cohere-openapi.yaml

openapi: 3.1.0
info:
  title: API Reference
  version: "1.0"
  description: |-
    The Cohere Platform is centered around models.

    Each model has a different amount of power (i.e. parameters), to be used for different tasks.

    Cohere also supports finetuning of baseline models to improve performance on downstream tasks or to teach the model large quantities of information which cannot be extracted with few-shot learning or prompt engineering.
  summary: Cohere's API
  contact:
    name: Cohere Developers
    url: https://cohere.com/
    email: team@cohere.com
  termsOfService: https://cohere.com/terms-of-use
  license:
    name: proprietary
    url: https://cohere.com/terms-of-use
servers:
  - url: https://api.cohere.com/v1
    description: production
security:
  - bearerAuth: []
tags:
  - name: /datasets
    description: Datasets API
  - name: /connectors
    description: Connectors API
  - name: /embed-jobs
    description: Embed Jobs API
  - name: /finetuning
    description: Finetuning API (Beta)
paths:
  /chat:
    post:
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-fern-audiences:
        - public
      summary: Chat
      operationId: chat
      x-fern-streaming:
        stream-condition: $request.stream
        response:
          required:
            - text
          x-fern-type-name: NonStreamedChatResponse
          properties:
            text:
              type: string
              x-fern-audiences:
                - public
              minLength: 1
              description: Contents of the reply generated by the model.
            generation_id:
              type: string
              x-fern-audiences:
                - public
              format: uuid
              description: Unique identifier for the generated reply. Useful for submitting
                feedback.
            citations:
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              description: Inline citations for the generated reply.
              items:
                $ref: "#/components/schemas/ChatCitation"
            documents:
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              description: Documents seen by the model when generating the reply.
              items:
                $ref: "#/components/schemas/ChatDocument"
            is_search_required:
              type: boolean
              x-fern-audiences:
                - public
              description: Denotes that a search for documents is required during the RAG
                flow.
            search_queries:
              type: array
              x-fern-audiences:
                - public
              description: Generated search queries, meant to be used as part of the RAG flow.
              items:
                $ref: "#/components/schemas/ChatSearchQuery"
            search_results:
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              description: Documents retrieved from each of the conducted searches.
              items:
                $ref: "#/components/schemas/ChatSearchResult"
            finish_reason:
              x-fern-audiences:
                - public
              $ref: "#/components/schemas/FinishReason"
            tool_calls:
              x-fern-audiences:
                - public
              type: array
              items:
                $ref: "#/components/schemas/ToolCall"
            chat_history:
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              items:
                $ref: "#/components/schemas/Message"
              description: |
                A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's `message`.
            meta:
              $ref: "#/components/schemas/ApiMeta"
              x-fern-audiences:
                - public
        response-stream:
          description: StreamedChatResponse is returned in streaming mode (specified with
            `stream=True` in the request).
          x-fern-type-name: StreamedChatResponse
          oneOf:
            - $ref: "#/components/schemas/ChatStreamStartEvent"
            - $ref: "#/components/schemas/ChatSearchQueriesGenerationEvent"
            - $ref: "#/components/schemas/ChatSearchResultsEvent"
            - $ref: "#/components/schemas/ChatTextGenerationEvent"
            - $ref: "#/components/schemas/ChatCitationGenerationEvent"
            - $ref: "#/components/schemas/ChatToolCallsGenerationEvent"
            - $ref: "#/components/schemas/ChatStreamEndEvent"
            - $ref: "#/components/schemas/ChatToolCallsChunkEvent"
          discriminator:
            propertyName: event_type
            mapping:
              stream-start: "#/components/schemas/ChatStreamStartEvent"
              search-queries-generation: "#/components/schemas/ChatSearchQueriesGenerationEvent"
              search-results: "#/components/schemas/ChatSearchResultsEvent"
              text-generation: "#/components/schemas/ChatTextGenerationEvent"
              citation-generation: "#/components/schemas/ChatCitationGenerationEvent"
              tool-calls-generation: "#/components/schemas/ChatToolCallsGenerationEvent"
              stream-end: "#/components/schemas/ChatStreamEndEvent"
              tool-calls-chunk: "#/components/schemas/ChatToolCallsChunkEvent"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Default
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Chat(
              		context.TODO(),
              		&cohere.ChatRequest{
              			ChatHistory: []*cohere.Message{
              				{
              					Role: "USER",
              					User: &cohere.ChatMessage{
              						Message: "Who discovered gravity?",
              					},
              				},
              				{
              					Role: "CHATBOT",
              					Chatbot: &cohere.ChatMessage{
              						Message: "The man who is widely credited with discovering gravity is Sir Isaac Newton",
              					},
              				}},
              			Message: "What year was he born?",
              			Connectors: []*cohere.ChatConnector{
              				{Id: "web-search"},
              			},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: go
            name: Documents
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"errors"
              	"io"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.ChatStream(
              		context.TODO(),
              		&cohere.ChatStreamRequest{
              			ChatHistory: []*cohere.Message{
              				{
              					Role: "USER",
              					User: &cohere.ChatMessage{
              						Message: "Who discovered gravity?",
              					},
              				},
              				{
              					Role: "CHATBOT",
              					Chatbot: &cohere.ChatMessage{
              						Message: "The man who is widely credited with discovering gravity is Sir Isaac Newton",
              					},
              				}},
              			Message: "What year was he born?",
              			Connectors: []*cohere.ChatConnector{
              				{Id: "web-search"},
              			},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	// Make sure to close the stream when you're done reading.
              	// This is easily handled with defer.
              	defer resp.Close()

              	for {
              		message, err := resp.Recv()

              		if errors.Is(err, io.EOF) {
              			// An io.EOF error means the server is done sending messages
              			// and should be treated as a success.
              			break
              		}

              		if message.TextGeneration != nil {
              			log.Printf("%+v", resp)
              		}
              	}

              }
          - language: go
            name: Streaming
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"errors"
              	"io"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.ChatStream(
              		context.TODO(),
              		&cohere.ChatStreamRequest{
              			ChatHistory: []*cohere.Message{
              				{
              					Role: "USER",
              					User: &cohere.ChatMessage{
              						Message: "Who discovered gravity?",
              					},
              				},
              				{
              					Role: "CHATBOT",
              					Chatbot: &cohere.ChatMessage{
              						Message: "The man who is widely credited with discovering gravity is Sir Isaac Newton",
              					},
              				}},
              			Message: "What year was he born?",
              			Connectors: []*cohere.ChatConnector{
              				{Id: "web-search"},
              			},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	// Make sure to close the stream when you're done reading.
              	// This is easily handled with defer.
              	defer resp.Close()

              	for {
              		message, err := resp.Recv()

              		if errors.Is(err, io.EOF) {
              			// An io.EOF error means the server is done sending messages
              			// and should be treated as a success.
              			break
              		}

              		if message.TextGeneration != nil {
              			log.Printf("%+v", resp)
              		}
              	}

              }
          - language: go
            name: Tools
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Chat(
              		context.TODO(),
              		&cohere.ChatRequest{
              			Message: "Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?",
              			Tools: []*cohere.Tool{
              				{
              					Name:        "query_daily_sales_report",
              					Description: "Connects to a database to retrieve overall sales volumes and sales information for a given day.",
              					ParameterDefinitions: map[string]*cohere.ToolParameterDefinitionsValue{
              						"day": {
              							Description: cohere.String("Retrieves sales data for this day, formatted as YYYY-MM-DD."),
              							Type:        "str",
              							Required:    cohere.Bool(true),
              						},
              					},
              				},
              				{
              					Name:        "query_product_catalog",
              					Description: "Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.",
              					ParameterDefinitions: map[string]*cohere.ToolParameterDefinitionsValue{
              						"category": {
              							Description: cohere.String("Retrieves product information data for all products in this category."),
              							Type:        "str",
              							Required:    cohere.Bool(true),
              						},
              					},
              				},
              			},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Default
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const response = await cohere.chat({
                  chatHistory: [
                    { role: 'USER', message: 'Who discovered gravity?' },
                    {
                      role: 'CHATBOT',
                      message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton',
                    },
                  ],
                  message: 'What year was he born?',
                  // perform web search before answering the question. You can also use your own custom connector.
                  connectors: [{ id: 'web-search' }],
                });

                console.log(response);
              })();
          - language: node
            name: Documents
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const response = await cohere.chat({
                  message: 'Who is more popular: Nsync or Backstreet Boys?',
                  documents: [
                    {
                      title: 'CSPC: Backstreet Boys Popularity Analysis - ChartMasters',
                      snippet:
                        '↓ Skip to Main Content\n\nMusic industry – One step closer to being accurate\n\nCSPC: Backstreet Boys Popularity Analysis\n\nHernán Lopez Posted on February 9, 2017 Posted in CSPC 72 Comments Tagged with Backstreet Boys, Boy band\n\nAt one point, Backstreet Boys defined success: massive albums sales across the globe, great singles sales, plenty of chart topping releases, hugely hyped tours and tremendous media coverage.\n\nIt is true that they benefited from extraordinarily good market conditions in all markets. After all, the all-time record year for the music business, as far as revenues in billion dollars are concerned, was actually 1999. That is, back when this five men group was at its peak.',
                    },
                    {
                      title: 'CSPC: NSYNC Popularity Analysis - ChartMasters',
                      snippet:
                        "↓ Skip to Main Content\n\nMusic industry – One step closer to being accurate\n\nCSPC: NSYNC Popularity Analysis\n\nMJD Posted on February 9, 2018 Posted in CSPC 27 Comments Tagged with Boy band, N'Sync\n\nAt the turn of the millennium three teen acts were huge in the US, the Backstreet Boys, Britney Spears and NSYNC. The latter is the only one we haven’t study so far. It took 15 years and Adele to break their record of 2,4 million units sold of No Strings Attached in its first week alone.\n\nIt wasn’t a fluke, as the second fastest selling album of the Soundscan era prior 2015, was also theirs since Celebrity debuted with 1,88 million units sold.",
                    },
                    {
                      title: 'CSPC: Backstreet Boys Popularity Analysis - ChartMasters',
                      snippet:
                        ' 1997, 1998, 2000 and 2001 also rank amongst some of the very best years.\n\nYet the way many music consumers – especially teenagers and young women’s – embraced their output deserves its own chapter. If Jonas Brothers and more recently One Direction reached a great level of popularity during the past decade, the type of success achieved by Backstreet Boys is in a completely different level as they really dominated the business for a few years all over the world, including in some countries that were traditionally hard to penetrate for Western artists.\n\nWe will try to analyze the extent of that hegemony with this new article with final results which will more than surprise many readers.',
                    },
                    {
                      title: 'CSPC: NSYNC Popularity Analysis - ChartMasters',
                      snippet:
                        ' Was the teen group led by Justin Timberlake really that big? Was it only in the US where they found success? Or were they a global phenomenon?\n\nAs usual, I’ll be using the Commensurate Sales to Popularity Concept in order to relevantly gauge their results. This concept will not only bring you sales information for all NSYNC‘s albums, physical and download singles, as well as audio and video streaming, but it will also determine their true popularity. If you are not yet familiar with the CSPC method, the next page explains it with a short video. I fully recommend watching the video before getting into the sales figures.',
                    },
                  ],
                });

                console.log(response);
              })();
          - language: node
            name: Streaming
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const chatStream = await cohere.chatStream({
                  chatHistory: [
                    { role: 'USER', message: 'Who discovered gravity?' },
                    {
                      role: 'CHATBOT',
                      message: 'The man who is widely credited with discovering gravity is Sir Isaac Newton',
                    },
                  ],
                  message: 'What year was he born?',
                  // perform web search before answering the question. You can also use your own custom connector.
                  connectors: [{ id: 'web-search' }],
                });

                for await (const message of chatStream) {
                  if (message.eventType === 'text-generation') {
                    process.stdout.write(message);
                  }
                }
              })();
          - language: node
            name: Tools
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const response = await cohere.chat({
                  message:
                    "Can you provide a sales summary for 29th September 2023, and also give me some details about the products in the 'Electronics' category, for example their prices and stock levels?",
                  tools: [
                    {
                      name: 'query_daily_sales_report',
                      description:
                        'Connects to a database to retrieve overall sales volumes and sales information for a given day.',
                      parameterDefinitions: {
                        day: {
                          description: 'Retrieves sales data for this day, formatted as YYYY-MM-DD.',
                          type: 'str',
                          required: true,
                        },
                      },
                    },
                    {
                      name: 'query_product_catalog',
                      description:
                        'Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.',
                      parameterDefinitions: {
                        category: {
                          description: 'Retrieves product information data for all products in this category.',
                          type: 'str',
                          required: true,
                        },
                      },
                    },
                  ],
                });

                console.log(response);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.chat(
                  chat_history=[
                      {"role": "USER", "message": "Who discovered gravity?"},
                      {
                          "role": "CHATBOT",
                          "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton",
                      },
                  ],
                  message="What year was he born?",
                  # perform web search before answering the question. You can also use your own custom connector.
                  connectors=[{"id": "web-search"}],
              )


              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  return await co.chat(
                      chat_history=[
                          {"role": "USER", "message": "Who discovered gravity?"},
                          {
                              "role": "CHATBOT",
                              "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton",
                          },
                      ],
                      message="What year was he born?",
                      # perform web search before answering the question. You can also use your own custom connector.
                      connectors=[{"id": "web-search"}],
                  )

              asyncio.run(main())
          - language: python
            name: Documents
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.chat(
                  model="command",
                  message="Who is more popular: Nsync or Backstreet Boys?",
                  documents=[
                      {
                        "title": "CSPC: Backstreet Boys Popularity Analysis - ChartMasters",
                        "snippet": "↓ Skip to Main Content\n\nMusic industry – One step closer to being accurate\n\nCSPC: Backstreet Boys Popularity Analysis\n\nHernán Lopez Posted on February 9, 2017 Posted in CSPC 72 Comments Tagged with Backstreet Boys, Boy band\n\nAt one point, Backstreet Boys defined success: massive albums sales across the globe, great singles sales, plenty of chart topping releases, hugely hyped tours and tremendous media coverage.\n\nIt is true that they benefited from extraordinarily good market conditions in all markets. After all, the all-time record year for the music business, as far as revenues in billion dollars are concerned, was actually 1999. That is, back when this five men group was at its peak."
                      },
                      {
                          "title": "CSPC: NSYNC Popularity Analysis - ChartMasters",
                          "snippet": "↓ Skip to Main Content\n\nMusic industry – One step closer to being accurate\n\nCSPC: NSYNC Popularity Analysis\n\nMJD Posted on February 9, 2018 Posted in CSPC 27 Comments Tagged with Boy band, N'Sync\n\nAt the turn of the millennium three teen acts were huge in the US, the Backstreet Boys, Britney Spears and NSYNC. The latter is the only one we haven’t study so far. It took 15 years and Adele to break their record of 2,4 million units sold of No Strings Attached in its first week alone.\n\nIt wasn’t a fluke, as the second fastest selling album of the Soundscan era prior 2015, was also theirs since Celebrity debuted with 1,88 million units sold."
                      },
                      {
                          "title": "CSPC: Backstreet Boys Popularity Analysis - ChartMasters",
                          "snippet": " 1997, 1998, 2000 and 2001 also rank amongst some of the very best years.\n\nYet the way many music consumers – especially teenagers and young women’s – embraced their output deserves its own chapter. If Jonas Brothers and more recently One Direction reached a great level of popularity during the past decade, the type of success achieved by Backstreet Boys is in a completely different level as they really dominated the business for a few years all over the world, including in some countries that were traditionally hard to penetrate for Western artists.\n\nWe will try to analyze the extent of that hegemony with this new article with final results which will more than surprise many readers."
                      },
                      {
                          "title": "CSPC: NSYNC Popularity Analysis - ChartMasters",
                          "snippet": " Was the teen group led by Justin Timberlake really that big? Was it only in the US where they found success? Or were they a global phenomenon?\n\nAs usual, I’ll be using the Commensurate Sales to Popularity Concept in order to relevantly gauge their results. This concept will not only bring you sales information for all NSYNC‘s albums, physical and download singles, as well as audio and video streaming, but it will also determine their true popularity. If you are not yet familiar with the CSPC method, the next page explains it with a short video. I fully recommend watching the video before getting into the sales figures."
                      }
                  ])

              print(response)
          - language: python
            name: Streaming
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.chat_stream(
                  chat_history=[
                      {"role": "USER", "message": "Who discovered gravity?"},
                      {
                          "role": "CHATBOT",
                          "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton",
                      },
                  ],
                  message="What year was he born?",
                  # perform web search before answering the question. You can also use your own custom connector.
                  connectors=[{"id": "web-search"}],
              )


              for event in response:
                  if event.event_type == "text-generation":
                      print(event.text, end='')
          - language: python
            name: Tools
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              # tool descriptions that the model has access to

              tools = [
                  {
                      "name": "query_daily_sales_report",
                      "description": "Connects to a database to retrieve overall sales volumes and sales information for a given day.",
                      "parameter_definitions": {
                          "day": {
                              "description": "Retrieves sales data for this day, formatted as YYYY-MM-DD.",
                              "type": "str",
                              "required": True
                          }
                      }
                  },
                  {
                      "name": "query_product_catalog",
                      "description": "Connects to a a product catalog with information about all the products being sold, including categories, prices, and stock levels.",
                      "parameter_definitions": {
                          "category": {
                              "description": "Retrieves product information data for all products in this category.",
                              "type": "str",
                              "required": True
                          }
                      }
                  }
              ]



              # user request

              message = "Can you provide a sales summary for 29th September
              2023, and also give me some details about the products in the
              'Electronics' category, for example their prices and stock
              levels?"


              response = co.chat(
                  message=message,
                  tools=tools,
              )


              print(response)
          - language: java
            name: Default
            install: implementation 'com.cohere:cohere-java:1.x.x'
            code: >
              /* (C)2024 */

              package chatpost;


              import com.cohere.api.Cohere;

              import com.cohere.api.requests.ChatRequest;

              import com.cohere.api.types.ChatMessage;

              import com.cohere.api.types.ChatMessageRole;

              import com.cohere.api.types.NonStreamedChatResponse;

              import java.util.List;


              public class Default {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      NonStreamedChatResponse response =
                              cohere.chat(
                                      ChatRequest.builder()
                                              .message("What year was he born?")
                                              .chatHistory(
                                                      List.of(
                                                              ChatMessage.builder()
                                                                      .role(ChatMessageRole.USER)
                                                                      .message("Who discovered gravity?")
                                                                      .build(),
                                                              ChatMessage.builder()
                                                                      .role(ChatMessageRole.CHATBOT)
                                                                      .message(
                                                                              "The man who is widely credited"
                                                                                  + " with discovering gravity is"
                                                                                  + " Sir Isaac Newton")
                                                                      .build()))
                                              .build());

                      System.out.println(response);
                  }
              }
          - language: java
            name: Documents
            install: implementation 'com.cohere:cohere-java:1.x.x'
            code: >
              /* (C)2024 */

              package chatpost;


              import com.cohere.api.Cohere;

              import com.cohere.api.requests.ChatRequest;

              import com.cohere.api.types.NonStreamedChatResponse;

              import java.util.List;

              import java.util.Map;


              public class Documents {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      NonStreamedChatResponse response =
                              cohere.chat(
                                      ChatRequest.builder()
                                              .message("What year was he born?")
                                              .documents(
                                                      List.of(
                                                              Map.of(
                                                                      "title",
                                                                              "CSPC: Backstreet Boys Popularity"
                                                                                  + " Analysis - ChartMasters",
                                                                      "snippet",
                                                                              "↓ Skip to Main Content\n\n"
                                                                                  + "Music industry – One step"
                                                                                  + " closer to being"
                                                                                  + " accurate\n\n"
                                                                                  + "CSPC: Backstreet Boys"
                                                                                  + " Popularity Analysis\n\n"
                                                                                  + "Hernán Lopez Posted on"
                                                                                  + " February 9, 2017 Posted in"
                                                                                  + " CSPC 72 Comments Tagged"
                                                                                  + " with Backstreet Boys, Boy"
                                                                                  + " band\n\n"
                                                                                  + "At one point, Backstreet"
                                                                                  + " Boys defined success:"
                                                                                  + " massive albums sales across"
                                                                                  + " the globe, great singles"
                                                                                  + " sales, plenty of chart"
                                                                                  + " topping releases, hugely"
                                                                                  + " hyped tours and tremendous"
                                                                                  + " media coverage.\n\n"
                                                                                  + "It is true that they"
                                                                                  + " benefited from"
                                                                                  + " extraordinarily good market"
                                                                                  + " conditions in all markets."
                                                                                  + " After all, the all-time"
                                                                                  + " record year for the music"
                                                                                  + " business, as far as"
                                                                                  + " revenues in billion dollars"
                                                                                  + " are concerned, was actually"
                                                                                  + " 1999. That is, back when"
                                                                                  + " this five men group was at"
                                                                                  + " its peak."),
                                                              Map.of(
                                                                      "title",
                                                                              "CSPC: NSYNC Popularity Analysis -"
                                                                                      + " ChartMasters",
                                                                      "snippet",
                                                                              "↓ Skip to Main Content\n\n"
                                                                                  + "Music industry – One step"
                                                                                  + " closer to being"
                                                                                  + " accurate\n\n"
                                                                                  + "CSPC: NSYNC Popularity"
                                                                                  + " Analysis\n\n"
                                                                                  + "MJD Posted on February 9,"
                                                                                  + " 2018 Posted in CSPC 27"
                                                                                  + " Comments Tagged with Boy"
                                                                                  + " band, N'Sync\n\n"
                                                                                  + "At the turn of the"
                                                                                  + " millennium three teen acts"
                                                                                  + " were huge in the US, the"
                                                                                  + " Backstreet Boys, Britney"
                                                                                  + " Spears and NSYNC. The"
                                                                                  + " latter is the only one we"
                                                                                  + " haven’t study so far. It"
                                                                                  + " took 15 years and Adele to"
                                                                                  + " break their record of 2,4"
                                                                                  + " million units sold of No"
                                                                                  + " Strings Attached in its"
                                                                                  + " first week alone.\n\n"
                                                                                  + "It wasn’t a fluke, as the"
                                                                                  + " second fastest selling"
                                                                                  + " album of the Soundscan era"
                                                                                  + " prior 2015, was also theirs"
                                                                                  + " since Celebrity debuted"
                                                                                  + " with 1,88 million units"
                                                                                  + " sold."),
                                                              Map.of(
                                                                      "title",
                                                                              "CSPC: Backstreet Boys Popularity"
                                                                                  + " Analysis - ChartMasters",
                                                                      "snippet",
                                                                              " 1997, 1998, 2000 and 2001 also"
                                                                                  + " rank amongst some of the"
                                                                                  + " very best years.\n\n"
                                                                                  + "Yet the way many music"
                                                                                  + " consumers – especially"
                                                                                  + " teenagers and young women’s"
                                                                                  + " – embraced their output"
                                                                                  + " deserves its own chapter."
                                                                                  + " If Jonas Brothers and more"
                                                                                  + " recently One Direction"
                                                                                  + " reached a great level of"
                                                                                  + " popularity during the past"
                                                                                  + " decade, the type of success"
                                                                                  + " achieved by Backstreet Boys"
                                                                                  + " is in a completely"
                                                                                  + " different level as they"
                                                                                  + " really dominated the"
                                                                                  + " business for a few years"
                                                                                  + " all over the world,"
                                                                                  + " including in some countries"
                                                                                  + " that were traditionally"
                                                                                  + " hard to penetrate for"
                                                                                  + " Western artists.\n\n"
                                                                                  + "We will try to analyze the"
                                                                                  + " extent of that hegemony"
                                                                                  + " with this new article with"
                                                                                  + " final results which will"
                                                                                  + " more than surprise many"
                                                                                  + " readers."),
                                                              Map.of(
                                                                      "title",
                                                                      "CSPC: NSYNC Popularity Analysis -"
                                                                              + " ChartMasters",
                                                                      "snippet",
                                                                      " Was the teen group led by Justin"
                                                                          + " Timberlake really that big? Was it"
                                                                          + " only in the US where they found"
                                                                          + " success? Or were they a global"
                                                                          + " phenomenon?\n\n"
                                                                          + "As usual, I’ll be using the"
                                                                          + " Commensurate Sales to Popularity"
                                                                          + " Concept in order to relevantly"
                                                                          + " gauge their results. This concept"
                                                                          + " will not only bring you sales"
                                                                          + " information for all NSYNC‘s albums,"
                                                                          + " physical and download singles, as"
                                                                          + " well as audio and video streaming,"
                                                                          + " but it will also determine their"
                                                                          + " true popularity. If you are not yet"
                                                                          + " familiar with the CSPC method, the"
                                                                          + " next page explains it with a short"
                                                                          + " video. I fully recommend watching"
                                                                          + " the video before getting into the"
                                                                          + " sales figures.")))
                                              .build());

