serialization.JsonRequired]
@@ -73,7 +73,7 @@ public sealed partial class Rerankv2Request
         /// A list of texts that will be compared to the `query`.<br/>
         /// For optimal performance we recommend against sending more than 1,000 documents in a single request.<br/>
         /// **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.<br/>
-        /// **Note**: structured data should be formatted as YAML strings for best performance.  
+        /// **Note**: structured data should be formatted as YAML strings for best performance.
         /// </param>
         /// <param name="topN">
         /// Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned.
diff --git a/src/libs/Cohere/openapi.yaml b/src/libs/Cohere/openapi.yaml
index 6679eb6..254ce1e 100644
--- a/src/libs/Cohere/openapi.yaml
+++ b/src/libs/Cohere/openapi.yaml
@@ -79,7 +79,7 @@ paths:
                     - AUTO
                     - AUTO_PRESERVE_ORDER
                   type: string
-                  description: "Defaults to `AUTO` when `connectors` are specified and `OFF` in all other cases.\n\nDictates how the prompt will be constructed.\n\nWith `prompt_truncation` set to \"AUTO\", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.\n\nWith `prompt_truncation` set to \"AUTO_PRESERVE_ORDER\", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.\n\nWith `prompt_truncation` set to \"OFF\", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.\n\nCompatible Deployments: \n - AUTO: Cohere Platform Only\n - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments\n"
+                  description: "Defaults to `AUTO` when `connectors` are specified and `OFF` in all other cases.\n\nDictates how the prompt will be constructed.\n\nWith `prompt_truncation` set to \"AUTO\", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be changed and ranked by relevance.\n\nWith `prompt_truncation` set to \"AUTO_PRESERVE_ORDER\", some elements from `chat_history` and `documents` will be dropped in an attempt to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved as they are inputted into the API.\n\nWith `prompt_truncation` set to \"OFF\", no elements will be dropped. If the sum of the inputs exceeds the model's context length limit, a `TooManyTokens` error will be returned.\n\nCompatible Deployments:\n - AUTO: Cohere Platform Only\n - AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments\n"
                   x-fern-audiences:
                     - public
                 connectors:
@@ -477,7 +477,7 @@ paths:
                   title: Isaac Newton (Stanford Encyclopedia of Philosophy)
                   url: https://plato.stanford.edu/Entries/newton/
                 - id: web-search_3
-                  snippet: "ShowsThis Day In HistoryScheduleTopicsStories\n\nFind History on Facebook (Opens in a new window)\n\nFind History on Twitter (Opens in a new window)\n\nFind History on YouTube (Opens in a new window)\n\nFind History on Instagram (Opens in a new window)\n\nFind History on TikTok (Opens in a new window)\n\nBy: History.com Editors\n\nUpdated: October 16, 2023 | Original: March 10, 2015\n\ncopy page linkPrint Page\n\nIsaac Newton is best know for his theory about the law of gravity, but his “Principia Mathematica” (1686) with its three laws of motion greatly influenced the Enlightenment in Europe. Born in 1643 in Woolsthorpe, England, Sir Isaac Newton began developing his theories on light, calculus and celestial mechanics while on break from Cambridge University. \n\nYears of research culminated with the 1687 publication of “Principia,” a landmark work that established the universal laws of motion and gravity. Newton’s second major book, “Opticks,” detailed his experiments to determine the properties of light. Also a student of Biblical history and alchemy, the famed scientist served as president of the Royal Society of London and master of England’s Royal Mint until his death in 1727.\n\nIsaac Newton: Early Life and Education\n\nIsaac Newton was born on January 4, 1643, in Woolsthorpe, Lincolnshire, England. The son of a farmer who died three months before he was born, Newton spent most of his early years with his maternal grandmother after his mother remarried. His education was interrupted by a failed attempt to turn him into a farmer, and he attended the King’s School in Grantham before enrolling at the University of Cambridge’s Trinity College in 1661.\n\nNewton studied a classical curriculum at Cambridge, but he became fascinated by the works of modern philosophers such as René Descartes, even devoting a set of notes to his outside readings he titled “Quaestiones Quaedam Philosophicae” (“Certain Philosophical Questions”). When the Great Plague shuttered Cambridge in 1665, Newton returned home and began formulating his theories on calculus, light and color, his farm the setting for the supposed falling apple that inspired his work on gravity.\n\nHistory Shorts: Isaac Newton's Genius in Quarantine (Forged in Crisis)\n\nIsaac Newton’s Telescope and Studies on Light\n\nNewton returned to Cambridge in 1667 and was elected a minor fellow. He constructed the first reflecting telescope in 1668, and the following year he received his Master of Arts degree and took over as Cambridge’s Lucasian Professor of Mathematics. Asked to give a demonstration of his telescope to the Royal Society of London in 1671, he was elected to the Royal Society the following year and published his notes on optics for his peers.