@ai-toolkit/cohere/dist/index.mjs.map

The **[Cohere provider](https://sdk.khulnasoft.com/providers/ai-toolkit-providers/cohere)** for the [AI TOOLKIT](https://sdk.khulnasoft.com/docs) contains language model support for the Cohere API.

{"version":3,"sources":["../src/cohere-provider.ts","../src/cohere-chat-language-model.ts","../src/cohere-error.ts","../src/convert-to-cohere-chat-prompt.ts","../src/map-cohere-finish-reason.ts","../src/cohere-prepare-tools.ts","../src/cohere-embedding-model.ts"],"sourcesContent":["import {\n  EmbeddingModelV1,\n  LanguageModelV1,\n  ProviderV1,\n} from '@ai-toolkit/provider';\nimport {\n  FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-toolkit/provider-utils';\nimport { CohereChatLanguageModel } from './cohere-chat-language-model';\nimport { CohereChatModelId, CohereChatSettings } from './cohere-chat-settings';\nimport { CohereEmbeddingModel } from './cohere-embedding-model';\nimport {\n  CohereEmbeddingModelId,\n  CohereEmbeddingSettings,\n} from './cohere-embedding-settings';\n\nexport interface CohereProvider extends ProviderV1 {\n  (modelId: CohereChatModelId, settings?: CohereChatSettings): LanguageModelV1;\n\n  /**\nCreates a model for text generation.\n*/\n  languageModel(\n    modelId: CohereChatModelId,\n    settings?: CohereChatSettings,\n  ): LanguageModelV1;\n\n  embedding(\n    modelId: CohereEmbeddingModelId,\n    settings?: CohereEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n\n  textEmbeddingModel(\n    modelId: CohereEmbeddingModelId,\n    settings?: CohereEmbeddingSettings,\n  ): EmbeddingModelV1<string>;\n}\n\nexport interface CohereProviderSettings {\n  /**\nUse a different URL prefix for API calls, e.g. to use proxy servers.\nThe default prefix is `https://api.cohere.com/v2`.\n   */\n  baseURL?: string;\n\n  /**\nAPI key that is being send using the `Authorization` header.\nIt defaults to the `COHERE_API_KEY` environment variable.\n   */\n  apiKey?: string;\n\n  /**\nCustom headers to include in the requests.\n     */\n  headers?: Record<string, string>;\n\n  /**\nCustom fetch implementation. You can use it as a middleware to intercept requests,\nor to provide a custom fetch implementation for e.g. testing.\n    */\n  fetch?: FetchFunction;\n}\n\n/**\nCreate a Cohere AI provider instance.\n */\nexport function createCohere(\n  options: CohereProviderSettings = {},\n): CohereProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? 'https://api.cohere.com/v2';\n\n  const getHeaders = () => ({\n    Authorization: `Bearer ${loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: 'COHERE_API_KEY',\n      description: 'Cohere',\n    })}`,\n    ...options.headers,\n  });\n\n  const createChatModel = (\n    modelId: CohereChatModelId,\n    settings: CohereChatSettings = {},\n  ) =>\n    new CohereChatLanguageModel(modelId, settings, {\n      provider: 'cohere.chat',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createTextEmbeddingModel = (\n    modelId: CohereEmbeddingModelId,\n    settings: CohereEmbeddingSettings = {},\n  ) =>\n    new CohereEmbeddingModel(modelId, settings, {\n      provider: 'cohere.textEmbedding',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (\n    modelId: CohereChatModelId,\n    settings?: CohereChatSettings,\n  ) {\n    if (new.target) {\n      throw new Error(\n        'The Cohere model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createChatModel(modelId, settings);\n  };\n\n  provider.languageModel = createChatModel;\n  provider.embedding = createTextEmbeddingModel;\n  provider.textEmbeddingModel = createTextEmbeddingModel;\n\n  return provider;\n}\n\n/**\nDefault Cohere provider instance.\n */\nexport const cohere = createCohere();\n","import {\n  LanguageModelV1,\n  LanguageModelV1FinishReason,\n  LanguageModelV1StreamPart,\n  UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport {\n  CohereChatModelId,\n  CohereChatSettings,\n} from '../src/cohere-chat-settings';\nimport { cohereFailedResponseHandler } from '../src/cohere-error';\nimport { convertToCohereChatPrompt } from '../src/convert-to-cohere-chat-prompt';\nimport { mapCohereFinishReason } from '../src/map-cohere-finish-reason';\nimport { prepareTools } from './cohere-prepare-tools';\nimport {\n  CohereChatPrompt,\n  CohereAssistantMessage,\n  CohereUserMessage,\n  CohereSystemMessage,\n} from './cohere-chat-prompt';\n\ntype CohereChatConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class CohereChatLanguageModel implements LanguageModelV1 {\n  readonly specificationVersion = 'v1';\n  