                      System.out.println(response);
                  }
              }
          - language: java
            name: Streaming
            install: implementation 'com.cohere:cohere-java:1.x.x'
            code: >
              /* (C)2024 */

              package chatpost;


              import com.cohere.api.Cohere;

              import com.cohere.api.requests.ChatStreamRequest;

              import com.cohere.api.types.ChatMessage;

              import com.cohere.api.types.ChatMessageRole;

              import com.cohere.api.types.ChatTextGenerationEvent;

              import com.cohere.api.types.StreamedChatResponse;

              import java.util.List;


              public class Stream {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      Iterable<StreamedChatResponse> response =
                              cohere.chatStream(
                                      ChatStreamRequest.builder()
                                              .message("What year was he born?")
                                              .chatHistory(
                                                      List.of(
                                                              ChatMessage.builder()
                                                                      .role(ChatMessageRole.USER)
                                                                      .message("Who discovered gravity?")
                                                                      .build(),
                                                              ChatMessage.builder()
                                                                      .role(ChatMessageRole.CHATBOT)
                                                                      .message(
                                                                              "The man who is widely credited"
                                                                                  + " with discovering gravity is"
                                                                                  + " Sir Isaac Newton")
                                                                      .build()))
                                              .build());

                      for (StreamedChatResponse chatResponse : response) {
                          if (chatResponse.isTextGeneration()) {
                              System.out.println(
                                      chatResponse
                                              .getTextGeneration()
                                              .map(ChatTextGenerationEvent::getText)
                                              .orElse(""));
                          }
                      }

                      System.out.println(response);
                  }
              }
          - language: java
            name: Tools
            install: implementation 'com.cohere:cohere-java:1.x.x'
            code: >
              /* (C)2024 */

              package chatpost;


              import com.cohere.api.Cohere;

              import com.cohere.api.requests.ChatRequest;

              import com.cohere.api.types.NonStreamedChatResponse;

              import com.cohere.api.types.Tool;

              import com.cohere.api.types.ToolParameterDefinitionsValue;

              import java.util.List;

              import java.util.Map;


              public class Tools {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      NonStreamedChatResponse response =
                              cohere.chat(
                                      ChatRequest.builder()
                                              .message(
                                                      "Can you provide a sales summary for 29th September 2023,"
                                                          + " and also give me some details about the products in"
                                                          + " the 'Electronics' category, for example their"
                                                          + " prices and stock levels?")
                                              .tools(
                                                      List.of(
                                                              Tool.builder()
                                                                      .name("query_daily_sales_report")
                                                                      .description(
                                                                              "Connects to a database to retrieve"
                                                                                  + " overall sales volumes and"
                                                                                  + " sales information for a"
                                                                                  + " given day.")
                                                                      .parameterDefinitions(
                                                                              Map.of(
                                                                                      "day",
                                                                                      ToolParameterDefinitionsValue
                                                                                              .builder()
                                                                                              .type("str")
                                                                                              .description(
                                                                                                      "Retrieves"
                                                                                                          + " sales"
                                                                                                          + " data"
                                                                                                          + " for this"
                                                                                                          + " day,"
                                                                                                          + " formatted"
                                                                                                          + " as YYYY-MM-DD.")
                                                                                              .required(true)
                                                                                              .build()))
                                                                      .build(),
                                                              Tool.builder()
                                                                      .name("query_product_catalog")
                                                                      .description(
                                                                              "Connects to a a product catalog"
                                                                                  + " with information about all"
                                                                                  + " the products being sold,"
                                                                                  + " including categories,"
                                                                                  + " prices, and stock levels.")
                                                                      .parameterDefinitions(
                                                                              Map.of(
                                                                                      "category",
                                                                                      ToolParameterDefinitionsValue
                                                                                              .builder()
                                                                                              .type("str")
                                                                                              .description(
                                                                                                      "Retrieves"
                                                                                                          + " product"
                                                                                                          + " information"
                                                                                                          + " data"
                                                                                                          + " for all"
                                                                                                          + " products"
                                                                                                          + " in this"
                                                                                                          + " category.")
                                                                                              .required(true)
                                                                                              .build()))
                                                                      .build()))
                                              .build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: >-
              curl --request POST \
                --url https://api.cohere.com/v1/chat \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "chat_history": [
                    {"role": "USER", "message": "Who discovered gravity?"},
                    {"role": "CHATBOT", "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton"}
                  ],
                  "message": "What year was he born?",
                  "connectors": [{"id": "web-search"}]
                }'
      description: |
        Generates a text response to a user message.
        To learn how to use the Chat API with Streaming and RAG follow our [Text Generation guides](https://docs.cohere.com/docs/chat-api).
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              x-examples:
                Chat:
                  value:
                    message: Can you give me a global market overview of solar panels?
                    temperature: 0.3
                    stream: false
                    chat_history:
                      - role: User
                        message: Hi!
                      - role: Chatbot
                        message: How can I help you today?
                    prompt_truncation: OFF
                Chat with web search:
                  value:
                    message: Can you give me a global market overview of solar panels?
                    temperature: 0.3
                    stream: false
                    chat_history: []
                    connectors:
                      - id: web-search
                    prompt_truncation: AUTO
                Chat with connector:
                  value:
                    message: How do I expense a meal?
                    stream: false
                    connectors:
                      - id: custom-connector-id
                    prompt_truncation: AUTO
              required:
                - message
              properties:
                message:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |
                    Text input for the model to respond to.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `command-r-plus`.

                    The name of a compatible [Cohere model](https://docs.cohere.com/docs/models) or the ID of a [fine-tuned](https://docs.cohere.com/docs/chat-fine-tuning) model.
                    Compatible Deployments: Cohere Platform, Private Deployments
                stream:
                  description: |
                    Defaults to `false`.

                    When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`.

                    Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  type: boolean
                  x-fern-audiences:
                    - public
                preamble:
                  description: |
                    When specified, the default Cohere preamble will be replaced with the provided one. Preambles are a part of the prompt used to adjust the model's overall behavior and conversation style, and use the `SYSTEM` role.

                    The `SYSTEM` role is also used for the contents of the optional `chat_history=` parameter. When used with the `chat_history=` parameter it adds content throughout a conversation. Conversely, when used with the `preamble=` parameter it adds content at the start of the conversation only.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  type: string
                  x-fern-audiences:
                    - public
                chat_history:
                  type: array
                  x-fern-audiences:
                    - public
                  minItems: 0
                  items:
                    $ref: "#/components/schemas/Message"
                  description: |
                    A list of previous messages between the user and the model, giving the model conversational context for responding to the user's `message`.

                    Each item represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content.

                    The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                conversation_id:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |
                    An alternative to `chat_history`.

                    Providing a `conversation_id` creates or resumes a persisted conversation with the specified ID. The ID can be any non empty string.
                    Compatible Deployments: Cohere Platform
                prompt_truncation:
                  type: string
                  x-fern-audiences:
                    - public
                  enum:
                    - OFF
                    - AUTO
                    - AUTO_PRESERVE_ORDER
                  description: |
                    Defaults to `AUTO` when `connectors` are specified and `OFF` in all other cases.

                    Dictates how the prompt will be constructed.

                    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.

                    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.

                    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.
                    Compatible Deployments: Cohere Platform Only AUTO_PRESERVE_ORDER: Azure, AWS Sagemaker/Bedrock, Private Deployments
                connectors:
                  type: array
                  x-fern-audiences:
                    - public
                  minItems: 0
                  items:
                    $ref: "#/components/schemas/ChatConnector"
                  description: |
                    Accepts `{"id": "web-search"}`, and/or the `"id"` for a custom [connector](https://docs.cohere.com/docs/connectors), if you've [created](https://docs.cohere.com/docs/creating-and-deploying-a-connector) one.

                    When specified, the model's reply will be enriched with information found by querying each of the connectors (RAG).
                    Compatible Deployments: Cohere Platform
                search_queries_only:
                  type: boolean
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `false`.

                    When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                documents:
                  description: |
                    A list of relevant documents that the model can cite to generate a more accurate reply. Each document is a string-string dictionary.

                    Example:
                    `[
                      { "title": "Tall penguins", "text": "Emperor penguins are the tallest." },
                      { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica." },
                    ]`

                    Keys and values from each document will be serialized to a string and passed to the model. The resulting generation will include citations that reference some of these documents.

                    Some suggested keys are "text", "author", and "date". For better generation quality, it is recommended to keep the total word count of the strings in the dictionary to under 300 words.

                    An `id` field (string) can be optionally supplied to identify the document in the citations. This field will not be passed to the model.

                    An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model. The omitted fields will still show up in the citation object. The "_excludes" field will not be passed to the model.

                    See ['Document Mode'](https://docs.cohere.com/docs/retrieval-augmented-generation-rag#document-mode) in the guide for more information.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  type: array
                  x-fern-audiences:
                    - public
                  items:
                    $ref: "#/components/schemas/ChatDocument"
                citation_quality:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `"accurate"`.

                    Dictates the approach taken to generating citations as part of the RAG flow by allowing the user to specify whether they want `"accurate"` results, `"fast"` results or no results.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  enum:
                    - fast
                    - accurate
                    - off
                temperature:
                  type: number
                  x-fern-audiences:
                    - public
                  format: float
                  minimum: 0
                  maximum: 1
                  description: |
                    Defaults to `0.3`.

                    A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations.

                    Randomness can be further maximized by increasing the  value of the `p` parameter.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                max_tokens:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  writeOnly: true
                max_input_tokens:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    The maximum number of input tokens to send to the model. If not specified, `max_input_tokens` is the model's context length limit minus a small buffer.

                    Input will be truncated according to the `prompt_truncation` parameter.
                    Compatible Deployments: Cohere Platform
                  writeOnly: true
                k:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    Ensures only the top `k` most likely tokens are considered for generation at each step.
                    Defaults to `0`, min value of `0`, max value of `500`.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  writeOnly: true
                  default: 0
                  minimum: 0
                  maximum: 500
                p:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
                    Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  writeOnly: true
                  default: 0.75
                  minimum: 0.01
                  maximum: 0.99
                seed:
                  x-fern-audiences:
                    - public
                  minimum: 0
                  maximum: 18446744073709552000
                  type: integer
                  description: |
                    If specified, the backend will make a best effort to sample tokens
                    deterministically, such that repeated requests with the same
                    seed and parameters should return the same result. However,
                    determinism cannot be totally guaranteed.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                stop_sequences:
                  x-fern-audiences:
                    - public
                  writeOnly: true
                  type: array
                  items:
                    type: string
                  description: |
                    A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                frequency_penalty:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `0.0`, min value of `0.0`, max value of `1.0`.

                    Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  writeOnly: true
                presence_penalty:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `0.0`, min value of `0.0`, max value of `1.0`.

                    Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  writeOnly: true
                tools:
                  x-fern-audiences:
                    - public
                  description: |
                    A list of available tools (functions) that the model may suggest invoking before producing a text response.

                    When `tools` is passed (without `tool_results`), the `text` field in the response will be `""` and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  type: array
                  items:
                    $ref: "#/components/schemas/Tool"
                  minItems: 0
                tool_results:
                  x-fern-audiences:
                    - public
                  description: |
                    A list of results from invoking tools recommended by the model in the previous chat turn. Results are used to produce a text response and will be referenced in citations. When using `tool_results`, `tools` must be passed as well.
                    Each tool_result contains information about how it was invoked, as well as a list of outputs in the form of dictionaries.

                    **Note**: `outputs` must be a list of objects. If your tool returns a single object (eg `{"status": 200}`), make sure to wrap it in a list.
                    ```
                    tool_results = [
                      {
                        "call": {
                          "name": <tool name>,
                          "parameters": {
                            <param name>: <param value>
                          }
                        },
                        "outputs": [{
                          <key>: <value>
                        }]
                      },
                      ...
                    ]
                    ```
                    **Note**: Chat calls with `tool_results` should not be included in the Chat history to avoid duplication of the message text.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                  type: array
                  minItems: 0
                  items:
                    $ref: "#/components/schemas/ToolResult"
                force_single_step:
                  x-fern-audiences:
                    - public
                  type: boolean
                  description: Forces the chat to be single step. Defaults to `false`.
                response_format:
                  x-fern-audiences:
                    - public
                  $ref: "#/components/schemas/ResponseFormat"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                oneOf:
                  - $ref: "#/components/schemas/NonStreamedChatResponse"
                  - $ref: "#/components/schemas/StreamedChatResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  /generate:
    post:
      x-fern-audiences:
        - public
      summary: Generate
      operationId: generate
      x-fern-streaming:
        stream-condition: $request.stream
        response:
          type: object
          x-fern-type-name: Generation
          required:
            - id
            - generations
          properties:
            id:
              type: string
              x-fern-audiences:
                - public
            prompt:
              type: string
              x-fern-audiences:
                - public
              description: Prompt used for generations.
            generations:
              type: array
              x-fern-audiences:
                - public
              description: List of generated results
              items:
                type: object
                x-fern-audiences:
                  - public
                $ref: "#/components/schemas/SingleGeneration"
            meta:
              $ref: "#/components/schemas/ApiMeta"
              x-fern-audiences:
                - public
        response-stream:
          description: Response in content type stream when `stream` is `true` in the
            request parameters. Generation tokens are streamed with the
            GenerationStream response. The final response is of type
            GenerationFinalResponse.
          x-fern-type-name: GenerateStreamedResponse
          oneOf:
            - $ref: "#/components/schemas/GenerateStreamText"
            - $ref: "#/components/schemas/GenerateStreamEnd"
            - $ref: "#/components/schemas/GenerateStreamError"
          discriminator:
            propertyName: event_type
            mapping:
              text-generation: "#/components/schemas/GenerateStreamText"
              stream-end: "#/components/schemas/GenerateStreamEnd"
              stream-error: "#/components/schemas/GenerateStreamError"
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"errors"
              	"io"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.GenerateStream(
              		context.TODO(),
              		&cohere.GenerateStreamRequest{
              			Prompt: "Please explain to me how LLMs work",
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	// Make sure to close the stream when you're done reading.
              	// This is easily handled with defer.
              	defer resp.Close()

              	for {
              		message, err := resp.Recv()

              		if errors.Is(err, io.EOF) {
              			// An io.EOF error means the server is done sending messages
              			// and should be treated as a success.
              			break
              		}

              		if message.TextGeneration != nil {
              			log.Printf("%+v", resp)
              		}
              	}
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const generate = await cohere.generate({
                  prompt: 'Please explain to me how LLMs work',
                });

                console.log(generate);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              response = co.generate(
                  prompt="Please explain to me how LLMs work",
              )
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.generate(
                      prompt="Please explain to me how LLMs work",
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.GenerateRequest;

              import com.cohere.api.types.Generation;



              public class GeneratePost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      Generation response = cohere.generate(GenerateRequest.builder().prompt("Please explain to me how LLMs work").build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/generate \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "prompt": "Please explain to me how LLMs work"
                }'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Generation"
            application/stream+json:
              schema:
                $ref: "#/components/schemas/GenerateStreamedResponse"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: |
        > 🚧 Warning
        > 
        > This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.

        Generates realistic text conditioned on a given input.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              x-examples:
                Example:
                  prompt: Please explain to me how LLMs work
                  max_tokens: 50
                  temperature: 1
                  k: 0
                  p: 0.75
              properties:
                prompt:
                  type: string
                  x-fern-audiences:
                    - public
                  example: Please explain to me how LLMs work
                  description: |
                    The input text that serves as the starting point for generating the response.
                    Note: The prompt will be pre-processed and modified before reaching the model.
                  writeOnly: true
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |-
                    The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
                    Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
                  writeOnly: true
                num_generations:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
                  writeOnly: true
                stream:
                  type: boolean
                  x-fern-audiences:
                    - public
                  description: |
                    When `true`, the response will be a JSON stream of events. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.

                    The final event will contain the complete response, and will contain an `is_finished` field set to `true`. The event will also contain a `finish_reason`, which can be one of the following:
                    - `COMPLETE` - the model sent back a finished reply
                    - `MAX_TOKENS` - the reply was cut off because the model reached the maximum number of tokens for its context length
                    - `ERROR` - something went wrong when generating the reply
                    - `ERROR_TOXIC` - the model generated a reply that was deemed toxic
                max_tokens:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.

                    This parameter is off by default, and if it's not specified, the model will continue generating until it emits an EOS completion token. See [BPE Tokens](/bpe-tokens-wiki) for more details.

                    Can only be set to `0` if `return_likelihoods` is set to `ALL` to get the likelihood of the prompt.
                  writeOnly: true
                truncate:
                  type: string
                  x-fern-audiences:
                    - public
                  default: END
                  enum:
                    - NONE
                    - START
                    - END
                  description: |-
                    One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.

                    Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.

                    If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
                  writeOnly: true
                temperature:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations. See [Temperature](/temperature-wiki) for more details.
                    Defaults to `0.75`, min value of `0.0`, max value of `5.0`.
                  writeOnly: true
                seed:
                  x-fern-audiences:
                    - public
                  minimum: 0
                  maximum: 18446744073709552000
                  type: integer
                  description: |
                    If specified, the backend will make a best effort to sample tokens
                    deterministically, such that repeated requests with the same
                    seed and parameters should return the same result. However,
                    determinism cannot be totally guaranteed.
                    Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
                preset:
                  type: string
                  x-fern-audiences:
                    - public
                  example: my-preset-a58sbd
                  description: |
                    Identifier of a custom preset. A preset is a combination of parameters, such as prompt, temperature etc. You can create presets in the [playground](https://dashboard.cohere.com/playground/generate).
                    When a preset is specified, the `prompt` parameter becomes optional, and any included parameters will override the preset's parameters.
                  writeOnly: true
                end_sequences:
                  type: array
                  x-fern-audiences:
                    - public
                  description: The generated text will be cut at the beginning of the earliest
                    occurrence of an end sequence. The sequence will be excluded
                    from the text.
                  items:
                    type: string
                    x-fern-audiences:
                      - public
                  writeOnly: true
                stop_sequences:
                  type: array
                  x-fern-audiences:
                    - public
                  description: The generated text will be cut at the end of the earliest
                    occurrence of a stop sequence. The sequence will be included
                    the text.
                  items:
                    type: string
                    x-fern-audiences:
                      - public
                  writeOnly: true
                k:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: |
                    Ensures only the top `k` most likely tokens are considered for generation at each step.
                    Defaults to `0`, min value of `0`, max value of `500`.
                  writeOnly: true
                p:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.
                    Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
                  writeOnly: true
                frequency_penalty:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.

                    Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
                  writeOnly: true
                presence_penalty:
                  type: number
                  x-fern-audiences:
                    - public
                  description: |
                    Defaults to `0.0`, min value of `0.0`, max value of `1.0`.

                    Can be used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.

                    Using `frequency_penalty` in combination with `presence_penalty` is not supported on newer models.
                  writeOnly: true
                return_likelihoods:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |-
                    One of `GENERATION|ALL|NONE` to specify how and if the token likelihoods are returned with the response. Defaults to `NONE`.

                    If `GENERATION` is selected, the token likelihoods will only be provided for generated text.

                    If `ALL` is selected, the token likelihoods will be provided both for the prompt and the generated text.
                  enum:
                    - GENERATION
                    - ALL
                    - NONE
                  default: NONE
                raw_prompting:
                  type: boolean
                  x-fern-audiences:
                    - public
                  description: When enabled, the user's prompt will be sent to the model without
                    any pre-processing.
              required:
                - prompt
              writeOnly: true
            examples:
              Example:
                value:
                  prompt: Please explain to me how LLMs work
        description: ""
  /embed:
    post:
      x-fern-audiences:
        - public
      summary: Embed
      operationId: embed
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Embed(
              		context.TODO(),
              		&cohere.EmbedRequest{
              			Texts:     []string{"hello", "goodbye"},
              			Model:     cohere.String("embed-english-v3.0"),
              			InputType: cohere.EmbedInputTypeSearchDocument.Ptr(),
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const embed = await cohere.embed({
                  texts: ['hello', 'goodbye'],
                  model: 'embed-english-v3.0',
                  inputType: 'classification',
                });
                console.log(embed);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.embed(
                  texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification"
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.embed(
                      texts=["hello", "goodbye"], model="embed-english-v3.0", input_type="classification"
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.EmbedRequest;

              import com.cohere.api.types.EmbedInputType;

              import com.cohere.api.types.EmbedResponse;


              import java.util.List;



              public class EmbedPost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      EmbedResponse response = cohere.embed(EmbedRequest.builder().texts(List.of("hello", "goodbye")).model("embed-english-v3.0").inputType(EmbedInputType.CLASSIFICATION).build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/embed \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "model": "embed-english-v3.0",
                  "texts": ["hello", "goodbye"],
                  "input_type": "classification"
                }'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                oneOf:
                  - $ref: "#/components/schemas/EmbedFloatsResponse"
                  - $ref: "#/components/schemas/EmbedByTypeResponse"
                discriminator:
                  propertyName: response_type
                  mapping:
                    embeddings_floats: "#/components/schemas/EmbedFloatsResponse"
                    embeddings_by_type: "#/components/schemas/EmbedByTypeResponse"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: |-
        This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents.

        Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.

        If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                texts:
                  type: array
                  x-fern-audiences:
                    - public
                  minItems: 1
                  maxItems: 96
                  description: An array of strings for the model to embed. Maximum number of texts
                    per call is `96`. We recommend reducing the length of each
                    text to be under `512` tokens for optimal quality.
                  items:
                    type: string
                    x-fern-audiences:
                      - public
                    writeOnly: true
                  writeOnly: true
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: |-
                    Defaults to embed-english-v2.0

                    The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.

                    Available models and corresponding embedding dimensions:

                    * `embed-english-v3.0`  1024
                    * `embed-multilingual-v3.0`  1024
                    * `embed-english-light-v3.0`  384
                    * `embed-multilingual-light-v3.0`  384

                    * `embed-english-v2.0`  4096
                    * `embed-english-light-v2.0`  1024
                    * `embed-multilingual-v2.0`  768
                  writeOnly: true
                input_type:
                  x-fern-audiences:
                    - public
                  $ref: "#/components/schemas/EmbedInputType"
                embedding_types:
                  type: array
                  x-fern-audiences:
                    - public
                  items:
                    $ref: "#/components/schemas/EmbeddingType"
                  description: |-
                    Specifies the types of embeddings you want to get back. Not required and default is None, which returns the Embed Floats response type. Can be one or more of the following types.

                    * `"float"`: Use this when you want to get back the default float embeddings. Valid for all models.
                    * `"int8"`: Use this when you want to get back signed int8 embeddings. Valid for only v3 models.
                    * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models.
                    * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for only v3 models.
                    * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for only v3 models.
                  writeOnly: true
                truncate:
                  type: string
                  x-fern-audiences:
                    - public
                  default: END
                  enum:
                    - NONE
                    - START
                    - END
                  description: |-
                    One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.

                    Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.