\n\nThrough his experiments with refraction, Newton determined that white light was a composite of all the colors on the spectrum, and he asserted that light was composed of particles instead of waves. His methods drew sharp rebuke from established Society member Robert Hooke, who was unsparing again with Newton’s follow-up paper in 1675. \n\nKnown for his temperamental defense of his work, Newton engaged in heated correspondence with Hooke before suffering a nervous breakdown and withdrawing from the public eye in 1678. In the following years, he returned to his earlier studies on the forces governing gravity and dabbled in alchemy.\n\nIsaac Newton and the Law of Gravity\n\nIn 1684, English astronomer Edmund Halley paid a visit to the secluded Newton. Upon learning that Newton had mathematically worked out the elliptical paths of celestial bodies, Halley urged him to organize his notes. \n\nThe result was the 1687 publication of “Philosophiae Naturalis Principia Mathematica” (Mathematical Principles of Natural Philosophy), which established the three laws of motion and the law of universal gravity. Newton’s three laws of motion state that (1) Every object in a state of uniform motion will remain in that state of motion unless an external force acts on it; (2) Force equals mass times acceleration: F=MA and (3) For every action there is an equal and opposite reaction.\n\n“Principia” propelled Newton to stardom in intellectual circles, eventually earning universal acclaim as one of the most important works of modern science. His work was a foundational part of the European Enlightenment.\n\nWith his newfound influence, Newton opposed the attempts of King James II to reinstitute Catholic teachings at English Universities. King James II was replaced by his protestant daughter Mary and her husband William of Orange as part of the Glorious Revolution of 1688, and Newton was elected to represent Cambridge in Parliament in 1689. \n\nNewton moved to London permanently after being named warden of the Royal Mint in 1696, earning a promotion to master of the Mint three years later. Determined to prove his position wasn’t merely symbolic, Newton moved the pound sterling from the silver to the gold standard and sought to punish counterfeiters.\n\nThe death of Hooke in 1703 allowed Newton to take over as president of the Royal Society, and the following year he published his second major work, “Opticks.” Composed largely from his earlier notes on the subject, the book detailed Newton’s painstaking experiments with refraction and the color spectrum, closing with his ruminations on such matters as energy and electricity. In 1705, he was knighted by Queen Anne of England.\n\nIsaac Newton: Founder of Calculus?\n\nAround this time, the debate over Newton’s claims to originating the field of calculus exploded into a nasty dispute. Newton had developed his concept of “fluxions” (differentials) in the mid 1660s to account for celestial orbits, though there was no public record of his work. \n\nIn the meantime, German mathematician Gottfried Leibniz formulated his own mathematical theories and published them in 1684. As president of the Royal Society, Newton oversaw an investigation that ruled his work to be the founding basis of the field, but the debate continued even after Leibniz’s death in 1716. Researchers later concluded that both men likely arrived at their conclusions independent of one another.\n\nDeath of Isaac Newton\n\nNewton was also an ardent student of history and religious doctrines, and his writings on those subjects were compiled into multiple books that were published posthumously. Having never married, Newton spent his later years living with his niece at Cranbury Park near Winchester, England. He died in his sleep on March 31, 1727, and was buried in Westminster Abbey.\n\nA giant even among the brilliant minds that drove the Scientific Revolution, Newton is remembered as a transformative scholar, inventor and writer. He eradicated any doubts about the heliocentric model of the universe by establishing celestial mechanics, his precise methodology giving birth to what is known as the scientific method. Although his theories of space-time and gravity eventually gave way to those of Albert Einstein, his work remains the bedrock on which modern physics was built.\n\n“If I have seen further it is by standing on the shoulders of Giants.”\n\n“I can calculate the motion of heavenly bodies but not the madness of people.”\n\n“What we know is a drop, what we don't know is an ocean.”\n\n“Gravity explains the motions of the planets, but it cannot explain who sets the planets in motion.”\n\n“No great discovery was ever made without a bold guess.”\n\nHISTORY Vault: Sir Isaac Newton: Gravity of Genius\n\nExplore the life of Sir Isaac Newton, who laid the foundations for calculus and defined the laws of gravity.\n\nSign up for Inside History\n\nGet HISTORY’s most fascinating stories delivered to your inbox three times a week.\n\nBy submitting your information, you agree to receive emails from HISTORY and A+E Networks. You can opt out at any time. You must be 16 years or older and a resident of the United States.\n\nMore details: Privacy Notice | Terms of Use | Contact Us"