readonly defaultObjectGenerationMode = undefined;\n\n  readonly modelId: CohereChatModelId;\n  readonly settings: CohereChatSettings;\n\n  private readonly config: CohereChatConfig;\n\n  constructor(\n    modelId: CohereChatModelId,\n    settings: CohereChatSettings,\n    config: CohereChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  private getArgs({\n    mode,\n    prompt,\n    maxTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n  }: Parameters<LanguageModelV1['doGenerate']>[0]) {\n    const type = mode.type;\n\n    const chatPrompt = convertToCohereChatPrompt(prompt);\n\n    const baseArgs = {\n      // model id:\n      model: this.modelId,\n\n      // model specific settings:\n      // none\n\n      // standardized settings:\n      frequency_penalty: frequencyPenalty,\n      presence_penalty: presencePenalty,\n      max_tokens: maxTokens,\n      temperature,\n      p: topP,\n      k: topK,\n      seed,\n      stop_sequences: stopSequences,\n\n      // response format:\n      response_format:\n        responseFormat?.type === 'json'\n          ? { type: 'json_object', schema: responseFormat.schema }\n          : undefined,\n\n      // messages:\n      messages: chatPrompt,\n    };\n\n    switch (type) {\n      case 'regular': {\n        const { tools, tool_choice, toolWarnings } = prepareTools(mode);\n        // TODO(shaper): Cohere API doesn't appear to support any form of\n        // explicit tool choice currently. In the future we may want to pass\n        // along the `tool_choice` value in some manner.\n        return {\n          ...baseArgs,\n          tools,\n          warnings: toolWarnings,\n        };\n      }\n\n      case 'object-json': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'object-json mode',\n        });\n      }\n\n      case 'object-tool': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'object-tool mode',\n        });\n      }\n\n      default: {\n        const _exhaustiveCheck: never = type;\n        throw new UnsupportedFunctionalityError({\n          functionality: `Unsupported mode: ${_exhaustiveCheck}`,\n        });\n      }\n    }\n  }\n\n  concatenateMessageText(messages: CohereChatPrompt): string {\n    return messages\n      .filter(\n        (\n          message,\n        ): message is\n          | CohereSystemMessage\n          | CohereUserMessage\n          | CohereAssistantMessage => 'content' in message,\n      )\n      .map(message => message.content)\n      .join('');\n  }\n\n  /*\n  Remove `additionalProperties` and `$schema` from the `parameters` object of each tool.\n  Though these are part of JSON schema, Cohere chokes if we include them in the request.\n  */\n  // TODO(shaper): Look at defining a type to simplify the params here and a couple of other places.\n  removeJsonSchemaExtras(\n    tools: Array<{\n      type: 'function';\n      function: {\n        name: string | undefined;\n        description: string | undefined;\n        parameters: unknown;\n      };\n    }>,\n  ) {\n    return tools.map(tool => {\n      if (\n        tool.type === 'function' &&\n        tool.function.parameters &&\n        typeof tool.function.parameters === 'object'\n      ) {\n        const { additionalProperties, $schema, ...restParameters } = tool\n          .function.parameters as Record<string, unknown>;\n        return {\n          ...tool,\n          function: {\n            ...tool.function,\n            parameters: restParameters,\n          },\n        };\n      }\n      return tool;\n    });\n  }\n\n  async doGenerate(\n    options: Parameters<LanguageModelV1['doGenerate']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doGenerate']>>> {\n    const { warnings, ...args } = this.getArgs(options);\n    args.tools = args.tools && this.removeJsonSchemaExtras(args.tools);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body: args,\n      failedResponseHandler: cohereFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        cohereChatResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages, ...rawSettings } = args;\n\n    const text = response.message.content?.[0]?.text ?? '';\n\n    return {\n      text,\n      toolCalls: response.message.tool_calls\n        ? response.message.tool_calls.map(toolCall => ({\n            toolCallId: toolCall.id,\n            toolName: toolCall.function.name,\n            // Cohere sometimes returns `null` for tool call arguments for tools\n            // defined as having no arguments.