                    If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
                  writeOnly: true
              required:
                - texts
            examples:
              Example:
                value:
                  texts:
                    - hello
                    - goodbye
        description: ""
  /embed-jobs:
    post:
      x-fern-audiences:
        - public
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      summary: Create an Embed Job
      operationId: create-embed-job
      tags:
        - /embed-jobs
      x-fern-sdk-group-name: embed-jobs
      x-fern-sdk-method-name: create
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.EmbedJobs.Create(
              		context.TODO(),
              		&cohere.CreateEmbedJobRequest{
              			DatasetId: "dataset_id",
              			InputType: cohere.EmbedInputTypeSearchDocument,
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              # start an embed job

              response = co.embed_jobs.create(
                  dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
              )


              # poll the server until the job is complete

              response = co.wait(job)


              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  # start an embed job
                  response = await co.embed_jobs.create(
                      dataset_id=ds.id, input_type="search_document", model="embed-english-v3.0"
                  )

                  # poll the server until the job is complete
                  response = await co.wait(job)

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.embedjobs.requests.CreateEmbedJobRequest;

              import com.cohere.api.types.CreateEmbedJobResponse;

              import com.cohere.api.types.EmbedInputType;



              public class EmbedJobsPost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      CreateEmbedJobResponse response = cohere.embedJobs().create(CreateEmbedJobRequest.builder().model("embed-english-v3.0").datasetId("ds.id").inputType(EmbedInputType.SEARCH_DOCUMENT).build());

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const embedJob = await cohere.embedJobs.create({
                  datasetId: 'my-dataset',
                  inputType: 'search_document',
                  model: 'embed-english-v3.0',
                });

                console.log(embedJob);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/embed-jobs \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "model": "embed-english-v3.0",
                  "dataset_id": "my-dataset"
                }'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateEmbedJobResponse"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: This API launches an async Embed job for a
        [Dataset](https://docs.cohere.com/docs/datasets) of type `embed-input`.
        The result of a completed embed job is new Dataset of type
        `embed-output`, which contains the original text entries and the
        corresponding embeddings.
      requestBody:
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateEmbedJobRequest"
    get:
      x-fern-audiences:
        - public
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      summary: List Embed Jobs
      operationId: list-embed-jobs
      tags:
        - /embed-jobs
      x-fern-sdk-group-name: embed-jobs
      x-fern-sdk-method-name: list
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.EmbedJobs.Get(context.TODO(), "embed_job_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # list embed jobs
              response = co.embed_jobs.list()

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.embed_jobs.list()

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.types.ListEmbedJobResponse;



              public class EmbedJobsGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListEmbedJobResponse response = cohere.embedJobs().list();

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const embedJobs = await cohere.embedJobs.list();

                console.log(embedJobs);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/embed-jobs \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListEmbedJobResponse"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: The list embed job endpoint allows users to view all embed jobs
        history for that specific user.
  "/embed-jobs/{id}":
    get:
      x-fern-audiences:
        - public
      parameters:
        - name: id
          in: path
          required: true
          description: The ID of the embed job to retrieve.
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      summary: Fetch an Embed Job
      operationId: get-embed-job
      tags:
        - /embed-jobs
      x-fern-sdk-group-name: embed-jobs
      x-fern-sdk-method-name: get
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.EmbedJobs.List(context.TODO())

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # get embed job
              response = co.embed_jobs.get("job_id")

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.embed_jobs.get("job_id")

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.types.ListEmbedJobResponse;



              public class EmbedJobsGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListEmbedJobResponse response = cohere.embedJobs().list();

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const embedJob = await cohere.embedJobs.get('job_id');

                console.log(embedJob);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/embed-jobs/id \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/EmbedJob"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning message for potentially incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: This API retrieves the details about an embed job started by the
        same user.
  "/embed-jobs/{id}/cancel":
    post:
      x-fern-audiences:
        - public
      parameters:
        - name: id
          in: path
          required: true
          description: The ID of the embed job to cancel.
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      summary: Cancel an Embed Job
      operationId: cancel-embed-job
      tags:
        - /embed-jobs
      x-fern-sdk-group-name: embed-jobs
      x-fern-sdk-method-name: cancel
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	err := co.EmbedJobs.Cancel(context.TODO(), "embed_job_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # cancel an embed job
              co.embed_jobs.cancel("job_id")
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  await co.embed_jobs.cancel("job_id")
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;



              public class EmbedJobsCancel {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      cohere.embedJobs().cancel("job_id");
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const embedJob = await cohere.embedJobs.cancel('job_id');

                console.log(embedJob);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/embed-jobs/id/cancel \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: OK
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: This API allows users to cancel an active embed job. Once invoked,
        the embedding process will be terminated, and users will be charged for
        the embeddings processed up to the cancellation point. It's important to
        note that partial results will not be available to users after
        cancellation.
  /rerank:
    post:
      x-fern-audiences:
        - public
      summary: Rerank
      operationId: rerank
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  id:
                    type: string
                    x-fern-audiences:
                      - public
                  results:
                    type: array
                    x-fern-audiences:
                      - public
                    description: An ordered list of ranked documents
                    items:
                      type: object
                      x-fern-audiences:
                        - public
                      properties:
                        document:
                          type: object
                          x-fern-audiences:
                            - public
                          description: If `return_documents` is set as `false` this will return none, if
                            `true` it will return the documents passed in
                          properties:
                            text:
                              type: string
                              x-fern-audiences:
                                - public
                              description: The text of the document to rerank
                          required:
                            - text
                        index:
                          type: integer
                          x-fern-audiences:
                            - public
                          description: Corresponds to the index in the original list of documents to which
                            the ranked document belongs. (i.e. if the first
                            value in the `results` object has an `index` value
                            of 3, it means in the list of documents passed in,
                            the document at `index=3` had the highest relevance)
                        relevance_score:
                          type: number
                          x-fern-audiences:
                            - public
                          format: float
                          description: Relevance scores are normalized to be in the range `[0, 1]`. Scores
                            close to `1` indicate a high relevance to the query,
                            and scores closer to `0` indicate low relevance. It
                            is not accurate to assume a score of 0.9 means the
                            document is 2x more relevant than a document with a
                            score of 0.45
                          readOnly: true
                      required:
                        - index
                        - relevance_score
                  meta:
                    $ref: "#/components/schemas/ApiMeta"
                    x-fern-audiences:
                      - public
                required:
                  - results
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: "The identifier of the model to use, one of :
                    `rerank-english-v3.0`, `rerank-multilingual-v3.0`,
                    `rerank-english-v2.0`, `rerank-multilingual-v2.0`"
                query:
                  type: string
                  x-fern-audiences:
                    - public
                  description: The search query
                documents:
                  type: array
                  x-fern-audiences:
                    - public
                  description: |-
                    A list of document objects or strings to rerank.
                    If a document is provided the text fields is required and all other fields will be preserved in the response.

                    The total max chunks (length of documents * max_chunks_per_doc) must be less than 10000.

                    We recommend a maximum of 1,000 documents for optimal endpoint performance.
                  items:
                    oneOf:
                      - type: string
                      - $ref: "#/components/schemas/RerankDocument"
                top_n:
                  type: integer
                  x-fern-audiences:
                    - public
                  minimum: 1
                  description: The number of most relevant documents or indices to return,
                    defaults to the length of the documents
                rank_fields:
                  type: array
                  x-fern-audiences:
                    - public
                  items:
                    type: string
                    x-fern-audiences:
                      - public
                  description: If a JSON object is provided, you can specify which keys you would
                    like to have considered for reranking. The model will rerank
                    based on order of the fields passed in (i.e.
                    rank_fields=['title','author','text'] will rerank using the
                    values in title, author, text  sequentially. If the length
                    of title, author, and text exceeds the context length of the
                    model, the chunking will not re-consider earlier fields). If
                    not provided, the model will use the default text field for
                    ranking.
                return_documents:
                  type: boolean
                  x-fern-audiences:
                    - public
                  default: false
                  description: |-
                    - If false, returns results without the doc text - the api will return a list of {index, relevance score} where index is inferred from the list passed into the request.
                    - If true, returns results with the doc text passed in - the api will return an ordered list of {index, text, relevance score} where index + text refers to the list passed into the request.
                max_chunks_per_doc:
                  type: integer
                  x-fern-audiences:
                    - public
                  description: The maximum number of chunks to produce internally from a document
                  default: 10
              required:
                - query
                - documents
            examples:
              Example:
                value:
                  model: rerank-english-v3.0
                  query: What is the capital of the United States?
                  documents:
                    - Carson City is the capital city of the American state of
                      Nevada.
                    - The Commonwealth of the Northern Mariana Islands is a
                      group of islands in the Pacific Ocean. Its capital is
                      Saipan.
                    - Washington, D.C. (also known as simply Washington or D.C.,
                      and officially as the District of Columbia) is the capital
                      of the United States. It is a federal district.
                    - Capital punishment (the death penalty) has existed in the
                      United States since beforethe United States was a country.
                      As of 2017, capital punishment is legal in 30 of the 50
                      states.
        description: ""
      description: This endpoint takes in a query and a list of texts and produces an
        ordered array with each text assigned a relevance score.
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Rerank(
              		context.TODO(),
              		&cohere.RerankRequest{
              			Query: "What is the capital of the United States?",
              			Documents: []*cohere.RerankRequestDocumentsItem{
              				{String: "Carson City is the capital city of the American state of Nevada."},
              				{String: "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan."},
              				{String: "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages."},
              				{String: "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district."},
              			},
              			Model: cohere.String("rerank-english-v3.0"),
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const rerank = await cohere.rerank({
                  documents: [
                    { text: 'Carson City is the capital city of the American state of Nevada.' },
                    {
                      text: 'The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.',
                    },
                    {
                      text: 'Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.',
                    },
                    {
                      text: 'Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.',
                    },
                    {
                      text: 'Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.',
                    },
                  ],
                  query: 'What is the capital of the United States?',
                  topN: 3,
                  model: 'rerank-english-v3.0',
                });

                console.log(rerank);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              docs = [
                  "Carson City is the capital city of the American state of Nevada.",
                  "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
                  "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.",
                  "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
                  "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
              ]


              response = co.rerank(
                  model="rerank-english-v3.0",
                  query="What is the capital of the United States?",
                  documents=docs,
                  top_n=3,
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")


              docs = [
                  "Carson City is the capital city of the American state of Nevada.",
                  "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
                  "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.",
                  "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
                  "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.",
              ]



              async def main():
                  response = await co.rerank(
                      model="rerank-english-v2.0",
                      query="What is the capital of the United States?",
                      documents=docs,
                      top_n=3,
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.RerankRequest;

              import com.cohere.api.types.RerankRequestDocumentsItem;

              import com.cohere.api.types.RerankResponse;


              import java.util.List;



              public class RerankPost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      RerankResponse response = cohere.rerank(RerankRequest.builder().query("What is the capital of the United States?").documents(List.of(
                              RerankRequestDocumentsItem.of("Carson City is the capital city of the American state of Nevada."),
                              RerankRequestDocumentsItem.of("The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan."),
                              RerankRequestDocumentsItem.of("Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages."),
                              RerankRequestDocumentsItem.of("Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district."),
                              RerankRequestDocumentsItem.of("Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states.")
                      )).model("rerank-english-v3.0").topN(3).build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: >-
              curl --request POST \
                --url https://api.cohere.com/v1/rerank \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "model": "rerank-english-v3.0",
                  "query": "What is the capital of the United States?",
                  "top_n": 3,
                  "documents": ["Carson City is the capital city of the American state of Nevada.",
                                "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
                                "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.",
                                "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.",
                                "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."]
                }'
  /classify:
    post:
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-fern-audiences:
        - public
      summary: Classify
      operationId: classify
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Classify(
              		context.TODO(),
              		&cohere.ClassifyRequest{
              			Examples: []*cohere.ClassifyExample{
              				{
              					Text:  cohere.String("orange"),
              					Label: cohere.String("fruit"),
              				},
              				{
              					Text:  cohere.String("pear"),
              					Label: cohere.String("fruit"),
              				},
              				{
              					Text:  cohere.String("lettuce"),
              					Label: cohere.String("vegetable"),
              				},
              				{
              					Text:  cohere.String("cauliflower"),
              					Label: cohere.String("vegetable"),
              				},
              			},
              			Inputs: []string{"peach"},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const classify = await cohere.classify({
                  examples: [
                    { text: "Dermatologists don't like her!", label: 'Spam' },
                    { text: "'Hello, open to this?'", label: 'Spam' },
                    { text: 'I need help please wire me $1000 right now', label: 'Spam' },
                    { text: 'Nice to know you ;)', label: 'Spam' },
                    { text: 'Please help me?', label: 'Spam' },
                    { text: 'Your parcel will be delivered today', label: 'Not spam' },
                    { text: 'Review changes to our Terms and Conditions', label: 'Not spam' },
                    { text: 'Weekly sync notes', label: 'Not spam' },
                    { text: "'Re: Follow up from today's meeting'", label: 'Not spam' },
                    { text: 'Pre-read for tomorrow', label: 'Not spam' },
                  ],
                  inputs: ['Confirm your email address', 'hey i need u to send some $'],
                });

                console.log(classify);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              from cohere import ClassifyExample


              co = cohere.Client("<<apiKey>>")

              examples = [
                  ClassifyExample(text="Dermatologists don't like her!", label="Spam"),
                  ClassifyExample(text="'Hello, open to this?'", label="Spam"),
                  ClassifyExample(
                      text="I need help please wire me $1000 right now", label="Spam"),
                  ClassifyExample(text="Nice to know you ;)", label="Spam"),
                  ClassifyExample(text="Please help me?", label="Spam"),
                  ClassifyExample(text="Your parcel will be delivered today",
                                  label="Not spam"),
                  ClassifyExample(
                      text="Review changes to our Terms and Conditions", label="Not spam"
                  ),
                  ClassifyExample(text="Weekly sync notes", label="Not spam"),
                  ClassifyExample(text="'Re: Follow up from today's meeting'",
                                  label="Not spam"),
                  ClassifyExample(text="Pre-read for tomorrow", label="Not spam"),
              ]

              inputs = [
                  "Confirm your email address",
                  "hey i need u to send some $",
              ]

              response = co.classify(
                  inputs=inputs,
                  examples=examples,
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio

              from cohere import ClassifyExample


              co = cohere.AsyncClient("<<apiKey>>")

              examples = [
                  ClassifyExample(text="Dermatologists don't like her!", label="Spam"),
                  ClassifyExample(text="'Hello, open to this?'", label="Spam"),
                  ClassifyExample(
                      text="I need help please wire me $1000 right now", label="Spam"),
                  ClassifyExample(text="Nice to know you ;)", label="Spam"),
                  ClassifyExample(text="Please help me?", label="Spam"),
                  ClassifyExample(text="Your parcel will be delivered today",
                                  label="Not spam"),
                  ClassifyExample(
                      text="Review changes to our Terms and Conditions", label="Not spam"
                  ),
                  ClassifyExample(text="Weekly sync notes", label="Not spam"),
                  ClassifyExample(text="'Re: Follow up from today's meeting'",
                                  label="Not spam"),
                  ClassifyExample(text="Pre-read for tomorrow", label="Not spam"),
              ]

              inputs = [
                  "Confirm your email address",
                  "hey i need u to send some $",
              ]



              async def main():
                  response = await co.classify(
                      inputs=inputs,
                      examples=examples,
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.ClassifyRequest;

              import com.cohere.api.types.ClassifyExample;

              import com.cohere.api.types.ClassifyResponse;


              import java.util.List;



              public class ClassifyPost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ClassifyResponse response = cohere.classify(ClassifyRequest.builder().addAllInputs(
                              List.of("Confirm your email address", "hey i need u to send some $")
                      ).examples(List.of(
                              ClassifyExample.builder().text("Dermatologists don't like her!").label("Spam").build(),
                              ClassifyExample.builder().text("'Hello, open to this?'").label("Spam").build(),
                              ClassifyExample.builder().text("I need help please wire me $1000 right now").label("Spam").build(),
                              ClassifyExample.builder().text("Nice to know you ;)").label("Spam").build(),
                              ClassifyExample.builder().text("Please help me?").label("Spam").build(),
                              ClassifyExample.builder().text("Your parcel will be delivered today").label("Not spam").build(),
                              ClassifyExample.builder().text("Review changes to our Terms and Conditions").label("Not spam").build(),
                              ClassifyExample.builder().text("Weekly sync notes").label("Not spam").build(),
                              ClassifyExample.builder().text("'Re: Follow up from today's meeting'").label("Not spam").build(),
                              ClassifyExample.builder().text("Pre-read for tomorrow").label("Not spam").build()
                      )).build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: >-
              curl --request POST \
                --url https://api.cohere.com/v1/classify \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "inputs": ["Confirm your email address", "hey i need u to send some $"],
                  "examples": [
                    {"text": "Dermatologists don'\''t like her!","label": "Spam"},
                    {"text": "'\''Hello, open to this?'\''","label": "Spam"},
                    {"text": "I need help please wire me $1000 right now","label": "Spam"},
                    {"text": "Nice to know you ;)","label": "Spam"},
                    {"text": "Please help me?","label": "Spam"},
                    {"text": "Your parcel will be delivered today","label": "Not spam"},
                    {"text": "Review changes to our Terms and Conditions","label": "Not spam"},
                    {"text": "Weekly sync notes","label": "Not spam"},
                    {"text": "'\''Re: Follow up from today'\''s meeting'\''","label": "Not spam"},
                    {"text": "Pre-read for tomorrow","label": "Not spam"}
                  ]
                }'
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                x-examples:
                  Example:
                    id: ca245541-8841-456e-b306-293370789a30
                    classifications:
                      - id: c50ab0d9-95d8-44de-ba90-9647a1718744
                        input: Confirm your email address
                        prediction: Not spam
                        predictions:
                          - Not spam
                        confidence: 0.7581943
                        confidences:
                          - 0.7581943
                        labels:
                          Not spam:
                            confidence: 0.7581943
                          Spam:
                            confidence: 0.24180566
                        classification_type: single-label
                      - id: 8a841de2-027f-49d4-8c91-e88fa1865b89
                        input: hey i need u to send some $
                        prediction: Spam
                        predictions:
                          - Spam
                        confidence: 0.9965721
                        confidences:
                          - 0.9965721
                        labels:
                          Not spam:
                            confidence: 0.0034279241
                          Spam:
                            confidence: 0.9965721
                        classification_type: single-label
                properties:
                  id:
                    type: string
                    x-fern-audiences:
                      - public
                    readOnly: true
                  classifications:
                    type: array
                    x-fern-audiences:
                      - public
                    items:
                      type: object
                      x-fern-audiences:
                        - public
                      properties:
                        id:
                          type: string
                          x-fern-audiences:
                            - public
                          readOnly: true
                        input:
                          type: string
                          x-fern-audiences:
                            - public
                          description: The input text that was classified
                          readOnly: true
                        prediction:
                          type: string
                          x-fern-audiences:
                            - public
                          description: The predicted label for the associated query (only filled for
                            single-label models)
                          readOnly: true
                          deprecated: true
                        predictions:
                          type: array
                          x-fern-audiences:
                            - public
                          items:
                            type: string
                            x-fern-audiences:
                              - public
                          description: An array containing the predicted labels for the associated query
                            (only filled for single-label classification)
                          readOnly: true
                        confidence:
                          type: number
                          x-fern-audiences:
                            - public
                          format: float
                          description: The confidence score for the top predicted class (only filled for
                            single-label classification)
                          readOnly: true
                          deprecated: true
                        confidences:
                          type: array
                          x-fern-audiences:
                            - public
                          items:
                            type: number
                            x-fern-audiences:
                              - public
                            format: float
                          description: An array containing the confidence scores of all the predictions in
                            the same order
                          readOnly: true
                        labels:
                          type: object
                          x-fern-audiences:
                            - public
                          additionalProperties:
                            type: object
                            x-fern-audiences:
                              - public
                            properties:
                              confidence:
                                type: number
                                x-fern-audiences:
                                  - public
                                format: float
                                readOnly: true
                            readOnly: true
                          description: A map containing each label and its confidence score according to
                            the classifier. All the confidence scores add up to
                            1 for single-label classification. For multi-label
                            classification the label confidences are independent
                            of each other, so they don't have to sum up to 1.
                          readOnly: true
                        classification_type:
                          type: string
                          x-fern-audiences:
                            - public
                          enum:
                            - single-label
                            - multi-label
                          description: The type of classification performed
                          readOnly: true
                      required:
                        - id
                        - predictions
                        - confidences
                        - labels
                        - classification_type
                      readOnly: true
                    readOnly: true
                  meta:
                    $ref: "#/components/schemas/ApiMeta"
                    x-fern-audiences:
                      - public
                required:
                  - id
                  - classifications
              examples:
                Example:
                  value:
                    id: ca245541-8841-456e-b306-293370789a30
                    classifications:
                      - id: c50ab0d9-95d8-44de-ba90-9647a1718744
                        input: Confirm your email address
                        prediction: Not spam
                        predictions:
                          - Not spam
                        confidence: 0.7581943
                        confidences:
                          - 0.7581943
                        labels:
                          Not spam:
                            confidence: 0.7581943
                          Spam:
                            confidence: 0.24180566
                        classification_type: single-label
                      - id: 8a841de2-027f-49d4-8c91-e88fa1865b89
                        input: hey i need u to send some $
                        prediction: Spam
                        predictions:
                          - Spam
                        confidence: 0.9965721
                        confidences:
                          - 0.9965721
                        labels:
                          Not spam:
                            confidence: 0.0034279241
                          Spam:
                            confidence: 0.9965721
                        classification_type: single-label
                    meta:
                      api_version:
                        version: "1"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: |-
        This endpoint makes a prediction about which label fits the specified text inputs best. To make a prediction, Classify uses the provided `examples` of text + label pairs as a reference.
        Note: [Fine-tuned models](https://docs.cohere.com/docs/classify-fine-tuning) trained on classification examples don't require the `examples` parameter to be passed in explicitly.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              x-examples:
                Example:
                  inputs:
                    - Confirm your email address
                    - hey i need u to send some $
                  examples:
                    - text: Dermatologists don't like her!
                      label: Spam
                    - text: Hello, open to this?
                      label: Spam
                    - text: I need help please wire me $1000 right now
                      label: Spam
                    - text: Nice to know you ;)
                      label: Spam
                    - text: Please help me?
                      label: Spam
                    - text: Your parcel will be delivered today
                      label: Not spam
                    - text: Review changes to our Terms and Conditions
                      label: Not spam
                    - text: Weekly sync notes
                      label: Not spam
                    - text: "Re: Follow up from today’s meeting"
                      label: Not spam
                    - text: Pre-read for tomorrow
                      label: Not spam
              properties:
                inputs:
                  type: array
                  x-fern-audiences:
                    - public
                  maxItems: 96
                  description: |-
                    A list of up to 96 texts to be classified. Each one must be a non-empty string.
                    There is, however, no consistent, universal limit to the length a particular input can be. We perform classification on the first `x` tokens of each input, and `x` varies depending on which underlying model is powering classification. The maximum token length for each model is listed in the "max tokens" column [here](https://docs.cohere.com/docs/models).
                    Note: by default the `truncate` parameter is set to `END`, so tokens exceeding the limit will be automatically dropped. This behavior can be disabled by setting `truncate` to `NONE`, which will result in validation errors for longer texts.
                  items:
                    type: string
                    x-fern-audiences:
                      - public
                    writeOnly: true
                  writeOnly: true
                examples:
                  type: array
                  x-fern-audiences:
                    - public
                  description: |-
                    An array of examples to provide context to the model. Each example is a text string and its associated label/class. Each unique label requires at least 2 examples associated with it; the maximum number of examples is 2500, and each example has a maximum length of 512 tokens. The values should be structured as `{text: "...",label: "..."}`.
                    Note: [Fine-tuned Models](https://docs.cohere.com/docs/classify-fine-tuning) trained on classification examples don't require the `examples` parameter to be passed in explicitly.
                  maxItems: 2500
                  items:
                    x-fern-audiences:
                      - public
                    $ref: "#/components/schemas/ClassifyExample"
                  writeOnly: true
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: The identifier of the model. Currently available models are
                    `embed-multilingual-v2.0`, `embed-english-light-v2.0`, and
                    `embed-english-v2.0` (default). Smaller "light" models are
                    faster, while larger models will perform better. [Fine-tuned
                    models](https://docs.cohere.com/docs/fine-tuning) can also
                    be supplied with their full ID.
                  writeOnly: true
                preset:
                  type: string
                  x-fern-audiences:
                    - public
                  example: my-preset-a58sbd
                  description: The ID of a custom playground preset. You can create presets in the
                    [playground](https://dashboard.cohere.com/playground/classify?model=large).
                    If you use a preset, all other parameters become optional,
                    and any included parameters will override the preset's
                    parameters.
                  writeOnly: true
                truncate:
                  type: string
                  x-fern-audiences:
                    - public
                  default: END
                  enum:
                    - NONE
                    - START
                    - END
                  description: |-
                    One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length.
                    Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
                    If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned.
                  writeOnly: true
              required:
                - inputs
            examples:
              Example:
                value:
                  inputs:
                    - Confirm your email address
                    - hey i need u to send some $
                  examples:
                    - text: Dermatologists don't like her!
                      label: Spam
                    - text: Hello, open to this?
                      label: Spam
                    - text: I need help please wire me $1000 right now
                      label: Spam
                    - text: Nice to know you ;)
                      label: Spam
                    - text: Please help me?
                      label: Spam
                    - text: Your parcel will be delivered today
                      label: Not spam
                    - text: Review changes to our Terms and Conditions
                      label: Not spam
                    - text: Weekly sync notes
                      label: Not spam
                    - text: "Re: Follow up from today’s meeting"
                      label: Not spam
                    - text: Pre-read for tomorrow
                      label: Not spam
        description: ""
  /datasets:
    post:
      x-fern-audiences:
        - public
      summary: Create a Dataset
      operationId: create-dataset
      description: Create a dataset by uploading a file. See ['Dataset
        Creation'](https://docs.cohere.com/docs/datasets#dataset-creation) for
        more information.
      tags:
        - /datasets
      x-fern-sdk-group-name: datasets
      x-fern-sdk-method-name: create
      requestBody:
        content:
          multipart/form-data:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                data:
                  type: string
                  x-fern-audiences:
                    - public
                  format: binary
                  description: The file to upload
                eval_data:
                  type: string
                  x-fern-audiences:
                    - public
                  format: binary
                  description: An optional evaluation file to upload
              required:
                - data
      parameters:
        - in: query
          name: name
          required: true
          schema:
            type: string
            x-fern-audiences:
              - public
          description: The name of the uploaded dataset.
        - in: query
          name: type
          required: true
          schema:
            $ref: "#/components/schemas/DatasetType"
          description: The dataset type, which is used to validate the data. Valid types
            are `embed-input`, `reranker-finetune-input`,
            `single-label-classification-finetune-input`, `chat-finetune-input`,
            and `multi-label-classification-finetune-input`.
        - in: query
          name: keep_original_file
          schema:
            type: boolean
            x-fern-audiences:
              - public
          description: Indicates if the original file should be stored.
        - in: query
          name: skip_malformed_input
          schema:
            type: boolean
            x-fern-audiences:
              - public
          description: Indicates whether rows with malformed input should be dropped
            (instead of failing the validation check). Dropped rows will be
            returned in the warnings field.
        - in: query
          name: keep_fields
          schema:
            type: array
            items:
              type: string
              x-fern-audiences:
                - public
          description: List of names of fields that will be persisted in the Dataset. By
            default the Dataset will retain only the required fields indicated
            in the [schema for the corresponding Dataset
            type](https://docs.cohere.com/docs/datasets#dataset-types). For
            example, datasets of type `embed-input` will drop all fields other
            than the required `text` field. If any of the fields in
            `keep_fields` are missing from the uploaded file, Dataset validation
            will fail.
        - in: query
          name: optional_fields
          schema:
            type: array
            items:
              type: string
              x-fern-audiences:
                - public
          description: List of names of fields that will be persisted in the Dataset. By
            default the Dataset will retain only the required fields indicated
            in the [schema for the corresponding Dataset
            type](https://docs.cohere.com/docs/datasets#dataset-types). For
            example, Datasets of type `embed-input` will drop all fields other
            than the required `text` field. If any of the fields in
            `optional_fields` are missing from the uploaded file, Dataset
            validation will pass.
        - in: query
          name: text_separator
          schema:
            type: string
            x-fern-audiences:
              - public
          description: Raw .txt uploads will be split into entries using the
            text_separator value.
        - in: query
          name: csv_delimiter
          schema:
            type: string
            x-fern-audiences:
              - public
          description: The delimiter used for .csv uploads.
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"io"
              	"log"
              	"strings"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              type MyReader struct {
              	io.Reader
              	name string
              }