+                  snippet: "ShowsThis Day In HistoryScheduleTopicsStories\n\nFind History on Facebook (Opens in a new window)\n\nFind History on Twitter (Opens in a new window)\n\nFind History on YouTube (Opens in a new window)\n\nFind History on Instagram (Opens in a new window)\n\nFind History on TikTok (Opens in a new window)\n\nBy: History.com Editors\n\nUpdated: October 16, 2023 | Original: March 10, 2015\n\ncopy page linkPrint Page\n\nIsaac Newton is best know for his theory about the law of gravity, but his “Principia Mathematica” (1686) with its three laws of motion greatly influenced the Enlightenment in Europe. Born in 1643 in Woolsthorpe, England, Sir Isaac Newton began developing his theories on light, calculus and celestial mechanics while on break from Cambridge University.\n\nYears of research culminated with the 1687 publication of “Principia,” a landmark work that established the universal laws of motion and gravity. Newton’s second major book, “Opticks,” detailed his experiments to determine the properties of light. Also a student of Biblical history and alchemy, the famed scientist served as president of the Royal Society of London and master of England’s Royal Mint until his death in 1727.\n\nIsaac Newton: Early Life and Education\n\nIsaac Newton was born on January 4, 1643, in Woolsthorpe, Lincolnshire, England. The son of a farmer who died three months before he was born, Newton spent most of his early years with his maternal grandmother after his mother remarried. His education was interrupted by a failed attempt to turn him into a farmer, and he attended the King’s School in Grantham before enrolling at the University of Cambridge’s Trinity College in 1661.\n\nNewton studied a classical curriculum at Cambridge, but he became fascinated by the works of modern philosophers such as René Descartes, even devoting a set of notes to his outside readings he titled “Quaestiones Quaedam Philosophicae” (“Certain Philosophical Questions”). When the Great Plague shuttered Cambridge in 1665, Newton returned home and began formulating his theories on calculus, light and color, his farm the setting for the supposed falling apple that inspired his work on gravity.\n\nHistory Shorts: Isaac Newton's Genius in Quarantine (Forged in Crisis)\n\nIsaac Newton’s Telescope and Studies on Light\n\nNewton returned to Cambridge in 1667 and was elected a minor fellow. He constructed the first reflecting telescope in 1668, and the following year he received his Master of Arts degree and took over as Cambridge’s Lucasian Professor of Mathematics. Asked to give a demonstration of his telescope to the Royal Society of London in 1671, he was elected to the Royal Society the following year and published his notes on optics for his peers.\n\nThrough his experiments with refraction, Newton determined that white light was a composite of all the colors on the spectrum, and he asserted that light was composed of particles instead of waves. His methods drew sharp rebuke from established Society member Robert Hooke, who was unsparing again with Newton’s follow-up paper in 1675.\n\nKnown for his temperamental defense of his work, Newton engaged in heated correspondence with Hooke before suffering a nervous breakdown and withdrawing from the public eye in 1678. In the following years, he returned to his earlier studies on the forces governing gravity and dabbled in alchemy.\n\nIsaac Newton and the Law of Gravity\n\nIn 1684, English astronomer Edmund Halley paid a visit to the secluded Newton. Upon learning that Newton had mathematically worked out the elliptical paths of celestial bodies, Halley urged him to organize his notes.\n\nThe result was the 1687 publication of “Philosophiae Naturalis Principia Mathematica” (Mathematical Principles of Natural Philosophy), which established the three laws of motion and the law of universal gravity. Newton’s three laws of motion state that (1) Every object in a state of uniform motion will remain in that state of motion unless an external force acts on it; (2) Force equals mass times acceleration: F=MA and (3) For every action there is an equal and opposite reaction.\n\n“Principia” propelled Newton to stardom in intellectual circles, eventually earning universal acclaim as one of the most important works of modern science. His work was a foundational part of the European Enlightenment.\n\nWith his newfound influence, Newton opposed the attempts of King James II to reinstitute Catholic teachings at English Universities. King James II was replaced by his protestant daughter Mary and her husband William of Orange as part of the Glorious Revolution of 1688, and Newton was elected to represent Cambridge in Parliament in 1689.\n\nNewton moved to London permanently after being named warden of the Royal Mint in 1696, earning a promotion to master of the Mint three years later. Determined to prove his position wasn’t merely symbolic, Newton moved the pound sterling from the silver to the gold standard and sought to punish counterfeiters.