\n            args: toolCall.function.arguments.replace(/^null$/, '{}'),\n            toolCallType: 'function',\n          }))\n        : [],\n      finishReason: mapCohereFinishReason(response.finish_reason),\n      usage: {\n        promptTokens: response.usage.tokens.input_tokens,\n        completionTokens: response.usage.tokens.output_tokens,\n      },\n      rawCall: {\n        rawPrompt: {\n          messages,\n        },\n        rawSettings,\n      },\n      response: {\n        id: response.generation_id ?? undefined,\n      },\n      rawResponse: {\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n      request: { body: JSON.stringify(args) },\n    };\n  }\n\n  async doStream(\n    options: Parameters<LanguageModelV1['doStream']>[0],\n  ): Promise<Awaited<ReturnType<LanguageModelV1['doStream']>>> {\n    const { warnings, ...args } = this.getArgs(options);\n    args.tools = args.tools && this.removeJsonSchemaExtras(args.tools);\n    const body = { ...args, stream: true };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: cohereFailedResponseHandler,\n      successfulResponseHandler: createEventSourceResponseHandler(\n        cohereChatChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const { messages, ...rawSettings } = args;\n\n    let finishReason: LanguageModelV1FinishReason = 'unknown';\n    let usage: { promptTokens: number; completionTokens: number } = {\n      promptTokens: Number.NaN,\n      completionTokens: Number.NaN,\n    };\n\n    let pendingToolCallDelta: {\n      toolCallId: string;\n      toolName: string;\n      argsTextDelta: string;\n    } = {\n      toolCallId: '',\n      toolName: '',\n      argsTextDelta: '',\n    };\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof cohereChatChunkSchema>>,\n          LanguageModelV1StreamPart\n        >({\n          transform(chunk, controller) {\n            // handle failed chunk parsing / validation:\n            if (!chunk.success) {\n              finishReason = 'error';\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n            const type = value.type;\n\n            switch (type) {\n              case 'content-delta': {\n                controller.enqueue({\n                  type: 'text-delta',\n                  textDelta: value.delta.message.content.text,\n                });\n                return;\n              }\n\n              case 'tool-call-start': {\n                // The start message is the only one that specifies the tool id and name.\n                pendingToolCallDelta = {\n                  toolCallId: value.delta.message.tool_calls.id,\n                  toolName: value.delta.message.tool_calls.function.name,\n                  argsTextDelta:\n                    value.delta.message.tool_calls.function.arguments,\n                };\n\n                // Provide visibility into the beginning of the tool call even\n                // though we likely don't have full arguments yet.\n                controller.enqueue({\n                  type: 'tool-call-delta',\n                  toolCallId: pendingToolCallDelta.toolCallId,\n                  toolName: pendingToolCallDelta.toolName,\n                  toolCallType: 'function',\n                  argsTextDelta: pendingToolCallDelta.argsTextDelta,\n                });\n                return;\n              }\n\n              case 'tool-call-delta': {\n                // Accumulate the arguments for the tool call.\n                pendingToolCallDelta.argsTextDelta +=\n                  value.delta.message.tool_calls.function.arguments;\n\n                // Provide visibility into the updated arguments for the tool call, even though we\n                // may have more arguments still coming.\n                controller.enqueue({\n                  type: 'tool-call-delta',\n                  toolCallId: pendingToolCallDelta.toolCallId,\n                  toolName: pendingToolCallDelta.toolName,\n                  toolCallType: 'function',\n                  argsTextDelta:\n                    value.delta.message.tool_calls.function.arguments,\n                });\n                return;\n              }\n\n              case 'tool-call-end': {\n                // Post the full tool call now that we have all of the arguments.