              func (m *MyReader) Name() string {
              	return m.name
              }


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Datasets.Create(
              		context.TODO(),
              		&MyReader{Reader: strings.NewReader(`{"text": "The quick brown fox jumps over the lazy dog"}`), name: "test.jsonl"},
              		&MyReader{Reader: strings.NewReader(""), name: "a.jsonl"},
              		&cohere.DatasetsCreateRequest{
              			Name: "prompt-completion-dataset",
              			Type: cohere.DatasetTypeEmbedResult,
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # upload a dataset
              response = co.datasets.create(
                  name="prompt-completion-dataset",
                  data=open("./prompt-completion.jsonl", "rb"),
                  type="prompt-completion-finetune-input",
              )

              # wait for validation to complete
              response = co.wait(my_dataset)

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():

                  # upload a dataset
                  response = await co.datasets.create(
                      name="prompt-completion-dataset",
                      data=open("./prompt-completion.jsonl", "rb"),
                      type="prompt-completion-finetune-input",
                  )

                  # wait for validation to complete
                  response = await co.wait(response)

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.datasets.requests.DatasetsCreateRequest;

              import
              com.cohere.api.resources.datasets.types.DatasetsCreateResponse;

              import com.cohere.api.types.DatasetType;


              import java.util.Optional;



              public class DatasetPost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      DatasetsCreateResponse response = cohere.datasets().create(null, Optional.empty(), DatasetsCreateRequest.builder().name("prompt-completion-dataset").type(DatasetType.PROMPT_COMPLETION_FINETUNE_INPUT).build());

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');

              const fs = require('fs');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const file = fs.createReadStream('embed_jobs_sample_data.jsonl'); // {"text": "The quick brown fox jumps over the lazy dog"}

                const dataset = await cohere.datasets.create({ name: 'my-dataset', type: 'embed-input' }, file);

                console.log(dataset);
              })();
          - language: curl
            name: cURL
            code: >-
              curl --request POST \
                --url "https://api.cohere.com/v1/datasets?name=my-dataset&type=generative-finetune-input" \
                --header 'Content-Type: multipart/form-data' \
                --header "Authorization: Bearer $CO_API_KEY" \
                --form file=@./path/to/file.jsonl
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  id:
                    type: string
                    x-fern-audiences:
                      - public
                    description: The dataset ID
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
    get:
      x-fern-audiences:
        - public
      summary: List Datasets
      operationId: list-datasets
      description: List datasets that have been created.
      tags:
        - /datasets
      x-fern-sdk-group-name: datasets
      x-fern-sdk-method-name: list
      parameters:
        - name: datasetType
          in: query
          description: optional filter by dataset type
          schema:
            type: string
            x-fern-audiences:
              - public
        - name: before
          in: query
          description: optional filter before a date
          schema:
            type: string
            x-fern-audiences:
              - public
            format: date-time
        - name: after
          in: query
          description: optional filter after a date
          schema:
            type: string
            x-fern-audiences:
              - public
            format: date-time
        - name: limit
          in: query
          description: optional limit to number of results
          schema:
            type: number
            x-fern-audiences:
              - public
        - name: offset
          in: query
          description: optional offset to start of results
          schema:
            type: number
            x-fern-audiences:
              - public
        - name: validationStatus
          in: query
          description: optional filter by validation status
          schema:
            $ref: "#/components/schemas/DatasetValidationStatus"
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Datasets.Get(context.TODO(), "dataset_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # get dataset
              response = co.datasets.get(id="<<datasetId>>")

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.datasets.get(id="<<datasetId>>")

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.datasets.types.DatasetsGetResponse;



              public class DatasetGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      DatasetsGetResponse response = cohere.datasets().get("dataset_id");

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const datasets = await cohere.datasets.get('<<datasetId>>');

                console.log(datasets);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/datasets \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  datasets:
                    type: array
                    x-fern-audiences:
                      - public
                    items:
                      $ref: "#/components/schemas/Dataset"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  /datasets/usage:
    get:
      x-fern-audiences:
        - public
      summary: Get Dataset Usage
      operationId: get-dataset-usage
      description: View the dataset storage usage for your Organization. Each
        Organization can have up to 10GB of storage across all their users.
      tags:
        - /datasets
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-fern-sdk-group-name: datasets
      x-fern-sdk-method-name: getUsage
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Datasets.GetUsage(context.TODO())

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # get usage
              response = co.datasets.get_usage()

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.datasets.get_usage()

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.datasets.types.DatasetsGetUsageResponse;



              public class DatasetUsageGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      DatasetsGetUsageResponse response = cohere.datasets().getUsage();

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const usage = await cohere.datasets.getUsage('id');

                console.log(usage);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/datasets/usage \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  organization_usage:
                    type: number
                    x-fern-audiences:
                      - public
                    format: int64
                    description: The total number of bytes used by the organization.
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  "/datasets/{id}":
    get:
      x-fern-audiences:
        - public
      summary: Get a Dataset
      operationId: get-dataset
      description: Retrieve a dataset by ID. See
        ['Datasets'](https://docs.cohere.com/docs/datasets) for more
        information.
      tags:
        - /datasets
      x-fern-sdk-group-name: datasets
      x-fern-sdk-method-name: get
      parameters:
        - name: id
          in: path
          required: true
          schema:
            type: string
            x-fern-audiences:
              - public
            pattern: ^(?!usage$).*$
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Datasets.Get(context.TODO(), "dataset_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # get dataset
              response = co.datasets.get(id="<<datasetId>>")

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.datasets.get(id="<<datasetId>>")

                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.datasets.types.DatasetsGetResponse;



              public class DatasetGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      DatasetsGetResponse response = cohere.datasets().get("dataset_id");

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const datasets = await cohere.datasets.get('<<datasetId>>');

                console.log(datasets);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/datasets \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                required:
                  - dataset
                properties:
                  dataset:
                    $ref: "#/components/schemas/Dataset"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
    delete:
      x-fern-audiences:
        - public
      summary: Delete a Dataset
      operationId: delete-dataset
      description: Delete a dataset by ID. Datasets are automatically deleted after 30
        days, but they can also be deleted manually.
      tags:
        - /datasets
      x-fern-sdk-group-name: datasets
      x-fern-sdk-method-name: delete
      parameters:
        - name: id
          in: path
          required: true
          schema:
            type: string
            x-fern-audiences:
              - public
            pattern: ^(?!usage$).*$
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	_, err := co.Datasets.Delete(context.TODO(), "dataset_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")

              # delete dataset
              co.datasets.delete("id")
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  await co.delete_dataset("id")

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;



              public class DatasetDelete {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      cohere.datasets().delete("id");

                  }
              }
          - language: curl
            name: cURL
            code: |-
              curl --request DELETE \
                --url https://api.cohere.com/v1/datasets/id \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  /summarize:
    post:
      x-fern-audiences:
        - public
      summary: Summarize
      operationId: summarize
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Summarize(
              		context.TODO(),
              		&cohere.SummarizeRequest{
              			Text: "the quick brown fox jumped over the lazy dog and then the dog jumped over the fox the quick brown fox jumped over the lazy dog the quick brown fox jumped over the lazy dog the quick brown fox jumped over the lazy dog the quick brown fox jumped over the lazy dog",
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const summarize = await cohere.summarize({
                  text:
                    'Ice cream is a sweetened frozen food typically eaten as a snack or dessert. ' +
                    'It may be made from milk or cream and is flavoured with a sweetener, ' +
                    'either sugar or an alternative, and a spice, such as cocoa or vanilla, ' +
                    'or with fruit such as strawberries or peaches. ' +
                    'It can also be made by whisking a flavored cream base and liquid nitrogen together. ' +
                    'Food coloring is sometimes added, in addition to stabilizers. ' +
                    'The mixture is cooled below the freezing point of water and stirred to incorporate air spaces ' +
                    'and to prevent detectable ice crystals from forming. The result is a smooth, ' +
                    'semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). ' +
                    'It becomes more malleable as its temperature increases.\n\n' +
                    'The meaning of the name "ice cream" varies from one country to another. ' +
                    'In some countries, such as the United States, "ice cream" applies only to a specific variety, ' +
                    'and most governments regulate the commercial use of the various terms according to the ' +
                    'relative quantities of the main ingredients, notably the amount of cream. ' +
                    'Products that do not meet the criteria to be called ice cream are sometimes labelled ' +
                    '"frozen dairy dessert" instead. In other countries, such as Italy and Argentina, ' +
                    'one word is used fo\r all variants. Analogues made from dairy alternatives, ' +
                    "such as goat's or sheep's milk, or milk substitutes " +
                    '(e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are ' +
                    'lactose intolerant, allergic to dairy protein or vegan.',
                });

                console.log(summarize);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              text = (
                  "Ice cream is a sweetened frozen food typically eaten as a snack or dessert. "
                  "It may be made from milk or cream and is flavoured with a sweetener, "
                  "either sugar or an alternative, and a spice, such as cocoa or vanilla, "
                  "or with fruit such as strawberries or peaches. "
                  "It can also be made by whisking a flavored cream base and liquid nitrogen together. "
                  "Food coloring is sometimes added, in addition to stabilizers. "
                  "The mixture is cooled below the freezing point of water and stirred to incorporate air spaces "
                  "and to prevent detectable ice crystals from forming. The result is a smooth, "
                  "semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). "
                  "It becomes more malleable as its temperature increases.\n\n"
                  'The meaning of the name "ice cream" varies from one country to another. '
                  'In some countries, such as the United States, "ice cream" applies only to a specific variety, '
                  "and most governments regulate the commercial use of the various terms according to the "
                  "relative quantities of the main ingredients, notably the amount of cream. "
                  "Products that do not meet the criteria to be called ice cream are sometimes labelled "
                  '"frozen dairy dessert" instead. In other countries, such as Italy and Argentina, '
                  "one word is used fo\r all variants. Analogues made from dairy alternatives, "
                  "such as goat's or sheep's milk, or milk substitutes "
                  "(e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are "
                  "lactose intolerant, allergic to dairy protein or vegan."
              )


              response = co.summarize(
                  text=text,
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")


              text = (
                  "Ice cream is a sweetened frozen food typically eaten as a snack or dessert. "
                  "It may be made from milk or cream and is flavoured with a sweetener, "
                  "either sugar or an alternative, and a spice, such as cocoa or vanilla, "
                  "or with fruit such as strawberries or peaches. "
                  "It can also be made by whisking a flavored cream base and liquid nitrogen together. "
                  "Food coloring is sometimes added, in addition to stabilizers. "
                  "The mixture is cooled below the freezing point of water and stirred to incorporate air spaces "
                  "and to prevent detectable ice crystals from forming. The result is a smooth, "
                  "semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). "
                  "It becomes more malleable as its temperature increases.\n\n"
                  'The meaning of the name "ice cream" varies from one country to another. '
                  'In some countries, such as the United States, "ice cream" applies only to a specific variety, '
                  "and most governments regulate the commercial use of the various terms according to the "
                  "relative quantities of the main ingredients, notably the amount of cream. "
                  "Products that do not meet the criteria to be called ice cream are sometimes labelled "
                  '"frozen dairy dessert" instead. In other countries, such as Italy and Argentina, '
                  "one word is used fo\r all variants. Analogues made from dairy alternatives, "
                  "such as goat's or sheep's milk, or milk substitutes "
                  "(e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are "
                  "lactose intolerant, allergic to dairy protein or vegan."
              )



              async def main():
                  response = await co.summarize(
                      text=text,
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.SummarizeRequest;

              import com.cohere.api.types.SummarizeResponse;



              public class SummarizePost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      SummarizeResponse response = cohere.summarize(SummarizeRequest.builder().text(
                              """
                                            Ice cream is a sweetened frozen food typically eaten as a snack or dessert.\s
                                            It may be made from milk or cream and is flavoured with a sweetener,\s
                                            either sugar or an alternative, and a spice, such as cocoa or vanilla,\s
                                            or with fruit such as strawberries or peaches.\s
                                            It can also be made by whisking a flavored cream base and liquid nitrogen together.\s
                                            Food coloring is sometimes added, in addition to stabilizers.\s
                                            The mixture is cooled below the freezing point of water and stirred to incorporate air spaces\s
                                            and to prevent detectable ice crystals from forming. The result is a smooth,\s
                                            semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F).\s
                                            It becomes more malleable as its temperature increases.\\n\\n
                                            The meaning of the name "ice cream" varies from one country to another.\s
                                            In some countries, such as the United States, "ice cream" applies only to a specific variety,\s
                                            and most governments regulate the commercial use of the various terms according to the\s
                                            relative quantities of the main ingredients, notably the amount of cream.\s
                                            Products that do not meet the criteria to be called ice cream are sometimes labelled\s
                                            "frozen dairy dessert" instead. In other countries, such as Italy and Argentina,\s
                                            one word is used fo\\r all variants. Analogues made from dairy alternatives,\s
                                            such as goat's or sheep's milk, or milk substitutes\s
                                            (e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are\s
                                            lactose intolerant, allergic to dairy protein or vegan.
                                      """
                      ).build());

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: >-
              curl --request POST \
                --url https://api.cohere.com/v1/summarize \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                  --data '{
                  "text": "Ice cream is a sweetened frozen food typically eaten as a snack or dessert. It may be made from milk or cream and is flavoured with a sweetener, either sugar or an alternative, and a spice, such as cocoa or vanilla, or with fruit such as strawberries or peaches. It can also be made by whisking a flavored cream base and liquid nitrogen together. Food coloring is sometimes added, in addition to stabilizers. The mixture is cooled below the freezing point of water and stirred to incorporate air spaces and to prevent detectable ice crystals from forming. The result is a smooth, semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). It becomes more malleable as its temperature increases.\n\nThe meaning of the name \"ice cream\" varies from one country to another. In some countries, such as the United States, \"ice cream\" applies only to a specific variety, and most governments regulate the commercial use of the various terms according to the relative quantities of the main ingredients, notably the amount of cream. Products that do not meet the criteria to be called ice cream are sometimes labelled \"frozen dairy dessert\" instead. In other countries, such as Italy and Argentina, one word is used for all variants. Analogues made from dairy alternatives, such as goat'\''s or sheep'\''s milk, or milk substitutes (e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are lactose intolerant, allergic to dairy protein or vegan."
                }'
      responses:
        "200":
          description: OK
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  id:
                    type: string
                    x-fern-audiences:
                      - public
                    description: Generated ID for the summary
                  summary:
                    type: string
                    x-fern-audiences:
                      - public
                    description: Generated summary for the text
                  meta:
                    $ref: "#/components/schemas/ApiMeta"
                    x-fern-audiences:
                      - public
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: |
        > 🚧 Warning
        > 
        > This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.

        Generates a summary in English for a given text.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                text:
                  type: string
                  x-fern-audiences:
                    - public
                  description: The text to generate a summary for. Can be up to 100,000 characters
                    long. Currently the only supported language is English.
                  minLength: 250
                  maxLength: 50000
                  writeOnly: true
                length:
                  type: string
                  x-fern-audiences:
                    - public
                  description: One of `short`, `medium`, `long`, or `auto` defaults to `auto`.
                    Indicates the approximate length of the summary. If `auto`
                    is selected, the best option will be picked based on the
                    input text.
                  default: medium
                  enum:
                    - short
                    - medium
                    - long
                  writeOnly: true
                format:
                  type: string
                  x-fern-audiences:
                    - public
                  default: paragraph
                  enum:
                    - paragraph
                    - bullets
                  description: One of `paragraph`, `bullets`, or `auto`, defaults to `auto`.
                    Indicates the style in which the summary will be delivered -
                    in a free form paragraph or in bullet points. If `auto` is
                    selected, the best option will be picked based on the input
                    text.
                  writeOnly: true
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: The identifier of the model to generate the summary with. Currently
                    available models are `command` (default), `command-nightly`
                    (experimental), `command-light`, and `command-light-nightly`
                    (experimental). Smaller, "light" models are faster, while
                    larger models will perform better.
                  writeOnly: true
                extractiveness:
                  type: string
                  x-fern-audiences:
                    - public
                  description: One of `low`, `medium`, `high`, or `auto`, defaults to `auto`.
                    Controls how close to the original text the summary is.
                    `high` extractiveness summaries will lean towards reusing
                    sentences verbatim, while `low` extractiveness summaries
                    will tend to paraphrase more. If `auto` is selected, the
                    best option will be picked based on the input text.
                  default: low
                  enum:
                    - low
                    - medium
                    - high
                  writeOnly: true
                temperature:
                  type: number
                  x-fern-audiences:
                    - public
                  description: Ranges from 0 to 5. Controls the randomness of the output. Lower
                    values tend to generate more “predictable” output, while
                    higher values tend to generate more “creative” output. The
                    sweet spot is typically between 0 and 1.
                  default: 0.3
                  minimum: 0
                  maximum: 5
                  writeOnly: true
                additional_command:
                  type: string
                  x-fern-audiences:
                    - public
                  description: A free-form instruction for modifying how the summaries get
                    generated. Should complete the sentence "Generate a summary
                    _". Eg. "focusing on the next steps" or "written by Yoda"
                  writeOnly: true
              required:
                - text
            examples:
              Example:
                value:
                  text: "Ice cream is a sweetened frozen food typically eaten as a snack or
                    dessert. It may be made from milk or cream and is flavoured
                    with a sweetener, either sugar or an alternative, and a
                    spice, such as cocoa or vanilla, or with fruit such as
                    strawberries or peaches. It can also be made by whisking a
                    flavored cream base and liquid nitrogen together. Food
                    coloring is sometimes added, in addition to stabilizers. The
                    mixture is cooled below the freezing point of water and
                    stirred to incorporate air spaces and to prevent detectable
                    ice crystals from forming. The result is a smooth,
                    semi-solid foam that is solid at very low temperatures
                    (below 2 °C or 35 °F). It becomes more malleable as its
                    temperature increases.