\n\nThe death of Hooke in 1703 allowed Newton to take over as president of the Royal Society, and the following year he published his second major work, “Opticks.” Composed largely from his earlier notes on the subject, the book detailed Newton’s painstaking experiments with refraction and the color spectrum, closing with his ruminations on such matters as energy and electricity. In 1705, he was knighted by Queen Anne of England.\n\nIsaac Newton: Founder of Calculus?\n\nAround this time, the debate over Newton’s claims to originating the field of calculus exploded into a nasty dispute. Newton had developed his concept of “fluxions” (differentials) in the mid 1660s to account for celestial orbits, though there was no public record of his work.\n\nIn the meantime, German mathematician Gottfried Leibniz formulated his own mathematical theories and published them in 1684. As president of the Royal Society, Newton oversaw an investigation that ruled his work to be the founding basis of the field, but the debate continued even after Leibniz’s death in 1716. Researchers later concluded that both men likely arrived at their conclusions independent of one another.\n\nDeath of Isaac Newton\n\nNewton was also an ardent student of history and religious doctrines, and his writings on those subjects were compiled into multiple books that were published posthumously. Having never married, Newton spent his later years living with his niece at Cranbury Park near Winchester, England. He died in his sleep on March 31, 1727, and was buried in Westminster Abbey.\n\nA giant even among the brilliant minds that drove the Scientific Revolution, Newton is remembered as a transformative scholar, inventor and writer. He eradicated any doubts about the heliocentric model of the universe by establishing celestial mechanics, his precise methodology giving birth to what is known as the scientific method. Although his theories of space-time and gravity eventually gave way to those of Albert Einstein, his work remains the bedrock on which modern physics was built.\n\n“If I have seen further it is by standing on the shoulders of Giants.”\n\n“I can calculate the motion of heavenly bodies but not the madness of people.”\n\n“What we know is a drop, what we don't know is an ocean.”\n\n“Gravity explains the motions of the planets, but it cannot explain who sets the planets in motion.”\n\n“No great discovery was ever made without a bold guess.”\n\nHISTORY Vault: Sir Isaac Newton: Gravity of Genius\n\nExplore the life of Sir Isaac Newton, who laid the foundations for calculus and defined the laws of gravity.\n\nSign up for Inside History\n\nGet HISTORY’s most fascinating stories delivered to your inbox three times a week.\n\nBy submitting your information, you agree to receive emails from HISTORY and A+E Networks. You can opt out at any time. You must be 16 years or older and a resident of the United States.\n\nMore details: Privacy Notice | Terms of Use | Contact Us"
                   timestamp: '2024-08-13T07:37:00.0000000+00:00'
                   title: 'Isaac Newton ‑ Facts, Biography & Laws'
                   url: https://www.history.com/topics/inventions/isaac-newton
@@ -971,7 +971,7 @@ paths:
                 role: assistant
                 content:
                   - type: text
-                    text: "LLMs stand for Large Language Models, which are a type of neural network model specialized in processing and generating human language. They are designed to understand and respond to natural language input and have become increasingly popular and valuable in recent years.\n\nLLMs are trained on vast amounts of text data, enabling them to learn patterns, grammar, and semantic meanings present in the language. These models can then be used for various natural language processing tasks, such as text generation, summarization, question answering, machine translation, sentiment analysis, and even some aspects of natural language understanding.\n\nSome well-known examples of LLMs include:\n\n1. GPT-3 (Generative Pre-trained Transformer 3) — An open-source LLM developed by OpenAI, capable of generating human-like text and performing various language tasks.\n\n2. BERT (Bidirectional Encoder Representations from Transformers) — A Google-developed LLM that is particularly good at understanding contextual relationships in text, and is widely used for natural language understanding tasks like sentiment analysis and named entity recognition.\n\n3. T5 (Text-to-Text Transfer Transformer) — Also from Google, T5 is a flexible LLM that frames all language tasks as text-to-text problems, where the model learns to generate output text based on input text prompts.\n\n4. RoBERTa (Robustly Optimized BERT Approach) — A variant of BERT that uses additional training techniques to improve performance.\n\n5. DeBERTa (Decoding-enhanced BERT with disentangled attention) — Another variant of BERT that introduces a new attention mechanism.\n\nLLMs have become increasingly powerful and larger in scale, improving the accuracy and sophistication of language tasks. They are also being used as a foundation for developing various applications, including chatbots, content recommendation systems, language translation services, and more. \n\nThe future of LLMs holds the potential for even more sophisticated language technologies, with ongoing research and development focused on enhancing their capabilities, improving efficiency, and exploring their applications in various domains."