\n                controller.enqueue({\n                  type: 'tool-call',\n                  toolCallId: pendingToolCallDelta.toolCallId,\n                  toolName: pendingToolCallDelta.toolName,\n                  toolCallType: 'function',\n                  args: JSON.stringify(\n                    JSON.parse(\n                      pendingToolCallDelta.argsTextDelta?.trim() || '{}',\n                    ),\n                  ),\n                });\n\n                // Clear the pending tool call. We rely on the API always\n                // following a start with an end. We do not defensively clear a\n                // previous accumulation of a pending tool call in\n                // non-tool-related events.\n                pendingToolCallDelta = {\n                  toolCallId: '',\n                  toolName: '',\n                  argsTextDelta: '',\n                };\n                return;\n              }\n\n              case 'message-start': {\n                controller.enqueue({\n                  type: 'response-metadata',\n                  id: value.id ?? undefined,\n                });\n\n                return;\n              }\n\n              case 'message-end': {\n                finishReason = mapCohereFinishReason(value.delta.finish_reason);\n                const tokens = value.delta.usage.tokens;\n\n                usage = {\n                  promptTokens: tokens.input_tokens,\n                  completionTokens: tokens.output_tokens,\n                };\n              }\n\n              default: {\n                return;\n              }\n            }\n          },\n\n          flush(controller) {\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage,\n            });\n          },\n        }),\n      ),\n      rawCall: {\n        rawPrompt: {\n          messages,\n        },\n        rawSettings,\n      },\n      rawResponse: { headers: responseHeaders },\n      warnings,\n      request: { body: JSON.stringify(body) },\n    };\n  }\n}\n\n// limited version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst cohereChatResponseSchema = z.object({\n  generation_id: z.string().nullish(),\n  message: z.object({\n    role: z.string(),\n    content: z\n      .array(\n        z.object({\n          type: z.string(),\n          text: z.string(),\n        }),\n      )\n      .nullish(),\n    tool_plan: z.string().nullish(),\n    tool_calls: z\n      .array(\n        z.object({\n          id: z.string(),\n          type: z.literal('function'),\n          function: z.object({\n            name: z.string(),\n            arguments: z.string(),\n          }),\n        }),\n      )\n      .nullish(),\n  }),\n  finish_reason: z.string(),\n  usage: z.object({\n    billed_units: z.object({\n      input_tokens: z.number(),\n      output_tokens: z.number(),\n    }),\n    tokens: z.object({\n      input_tokens: z.number(),\n      output_tokens: z.number(),\n    }),\n  }),\n});\n\n// limited version of the schema, focused on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst cohereChatChunkSchema = z.discriminatedUnion('type', [\n  z.object({\n    type: z.literal('citation-start'),\n  }),\n  z.object({\n    type: z.literal('citation-end'),\n  }),\n  z.object({\n    type: z.literal('content-start'),\n  }),\n  z.object({\n    type: z.literal('content-delta'),\n    delta: z.object({\n      message: z.object({\n        content: z.object({\n          text: z.string(),\n        }),\n      }),\n    }),\n  }),\n  z.object({\n    type: z.literal('content-end'),\n  }),\n  z.object({\n    type: z.literal('message-start'),\n    id: z.string().nullish(),\n  }),\n  z.object({\n    type: z.literal('message-end'),\n    delta: z.object({\n      finish_reason: z.string(),\n      usage: z.object({\n        tokens: z.object({\n          input_tokens: z.number(),\n          output_tokens: z.number(),\n        }),\n      }),\n    }),\n  }),\n  // https://docs.cohere.com/v2/docs/streaming#tool-use-stream-events-for-tool-calling\n  z.object({\n    type: z.literal('tool-plan-delta'),\n    delta: z.object({\n      message: z.object({\n        tool_plan: z.string(),\n      }),\n    }),\n  }),\n  z.object({\n    type: z.literal('tool-call-start'),\n    delta: z.object({\n      message: z.object({\n        tool_calls: z.object({\n          id: z.string(),\n          type: z.literal('function'),\n          function: z.object({\n            name: z.string(),\n            arguments: z.string(),\n          }),\n        }),\n      }),\n    }),\n  }),\n  // A single tool call's `arguments` stream in chunks and must be accumulated\n  // in a string and so the full tool object info can only be parsed once we see\n  // `tool-call-end`.