                    The meaning of the name \"ice cream\" varies from one
                    country to another. In some countries, such as the United
                    States, \"ice cream\" applies only to a specific variety,
                    and most governments regulate the commercial use of the
                    various terms according to the relative quantities of the
                    main ingredients, notably the amount of cream. Products that
                    do not meet the criteria to be called ice cream are
                    sometimes labelled \"frozen dairy dessert\" instead. In
                    other countries, such as Italy and Argentina, one word is
                    used fo\r all variants. Analogues made from dairy
                    alternatives, such as goat's or sheep's milk, or milk
                    substitutes (e.g., soy, cashew, coconut, almond milk or
                    tofu), are available for those who are lactose intolerant,
                    allergic to dairy protein or vegan."
        description: ""
  /tokenize:
    post:
      x-fern-audiences:
        - public
      summary: Tokenize
      operationId: tokenize
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Tokenize(
              		context.TODO(),
              		&cohere.TokenizeRequest{
              			Text:  "cohere <3",
              			Model: "base",
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const cohere = require('cohere-ai');
              cohere.init('<<apiKey>>')(async () => {
                const response = await cohere.tokenize({
                  text: 'tokenize me! :D',
                  model: 'command', // optional
                });
                console.log(response);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.tokenize(text="tokenize me! :D", model="command")  #
              optional

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.tokenize(text="tokenize me! :D", model="command")
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.TokenizeRequest;

              import com.cohere.api.types.TokenizeResponse;



              public class TokenizePost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      TokenizeResponse response = cohere.tokenize(TokenizeRequest.builder().text("tokenize me").model("command").build());

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere Node.js SDK
            install: npm install cohere-ai
            code: |
              const cohere = require('cohere-ai');
              cohere.init('<<apiKey>>')(async () => {
                const response = await cohere.tokenize({
                  text: 'tokenize me! :D',
                  model: 'command', // optional
                });
                console.log(response);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/tokenize \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "model": "command",
                  "text": "tokenize me! :D"
                }'
      responses:
        "200":
          description: OK
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  tokens:
                    type: array
                    x-fern-audiences:
                      - public
                    description: An array of tokens, where each token is an integer.
                    items:
                      type: integer
                      x-fern-audiences:
                        - public
                      readOnly: true
                    readOnly: true
                  token_strings:
                    type: array
                    x-fern-audiences:
                      - public
                    items:
                      type: string
                      x-fern-audiences:
                        - public
                  meta:
                    $ref: "#/components/schemas/ApiMeta"
                    x-fern-audiences:
                      - public
                required:
                  - tokens
                  - token_strings
              examples:
                Example:
                  value:
                    tokens:
                      - 34160
                      - 974
                      - 514
                      - 34
                      - 1420
                      - 69
                    token_strings:
                      - token
                      - ize'
                      - " me"
                      - "!"
                      - " :"
                      - D
                    meta:
                      api_version:
                        version: "1"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: This endpoint splits input text into smaller units called tokens
        using byte-pair encoding (BPE). To learn more about tokenization and
        byte pair encoding, see the tokens page.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                text:
                  type: string
                  x-fern-audiences:
                    - public
                  description: The string to be tokenized, the minimum text length is 1 character,
                    and the maximum text length is 65536 characters.
                  minLength: 1
                  maxLength: 65536
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: An optional parameter to provide the model name. This will ensure
                    that the tokenization uses the tokenizer used by that model.
              required:
                - text
                - model
            examples:
              Example:
                value:
                  text: tokenize me! :D
                  model: command
        description: ""
  /detokenize:
    post:
      x-fern-audiences:
        - public
      summary: Detokenize
      operationId: detokenize
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - curl
          - node
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Detokenize(
              		context.TODO(),
              		&cohere.DetokenizeRequest{
              			Tokens: []int{10002, 1706, 1722, 5169, 4328},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const detokenize = await cohere.detokenize({
                  tokens: [10002, 2261, 2012, 8, 2792, 43],
                  model: 'command',
                });

                console.log(detokenize);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")


              response = co.detokenize(
                  tokens=[8466, 5169, 2594, 8, 2792, 43], model="command"  # optional
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.detokenize(
                      tokens=[8466, 5169, 2594, 8, 2792, 43], model="command"  # optional
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.requests.DetokenizeRequest;

              import com.cohere.api.types.DetokenizeResponse;


              import java.util.List;



              public class DetokenizePost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      DetokenizeResponse response = cohere.detokenize(
                              DetokenizeRequest.builder().model("command").tokens(List.of(8466, 5169, 2594, 8, 2792, 43)).build()
                      );

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/detokenize \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "model": "command",
                  "tokens": [8466, 5169, 2594, 8, 2792, 43]
                }'
      responses:
        "200":
          description: OK
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
          content:
            application/json:
              schema:
                type: object
                x-fern-audiences:
                  - public
                properties:
                  text:
                    type: string
                    x-fern-audiences:
                      - public
                    description: A string representing the list of tokens.
                    readOnly: true
                  meta:
                    $ref: "#/components/schemas/ApiMeta"
                    x-fern-audiences:
                      - public
                required:
                  - text
              examples:
                Example:
                  value:
                    text: detokenized! :D
                    meta:
                      api_version:
                        version: "1"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      description: This endpoint takes tokens using byte-pair encoding and returns
        their text representation. To learn more about tokenization and byte
        pair encoding, see the tokens page.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              x-fern-audiences:
                - public
              properties:
                tokens:
                  type: array
                  x-fern-audiences:
                    - public
                  description: The list of tokens to be detokenized.
                  minItems: 1
                  maxItems: 65536
                  items:
                    type: integer
                    x-fern-audiences:
                      - public
                  writeOnly: true
                model:
                  type: string
                  x-fern-audiences:
                    - public
                  description: An optional parameter to provide the model name. This will ensure
                    that the detokenization is done by the tokenizer used by
                    that model.
              required:
                - tokens
                - model
            examples:
              Example:
                value:
                  tokens:
                    - 10104
                    - 12221
                    - 1315
                    - 34
                    - 1420
                    - 69
                  model: command
        description: ""
    parameters: []
  /connectors:
    get:
      x-fern-audiences:
        - public
      summary: List Connectors
      tags:
        - /connectors
      operationId: list-connectors
      x-fern-sdk-method-name: list
      x-fern-sdk-group-name: connectors
      description: Returns a list of connectors ordered by descending creation date
        (newer first). See ['Managing your
        Connector'](https://docs.cohere.com/docs/managing-your-connector) for
        more information.
      parameters:
        - name: limit
          in: query
          description: Maximum number of connectors to return [0, 100].
          schema:
            default: 30
            type: number
            x-fern-audiences:
              - public
        - name: offset
          in: query
          description: Number of connectors to skip before returning results [0, inf].
          schema:
            default: 0
            type: number
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListConnectorsResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.List(
              		context.TODO(),
              		&cohere.ConnectorsListRequest{})

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.connectors.list()
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.connectors.list()
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.types.ListConnectorsResponse;



              public class ConnectorsList {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListConnectorsResponse list = cohere.connectors().list();

                      System.out.println(list);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const connectors = await cohere.connectors.list();

                console.log(connectors);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/connectors \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
    post:
      x-fern-audiences:
        - public
      summary: Create a Connector
      tags:
        - /connectors
      operationId: create-connector
      x-fern-sdk-method-name: create
      x-fern-sdk-group-name: connectors
      description: Creates a new connector. The connector is tested during
        registration and will cancel registration when the test is unsuccessful.
        See ['Creating and Deploying a
        Connector'](https://docs.cohere.com/docs/creating-and-deploying-a-connector)
        for more information.
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      requestBody:
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateConnectorRequest"
        required: true
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateConnectorResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.Create(
              		context.TODO(),
              		&cohere.CreateConnectorRequest{
              			Name: "Example connector",
              			Url:  "https://you-connector-url",
              			ServiceAuth: &cohere.CreateConnectorServiceAuth{
              				Token: "dummy-connector-token",
              				Type:  "bearer",
              			},
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.connectors.create(
                  name="Example connector",
                  url="https://connector-example.com/search",
              )
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.connectors.create(
                      name="Example connector",
                      url="https://connector-example.com/search",
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.connectors.requests.CreateConnectorRequest;

              import com.cohere.api.types.CreateConnectorResponse;



              public class ConnectorCreate {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      CreateConnectorResponse response = cohere.connectors().create(CreateConnectorRequest.builder()
                              .name("Example connector")
                              .url("https://connector-example.com/search").build());

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const connector = await cohere.connectors.create({
                  name: 'test-connector',
                  url: 'https://example.com/search',
                  description: 'A test connector',
                });

                console.log(connector);
              })();
          - language: curl
            name: Curl
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/connectors \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "name": "Example connector",
                  "url": "https://connector-example.com/search"
                }'
  "/connectors/{id}":
    get:
      x-fern-audiences:
        - public
      summary: Get a Connector
      tags:
        - /connectors
      description: Retrieve a connector by ID. See
        ['Connectors'](https://docs.cohere.com/docs/connectors) for more
        information.
      operationId: get-connector
      x-fern-sdk-method-name: get
      x-fern-sdk-group-name: connectors
      parameters:
        - name: id
          in: path
          required: true
          description: The ID of the connector to retrieve.
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/GetConnectorResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.Get(context.TODO(), "connector_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.connectors.get("test-id")
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.connectors.get("test-id")
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.types.GetConnectorResponse;



              public class ConnectorGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      GetConnectorResponse response = cohere.connectors().get("test-id");

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const connector = await cohere.connectors.get('connector-id');

                console.log(connector);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/connectors/id \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
    patch:
      x-fern-audiences:
        - public
      summary: Update a Connector
      tags:
        - /connectors
      operationId: update-connector
      x-fern-sdk-method-name: update
      x-fern-sdk-group-name: connectors
      description: Update a connector by ID. Omitted fields will not be updated. See
        ['Managing your
        Connector'](https://docs.cohere.com/docs/managing-your-connector) for
        more information.
      parameters:
        - name: id
          in: path
          required: true
          description: The ID of the connector to update.
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      requestBody:
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/UpdateConnectorRequest"
        required: true
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/UpdateConnectorResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.Update(
              		context.TODO(),
              		"connector_id",
              		&cohere.UpdateConnectorRequest{
              			Name: cohere.String("Example connector renamed"),
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")

              response = co.connectors.update(
                  connector_id="test-id", name="new name", url="https://example.com/search"
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.connectors.update(
                      connector_id="test-id", name="new name", url="https://example.com/search"
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.connectors.requests.UpdateConnectorRequest;



              public class ConnectorPatch {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      cohere.connectors().update("test-id", UpdateConnectorRequest.builder()
                              .name("new name")
                              .url("https://connector-example.com/search").build());
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const connector = await cohere.connectors.update(connector.id, {
                  name: 'test-connector-renamed',
                  description: 'A test connector renamed',
                });

                console.log(connector);
              })();
          - language: curl
            name: Curl
            code: |-
              curl --request PATCH \
                --url https://api.cohere.com/v1/connectors/id \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                  "name": "new name",
                  "url": "https://example.com/search"
                }'
    delete:
      x-fern-audiences:
        - public
      summary: Delete a Connector
      tags:
        - /connectors
      description: Delete a connector by ID. See
        ['Connectors'](https://docs.cohere.com/docs/connectors) for more
        information.
      operationId: delete-connector
      x-fern-sdk-method-name: delete
      x-fern-sdk-group-name: connectors
      parameters:
        - name: id
          description: The ID of the connector to delete.
          in: path
          required: true
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/DeleteConnectorResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.Delete(context.TODO(), "connector_id")

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                await cohere.connectors.delete('connector-id');
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              co.connectors.delete("test-id")
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  await co.connectors.delete("test-id")

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >+
              import com.cohere.api.Cohere;



              public class ConnectorDelete {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      cohere.connectors().delete("test-id");
                  }
              }

          - language: curl
            name: cURL
            code: |-
              curl --request DELETE \
                --url https://api.cohere.com/v1/connectors/id \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
  "/connectors/{id}/oauth/authorize":
    post:
      x-fern-audiences:
        - public
      summary: Authorize with oAuth
      tags:
        - /connectors
      description: Authorize the connector with the given ID for the connector oauth
        app.  See ['Connector
        Authentication'](https://docs.cohere.com/docs/connector-authentication)
        for more information.
      operationId: oAuthAuthorize-connector
      x-fern-sdk-method-name: oAuthAuthorize
      x-fern-sdk-group-name: connectors
      parameters:
        - name: id
          in: path
          required: true
          description: The ID of the connector to authorize.
          schema:
            type: string
            x-fern-audiences:
              - public
        - name: after_token_redirect
          in: query
          description: The URL to redirect to after the connector has been authorized.
          schema:
            type: string
            x-fern-audiences:
              - public
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/OAuthAuthorizeResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - python
          - java
          - python
          - java
          - node
          - curl
          - go
        code-samples:
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Connectors.OAuthAuthorize(
              		context.TODO(),
              		"connector_id",
              		&cohere.ConnectorsOAuthAuthorizeRequest{
              			AfterTokenRedirect: cohere.String("https://test.com"),
              		},
              	)

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: >
              import cohere


              co = cohere.Client("<<apiKey>>")

              response = co.connectors.o_auth_authorize(
                  connector_id="test-id", after_token_redirect="https://test.com"
              )

              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.connectors.o_auth_authorize(
                      connector_id="test-id", after_token_redirect="https://test.com"
                  )
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.connectors.requests.ConnectorsOAuthAuthorizeRequest;

              import com.cohere.api.types.OAuthAuthorizeResponse;



              public class ConnectorsIdOauthAuthorizePost {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      OAuthAuthorizeResponse response = cohere.connectors().oAuthAuthorize("test-id", ConnectorsOAuthAuthorizeRequest.builder().afterTokenRedirect("https://connector-example.com/search").build());

                      System.out.println(response);
                  }
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const connector = await cohere.connectors.oAuthAuthorize('connector-id', {
                  redirect_uri: 'https://example.com/oauth/callback',
                });

                console.log(connector);
              })();
          - language: curl
            name: cURL
            code: |-
              curl --request POST \
                --url https://api.cohere.com/v1/connectors/id/oauth/authorize \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
  "/models/{model}":
    get:
      summary: Get a Model
      tags:
        - /models
      operationId: get-model
      x-fern-sdk-method-name: get
      x-fern-sdk-group-name: models
      x-fern-audiences:
        - public
      description: Returns the details of a model, provided its name.
      parameters:
        - name: model
          in: path
          required: true
          schema:
            type: string
            x-fern-audiences:
              - public
            example: command-r
        - $ref: "#/components/parameters/RequestSource"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/GetModelResponse"
          headers:
            X-API-Warning:
              schema:
                type: string
                x-fern-audiences:
                  - public
              description: Warning description for incorrect usage of the API
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  /models:
    get:
      x-fern-audiences:
        - public
      summary: List Models
      tags:
        - /models
      operationId: list-models
      x-fern-sdk-method-name: list
      x-fern-sdk-group-name: models
      description: Returns a list of models available for use. The list contains
        models from Cohere as well as your fine-tuned models.
      parameters:
        - name: page_size
          in: query
          description: |-
            Maximum number of models to include in a page
            Defaults to `20`, min value of `1`, max value of `1000`.
          schema:
            type: number
            x-fern-audiences:
              - public
        - name: page_token
          description: Page token provided in the `next_page_token` field of a previous
            response.
          in: query
          schema:
            type: string
            x-fern-audiences:
              - public
        - name: endpoint
          in: query
          description: When provided, filters the list of models to only those that are
            compatible with the specified endpoint.
          schema:
            $ref: "#/components/schemas/CompatibleEndpoint"
            x-fern-audiences:
              - public
        - name: default_only
          in: query
          description: When provided, filters the list of models to only the default model
            to the endpoint. This parameter is only valid when `endpoint` is
            provided.
          schema:
            type: boolean
            x-fern-audiences:
              - public
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListModelsResponse"
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
      x-readme:
        samples-languages:
          - node
          - curl
          - python
          - java
          - go
        code-samples:
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.models.list()
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.models.list()
                  print(response)

              asyncio.run(main())
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java:1.0.4'
            code: >
              import com.cohere.api.Cohere;

              import com.cohere.api.types.ListModelsResponse;



              public class ModelsListGet {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListModelsResponse response = cohere.models().list();

                      System.out.println(response);
                  }
              }
          - language: curl
            name: cURL
            code: |-
              curl --request GET \
                --url https://api.cohere.com/v1/models \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                const models = await cohere.models.list();

                console.log(models);
              })();
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	client "github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Models.List(context.TODO(), &cohere.ModelsListRequest{})

              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp)
              }
  /check-api-key:
    post:
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-fern-audiences:
        - public
      summary: Check API key
      operationId: checkAPIKey
      description: |
        Checks that the api key in the Authorization header is valid and active
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                type: object
                required:
                  - valid
                properties:
                  valid:
                    type: boolean
                  organization_id:
                    type: string
                  owner_id:
                    type: string
        "400":
          $ref: "#/components/responses/BadRequest"
        "401":
          $ref: "#/components/responses/Unauthorized"
        "403":
          $ref: "#/components/responses/Forbidden"
        "404":
          $ref: "#/components/responses/NotFound"
        "422":
          $ref: "#/components/responses/UnprocessableEntity"
        "429":
          $ref: "#/components/responses/RateLimit"
        "498":
          $ref: "#/components/responses/InvalidToken"
        "499":
          $ref: "#/components/responses/RequestCancelled"
        "500":
          $ref: "#/components/responses/InternalServerError"
        "501":
          $ref: "#/components/responses/NotImplemented"
        "503":
          $ref: "#/components/responses/ServiceUnavailable"
        "504":
          $ref: "#/components/responses/GatewayTimeout"
  /finetuning/finetuned-models:
    get:
      summary: Lists fine-tuned models.
      operationId: ListFinetunedModels
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListFinetunedModelsResponse"
              example:
                finetuned_models:
                  - id: fee37446-7fc7-42f9-a026-c6ba2fcc422d
                    name: prompt-completion-ft
                    creator_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                    organization_id: 6bdca3d5-3eae-4de0-ac34-786d8063b7ee
                    settings:
                      base_model:
                        name: medium
                        version: 14.2.0
                        base_type: BASE_TYPE_GENERATIVE
                        strategy: STRATEGY_TFEW
                      dataset_id: my-dataset-d701tr
                      hyperparameters:
                        early_stopping_patience: 6
                        early_stopping_threshold: 0.01
                        train_batch_size: 16
                        train_epochs: 1
                        learning_rate: 0.01
                    status: STATUS_READY
                    created_at: 2024-01-17T20:11:42.907112Z
                    updated_at: 2024-01-17T20:31:06.464015Z
                    completed_at: 2024-01-17T20:31:05.047066Z
                  - id: 9d927c5e-7598-4772-98b7-cdf2014e8874
                    name: rerank-ft
                    creator_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                    organization_id: 6bdca3d5-3eae-4de0-ac34-786d8063b7ee
                    settings:
                      base_model:
                        name: english
                        version: 2.0.0
                        base_type: BASE_TYPE_RERANK
                        strategy: STRATEGY_VANILLA
                      dataset_id: rerank-dataset-d820xf
                    status: STATUS_READY
                    created_at: 2024-01-17T20:17:16.997608Z
                    updated_at: 2024-01-17T20:50:11.148769Z
                    completed_at: 2024-01-17T20:42:55.821051Z
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: page_size
          description: |-
            Maximum number of results to be returned by the server. If 0, defaults to
            50.
          in: query
          required: false
          schema:
            type: integer
            format: int32
        - name: page_token
          description: Request a specific page of the list results.
          in: query
          required: false
          schema:
            type: string
        - name: order_by
          description: |-
            Comma separated list of fields. For example: "created_at,name". The default
            sorting order is ascending. To specify descending order for a field, append
            " desc" to the field name. For example: "created_at desc,name".

            Supported sorting fields:
              - created_at (default)
          in: query
          required: false
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >
              package finetuning;


              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.finetuning.finetuning.types.ListFinetunedModelsResponse;



              public class ListFinetunedModels {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListFinetunedModelsResponse response = cohere.finetuning().listFinetunedModels();

                      System.out.println(response);
                  }
              }
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.ListFinetunedModels(context.TODO(), nil)
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.FinetunedModels)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const finetunedModels = await cohere.finetuning.listFinetunedModels();

                console.log(finetunedModels);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.finetuning.list_finetuned_models()
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.finetuning.list_finetuned_models()
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: |
              curl --request GET \
                --url https://api.cohere.com/v1/finetuning/finetuned-models \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
      x-fern-request-name: FinetuningListFinetunedModelsRequest
    post:
      summary: Trains and deploys a fine-tuned model.
      operationId: CreateFinetunedModel
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateFinetunedModelResponse"
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      tags:
        - /finetuning
      requestBody:
        description: Information about the fine-tuned model. Must contain name and settings.
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/FinetunedModel"
              required:
                - finetuned_model
            example:
              name: api-test
              settings:
                base_model:
                  base_type: BASE_TYPE_GENERATIVE
                dataset_id: my-dataset-id
        required: true
      parameters:
        - $ref: "#/components/parameters/RequestSource"
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >+
              package finetuning;


              import com.cohere.api.Cohere;

              import com.cohere.api.resources.finetuning.finetuning.types.*;


              public class CreateFinetunedModel {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      CreateFinetunedModelResponse response = cohere.finetuning().createFinetunedModel(FinetunedModel.builder().name("test-finetuned-model").settings(Settings.builder().baseModel(BaseModel.builder().baseType(BaseType.BASE_TYPE_GENERATIVE).build()).datasetId("my-dataset-id").build()).build());

                      System.out.println(response);
                  }
              }


          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              	"github.com/cohere-ai/cohere-go/v2/finetuning"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.CreateFinetunedModel(
              		context.TODO(),
              		&finetuning.FinetunedModel{
              			Name: "test-finetuned-model",
              			Settings: &finetuning.Settings{
              				DatasetId: "my-dataset-id",
              				BaseModel: &finetuning.BaseModel{
              					BaseType: finetuning.BaseTypeBaseTypeGenerative,
              				},
              			},
              		},
              	)
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.FinetunedModel)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { Cohere, CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const finetunedModel = await cohere.finetuning.createFinetunedModel({
                  name: 'test-finetuned-model',
                  settings: {
                    base_model: {
                      base_type: Cohere.Finetuning.BaseType.BaseTypeGenerative,
                    },
                    dataset_id: 'test-dataset-id',
                  },
                });

                console.log(finetunedModel);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              from cohere.finetuning import (
                  BaseModel,
                  FinetunedModel,
                  Hyperparameters,
                  Settings,
              )
              import cohere

              co = cohere.Client("<<apiKey>>")
              hp = Hyperparameters(
                  early_stopping_patience=10,
                  early_stopping_threshold=0.001,
                  train_batch_size=16,
                  train_epoch=1,
                  learning_rate=0.01,
              )
              finetuned_model = co.finetuning.create_finetuned_model(
                  request=FinetunedModel(
                      name="test-finetuned-model",
                      settings=Settings(
                          base_model=BaseModel(
                              base_type="BASE_TYPE_CHAT",
                          ),
                          dataset_id="my-dataset-id",
                          hyperparameters=hp,
                      ),
                  )
              )
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              from cohere.finetuning import (
                  BaseModel,
                  BaseType,
                  FinetunedModel,
                  Settings,
              )
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.finetuning.create_finetuned_model(
                      request=FinetunedModel(
                          name="test-finetuned-model",
                          settings=Settings(
                              base_model=BaseModel(
                                  base_type=BaseType.BASE_TYPE_GENERATIVE,
                              ),
                              dataset_id="my-dataset-id",
                          ),
                      )
                  )
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: |
              curl --request POST \
                --url https://api.cohere.com/v1/finetuning/finetuned-models \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{
                    "name": "test-finetuned-model",
                    "settings": {
                      "base_model": {
                        "base_type": "BASE_TYPE_GENERATIVE",
                      },
                      "dataset_id": "test-dataset-id"
                    }
                }'
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
  "/finetuning/finetuned-models/{id}":
    patch:
      summary: Updates a fine-tuned model.
      operationId: UpdateFinetunedModel
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/UpdateFinetunedModelResponse"
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: id
          description: FinetunedModel ID.
          in: path
          required: true
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      requestBody:
        description: Information about the fine-tuned model. Must contain name and settings.
        content:
          application/json:
            schema:
              type: object
              properties:
                name:
                  type: string
                  description: FinetunedModel name (e.g. `foobar`).
                creator_id:
                  type: string
                  description: User ID of the creator.
                  readOnly: true
                organization_id:
                  type: string
                  description: Organization ID.
                  readOnly: true
                settings:
                  $ref: "#/components/schemas/Settings"
                  description: FinetunedModel settings such as dataset, hyperparameters...
                status:
                  $ref: "#/components/schemas/Status"
                  description: Current stage in the life-cycle of the fine-tuned model.
                  readOnly: true
                created_at:
                  type: string
                  format: date-time
                  description: Creation timestamp.
                  readOnly: true
                updated_at:
                  type: string
                  format: date-time
                  description: Latest update timestamp.
                  readOnly: true
                completed_at:
                  type: string
                  format: date-time
                  description: Timestamp for the completed fine-tuning.
                  readOnly: true
                last_used:
                  type: string
                  format: date-time
                  description: Timestamp for the latest request to this fine-tuned model.
                  readOnly: true
              title: Information about the fine-tuned model. Must contain name and settings.
              required:
                - name
                - settings
        required: true
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >
              package finetuning;


              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.finetuning.finetuning.types.BaseModel;

              import
              com.cohere.api.resources.finetuning.finetuning.types.BaseType;

              import
              com.cohere.api.resources.finetuning.finetuning.types.Settings;

              import
              com.cohere.api.resources.finetuning.finetuning.types.UpdateFinetunedModelResponse;

              import
              com.cohere.api.resources.finetuning.requests.FinetuningUpdateFinetunedModelRequest;



              public class UpdateFinetunedModel {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      UpdateFinetunedModelResponse response = cohere.finetuning().updateFinetunedModel("test-id", FinetuningUpdateFinetunedModelRequest.builder().name("new name").settings(Settings.builder().baseModel(BaseModel.builder().baseType(BaseType.BASE_TYPE_GENERATIVE).build()).datasetId("my-dataset-id").build()).build());

                      System.out.println(response);
                  }
              }
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	cohere "github.com/cohere-ai/cohere-go/v2"
              	"github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.UpdateFinetunedModel(
              		context.TODO(),
              		"test-id",
              		&cohere.FinetuningUpdateFinetunedModelRequest{
              			Name: "new-name",
              		},
              	)
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.FinetunedModel)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const finetunedModel = await cohere.finetuning.updateFinetunedModel('test-id', {
                  name: 'new name',
                });

                console.log(finetunedModel);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              finetuned_model = co.finetuning.update_finetuned_model(
                  id="test-id",
                  name="new name",
                  settings=Settings(
                      base_model=BaseModel(
                          base_type="BASE_TYPE_CHAT",
                      ),
                      dataset_id="my-dataset-id",)
              )

              print(finetuned_model)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.finetuning.update_finetuned_model(id="test-id", name="new name")
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: >
              curl --request PATCH \
                --url https://api.cohere.com/v1/finetuning/finetuned-models/test-id \
                --header 'accept: application/json' \
                --header 'content-type: application/json' \
                --header "Authorization: bearer $CO_API_KEY" \
                --data '{ "name": "new name" }'
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
      x-fern-request-name: FinetuningUpdateFinetunedModelRequest
    get:
      summary: Returns a fine-tuned model by ID.
      operationId: GetFinetunedModel
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/GetFinetunedModelResponse"
              example:
                finetuned_model:
                  id: fee37446-7fc7-42f9-a026-c6ba2fcc422d
                  name: prompt-completion-ft
                  creator_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                  organization_id: 6bdca3d5-3eae-4de0-ac34-786d8063b7ee
                  settings:
                    base_model:
                      name: medium
                      version: 14.2.0
                      base_type: BASE_TYPE_GENERATIVE
                      strategy: STRATEGY_TFEW
                    dataset_id: my-dataset-d701tr
                    hyperparameters:
                      early_stopping_patience: 6
                      early_stopping_threshold: 0.01
                      train_batch_size: 16
                      train_epochs: 1
                      learning_rate: 0.01
                  status: STATUS_READY
                  created_at: 2024-01-17T20:11:42.907112Z
                  updated_at: 2024-01-17T20:31:06.464015Z
                  completed_at: 2024-01-17T20:31:05.047066Z
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: id
          description: The fine-tuned model ID.
          in: path
          required: true
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >+
              package finetuning;