+                    text: "LLMs stand for Large Language Models, which are a type of neural network model specialized in processing and generating human language. They are designed to understand and respond to natural language input and have become increasingly popular and valuable in recent years.\n\nLLMs are trained on vast amounts of text data, enabling them to learn patterns, grammar, and semantic meanings present in the language. These models can then be used for various natural language processing tasks, such as text generation, summarization, question answering, machine translation, sentiment analysis, and even some aspects of natural language understanding.\n\nSome well-known examples of LLMs include:\n\n1. GPT-3 (Generative Pre-trained Transformer 3) — An open-source LLM developed by OpenAI, capable of generating human-like text and performing various language tasks.\n\n2. BERT (Bidirectional Encoder Representations from Transformers) — A Google-developed LLM that is particularly good at understanding contextual relationships in text, and is widely used for natural language understanding tasks like sentiment analysis and named entity recognition.\n\n3. T5 (Text-to-Text Transfer Transformer) — Also from Google, T5 is a flexible LLM that frames all language tasks as text-to-text problems, where the model learns to generate output text based on input text prompts.\n\n4. RoBERTa (Robustly Optimized BERT Approach) — A variant of BERT that uses additional training techniques to improve performance.\n\n5. DeBERTa (Decoding-enhanced BERT with disentangled attention) — Another variant of BERT that introduces a new attention mechanism.\n\nLLMs have become increasingly powerful and larger in scale, improving the accuracy and sophistication of language tasks. They are also being used as a foundation for developing various applications, including chatbots, content recommendation systems, language translation services, and more.\n\nThe future of LLMs holds the potential for even more sophisticated language technologies, with ongoing research and development focused on enhancing their capabilities, improving efficiency, and exploring their applications in various domains."
               finish_reason: COMPLETE
               usage:
                 billed_units:
@@ -1558,7 +1558,7 @@ paths:
               id: 6afae9c2-3375-4d0e-8d18-2e9eb7f2c3ec
               generations:
                 - id: 8e6de35d-3007-43ab-9253-ac4f95dcb8a2
-                  text: "LLMs, or Large Language Models, are a type of neural network-based AI model that has been trained on massive amounts of text data and have become ubiquitous in the AI landscape. They possess astounding capabilities for comprehending and generating human-like language.\nThese models leverage neural networks that operate on a large scale, often involving millions or even billions of parameters. This substantial scale enables them to capture intricate patterns and connections within the vast amounts of text they have been trained on.\n\nThe training process for LLMs is fueled by colossal datasets of textual information, ranging from books and articles to websites and conversational transcripts. This extensive training enables them to develop a nuanced understanding of language patterns, grammar, and semantics.\n\nWhen posed with a new text input, LLMs employ their finely honed understanding of language to generate informed responses or undertake tasks such as language translation, text completion, or question answering. They do this by manipulating the input text through adding, removing, or altering elements to craft a desired output.\n\nOne of the underlying principles of their efficacy is the recurrent neural network (RNN) architecture they often adopt. This design enables them to process sequential data like natural language effectively. RNNs possess \"memory\" aspects via loops between layers, which allows them to retain and manipulate information gathered across long sequences, akin to the way humans process information.\n\nHowever, it's their size that arguably constitutes their most notable aspect. The sheer volume of these models – with counts of parameters often exceeding 100 million – enables them to capture correlations and patterns within language data effectively. This empowers them to generate coherent and contextually appropriate responses, posing a remarkable advancement in conversational AI.\n\nWhile LLMs have demonstrated extraordinary language prowess, it's vital to acknowledge their limitations and potential for improvement. Their biases often reflect those of the training data, and they may struggle with logical inconsistencies or factual errors. Ongoing research aims to enhance their robustness, diversity, and overall usability.\n\nIn essence, LLMs are a groundbreaking manifestation of AI's potential to simulate and even extend human language capabilities, while also serving as a testament to the ongoing journey towards refining and perfecting these technologies. "