\n  z.object({\n    type: z.literal('tool-call-delta'),\n    delta: z.object({\n      message: z.object({\n        tool_calls: z.object({\n          function: z.object({\n            arguments: z.string(),\n          }),\n        }),\n      }),\n    }),\n  }),\n  z.object({\n    type: z.literal('tool-call-end'),\n  }),\n]);\n","import { createJsonErrorResponseHandler } from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\n\nconst cohereErrorDataSchema = z.object({\n  message: z.string(),\n});\n\nexport type CohereErrorData = z.infer<typeof cohereErrorDataSchema>;\n\nexport const cohereFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: cohereErrorDataSchema,\n  errorToMessage: data => data.message,\n});\n","import {\n  LanguageModelV1Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\nimport { CohereAssistantMessage, CohereChatPrompt } from './cohere-chat-prompt';\n\nexport function convertToCohereChatPrompt(\n  prompt: LanguageModelV1Prompt,\n): CohereChatPrompt {\n  const messages: CohereChatPrompt = [];\n\n  for (const { role, content } of prompt) {\n    switch (role) {\n      case 'system': {\n        messages.push({ role: 'system', content });\n        break;\n      }\n\n      case 'user': {\n        messages.push({\n          role: 'user',\n          content: content\n            .map(part => {\n              switch (part.type) {\n                case 'text': {\n                  return part.text;\n                }\n                case 'image': {\n                  throw new UnsupportedFunctionalityError({\n                    functionality: 'image-part',\n                  });\n                }\n              }\n            })\n            .join(''),\n        });\n        break;\n      }\n\n      case 'assistant': {\n        let text = '';\n        const toolCalls: CohereAssistantMessage['tool_calls'] = [];\n\n        for (const part of content) {\n          switch (part.type) {\n            case 'text': {\n              text += part.text;\n              break;\n            }\n            case 'redacted-reasoning':\n            case 'reasoning': {\n              break; // ignored\n            }\n            case 'tool-call': {\n              toolCalls.push({\n                id: part.toolCallId,\n                type: 'function' as const,\n                function: {\n                  name: part.toolName,\n                  arguments: JSON.stringify(part.args),\n                },\n              });\n              break;\n            }\n            default: {\n              const _exhaustiveCheck: never = part;\n              throw new Error(`Unsupported part: ${_exhaustiveCheck}`);\n            }\n          }\n        }\n\n        messages.push({\n          role: 'assistant',\n          // note: this is a workaround for a Cohere API bug\n          // that requires content to be provided\n          // even if there are tool calls\n          content: text !== '' ? text : 'call tool',\n          tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n          tool_plan: undefined,\n        });\n\n        break;\n      }\n      case 'tool': {\n        // Cohere uses one tool message per tool result\n        messages.push(\n          ...content.map(toolResult => ({\n            role: 'tool' as const,\n            content: JSON.stringify(toolResult.result),\n            tool_call_id: toolResult.toolCallId,\n          })),\n        );\n\n        break;\n      }\n      default: {\n        const _exhaustiveCheck: never = role;\n        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);\n      }\n    }\n  }\n\n  return messages;\n}\n","import { LanguageModelV1FinishReason } from '@ai-toolkit/provider';\n\nexport function mapCohereFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV1FinishReason {\n  switch (finishReason) {\n    case 'COMPLETE':\n    case 'STOP_SEQUENCE':\n      return 'stop';\n\n    case 'MAX_TOKENS':\n      return 'length';\n\n    case 'ERROR':\n    case 'ERROR_LIMIT':\n      return 'error';\n\n    case 'ERROR_TOXIC':\n      return 'content-filter';\n\n    case 'USER_CANCEL':\n      return 'other';\n\n    default:\n      return 'unknown';\n  }\n}\n","import {\n  LanguageModelV1,\n  LanguageModelV1CallWarning,\n  UnsupportedFunctionalityError,\n} from '@ai-toolkit/provider';\n\n// For reference: https://docs.cohere.com/docs/parameter-types-in-tool-use\nexport function prepareTools(\n  mode: Parameters<LanguageModelV1['doGenerate']>[0]['mode'] & {\n    type: 'regular';\n  },\n): {\n  tools:\n    | Array<{\n        type: 'function';\n        function: {\n          name: string | undefined;\n          description: string | undefined;\n          parameters: unknown;\n        };\n      }>\n    | undefined;\n  tool_choice:\n    | { type: 'function'; function: { name: string } }\n    | 'auto'\n    | 'none'\n    | 'any'\n    | undefined;\n  toolWarnings: LanguageModelV1CallWarning[];\n} {\n  const tools = mode.tools?.length ? mode.tools : undefined;\n  const toolWarnings: LanguageModelV1CallWarning[] = [];\n\n  if (tools == null) {\n    return { tools: undefined, tool_choice: undefined, toolWarnings };\n  }\n\n  const cohereTools: Array<{\n    type: 'function';\n    function: {\n      name: string;\n      description: string | undefined;\n      parameters: unknown;\n    };\n  }> = [];\n\n  for (const tool of tools) {\n    if (tool.type === 'provider-defined') {\n      toolWarnings.push({ type: 'unsupported-tool', tool });\n    } else {\n      cohereTools.push({\n        type: 'function',\n        function: {\n          name: tool.name,\n          description: tool.description,\n          parameters: tool.parameters,\n        },\n      });\n    }\n  }\n\n  const toolChoice = mode.toolChoice;\n\n  if (toolChoice == null) {\n    return { tools: cohereTools, tool_choice: undefined, toolWarnings };\n  }\n\n  const type = toolChoice.type;\n\n  switch (type) {\n    case 'auto':\n      return { tools: cohereTools, tool_choice: type, toolWarnings };\n\n    case 'none':\n      // Cohere does not support 'none' tool choice, so we remove the tools.\n      return { tools: undefined, tool_choice: 'any', toolWarnings };\n\n    case 'required':\n    case 'tool':\n      // Cohere does not support forcing tool calls\n      throw new UnsupportedFunctionalityError({\n        functionality: `Unsupported tool choice type: ${type}`,\n      });\n\n    default: {\n      const _exhaustiveCheck: never = type;\n      throw new UnsupportedFunctionalityError({\n        functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`,\n      });\n    }\n  }\n}\n","import {\n  EmbeddingModelV1,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-toolkit/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  FetchFunction,\n  postJsonToApi,\n} from '@ai-toolkit/provider-utils';\nimport { z } from 'zod';\nimport {\n  CohereEmbeddingModelId,\n  CohereEmbeddingSettings,\n} from './cohere-embedding-settings';\nimport { cohereFailedResponseHandler } from './cohere-error';\n\ntype CohereEmbeddingConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class CohereEmbeddingModel implements EmbeddingModelV1<string> {\n  readonly specificationVersion = 'v1';\n  readonly modelId: CohereEmbeddingModelId;\n\n  readonly maxEmbeddingsPerCall = 96;\n  readonly supportsParallelCalls = true;\n\n  private readonly config: CohereEmbeddingConfig;\n  private readonly settings: CohereEmbeddingSettings;\n\n  constructor(\n    modelId: CohereEmbeddingModelId,\n    settings: CohereEmbeddingSettings,\n    config: CohereEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.settings = settings;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  async doEmbed({\n    values,\n    headers,\n    abortSignal,\n  }: Parameters<EmbeddingModelV1<string>['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV1<string>['doEmbed']>>\n  > {\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        provider: this.provider,\n        modelId: this.modelId,\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        values,\n      });\n    }\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/embed`,\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        // The AI TOOLKIT only supports 'float' embeddings which are also the only ones\n        // the Cohere API docs state are supported for all models.\n        // https://docs.cohere.com/v2/reference/embed#request.body.embedding_types\n        embedding_types: ['float'],\n        texts: values,\n        input_type: this.settings.inputType ?? 'search_query',\n        truncate: this.settings.truncate,\n      },\n      failedResponseHandler: cohereFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        cohereTextEmbeddingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      embeddings: response.embeddings.float,\n      usage: { tokens: response.meta.billed_units.input_tokens },\n      rawResponse: { headers: responseHeaders },\n    };\n  }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst cohereTextEmbeddingResponseSchema = z.object({\n  embeddings: z.object({\n    float: z.array(z.array(z.number())),\n  }),\n  meta: z.object({\n    billed_units: z.object({\n      input_tokens: z.number(),\n    }),\n  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