              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.finetuning.finetuning.types.GetFinetunedModelResponse;


              public class GetFinetunedModel {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      GetFinetunedModelResponse response = cohere.finetuning().getFinetunedModel("test-id");

                      System.out.println(response);
                  }
              }

          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.GetFinetunedModel(context.TODO(), "test-id")
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.FinetunedModel)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const finetunedModel = await cohere.finetuning.getFinetunedModel('test-id');

                console.log(finetunedModel);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.finetuning.get_finetuned_model("test-id")
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  response = await co.finetuning.get_finetuned_model("test-id")
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: >
              curl --request GET \
                --url https://api.cohere.com/v1/finetuning/finetuned-models/test-id \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
      x-fern-request-name: FinetuningGetFinetunedModelRequest
    delete:
      summary: Deletes a fine-tuned model.
      operationId: DeleteFinetunedModel
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/DeleteFinetunedModelResponse"
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: id
          description: The fine-tuned model ID.
          in: path
          required: true
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >
              package finetuning;


              import com.cohere.api.Cohere;



              public class DeleteFinetunedModel {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      cohere.finetuning().deleteFinetunedModel("test-id");
                  }
              }
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: >
              package main


              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              )


              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	_, err := co.Finetuning.DeleteFinetunedModel(context.TODO(), "test-id")
              	if err != nil {
              		log.Fatal(err)
              	}
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: |
              const { CohereClient } = require('cohere-ai');

              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });

              (async () => {
                await cohere.finetuning.deleteFinetunedModel('test-id');
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              co.finetuning.delete_finetuned_model("test-id")
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: |
              import cohere
              import asyncio

              co = cohere.AsyncClient("<<apiKey>>")


              async def main():
                  await co.finetuning.delete_finetuned_model("test-id")

              asyncio.run(main())
          - language: curl
            name: cURL
            code: >
              curl --request DELETE \
                --url https://api.cohere.com/v1/finetuning/finetuned-models/test-id \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
  "/finetuning/finetuned-models/{finetuned_model_id}/events":
    get:
      summary: Retrieves the chronology of statuses the fine-tuned model has been
        through.
      operationId: ListEvents
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListEventsResponse"
              example:
                events:
                  - user_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                    status: STATUS_QUEUED
                    created_at: 2024-01-17T20:11:45.267960Z
                  - user_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                    status: STATUS_FINETUNING
                    created_at: 2024-01-17T20:11:46.000862Z
                  - user_id: 7a317d97-4d05-427d-9396-f31b9fb92c55
                    status: STATUS_DEPLOYING_API
                    created_at: 2024-01-17T20:31:05.047066Z
                  - status: STATUS_READY
                    created_at: 2024-01-17T20:31:06.988655Z
                total_size: 5
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: finetuned_model_id
          description: The parent fine-tuned model ID.
          in: path
          required: true
          schema:
            type: string
        - name: page_size
          description: |-
            Maximum number of results to be returned by the server. If 0, defaults to
            50.
          in: query
          required: false
          schema:
            type: integer
            format: int32
        - name: page_token
          description: Request a specific page of the list results.
          in: query
          required: false
          schema:
            type: string
        - name: order_by
          description: |-
            Comma separated list of fields. For example: "created_at,name". The default
            sorting order is ascending. To specify descending order for a field, append
            " desc" to the field name. For example: "created_at desc,name".

            Supported sorting fields:
              - created_at (default)
          in: query
          required: false
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >
              package finetuning;


              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.finetuning.finetuning.types.ListEventsResponse;


              public class ListEvents {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListEventsResponse response = cohere.finetuning().listEvents("test-id");

                      System.out.println(response);
                  }
              }
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.ListEvents(
              		context.TODO(),
              		"test-finetuned-model-id",
              		nil,
              	)
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.Events)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const events = await cohere.finetuning.listEvents('test-finetuned-model-id');

                console.log(events);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              response = co.finetuning.list_events(finetuned_model_id="test-id")
              print(response)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.finetuning.list_events(finetuned_model_id="test-id")
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: >
              curl --request GET \
                --url https://api.cohere.com/v1/finetuning/finetuned-models/test-id/events \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
      x-fern-request-name: FinetuningListEventsRequest
  "/finetuning/finetuned-models/{finetuned_model_id}/training-step-metrics":
    get:
      summary: Retrieves metrics measured during the training of a fine-tuned model.
      operationId: ListTrainingStepMetrics
      responses:
        "200":
          description: A successful response.
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/ListTrainingStepMetricsResponse"
              example:
                step_metrics:
                  - created_at: 2024-01-17T20:24:26.952802658Z
                    metrics:
                      accuracy: 0.4557601809501648
                      cross_entropy: 4.264331340789795
                      generation_accuracy: 0.4557601809501648
                      generation_cross_entropy: 4.264331340789795
                      step: 0
                  - created_at: 2024-01-17T20:25:19.331940174Z
                    step_number: 9
                    metrics:
                      accuracy: 0.7393720149993896
                      cross_entropy: 0.7702581286430359
                      generation_accuracy: 0.7393720149993896
                      generation_cross_entropy: 0.7702581286430359
                      step: 9
        "400":
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "401":
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "403":
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "404":
          description: Not Found
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "500":
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
        "503":
          description: Status Service Unavailable
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
      parameters:
        - name: finetuned_model_id
          description: The parent fine-tuned model ID.
          in: path
          required: true
          schema:
            type: string
        - name: page_size
          description: |-
            Maximum number of results to be returned by the server. If 0, defaults to
            50.
          in: query
          required: false
          schema:
            type: integer
            format: int32
        - name: page_token
          description: Request a specific page of the list results.
          in: query
          required: false
          schema:
            type: string
        - $ref: "#/components/parameters/RequestSource"
      tags:
        - /finetuning
      x-readme:
        samples-languages:
          - python
          - java
          - go
          - node
          - curl
        code-samples:
          - language: java
            name: Cohere java SDK
            install: implementation 'com.cohere:cohere-java'
            code: >
              package finetuning;


              import com.cohere.api.Cohere;

              import
              com.cohere.api.resources.finetuning.finetuning.types.ListTrainingStepMetricsResponse;



              public class ListTrainingStepMetrics {
                  public static void main(String[] args) {
                      Cohere cohere = Cohere.builder().token("<<apiKey>>").clientName("snippet").build();

                      ListTrainingStepMetricsResponse response = cohere.finetuning().listTrainingStepMetrics("test-id");

                      System.out.println(response);
                  }
              }
          - language: go
            name: Cohere Go SDK
            install: go get github.com/cohere-ai/cohere-go/v2
            code: |
              package main

              import (
              	"context"
              	"log"

              	"github.com/cohere-ai/cohere-go/v2/client"
              )

              func main() {
              	co := client.NewClient(client.WithToken("<<apiKey>>"))

              	resp, err := co.Finetuning.ListTrainingStepMetrics(
              		context.TODO(),
              		"test-finetuned-model-id",
              		nil,
              	)
              	if err != nil {
              		log.Fatal(err)
              	}

              	log.Printf("%+v", resp.StepMetrics)
              }
          - language: node
            name: Cohere TypeScript SDK
            install: npm i cohere-ai
            code: >
              const { CohereClient } = require('cohere-ai');


              const cohere = new CohereClient({
                token: '<<apiKey>>',
              });


              (async () => {
                const trainingStepMetrics = await cohere.finetuning.listTrainingStepMetrics(
                  'test-finetuned-model-id'
                );

                console.log(trainingStepMetrics);
              })();
          - language: python
            name: Sync
            install: python -m pip install cohere --upgrade
            code: |
              import cohere

              co = cohere.Client("<<apiKey>>")
              train_step_metrics = co.finetuning.list_training_step_metrics(
                  finetuned_model_id="test-id")
              print(train_step_metrics)
          - language: python
            name: Async
            install: python -m pip install cohere --upgrade
            code: >
              import cohere

              import asyncio


              co = cohere.AsyncClient("<<apiKey>>")



              async def main():
                  response = await co.finetuning.list_train_step_metrics(finetuned_model_id="test-id")
                  print(response)

              asyncio.run(main())
          - language: curl
            name: cURL
            code: >
              curl --request GET \
                --url https://api.cohere.com/v1/finetuning/finetuned-models/test-id/training-step-metrics \
                --header 'accept: application/json' \
                --header "Authorization: bearer $CO_API_KEY"
      x-fern-sdk-group-name: finetuning
      x-fern-audiences:
        - public
      x-fern-request-name: FinetuningListTrainingStepMetricsRequest
components:
  securitySchemes:
    bearerAuth:
      type: http
      x-fern-audiences:
        - public
      scheme: bearer
      x-fern-bearer:
        env: CO_API_KEY
  parameters:
    RequestSource:
      name: X-Client-Name
      description: |
        The name of the project that is making the request.
      x-fern-audiences:
        - public
      schema:
        type: string
      in: header
      required: false
      example: my-cool-project
  schemas:
    ChatRole:
      type: string
      description: |
        One of `CHATBOT`, `SYSTEM`, `TOOL` or `USER` to identify who the message is coming from.
      enum:
        - CHATBOT
        - SYSTEM
        - USER
        - TOOL
    ToolCall:
      description: |
        Contains the tool calls generated by the model. Use it to invoke your tools.
      type: object
      required:
        - name
        - parameters
      properties:
        name:
          x-fern-audiences:
            - public
          description: Name of the tool to call.
          type: string
        parameters:
          x-fern-audiences:
            - public
          description: The name and value of the parameters to use when invoking a tool.
          type: object
          additionalProperties: true
    ChatMessage:
      description: |
        Represents a single message in the chat history, excluding the current user turn. It has two properties: `role` and `message`. The `role` identifies the sender (`CHATBOT`, `SYSTEM`, or `USER`), while the `message` contains the text content.

        The chat_history parameter should not be used for `SYSTEM` messages in most cases. Instead, to add a `SYSTEM` role message at the beginning of a conversation, the `preamble` parameter should be used.
      required:
        - role
        - message
      properties:
        role:
          $ref: "#/components/schemas/ChatRole"
          x-fern-audiences:
            - public
        message:
          type: string
          description: |
            Contents of the chat message.
          minLength: 1
          x-fern-audiences:
            - public
        tool_calls:
          x-fern-audiences:
            - public
          type: array
          items:
            $ref: "#/components/schemas/ToolCall"
    ToolResult:
      type: object
      required:
        - call
        - outputs
      properties:
        call:
          $ref: "#/components/schemas/ToolCall"
        outputs:
          type: array
          minItems: 0
          items:
            type: object
            additionalProperties: true
    ToolMessage:
      description: |
        Represents tool result in the chat history.
      required:
        - role
      properties:
        role:
          $ref: "#/components/schemas/ChatRole"
        tool_results:
          type: array
          items:
            $ref: "#/components/schemas/ToolResult"
    Message:
      oneOf:
        - $ref: "#/components/schemas/ChatMessage"
        - $ref: "#/components/schemas/ToolMessage"
      discriminator:
        propertyName: role
        mapping:
          CHATBOT: "#/components/schemas/ChatMessage"
          SYSTEM: "#/components/schemas/ChatMessage"
          USER: "#/components/schemas/ChatMessage"
          TOOL: "#/components/schemas/ToolMessage"
    ChatConnector:
      description: |
        The connector used for fetching documents.
      type: object
      required:
        - id
      properties:
        id:
          description: |
            The identifier of the connector.
          type: string
          x-fern-audiences:
            - public
          minLength: 1
        user_access_token:
          description: |
            When specified, this user access token will be passed to the connector in the Authorization header instead of the Cohere generated one.
          type: string
          x-fern-audiences:
            - public
        continue_on_failure:
          description: |
            Defaults to `false`.

            When `true`, the request will continue if this connector returned an error.
          type: boolean
          x-fern-audiences:
            - public
        options:
          description: |
            Provides the connector with different settings at request time. The key/value pairs of this object are specific to each connector.

            For example, the connector `web-search` supports the `site` option, which limits search results to the specified domain.
          type: object
          x-fern-audiences:
            - public
    ChatDocument:
      description: |
        Relevant information that could be used by the model to generate a more accurate reply.
        The contents of each document are generally short (under 300 words), and are passed in the form of a
        dictionary of strings. Some suggested keys are "text", "author", "date". Both the key name and the value will be
        passed to the model.
      type: object
      properties:
        id:
          description: Unique identifier for this document.
          type: string
          x-fern-audiences:
            - public
      additionalProperties:
        type: string
        x-fern-audiences:
          - public
      x-fern-type: map<string, string>
    Tool:
      type: object
      required:
        - name
        - description
      properties:
        name:
          type: string
          description: |
            The name of the tool to be called. Valid names contain only the characters `a-z`, `A-Z`, `0-9`, `_` and must not begin with a digit.
        description:
          description: |
            The description of what the tool does, the model uses the description to choose when and how to call the function.
          type: string
        parameter_definitions:
          description: |
            The input parameters of the tool. Accepts a dictionary where the key is the name of the parameter and the value is the parameter spec. Valid parameter names contain only the characters `a-z`, `A-Z`, `0-9`, `_` and must not begin with a digit.
            ```
            {
              "my_param": {
                "description": <string>,
                "type": <string>, // any python data type, such as 'str', 'bool'
                "required": <boolean>
              }
            }
            ```
          type: object
          additionalProperties:
            type: object
            required:
              - type
            properties:
              description:
                description: |
                  The description of the parameter.
                type: string
              type:
                description: |
                  The type of the parameter. Must be a valid Python type.
                type: string
              required:
                description: |
                  Denotes whether the parameter is always present (required) or not. Defaults to not required.
                type: boolean
                default: false
    ResponseFormatType:
      x-fern-audiences:
        - public
      description: |
        Defaults to `"text"`.

        When set to `"json_object"`, the model's output will be a valid JSON Object.
      type: string
      enum:
        - text
        - json_object
    TextResponseFormat:
      x-fern-audiences:
        - public
      type: object
      properties:
        type:
          $ref: "#/components/schemas/ResponseFormatType"
      required:
        - type
    JSONResponseFormat:
      x-fern-audiences:
        - public
      type: object
      properties:
        type:
          $ref: "#/components/schemas/ResponseFormatType"
        schema:
          description: |
            [BETA] A JSON schema object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](/docs/structured-outputs-json#schema-constraints) for more information.
            Example (required name and age object):
            ```json
            {
              "type": "object",
              "properties": {
                "name": {"type": "string"},
                "age": {"type": "integer"}
              },
              "required": ["name", "age"]
            }
            ```

            **Note**: This field must not be specified when the `type` is set to `"text"`.
          type: object
      required:
        - type
    ResponseFormat:
      description: |
        Configuration for forcing the model output to adhere to the specified format. Supported on [Command R](https://docs.cohere.com/docs/command-r), [Command R+](https://docs.cohere.com/docs/command-r-plus) and newer models.

        The model can be forced into outputting JSON objects (with up to 5 levels of nesting) by setting `{ "type": "json_object" }`.

        A [JSON Schema](https://json-schema.org/) can optionally be provided, to ensure a specific structure.

        **Note**: When using  `{ "type": "json_object" }` your `message` should always explicitly instruct the model to generate a JSON (eg: _"Generate a JSON ..."_) . Otherwise the model may end up getting stuck generating an infinite stream of characters and eventually run out of context length.
        **Limitation**: The parameter is not supported in RAG mode (when any of `connectors`, `documents`, `tools`, `tool_results` are provided).
      oneOf:
        - $ref: "#/components/schemas/TextResponseFormat"
        - $ref: "#/components/schemas/JSONResponseFormat"
      discriminator:
        propertyName: type
        mapping:
          text: "#/components/schemas/TextResponseFormat"
          json_object: "#/components/schemas/JSONResponseFormat"
    ChatCitation:
      description: |
        A section of the generated reply which cites external knowledge.
      required:
        - start
        - end
        - text
        - document_ids
      properties:
        start:
          description: |
            The index of text that the citation starts at, counting from zero. For example, a generation of `Hello, world!` with a citation on `world` would have a start value of `7`. This is because the citation starts at `w`, which is the seventh character.
          type: integer
          x-fern-audiences:
            - public
        end:
          description: |
            The index of text that the citation ends after, counting from zero. For example, a generation of `Hello, world!` with a citation on `world` would have an end value of `11`. This is because the citation ends after `d`, which is the eleventh character.
          type: integer
          x-fern-audiences:
            - public
        text:
          description: |
            The text of the citation. For example, a generation of `Hello, world!` with a citation of `world` would have a text value of `world`.
          type: string
          x-fern-audiences:
            - public
          minLength: 1
        document_ids:
          description: |
            Identifiers of documents cited by this section of the generated reply.
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
          minItems: 1
    ChatSearchQuery:
      description: |
        The generated search query. Contains the text of the query and a unique identifier for the query.
      type: object
      required:
        - text
        - generation_id
      properties:
        text:
          description: |
            The text of the search query.
          type: string
          x-fern-audiences:
            - public
        generation_id:
          description: |
            Unique identifier for the generated search query. Useful for submitting feedback.
          type: string
          x-fern-audiences:
            - public
          format: uuid
    ChatSearchResultConnector:
      description: |
        The connector used for fetching documents.
      type: object
      required:
        - id
      properties:
        id:
          description: |
            The identifier of the connector.
          type: string
          x-fern-audiences:
            - public
          minLength: 1
    ChatSearchResult:
      type: object
      required:
        - connector
        - document_ids
      properties:
        search_query:
          $ref: "#/components/schemas/ChatSearchQuery"
          x-fern-audiences:
            - public
        connector:
          description: |
            The connector from which this result comes from.
          $ref: "#/components/schemas/ChatSearchResultConnector"
          x-fern-audiences:
            - public
        document_ids:
          description: |
            Identifiers of documents found by this search query.
          type: array
          x-fern-audiences:
            - public
          minLength: 0
          items:
            type: string
            x-fern-audiences:
              - public
            minLength: 1
        error_message:
          description: |
            An error message if the search failed.
          type: string
          x-fern-audiences:
            - public
        continue_on_failure:
          description: |
            Whether a chat request should continue or not if the request to this connector fails.
          type: boolean
          x-fern-audiences:
            - public
    FinishReason:
      type: string
      enum:
        - COMPLETE
        - STOP_SEQUENCE
        - ERROR
        - ERROR_TOXIC
        - ERROR_LIMIT
        - USER_CANCEL
        - MAX_TOKENS
    ApiMeta:
      type: object
      properties:
        api_version:
          type: object
          x-fern-audiences:
            - public
          properties:
            version:
              type: string
              x-fern-audiences:
                - public
            is_deprecated:
              type: boolean
              x-fern-audiences:
                - public
            is_experimental:
              type: boolean
              x-fern-audiences:
                - public
          required:
            - version
        billed_units:
          type: object
          x-fern-audiences:
            - public
          properties:
            input_tokens:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of billed input tokens.
            output_tokens:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of billed output tokens.
            search_units:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of billed search units.
            classifications:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of billed classifications units.
        tokens:
          type: object
          x-fern-audiences:
            - public
          properties:
            input_tokens:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of tokens used as input to the model.
            output_tokens:
              type: number
              x-fern-audiences:
                - public
              description: |
                The number of tokens produced by the model.
        warnings:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
    NonStreamedChatResponse:
      required:
        - text
      x-fern-type-name: NonStreamedChatResponse
      properties:
        text:
          type: string
          x-fern-audiences:
            - public
          minLength: 1
          description: Contents of the reply generated by the model.
        generation_id:
          type: string
          x-fern-audiences:
            - public
          format: uuid
          description: Unique identifier for the generated reply. Useful for submitting
            feedback.
        citations:
          type: array
          x-fern-audiences:
            - public
          minItems: 0
          description: Inline citations for the generated reply.
          items:
            $ref: "#/components/schemas/ChatCitation"
        documents:
          type: array
          x-fern-audiences:
            - public
          minItems: 0
          description: Documents seen by the model when generating the reply.
          items:
            $ref: "#/components/schemas/ChatDocument"
        is_search_required:
          type: boolean
          x-fern-audiences:
            - public
          description: Denotes that a search for documents is required during the RAG flow.
        search_queries:
          type: array
          x-fern-audiences:
            - public
          description: Generated search queries, meant to be used as part of the RAG flow.
          items:
            $ref: "#/components/schemas/ChatSearchQuery"
        search_results:
          type: array
          x-fern-audiences:
            - public
          minItems: 0
          description: Documents retrieved from each of the conducted searches.
          items:
            $ref: "#/components/schemas/ChatSearchResult"
        finish_reason:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/FinishReason"
        tool_calls:
          x-fern-audiences:
            - public
          type: array
          items:
            $ref: "#/components/schemas/ToolCall"
        chat_history:
          type: array
          x-fern-audiences:
            - public
          minItems: 0
          items:
            $ref: "#/components/schemas/Message"
          description: |
            A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's `message`.
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
    ChatStreamEvent:
      required:
        - event_type
      properties:
        event_type:
          type: string
          x-fern-audiences:
            - public
          enum:
            - stream-start
            - search-queries-generation
            - search-results
            - text-generation
            - citation-generation
            - stream-end
    ChatStreamStartEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - generation_id
          properties:
            generation_id:
              description: |
                Unique identifier for the generated reply. Useful for submitting feedback.
              type: string
              x-fern-audiences:
                - public
              format: uuid
              minLength: 1
    ChatSearchQueriesGenerationEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - search_queries
          properties:
            search_queries:
              type: array
              x-fern-audiences:
                - public
              description: Generated search queries, meant to be used as part of the RAG flow.
              items:
                $ref: "#/components/schemas/ChatSearchQuery"
    ChatSearchResultsEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          properties:
            search_results:
              description: |
                Conducted searches and the ids of documents retrieved from each of them.
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              items:
                $ref: "#/components/schemas/ChatSearchResult"
            documents:
              description: |
                Documents fetched from searches or provided by the user.
              type: array
              x-fern-audiences:
                - public
              minItems: 0
              items:
                $ref: "#/components/schemas/ChatDocument"
    ChatTextGenerationEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - text
          properties:
            text:
              description: |
                The next batch of text generated by the model.
              type: string
              x-fern-audiences:
                - public
              minLength: 1
    ChatCitationGenerationEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - citations
          properties:
            citations:
              description: |
                Citations for the generated reply.
              type: array
              x-fern-audiences:
                - public
              items:
                $ref: "#/components/schemas/ChatCitation"
    ChatToolCallsGenerationEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - tool_calls
          properties:
            text:
              description: |
                The text generated related to the tool calls generated
              type: string
              x-fern-audiences:
                - public
            tool_calls:
              x-fern-audiences:
                - public
              type: array
              items:
                $ref: "#/components/schemas/ToolCall"
    ChatStreamEndEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - finish_reason
            - response
          properties:
            finish_reason:
              description: |
                - `COMPLETE` - the model sent back a finished reply
                - `ERROR_LIMIT` - the reply was cut off because the model reached the maximum number of tokens for its context length
                - `MAX_TOKENS` - the reply was cut off because the model reached the maximum number of tokens specified by the max_tokens parameter
                - `ERROR` - something went wrong when generating the reply
                - `ERROR_TOXIC` - the model generated a reply that was deemed toxic
              type: string
              x-fern-audiences:
                - public
              enum:
                - COMPLETE
                - ERROR_LIMIT
                - MAX_TOKENS
                - ERROR
                - ERROR_TOXIC
            response:
              x-fern-audiences:
                - public
              description: |
                The consolidated response from the model. Contains the generated reply and all the other information streamed back in the previous events.
              $ref: "#/components/schemas/NonStreamedChatResponse"
    ToolCallDelta:
      description: |
        Contains the chunk of the tool call generation in the stream.
      type: object
      properties:
        name:
          description: |
            Name of the tool call
          type: string
          x-fern-audiences:
            - public
        index:
          description: |
            Index of the tool call generated
          type: number
          x-fern-audiences:
            - public
        parameters:
          description: |
            Chunk of the tool parameters
          type: string
          x-fern-audiences:
            - public
        text:
          description: |
            Chunk of the tool plan text
          type: string
          x-fern-audiences:
            - public
    ChatToolCallsChunkEvent:
      allOf:
        - $ref: "#/components/schemas/ChatStreamEvent"
        - type: object
          required:
            - tool_call_delta
          properties:
            tool_call_delta:
              $ref: "#/components/schemas/ToolCallDelta"
              x-fern-audiences:
                - public
    StreamedChatResponse:
      description: StreamedChatResponse is returned in streaming mode (specified with
        `stream=True` in the request).
      x-fern-type-name: StreamedChatResponse
      oneOf:
        - $ref: "#/components/schemas/ChatStreamStartEvent"
        - $ref: "#/components/schemas/ChatSearchQueriesGenerationEvent"
        - $ref: "#/components/schemas/ChatSearchResultsEvent"
        - $ref: "#/components/schemas/ChatTextGenerationEvent"
        - $ref: "#/components/schemas/ChatCitationGenerationEvent"
        - $ref: "#/components/schemas/ChatToolCallsGenerationEvent"
        - $ref: "#/components/schemas/ChatStreamEndEvent"
        - $ref: "#/components/schemas/ChatToolCallsChunkEvent"
      discriminator:
        propertyName: event_type
        mapping:
          stream-start: "#/components/schemas/ChatStreamStartEvent"
          search-queries-generation: "#/components/schemas/ChatSearchQueriesGenerationEvent"
          search-results: "#/components/schemas/ChatSearchResultsEvent"
          text-generation: "#/components/schemas/ChatTextGenerationEvent"
          citation-generation: "#/components/schemas/ChatCitationGenerationEvent"
          tool-calls-generation: "#/components/schemas/ChatToolCallsGenerationEvent"
          stream-end: "#/components/schemas/ChatStreamEndEvent"
          tool-calls-chunk: "#/components/schemas/ChatToolCallsChunkEvent"
    SingleGeneration:
      type: object
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
        text:
          type: string
          x-fern-audiences:
            - public
        index:
          type: integer
          x-fern-audiences:
            - public
          description: Refers to the nth generation. Only present when `num_generations`
            is greater than zero.
        likelihood:
          type: number
          x-fern-audiences:
            - public
        token_likelihoods:
          type: array
          x-fern-audiences:
            - public
          description: Only returned if `return_likelihoods` is set to `GENERATION` or
            `ALL`. The likelihood refers to the average log-likelihood of the
            entire specified string, which is useful for [evaluating the
            performance of your model](likelihood-eval), especially if you've
            created a [custom model](/docs/training-custom-models). Individual
            token likelihoods provide the log-likelihood of each token. The
            first token will not have a likelihood.
          items:
            type: object
            x-fern-audiences:
              - public
            properties:
              token:
                type: string
                x-fern-audiences:
                  - public
              likelihood:
                type: number
                x-fern-audiences:
                  - public
            required:
              - id
              - token
              - likelihood
      required:
        - text
        - id
    Generation:
      type: object
      x-fern-type-name: Generation
      required:
        - id
        - generations
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
        prompt:
          type: string
          x-fern-audiences:
            - public
          description: Prompt used for generations.
        generations:
          type: array
          x-fern-audiences:
            - public
          description: List of generated results
          items:
            type: object
            x-fern-audiences:
              - public
            $ref: "#/components/schemas/SingleGeneration"
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
    GenerateStreamEvent:
      required:
        - event_type
      properties:
        event_type:
          type: string
          enum:
            - text-generation
            - stream-end
            - stream-error
    GenerateStreamText:
      allOf:
        - $ref: "#/components/schemas/GenerateStreamEvent"
        - type: object
          required:
            - is_finished
            - text
          properties:
            text:
              type: string
              description: A segment of text of the generation.
            index:
              type: integer
              description: Refers to the nth generation. Only present when `num_generations`
                is greater than zero, and only when text responses are being
                streamed.
            is_finished:
              type: boolean
    SingleGenerationInStream:
      type: object
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
        text:
          type: string
          x-fern-audiences:
            - public
          description: Full text of the generation.
        index:
          type: integer
          x-fern-audiences:
            - public
          description: Refers to the nth generation. Only present when `num_generations`
            is greater than zero.
        finish_reason:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/FinishReason"
      required:
        - text
        - id
        - finish_reason
    GenerateStreamEnd:
      allOf:
        - $ref: "#/components/schemas/GenerateStreamEvent"
        - type: object
          required:
            - is_finished
            - finish_response
            - response
          properties:
            is_finished:
              x-fern-audiences:
                - public
              type: boolean
            finish_reason:
              x-fern-audiences:
                - public
              $ref: "#/components/schemas/FinishReason"
            response:
              type: object
              x-fern-audiences:
                - public
              required:
                - id
              properties:
                id:
                  x-fern-audiences:
                    - public
                  type: string
                prompt:
                  x-fern-audiences:
                    - public
                  type: string
                generations:
                  x-fern-audiences:
                    - public
                  type: array
                  items:
                    $ref: "#/components/schemas/SingleGenerationInStream"
    GenerateStreamError:
      allOf:
        - $ref: "#/components/schemas/GenerateStreamEvent"
        - type: object
          required:
            - is_finished
            - finish_reason
            - err
          properties:
            index:
              x-fern-audiences:
                - public
              type: integer
              description: Refers to the nth generation. Only present when `num_generations`
                is greater than zero.
            is_finished:
              x-fern-audiences:
                - public
              type: boolean
            finish_reason:
              x-fern-audiences:
                - public
              $ref: "#/components/schemas/FinishReason"
            err:
              x-fern-audiences:
                - public
              type: string
              description: Error message
    GenerateStreamedResponse:
      description: Response in content type stream when `stream` is `true` in the
        request parameters. Generation tokens are streamed with the
        GenerationStream response. The final response is of type
        GenerationFinalResponse.
      x-fern-type-name: GenerateStreamedResponse
      oneOf:
        - $ref: "#/components/schemas/GenerateStreamText"
        - $ref: "#/components/schemas/GenerateStreamEnd"
        - $ref: "#/components/schemas/GenerateStreamError"
      discriminator:
        propertyName: event_type
        mapping:
          text-generation: "#/components/schemas/GenerateStreamText"
          stream-end: "#/components/schemas/GenerateStreamEnd"
          stream-error: "#/components/schemas/GenerateStreamError"
    EmbedInputType:
      type: string
      enum:
        - search_document
        - search_query
        - classification
        - clustering
      description: |
        Specifies the type of input passed to the model. Required for embedding models v3 and higher.