+                  text: "LLMs, or Large Language Models, are a type of neural network-based AI model that has been trained on massive amounts of text data and have become ubiquitous in the AI landscape. They possess astounding capabilities for comprehending and generating human-like language.\nThese models leverage neural networks that operate on a large scale, often involving millions or even billions of parameters. This substantial scale enables them to capture intricate patterns and connections within the vast amounts of text they have been trained on.\n\nThe training process for LLMs is fueled by colossal datasets of textual information, ranging from books and articles to websites and conversational transcripts. This extensive training enables them to develop a nuanced understanding of language patterns, grammar, and semantics.\n\nWhen posed with a new text input, LLMs employ their finely honed understanding of language to generate informed responses or undertake tasks such as language translation, text completion, or question answering. They do this by manipulating the input text through adding, removing, or altering elements to craft a desired output.\n\nOne of the underlying principles of their efficacy is the recurrent neural network (RNN) architecture they often adopt. This design enables them to process sequential data like natural language effectively. RNNs possess \"memory\" aspects via loops between layers, which allows them to retain and manipulate information gathered across long sequences, akin to the way humans process information.\n\nHowever, it's their size that arguably constitutes their most notable aspect. The sheer volume of these models – with counts of parameters often exceeding 100 million – enables them to capture correlations and patterns within language data effectively. This empowers them to generate coherent and contextually appropriate responses, posing a remarkable advancement in conversational AI.\n\nWhile LLMs have demonstrated extraordinary language prowess, it's vital to acknowledge their limitations and potential for improvement. Their biases often reflect those of the training data, and they may struggle with logical inconsistencies or factual errors. Ongoing research aims to enhance their robustness, diversity, and overall usability.\n\nIn essence, LLMs are a groundbreaking manifestation of AI's potential to simulate and even extend human language capabilities, while also serving as a testament to the ongoing journey towards refining and perfecting these technologies."
               prompt: Please explain to me how LLMs work
               meta:
                 api_version:
@@ -1688,7 +1688,7 @@ paths:
               code: "package embedpost; /* (C)2024 */\n\nimport com.cohere.api.Cohere;\nimport com.cohere.api.requests.EmbedRequest;\nimport com.cohere.api.types.EmbedInputType;\nimport com.cohere.api.types.EmbedResponse;\nimport com.cohere.api.types.EmbeddingType;\nimport java.io.InputStream;\nimport java.net.HttpURLConnection;\nimport java.net.URI;\nimport java.net.URL;\nimport java.util.Base64;\nimport java.util.List;\n\npublic class EmbedImagePost {\n  public static void main(String[] args) {\n    Cohere cohere = Cohere.builder().clientName(\"snippet\").build();\n\n    URL url = URI.toUrl(\"https://cohere.com/favicon-32x32.png\");\n    HttpURLConnection connection = (HttpURLConnection) url.openConnection();\n    connection.connect();\n\n    InputStream inputStream = connection.getInputStream();\n    byte[] buffer = inputStream.readAllBytes();\n    inputStream.close();\n\n    String imageBase64 =\n        String.format(\n            \"data:%s;base64,%s\",\n            connection.getHeaderField(\"Content-Type\"), Base64.getEncoder().encodeToString(buffer));\n\n    EmbedResponse response =\n        cohere.embed(\n            EmbedRequest.builder()\n                .images(List.of(imageBase64))\n                .model(\"embed-english-v3.0\")\n                .inputType(EmbedInputType.IMAGE)\n                .embeddingTypes(List.of(EmbeddingType.FLOAT))\n                .build());\n\n    System.out.println(response);\n  }\n}\n"