        - `"search_document"`: Used for embeddings stored in a vector database for search use-cases.
        - `"search_query"`: Used for embeddings of search queries run against a vector DB to find relevant documents.
        - `"classification"`: Used for embeddings passed through a text classifier.
        - `"clustering"`: Used for the embeddings run through a clustering algorithm.
    EmbeddingType:
      x-fern-audiences:
        - public
      type: string
      writeOnly: true
      enum:
        - float
        - int8
        - uint8
        - binary
        - ubinary
    EmbedFloatsResponse:
      type: object
      required:
        - id
        - embeddings
        - texts
        - api_version
      properties:
        response_type:
          type: string
          x-fern-audiences:
            - public
          enum:
            - embeddings_floats
            - embeddings_by_type
        id:
          type: string
          x-fern-audiences:
            - public
        embeddings:
          type: array
          x-fern-audiences:
            - public
          description: An array of embeddings, where each embedding is an array of floats.
            The length of the `embeddings` array will be the same as the length
            of the original `texts` array.
          items:
            type: array
            x-fern-audiences:
              - public
            items:
              type: number
              x-fern-audiences:
                - public
            readOnly: true
          readOnly: true
        texts:
          type: array
          x-fern-audiences:
            - public
          description: The text entries for which embeddings were returned.
          items:
            type: string
            x-fern-audiences:
              - public
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
      x-examples:
        Example:
          value:
            id: bc57846a-3e56-4327-8acc-588ca1a37b8a
            texts:
              - hello world
            embeddings:
              - - -0.039520264
                - -0.04574585
                - -0.08807373
                - -0.084106445
                - -0.009742737
                - -0.0090408325
                - -0.048187256
                - 0.07128906
                - 0.010688782
                - 0.016067505
                - -0.02822876
                - 0.036224365
                - 0.037994385
                - -0.041992188
                - 0.052124023
                - -0.027297974
                - 0.048309326
                - 0.017410278
                - 0.004283905
                - -0.0058555603
                - -0.012992859
                - 0.023040771
                - 0.00121212
                - 0.02078247
                - 0.04827881
                - -0.021530151
                - 0.017807007
                - 0.0075531006
                - -0.047912598
                - -0.042633057
                - 0.0016927719
                - 0.04547119
                - 0.0143585205
                - 0.022354126
                - 0.0017910004
                - 0.048614502
                - -0.02609253
                - 0.007850647
                - 0.00907135
                - 0.0035381317
                - 0.015289307
                - -0.008132935
                - 0.016296387
                - -0.02859497
                - -0.023712158
                - 0.008857727
                - -0.04309082
                - -0.025115967
                - 0.032928467
                - -0.029663086
                - 0.00434494
                - -0.022338867
                - -0.004371643
                - -0.0075035095
                - -0.009529114
                - 0.0038223267
                - -0.06048584
                - -0.018127441
                - 0.010131836
                - 0.08380127
                - 0.03652954
                - 0.0030384064
                - 0.006034851
                - -0.006286621
                - 0.016784668
                - -0.00308609
                - 0.01612854
                - 0.011413574
                - 0.022491455
                - 0.02482605
                - 0.0047683716
                - 0.012245178
                - -0.017593384
                - -0.023254395
                - -0.012641907
                - 0.030181885
                - -0.015365601
                - 0.031021118
                - 0.015144348
                - 0.016708374
                - 0.02180481
                - 0.010765076
                - -0.015586853
                - -0.05819702
                - 0.02128601
                - -0.021713257
                - -0.013320923
                - -0.0064849854
                - -0.001657486
                - -0.030715942
                - -0.008666992
                - 0.060668945
                - -0.0011959076
                - -0.032104492
                - -0.057006836
                - -0.015655518
                - -0.014015198
                - 0.036987305
                - 0.011016846
                - 0.037475586
                - -0.015319824
                - -0.016479492
                - 0.0030403137
                - -0.028167725
                - -0.0067825317
                - -0.00894928
                - -0.026107788
                - 0.010231018
                - 0.031707764
                - 0.050354004
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                - 0.04345703
                - -0.008888245
                - -0.0037784576
                - -0.034576416
                - 0.002565384
                - -0.0018634796
                - 0.013954163
                - 0.012062073
                - 0.046813965
                - 0.033081055
                - 0.028778076
                - 0.0051231384
                - 0.022094727
                - -0.021881104
                - -0.009788513
                - -0.04647827
                - -0.025482178
                - 0.066833496
                - -0.040863037
                - -0.019714355
                - 0.06866455
                - -0.07519531
                - -0.045532227
                - -0.020507812
                - 0.021331787
                - 0.036376953
                - -0.034210205
                - 0.017669678
                - 0.0155181885
                - 0.047698975
                - -0.010368347
                - 0.010879517
                - 0.016555786
                - -0.028076172
                - 0.03225708
                - -0.0056152344
                - -0.020690918
                - 0.00011438131
                - -0.021011353
                - -0.04425049
                - -0.029922485
                - -0.0014362335
                - 0.013618469
                - 0.011260986
                - 0.017532349
                - -0.043426514
                - 0.012557983
                - 0.026748657
                - 0.039154053
                - -0.030944824
                - 0.024169922
                - 0.06573486
                - -0.03817749
                - -0.025314331
                - -0.039794922
                - -0.050445557
                - -0.0020828247
                - -0.0010194778
                - 0.01725769
                - 0.0017976761
                - 0.029067993
                - 0.0115356445
                - 0.005214691
                - 0.0066604614
                - -0.014801025
                - -0.033172607
                - 0.023956299
                - 0.008865356
                - 0.09539795
                - 0.062286377
                - -0.0076408386
                - -0.035125732
                - 0.09082031
                - -0.015350342
                - -0.015625
                - -0.0090789795
                - -0.01876831
                - 0.05239868
                - -0.010406494
                - 0.05078125
                - -0.013694763
                - -0.07244873
                - 0.018539429
                - -0.016983032
                - -0.03353882
                - 0.01739502
                - 0.004055023
                - -0.010147095
                - -0.0027599335
                - -0.002714157
                - -0.0107040405
                - 0.021438599
                - -0.029525757
                - 0.01902771
                - -0.025909424
                - 0.0008072853
                - 0.026672363
                - 0.0019044876
                - 0.016937256
                - -0.018814087
                - -0.03668213
                - 0.03338623
                - -0.09240723
                - -0.024765015
                - -0.019378662
                - -0.051757812
                - 0.050109863
                - 0.018859863
                - 0.01687622
                - 0.020111084
                - -0.041534424
                - 0.009559631
                - -0.018859863
                - 0.00005853176
                - 0.032684326
                - -0.04058838
                - 0.015823364
                - -0.038635254
                - 0.0055656433
                - -0.021560669
                - 0.012481689
                - -0.050872803
                - -0.036071777
                - -0.046844482
                - -0.041900635
                - -0.06561279
                - 0.014785767
                - 0.010124207
                - 0.030426025
                - 0.024261475
                - 0.0057411194
                - -0.026275635
                - -0.006919861
                - 0.022750854
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                - 0.022964478
                - -0.0033664703
                - 0.02734375
                - 0.03201294
                - 0.025939941
                - 0.019104004
                - 0.028030396
                - -0.014328003
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                - 0.06982422
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                - 0.006816864
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                - 0.026245117
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                - 0.014968872
                - 0.0054244995
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                - -0.0030822754
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                - 0.029953003
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                - 0.064331055
                - -0.028533936
                - -0.07287598
                - 0.028839111
                - -0.0029792786
                - -0.02104187
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                - -0.015106201
                - 0.0027313232
                - 0.031051636
                - -0.025680542
                - 0.004722595
                - 0.032348633
                - 0.020019531
                - 0.056365967
                - 0.07208252
                - 0.01235199
                - 0.00084495544
                - 0.035369873
                - -0.018569946
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                - 0.03161621
                - 0.064575195
                - -0.02557373
                - -0.007232666
                - -0.017700195
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                - 0.007381439
                - -0.0385437
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                - 0.044128418
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            meta:
              api_version:
                version: "1"
            api_version: v1
    EmbedByTypeResponse:
      type: object
      required:
        - id
        - embeddings
        - texts
        - api_version
      properties:
        response_type:
          type: string
          x-fern-audiences:
            - public
          enum:
            - embeddings_floats
            - embeddings_by_type
        id:
          type: string
          x-fern-audiences:
            - public
        embeddings:
          type: object
          x-fern-audiences:
            - public
          description: An object with different embedding types. The length of each
            embedding type array will be the same as the length of the original
            `texts` array.
          properties:
            float:
              type: array
              x-fern-audiences:
                - public
              description: An array of float embeddings.
              items:
                type: array
                x-fern-audiences:
                  - public
                items:
                  type: number
                  x-fern-audiences:
                    - public
                readOnly: true
              readOnly: true
            int8:
              type: array
              x-fern-audiences:
                - public
              description: An array of signed int8 embeddings. Each value is between -128 and
                127.
              items:
                type: array
                x-fern-audiences:
                  - public
                items:
                  type: integer
                  x-fern-audiences:
                    - public
                readOnly: true
              readOnly: true
            uint8:
              type: array
              x-fern-audiences:
                - public
              description: An array of unsigned int8 embeddings. Each value is between 0 and
                255.
              items:
                type: array
                x-fern-audiences:
                  - public
                items:
                  type: integer
                  x-fern-audiences:
                    - public
                readOnly: true
              readOnly: true
            binary:
              type: array
              x-fern-audiences:
                - public
              description: An array of packed signed binary embeddings. The length of each
                binary embedding is 1/8 the length of the float embeddings of
                the provided model. Each value is between -128 and 127.
              items:
                type: array
                x-fern-audiences:
                  - public
                items:
                  type: integer
                  x-fern-audiences:
                    - public
                readOnly: true
              readOnly: true
            ubinary:
              type: array
              x-fern-audiences:
                - public
              description: An array of packed unsigned binary embeddings. The length of each
                binary embedding is 1/8 the length of the float embeddings of
                the provided model. Each value is between 0 and 255.
              items:
                type: array
                x-fern-audiences:
                  - public
                items:
                  type: integer
                  x-fern-audiences:
                    - public
                readOnly: true
          items:
            type: array
            x-fern-audiences:
              - public
            items:
              type: number
              x-fern-audiences:
                - public
            readOnly: true
          readOnly: true
        texts:
          type: array
          x-fern-audiences:
            - public
          description: The text entries for which embeddings were returned.
          items:
            type: string
            x-fern-audiences:
              - public
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
      x-examples:
        Example:
          value:
            id: bc57846a-3e56-4327-8acc-588ca1a37b8a
            texts:
              - hello world
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            api_version: v2
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      required:
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        - status
        - created_at
        - input_dataset_id
        - model
        - truncate
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          x-fern-audiences:
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          description: ID of the embed job
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        status:
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        output_dataset_id:
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          x-fern-audiences:
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          x-fern-audiences:
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        truncate:
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      properties:
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          format: string
          description: |
            ID of the embedding model.

            Available models and corresponding embedding dimensions:

            - `embed-english-v3.0` : 1024
            - `embed-multilingual-v3.0` : 1024
            - `embed-english-light-v3.0` : 384
            - `embed-multilingual-light-v3.0` : 384
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        embedding_types:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/EmbeddingType"
          description: |-
            Specifies the types of embeddings you want to get back. Not required and default is None, which returns the Embed Floats response type. Can be one or more of the following types.

            * `"float"`: Use this when you want to get back the default float embeddings. Valid for all models.
            * `"int8"`: Use this when you want to get back signed int8 embeddings. Valid for only v3 models.
            * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models.
            * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for only v3 models.
            * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for only v3 models.
        truncate:
          type: string
          x-fern-audiences:
            - public
          description: |
            One of `START|END` to specify how the API will handle inputs longer than the maximum token length.

            Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.
          default: END
          enum:
            - START
            - END
    CreateEmbedJobResponse:
      type: object
      required:
        - job_id
      description: Response from creating an embed job.
      properties:
        job_id:
          type: string
          x-fern-audiences:
            - public
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
    RerankDocument:
      type: object
      x-fern-type: map<string, string>
      properties:
        text:
          type: string
          x-fern-audiences:
            - public
          description: The text of the document to rerank.
      required:
        - text
    ClassifyExample:
      type: object
      properties:
        text:
          type: string
          x-fern-audiences:
            - public
          writeOnly: true
        label:
          type: string
          x-fern-audiences:
            - public
          writeOnly: true
      writeOnly: true
    DatasetValidationStatus:
      x-fern-audiences:
        - public
      description: The validation status of the dataset
      type: string
      enum:
        - unknown
        - queued
        - processing
        - failed
        - validated
        - skipped
    DatasetType:
      x-fern-audiences:
        - public
      description: The type of the dataset
      type: string
      enum:
        - embed-input
        - embed-result
        - cluster-result
        - cluster-outliers
        - reranker-finetune-input
        - single-label-classification-finetune-input
        - chat-finetune-input
        - multi-label-classification-finetune-input
    DatasetPart:
      type: object
      required:
        - id
        - name
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
          description: The dataset part ID
        name:
          type: string
          x-fern-audiences:
            - public
          description: The name of the dataset part
        url:
          type: string
          x-fern-audiences:
            - public
          description: The download url of the file
        index:
          type: integer
          x-fern-audiences:
            - public
          format: int32
          description: The index of the file
        size_bytes:
          type: integer
          x-fern-audiences:
            - public
          format: int32
          description: The size of the file in bytes
        num_rows:
          type: integer
          x-fern-audiences:
            - public
          format: int32
          description: The number of rows in the file
        original_url:
          type: string
          x-fern-audiences:
            - public
          description: The download url of the original file
        samples:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
          description: The first few rows of the parsed file
    ParseInfo:
      type: object
      properties:
        separator:
          type: string
          x-fern-audiences:
            - public
          writeOnly: true
        delimiter:
          type: string
          x-fern-audiences:
            - public
          writeOnly: true
      writeOnly: true
    RerankerDataMetrics:
      type: object
      properties:
        num_train_queries:
          type: number
          format: int64
          description: The number of training queries.
          x-fern-audiences:
            - public
        num_train_relevant_passages:
          type: number
          format: int64
          description: The sum of all relevant passages of valid training examples.
          x-fern-audiences:
            - public
        num_train_hard_negatives:
          type: number
          format: int64
          description: The sum of all hard negatives of valid training examples.
          x-fern-audiences:
            - public
        num_eval_queries:
          type: number
          format: int64
          description: The number of evaluation queries.
          x-fern-audiences:
            - public
        num_eval_relevant_passages:
          type: number
          format: int64
          description: The sum of all relevant passages of valid eval examples.
          x-fern-audiences:
            - public
        num_eval_hard_negatives:
          type: number
          format: int64
          description: The sum of all hard negatives of valid eval examples.
          x-fern-audiences:
            - public
    ChatDataMetrics:
      type: object
      properties:
        num_train_turns:
          type: number
          format: int64
          description: The sum of all turns of valid train examples.
          x-fern-audiences:
            - public
        num_eval_turns:
          type: number
          format: int64
          description: The sum of all turns of valid eval examples.
          x-fern-audiences:
            - public
        preamble:
          type: string
          description: The preamble of this dataset.
          x-fern-audiences:
            - public
    LabelMetric:
      type: object
      properties:
        total_examples:
          type: number
          format: int64
          description: Total number of examples for this label
          x-fern-audiences:
            - public
        label:
          type: string
          description: value of the label
          x-fern-audiences:
            - public
        samples:
          type: array
          items:
            type: string
          description: samples for this label
          x-fern-audiences:
            - public
    ClassifyDataMetrics:
      type: object
      properties:
        label_metrics:
          type: array
          items:
            $ref: "#/components/schemas/LabelMetric"
    FinetuneDatasetMetrics:
      type: object
      properties:
        trainable_token_count:
          type: number
          format: int64
          description: The number of tokens of valid examples that can be used for training.
          x-fern-audiences:
            - public
        total_examples:
          type: number
          format: int64
          description: The overall number of examples.
          x-fern-audiences:
            - public
        train_examples:
          type: number
          format: int64
          description: The number of training examples.
          x-fern-audiences:
            - public
        train_size_bytes:
          type: number
          format: int64
          description: The size in bytes of all training examples.
          x-fern-audiences:
            - public
        eval_examples:
          type: number
          format: int64
          description: Number of evaluation examples.
          x-fern-audiences:
            - public
        eval_size_bytes:
          type: number
          format: int64
          description: The size in bytes of all eval examples.
          x-fern-audiences:
            - public
        reranker_data_metrics:
          $ref: "#/components/schemas/RerankerDataMetrics"
        chat_data_metrics:
          $ref: "#/components/schemas/ChatDataMetrics"
        classify_data_metrics:
          $ref: "#/components/schemas/ClassifyDataMetrics"
    Metrics:
      type: object
      properties:
        finetune_dataset_metrics:
          $ref: "#/components/schemas/FinetuneDatasetMetrics"
    Dataset:
      type: object
      required:
        - id
        - name
        - created_at
        - updated_at
        - dataset_type
        - validation_status
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
          description: The dataset ID
        name:
          type: string
          x-fern-audiences:
            - public
          description: The name of the dataset
        created_at:
          type: string
          x-fern-audiences:
            - public
          format: date-time
          description: The creation date
        updated_at:
          type: string
          x-fern-audiences:
            - public
          format: date-time
          description: The last update date
        dataset_type:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/DatasetType"
        validation_status:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/DatasetValidationStatus"
        validation_error:
          type: string
          x-fern-audiences:
            - public
          description: Errors found during validation
        schema:
          type: string
          x-fern-audiences:
            - public
          description: the avro schema of the dataset
        required_fields:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        preserve_fields:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        dataset_parts:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/DatasetPart"
          description: the underlying files that make up the dataset
        validation_warnings:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
          description: warnings found during validation
        parse_info:
          $ref: "#/components/schemas/ParseInfo"
        metrics:
          $ref: "#/components/schemas/Metrics"
    ConnectorOAuth:
      type: object
      required:
        - authorize_url
        - token_url
      properties:
        client_id:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 client ID. This field is encrypted at rest.
        client_secret:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 client Secret. This field is encrypted at rest and
            never returned in a response.
        authorize_url:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 /authorize endpoint to use when users authorize the
            connector.
        token_url:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 /token endpoint to use when users authorize the
            connector.
        scope:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth scopes to request when users authorize the connector.
    Connector:
      description: |-
        A connector allows you to integrate data sources with the '/chat' endpoint to create grounded generations with citations to the data source.
        documents to help answer users.
      type: object
      required:
        - id
        - name
        - created_at
        - updated_at
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
          description: |-
            The unique identifier of the connector (used in both `/connectors` & `/chat` endpoints).
            This is automatically created from the name of the connector upon registration.
        organization_id:
          type: string
          x-fern-audiences:
            - public
          description: |-
            The organization to which this connector belongs. This is automatically set to
            the organization of the user who created the connector.
        name:
          type: string
          x-fern-audiences:
            - public
          description: A human-readable name for the connector.
        description:
          type: string
          x-fern-audiences:
            - public
          description: A description of the connector.
        url:
          type: string
          x-fern-audiences:
            - public
          description: The URL of the connector that will be used to search for documents.
        created_at:
          type: string
          x-fern-audiences:
            - public
          format: date-time
          description: The UTC time at which the connector was created.
        updated_at:
          type: string
          x-fern-audiences:
            - public
          format: date-time
          description: The UTC time at which the connector was last updated.
        excludes:
          description: A list of fields to exclude from the prompt (fields remain in the
            document).
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        auth_type:
          description: "The type of authentication/authorization used by the connector.
            Possible values: [oauth, service_auth]"
          type: string
          x-fern-audiences:
            - public
          format: enum
        oauth:
          $ref: "#/components/schemas/ConnectorOAuth"
          x-fern-audiences:
            - public
          description: The OAuth 2.0 configuration for the connector.
        auth_status:
          type: string
          x-fern-audiences:
            - public
          enum:
            - valid
            - expired
          description: The OAuth status for the user making the request. One of ["valid",
            "expired", ""]. Empty string (field is omitted) means the user has
            not authorized the connector yet.
        active:
          type: boolean
          x-fern-audiences:
            - public
          description: Whether the connector is active or not.
        continue_on_failure:
          type: boolean
          x-fern-audiences:
            - public
          description: Whether a chat request should continue or not if the request to
            this connector fails.
    ListConnectorsResponse:
      type: object
      required:
        - connectors
      properties:
        connectors:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/Connector"
        total_count:
          type: number
          x-fern-audiences:
            - public
          description: Total number of connectors.
          readOnly: true
    CreateConnectorOAuth:
      required:
        - clientId
        - clientSecret
        - authorizeUrl
        - tokenUrl
      type: object
      properties:
        client_id:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 client ID. This fields is encrypted at rest.
        client_secret:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 client Secret. This field is encrypted at rest and
            never returned in a response.
        authorize_url:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 /authorize endpoint to use when users authorize the
            connector.
        token_url:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 /token endpoint to use when users authorize the
            connector.
        scope:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth scopes to request when users authorize the connector.
    AuthTokenType:
      type: string
      description: The token_type specifies the way the token is passed in the
        Authorization header. Valid values are "bearer", "basic", and
        "noscheme".
      enum:
        - bearer
        - basic
        - noscheme
      default: noscheme
    CreateConnectorServiceAuth:
      required:
        - type
        - token
      type: object
      properties:
        type:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/AuthTokenType"
        token:
          type: string
          x-fern-audiences:
            - public
          description: The token that will be used in the HTTP Authorization header when
            making requests to the connector. This field is encrypted at rest
            and never returned in a response.
    CreateConnectorRequest:
      type: object
      required:
        - name
        - url
      properties:
        name:
          type: string
          x-fern-audiences:
            - public
          description: A human-readable name for the connector.
        description:
          type: string
          x-fern-audiences:
            - public
          description: A description of the connector.
        url:
          type: string
          x-fern-audiences:
            - public
          description: The URL of the connector that will be used to search for documents.
        excludes:
          description: A list of fields to exclude from the prompt (fields remain in the
            document).
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        oauth:
          $ref: "#/components/schemas/CreateConnectorOAuth"
          x-fern-audiences:
            - public
          description: The OAuth 2.0 configuration for the connector. Cannot be specified
            if service_auth is specified.
        active:
          type: boolean
          x-fern-audiences:
            - public
          default: true
          description: Whether the connector is active or not.
        continue_on_failure:
          type: boolean
          x-fern-audiences:
            - public
          default: false
          description: Whether a chat request should continue or not if the request to
            this connector fails.
        service_auth:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/CreateConnectorServiceAuth"
          description: The service to service authentication configuration for the
            connector. Cannot be specified if oauth is specified.
    CreateConnectorResponse:
      type: object
      required:
        - connector
      properties:
        connector:
          $ref: "#/components/schemas/Connector"
    GetConnectorResponse:
      type: object
      required:
        - connector
      properties:
        connector:
          $ref: "#/components/schemas/Connector"
    DeleteConnectorResponse:
      type: object
    UpdateConnectorRequest:
      type: object
      properties:
        name:
          type: string
          x-fern-audiences:
            - public
          description: A human-readable name for the connector.
        url:
          type: string
          x-fern-audiences:
            - public
          description: The URL of the connector that will be used to search for documents.
        excludes:
          description: A list of fields to exclude from the prompt (fields remain in the
            document).
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        oauth:
          $ref: "#/components/schemas/CreateConnectorOAuth"
          x-fern-audiences:
            - public
          description: The OAuth 2.0 configuration for the connector. Cannot be specified
            if service_auth is specified.
        active:
          type: boolean
          x-fern-audiences:
            - public
          default: true
        continue_on_failure:
          type: boolean
          x-fern-audiences:
            - public
          default: false
        service_auth:
          x-fern-audiences:
            - public
          $ref: "#/components/schemas/CreateConnectorServiceAuth"
          description: The service to service authentication configuration for the
            connector. Cannot be specified if oauth is specified.
    UpdateConnectorResponse:
      type: object
      required:
        - connector
      properties:
        connector:
          $ref: "#/components/schemas/Connector"
    OAuthAuthorizeResponse:
      type: object
      properties:
        redirect_url:
          type: string
          x-fern-audiences:
            - public
          description: The OAuth 2.0 redirect url. Redirect the user to this url to
            authorize the connector.
    ConnectorLog:
      type: object
      required:
        - id
        - connector_id
        - request_id
        - user_id
        - organization_id
        - created_at
        - status_code
        - duration_millis
      properties:
        id:
          type: string
          description: Unique identifier for the connector log
        connector_id:
          type: string
          description: Unique identifier for the connector
        request_id:
          type: string
          description: Unique identifier for the request
        user_id:
          type: string
          description: Unique identifier for the user
        organization_id:
          type: string
          description: Unique identifier for the organization
        created_at:
          type: string
          description: Time of connector log creation in RFC3339 format
          format: date-time
          example: 2023-04-28T21:00:56.646257Z
        status_code:
          type: integer
          description: HTTP status code of the request
        duration_millis:
          type: integer
          description: Duration of the request in milliseconds
        error_message:
          type: string
          description: Error message of the request
    GetConnectorsLogsResponse:
      type: object
      required:
        - connector_logs
        - total
      properties:
        connector_logs:
          type: array
          items:
            $ref: "#/components/schemas/ConnectorLog"
        total:
          type: number
          description: Total number of connector logs
          readOnly: true
    TokenLikelihood:
      type: object
      properties:
        encoded:
          description: Tokenized text
          type: integer
          x-fern-audiences:
            - public
        decoded:
          description: Detokenized text
          type: string
          x-fern-audiences:
            - public
        log_likelihood:
          description: Non normalized probability of the token
          type: number
          x-fern-audiences:
            - public
    LogLikelihoodResponse:
      type: object
      required:
        - id
        - prompt_tokens
        - completion_tokens
        - raw_prompt_tokens
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
        prompt_tokens:
          description: Probabilities for tokens in the request prompt
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/TokenLikelihood"
        completion_tokens:
          description: Probabilities for tokens in the request completion
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/TokenLikelihood"
        raw_prompt_tokens:
          description: Probabilities for tokens in the request raw_prompt
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/TokenLikelihood"
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
    Cluster:
      type: object
      properties:
        id:
          type: string
          x-fern-audiences:
            - public
        keywords:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
        description:
          type: string
          x-fern-audiences:
            - public
        size:
          type: integer
          x-fern-audiences:
            - public
        sample_elements:
          type: array
          x-fern-audiences:
            - public
          items:
            type: string
            x-fern-audiences:
              - public
    GetClusterJobResponse:
      type: object
      required:
        - job_id
      properties:
        job_id:
          type: string
          x-fern-audiences:
            - public
        created_at:
          type: string
          x-fern-audiences:
            - public
          description: Time of job creation in RFC3339 format
          format: date-time
          example: 2023-04-28T21:00:56.646257Z
          readOnly: true
        embeddings_url:
          type: string
          x-fern-audiences:
            - public
          description: The input file URL used for the job
          readOnly: true
        input_dataset_id:
          type: string
          x-fern-audiences:
            - public
          description: The input dataset ID used for the job
          readOnly: true
        min_cluster_size:
          type: integer
          x-fern-audiences:
            - public
          description: The parameter used in the job creation. Please refer to the job
            creation endpoint for more details
          readOnly: true
        n_neighbors:
          type: integer
          x-fern-audiences:
            - public
          description: The parameter used in the job creation. Please refer to the job
            creation endpoint for more details
          readOnly: true
        is_deterministic:
          type: boolean
          x-fern-audiences:
            - public
          description: The parameter used in the job creation. Please refer to the job
            creation endpoint for more details
          readOnly: true
        status:
          type: string
          x-fern-audiences:
            - public
          enum:
            - unknown
            - processing
            - failed
            - complete
            - queued
        is_final_state:
          type: boolean
          x-fern-audiences:
            - public
          description: A boolean indicating whether the job is in a final state, whether
            completed or failed
          readOnly: true
        output_clusters_url:
          type: string
          x-fern-audiences:
            - public
          description: The output file URL for the clusters (signed url that expires)
          readOnly: true
        output_outliers_url:
          type: string
          x-fern-audiences:
            - public
          description: The output file URL for the outliers (signed url that expires)
          readOnly: true
        clusters:
          type: array
          x-fern-audiences:
            - public
          description: The list of cluster summaries for the job
          readOnly: true
          items:
            $ref: "#/components/schemas/Cluster"
        error:
          type: string
          x-fern-audiences:
            - public
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
      description: Response for getting a cluster job.
    ListClusterJobsResponse:
      type: object
      required:
        - jobs
      properties:
        jobs:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/GetClusterJobResponse"
        total_count:
          type: integer
          x-fern-audiences:
            - public
        meta:
          $ref: "#/components/schemas/ApiMeta"
          x-fern-audiences:
            - public
    CreateClusterJobRequest:
      type: object
      required:
        - embeddings_url
      properties:
        embeddings_url:
          type: string
          x-fern-audiences:
            - public
        input_dataset_id:
          type: string
          x-fern-audiences:
            - public
        min_cluster_size:
          type: integer
          x-fern-audiences:
            - public
          description: Defaults to `10`. Parameter for HDBSCAN. Only clusters with this
            number of elements will be returned with a positive cluster number.
          default: 10
          minimum: 1
        n_neighbors:
          type: integer
          x-fern-audiences:
            - public
          description: Parameter for UMAP. A scalar governing how to balance global vs
            local structure in the data.
          minimum: 2
          maximum: 100
          default: 15
        is_deterministic:
          type: boolean
          x-fern-audiences:
            - public
          description: Parameter for UMAP. A boolean governing whether the embeddings from
            UMAP (that will be clustered with HDBSCAN) are deterministic.
          default: true
        generate_descriptions:
          type: boolean
          x-fern-audiences:
            - public
    CreateClusterJobResponse:
      type: object
      required:
        - job_id
      properties:
        job_id:
          type: string
          x-fern-audiences:
            - public
      description: Response for creating a cluster job.
    UpdateClusterJobRequest:
      type: object
      properties:
        status:
          type: string
          x-fern-audiences:
            - public
          enum:
            - unknown
            - processing
            - failed
            - complete
            - queued
        clusters:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/Cluster"
        output_clusters_gs_path:
          type: string
          x-fern-audiences:
            - public
        output_outliers_gs_path:
          type: string
          x-fern-audiences:
            - public
        error:
          type: string
          x-fern-audiences:
            - public
        input_tracking_metrics:
          type: object
          x-fern-audiences:
            - public
        output_tracking_metrics:
          type: object
          x-fern-audiences:
            - public
    UpdateClusterJobResponse:
      type: object
      required:
        - job_id
      properties:
        job_id:
          type: string
          x-fern-audiences:
            - public
      description: Response for updating a cluster job.
    CompatibleEndpoint:
      type: string
      description: One of the Cohere API endpoints that the model can be used with.
      enum:
        - chat
        - embed
        - classify
        - summarize
        - rerank
        - rate
        - generate
    GetModelResponse:
      description: Contains information about the model and which API endpoints it can
        be used with.
      type: object
      properties:
        name:
          type: string
          x-fern-audiences:
            - public
          description: Specify this name in the `model` parameter of API requests to use
            your chosen model.
        endpoints:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/CompatibleEndpoint"
          description: The API endpoints that the model is compatible with.
        finetuned:
          type: boolean
          x-fern-audiences:
            - public
          description: Whether the model has been fine-tuned or not.
        context_length:
          type: number
          x-fern-audiences:
            - public
          description: The maximum number of tokens that the model can process in a single
            request. Note that not all of these tokens are always available due
            to special tokens and preambles that Cohere has added by default.
        tokenizer_url:
          type: string
          x-fern-audiences:
            - public
          description: Public URL to the tokenizer's configuration file.
        default_endpoints:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/CompatibleEndpoint"
          description: The API endpoints that the model is default to.
    ListModelsResponse:
      type: object
      required:
        - models
      properties:
        models:
          type: array
          x-fern-audiences:
            - public
          items:
            $ref: "#/components/schemas/GetModelResponse"
        next_page_token:
          type: string
          x-fern-audiences:
            - public
          description: A token to retrieve the next page of results. Provide in the
            page_token parameter of the next request.
    BaseType:
      type: string
      enum:
        - BASE_TYPE_UNSPECIFIED
        - BASE_TYPE_GENERATIVE
        - BASE_TYPE_CLASSIFICATION
        - BASE_TYPE_RERANK
        - BASE_TYPE_CHAT
      default: BASE_TYPE_UNSPECIFIED
      description: |-
        The possible types of fine-tuned models.

         - BASE_TYPE_UNSPECIFIED: Unspecified model.
         - BASE_TYPE_GENERATIVE: Generative model.
         - BASE_TYPE_CLASSIFICATION: Classification model.
         - BASE_TYPE_RERANK: Rerank model.
         - BASE_TYPE_CHAT: Chat model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Strategy:
      type: string
      enum:
        - STRATEGY_UNSPECIFIED
        - STRATEGY_VANILLA
        - STRATEGY_TFEW
      default: STRATEGY_UNSPECIFIED
      description: |-
        The possible strategy used to serve a fine-tuned models.

         - STRATEGY_UNSPECIFIED: Unspecified strategy.
         - STRATEGY_VANILLA: Serve the fine-tuned model on a dedicated GPU.
         - STRATEGY_TFEW: Serve the fine-tuned model on a shared GPU.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    BaseModel:
      type: object
      properties:
        name:
          type: string
          description: The name of the base model.
        version:
          type: string
          description: read-only. The version of the base model.
          readOnly: true
        base_type:
          $ref: "#/components/schemas/BaseType"
          description: The type of the base model.
        strategy:
          $ref: "#/components/schemas/Strategy"
          description: The fine-tuning strategy.
      description: The base model used for fine-tuning.
      required:
        - base_type
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Hyperparameters:
      type: object
      properties:
        early_stopping_patience:
          type: integer
          format: int32
          description: |-
            Stops training if the loss metric does not improve beyond the value of
            `early_stopping_threshold` after this many times of evaluation.
        early_stopping_threshold:
          type: number
          format: double
          description: How much the loss must improve to prevent early stopping.
        train_batch_size:
          type: integer
          format: int32
          description: |-
            The batch size is the number of training examples included in a single
            training pass.
        train_epochs:
          type: integer
          format: int32
          description: The number of epochs to train for.
        learning_rate:
          type: number
          format: double
          description: The learning rate to be used during training.
      description: The fine-tuning hyperparameters.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Settings:
      type: object
      properties:
        base_model:
          $ref: "#/components/schemas/BaseModel"
          description: The base model to fine-tune.
        dataset_id:
          type: string
          description: The data used for training and evaluating the fine-tuned model.
        hyperparameters:
          $ref: "#/components/schemas/Hyperparameters"
          description: Fine-tuning hyper-parameters.
        multi_label:
          type: boolean
          description: read-only. Whether the model is single-label or multi-label (only
            for classification).
          readOnly: true
      description: The configuration used for fine-tuning.
      required:
        - base_model
        - dataset_id
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Status:
      type: string
      enum:
        - STATUS_UNSPECIFIED
        - STATUS_FINETUNING
        - STATUS_DEPLOYING_API
        - STATUS_READY
        - STATUS_FAILED
        - STATUS_DELETED
        - STATUS_TEMPORARILY_OFFLINE
        - STATUS_PAUSED
        - STATUS_QUEUED
      default: STATUS_UNSPECIFIED
      description: |-
        The possible stages of a fine-tuned model life-cycle.

         - STATUS_UNSPECIFIED: Unspecified status.
         - STATUS_FINETUNING: The fine-tuned model is being fine-tuned.
         - STATUS_DEPLOYING_API: The fine-tuned model is being deployed.
         - STATUS_READY: The fine-tuned model is ready to receive requests.
         - STATUS_FAILED: The fine-tuned model failed.
         - STATUS_DELETED: The fine-tuned model was deleted.
         - STATUS_TEMPORARILY_OFFLINE: The fine-tuned model is temporarily unavailable.
         - STATUS_PAUSED: The fine-tuned model is paused (Vanilla only).
         - STATUS_QUEUED: The fine-tuned model is queued for training.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    FinetunedModel:
      type: object
      properties:
        id:
          type: string
          description: read-only. FinetunedModel ID.
          readOnly: true
        name:
          type: string
          description: FinetunedModel name (e.g. `foobar`).
        creator_id:
          type: string
          description: read-only. User ID of the creator.
          readOnly: true
        organization_id:
          type: string
          description: read-only. Organization ID.
          readOnly: true
        settings:
          $ref: "#/components/schemas/Settings"
          description: FinetunedModel settings such as dataset, hyperparameters...
        status:
          $ref: "#/components/schemas/Status"
          description: read-only. Current stage in the life-cycle of the fine-tuned model.
          readOnly: true
        created_at:
          type: string
          format: date-time
          description: read-only. Creation timestamp.
          readOnly: true
        updated_at:
          type: string
          format: date-time
          description: read-only. Latest update timestamp.
          readOnly: true
        completed_at:
          type: string
          format: date-time
          description: read-only. Timestamp for the completed fine-tuning.
          readOnly: true
        last_used:
          type: string
          format: date-time
          description: read-only. Timestamp for the latest request to this fine-tuned model.
          readOnly: true
      description: This resource represents a fine-tuned model.
      required:
        - name
        - settings
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    ListFinetunedModelsResponse:
      type: object
      properties:
        finetuned_models:
          type: array
          items:
            type: object
            $ref: "#/components/schemas/FinetunedModel"
          description: List of fine-tuned models matching the request.
        next_page_token:
          type: string
          description: |-
            Pagination token to retrieve the next page of results. If the value is "",
            it means no further results for the request.
        total_size:
          type: integer
          format: int32
          description: Total count of results.
      description: Response to a request to list fine-tuned models.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Error:
      type: object
      properties:
        message:
          type: string
          description: A developer-facing error message.
      description: Error is the response for any unsuccessful event.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    CreateFinetunedModelResponse:
      type: object
      properties:
        finetuned_model:
          $ref: "#/components/schemas/FinetunedModel"
          description: Information about the fine-tuned model.
      description: Response to request to create a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    GetFinetunedModelResponse:
      type: object
      properties:
        finetuned_model:
          $ref: "#/components/schemas/FinetunedModel"
          description: Information about the fine-tuned model.
      description: Response to a request to get a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    DeleteFinetunedModelResponse:
      type: object
      description: Response to request to delete a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    UpdateFinetunedModelResponse:
      type: object
      properties:
        finetuned_model:
          $ref: "#/components/schemas/FinetunedModel"
          description: Information about the fine-tuned model.
      description: Response to a request to update a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    Event:
      type: object
      properties:
        user_id:
          type: string
          description: ID of the user who initiated the event. Empty if initiated by the
            system.
        status:
          $ref: "#/components/schemas/Status"
          description: Status of the fine-tuned model.
        created_at:
          type: string
          format: date-time
          description: Timestamp when the event happened.
      description: A change in status of a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    ListEventsResponse:
      type: object
      properties:
        events:
          type: array
          items:
            type: object
            $ref: "#/components/schemas/Event"
          description: List of events for the fine-tuned model.
        next_page_token:
          type: string
          description: |-
            Pagination token to retrieve the next page of results. If the value is "",
            it means no further results for the request.
        total_size:
          type: integer
          format: int32
          description: Total count of results.
      description: Response to a request to list events of a fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    TrainingStepMetrics:
      type: object
      properties:
        created_at:
          type: string
          format: date-time
          description: Creation timestamp.
        step_number:
          type: integer
          format: int32
          description: Step number.
        metrics:
          type: object
          additionalProperties:
            type: number
            format: double
          description: Map of names and values for each evaluation metrics.
      description: The evaluation metrics at a given step of the training of a
        fine-tuned model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
    ListTrainingStepMetricsResponse:
      type: object
      properties:
        step_metrics:
          type: array
          items:
            type: object
            $ref: "#/components/schemas/TrainingStepMetrics"
          description: The metrics for each step the evaluation was run on.
        next_page_token:
          type: string
          description: |-
            Pagination token to retrieve the next page of results. If the value is "",
            it means no further results for the request.
      description: Response to a request to list training-step metrics of a fine-tuned
        model.
      x-fern-sdk-group-name:
        - finetuning
        - finetuning
  responses:
    BadRequest:
      description: |
        This error is returned when the request is not well formed. This could be because:
          - JSON is invalid
          - The request is missing required fields
          - The request contains an invalid combination of fields
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    Unauthorized:
      description: |
        This error indicates that the operation attempted to be performed is not allowed. This could be because:
          - The api token is invalid
          - The user does not have the necessary permissions
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    Forbidden:
      description: |
        This error indicates that the operation attempted to be performed is not allowed. This could be because:
          - The api token is invalid
          - The user does not have the necessary permissions
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    NotFound:
      description: |
        This error is returned when a resource is not found. This could be because:
          - The endpoint does not exist
          - The resource does not exist eg model id, dataset id
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    UnprocessableEntity:
      description: |
        This error is returned when the request is not well formed. This could be because:
          - JSON is invalid
          - The request is missing required fields
          - The request contains an invalid combination of fields
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    RateLimit:
      description: Too many requests
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    InvalidToken:
      description: |
        This error is returned when a request or response contains a deny-listed token.
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    RequestCancelled:
      description: |
        This error is returned when a request is cancelled by the user.
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    InternalServerError:
      description: |
        This error is returned when an uncategorised internal server error occurs.
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    NotImplemented:
      description: |
        This error is returned when the requested feature is not implemented.
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    ServiceUnavailable:
      description: |
        This error is returned when the service is unavailable. This could be due to:
          - Too many users trying to access the service at the same time
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
    GatewayTimeout:
      description: |
        This error is returned when a request to the server times out. This could be due to:
          - An internal services taking too long to respond
      content:
        application/json:
          schema:
            type: object
            properties:
              data:
                type: string
x-readme:
  disable-tag-sorting: true