             - sdk: curl
               name: Images
-              code: "curl --request POST \\\n  --url https://api.cohere.com/v1/embed \\\n  --header 'accept: application/json' \\\n  --header 'content-type: application/json' \\\n  --header \"Authorization: bearer $CO_API_KEY\" \\\n  --data '{\n    \"model\": \"embed-english-v3.0\",\n    \"input_type\": \"image\", \n    \"embedding_types\": [\"float\"],\n    \"images\": [\"data:image/jpeg;base64,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\"]\n  }'"
+              code: "curl --request POST \\\n  --url https://api.cohere.com/v1/embed \\\n  --header 'accept: application/json' \\\n  --header 'content-type: application/json' \\\n  --header \"Authorization: bearer $CO_API_KEY\" \\\n  --data '{\n    \"model\": \"embed-english-v3.0\",\n    \"input_type\": \"image\",\n    \"embedding_types\": [\"float\"],\n    \"images\": [\"data:image/jpeg;base64,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\"]\n  }'"
           request:
             model: embed-english-v3.0
             input_type: image
@@ -4952,7 +4952,7 @@ paths:
               code: "package embedv2post; /* (C)2024 */\n\nimport com.cohere.api.Cohere;\nimport com.cohere.api.resources.v2.requests.V2EmbedRequest;\nimport com.cohere.api.types.EmbedByTypeResponse;\nimport com.cohere.api.types.EmbedInputType;\nimport com.cohere.api.types.EmbeddingType;\nimport java.io.InputStream;\nimport java.net.HttpURLConnection;\nimport java.net.URI;\nimport java.net.URL;\nimport java.util.Base64;\nimport java.util.List;\n\npublic class EmbedImagePost {\n  public static void main(String[] args) {\n    Cohere cohere = Cohere.builder().clientName(\"snippet\").build();\n\n    URL url = URI.toUrl(\"https://cohere.com/favicon-32x32.png\");\n    HttpURLConnection connection = (HttpURLConnection) url.openConnection();\n    connection.connect();\n\n    InputStream inputStream = connection.getInputStream();\n    byte[] buffer = inputStream.readAllBytes();\n    inputStream.close();\n\n    String imageBase64 =\n        String.format(\n            \"data:%s;base64,%s\",\n            connection.getHeaderField(\"Content-Type\"), Base64.getEncoder().encodeToString(buffer));\n\n    EmbedByTypeResponse response =\n        cohere\n            .v2()\n            .embed(\n                V2EmbedRequest.builder()\n                    .model(\"embed-english-v3.0\")\n                    .inputType(EmbedInputType.IMAGE)\n                    .images(List.of(imageBase64))\n                    .embeddingTypes(List.of(EmbeddingType.FLOAT))\n                    .build());\n\n    System.out.println(response);\n  }\n}\n"
             - sdk: curl
               name: Images
-              code: "curl --request POST \\\n  --url https://api.cohere.com/v2/embed \\\n  --header 'accept: application/json' \\\n  --header 'content-type: application/json' \\\n  --header \"Authorization: bearer $CO_API_KEY\" \\\n  --data '{\n    \"model\": \"embed-english-v3.0\",\n    \"input_type\": \"image\", \n    \"embedding_types\": [\"float\"],\n    \"images\": [\"data:image/jpeg;base64,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\"]\n  }'"
+              code: "curl --request POST \\\n  --url https://api.cohere.com/v2/embed \\\n  --header 'accept: application/json' \\\n  --header 'content-type: application/json' \\\n  --header \"Authorization: bearer $CO_API_KEY\" \\\n  --data '{\n    \"model\": \"embed-english-v3.0\",\n    \"input_type\": \"image\",\n    \"embedding_types\": [\"float\"],\n    \"images\": [\"data:image/jpeg;base64,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\"]\n  }'"
           request:
             model: embed-english-v3.0
             input_type: image
@@ -8602,7 +8602,7 @@ paths:
                   type: array
                   items:
                     type: string
-                  description: "A list of texts that will be compared to the `query`.\nFor optimal performance we recommend against sending more than 1,000 documents in a single request.\n\n**Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.\n\n**Note**: structured data should be formatted as YAML strings for best performance.  "
+                  description: "A list of texts that will be compared to the `query`.\nFor optimal performance we recommend against sending more than 1,000 documents in a single request.\n\n**Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`.\n\n**Note**: structured data should be formatted as YAML strings for best performance."
                   x-fern-audiences:
                     - public
                 top_n: