@ai-sdk/openai/dist/index.mjs

The **[OpenAI provider](https://sdk.vercel.ai/providers/ai-sdk-providers/openai)** for the [AI SDK](https://sdk.vercel.ai/docs) contains language model support for the OpenAI chat and completion APIs and embedding model support for the OpenAI embeddings A

// src/openai-facade.ts
import { loadApiKey, withoutTrailingSlash } from "@ai-sdk/provider-utils";

// src/openai-chat-language-model.ts
import {
  InvalidResponseDataError,
  UnsupportedFunctionalityError as UnsupportedFunctionalityError2
} from "@ai-sdk/provider";
import {
  combineHeaders,
  createEventSourceResponseHandler,
  createJsonResponseHandler,
  generateId,
  isParsableJson,
  postJsonToApi
} from "@ai-sdk/provider-utils";
import { z as z2 } from "zod";

// src/convert-to-openai-chat-messages.ts
import {
  UnsupportedFunctionalityError
} from "@ai-sdk/provider";
import { convertUint8ArrayToBase64 } from "@ai-sdk/provider-utils";
function convertToOpenAIChatMessages({
  prompt,
  useLegacyFunctionCalling = false
}) {
  const messages = [];
  for (const { role, content } of prompt) {
    switch (role) {
      case "system": {
        messages.push({ role: "system", content });
        break;
      }
      case "user": {
        if (content.length === 1 && content[0].type === "text") {
          messages.push({ role: "user", content: content[0].text });
          break;
        }
        messages.push({
          role: "user",
          content: content.map((part) => {
            var _a, _b, _c;
            switch (part.type) {
              case "text": {
                return { type: "text", text: part.text };
              }
              case "image": {
                return {
                  type: "image_url",
                  image_url: {
                    url: part.image instanceof URL ? part.image.toString() : `data:${(_a = part.mimeType) != null ? _a : "image/jpeg"};base64,${convertUint8ArrayToBase64(part.image)}`,
                    // OpenAI specific extension: image detail
                    detail: (_c = (_b = part.providerMetadata) == null ? void 0 : _b.openai) == null ? void 0 : _c.imageDetail
                  }
                };
              }
              case "file": {
                throw new UnsupportedFunctionalityError({
                  functionality: "File content parts in user messages"
                });
              }
            }
          })
        });
        break;
      }
      case "assistant": {
        let text = "";
        const toolCalls = [];
        for (const part of content) {
          switch (part.type) {
            case "text": {
              text += part.text;
              break;
            }
            case "tool-call": {
              toolCalls.push({
                id: part.toolCallId,
                type: "function",
                function: {
                  name: part.toolName,
                  arguments: JSON.stringify(part.args)
                }
              });
              break;
            }
            default: {
              const _exhaustiveCheck = part;
              throw new Error(`Unsupported part: ${_exhaustiveCheck}`);
            }
          }
        }
        if (useLegacyFunctionCalling) {
          if (toolCalls.length > 1) {
            throw new UnsupportedFunctionalityError({
              functionality: "useLegacyFunctionCalling with multiple tool calls in one message"
            });
          }
          messages.push({
            role: "assistant",
            content: text,
            function_call: toolCalls.length > 0 ? toolCalls[0].function : void 0
          });
        } else {
          messages.push({
            role: "assistant",
            content: text,
            tool_calls: toolCalls.length > 0 ? toolCalls : void 0
          });
        }
        break;
      }
      case "tool": {
        for (const toolResponse of content) {
          if (useLegacyFunctionCalling) {
            messages.push({
              role: "function",
              name: toolResponse.toolName,
              content: JSON.stringify(toolResponse.result)
            });
          } else {
            messages.push({
              role: "tool",
              tool_call_id: toolResponse.toolCallId,
              content: JSON.stringify(toolResponse.result)
            });
          }
        }
        break;
      }
      default: {
        const _exhaustiveCheck = role;
        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);
      }
    }
  }
  return messages;
}

// src/map-openai-chat-logprobs.ts
function mapOpenAIChatLogProbsOutput(logprobs) {
  var _a, _b;
  return (_b = (_a = logprobs == null ? void 0 : logprobs.content) == null ? void 0 : _a.map(({ token, logprob, top_logprobs }) => ({
    token,
    logprob,
    topLogprobs: top_logprobs ? top_logprobs.map(({ token: token2, logprob: logprob2 }) => ({
      token: token2,
      logprob: logprob2
    })) : []
  }))) != null ? _b : void 0;
}

// src/map-openai-finish-reason.ts
function mapOpenAIFinishReason(finishReason) {
  switch (finishReason) {
    case "stop":
      return "stop";
    case "length":
      return "length";
    case "content_filter":
      return "content-filter";
    case "function_call":
    case "tool_calls":
      return "tool-calls";
    default:
      return "unknown";
  }
}

// src/openai-error.ts
import { z } from "zod";
import { createJsonErrorResponseHandler } from "@ai-sdk/provider-utils";
var openAIErrorDataSchema = z.object({
  error: z.object({
    message: z.string(),
    // The additional information below is handled loosely to support
    // OpenAI-compatible providers that have slightly different error
    // responses:
    type: z.string().nullish(),
    param: z.any().nullish(),
    code: z.union([z.string(), z.number()]).nullish()
  })
});
var openaiFailedResponseHandler = createJsonErrorResponseHandler({
  errorSchema: openAIErrorDataSchema,
  errorToMessage: (data) => data.error.message
});

// src/get-response-metadata.ts
function getResponseMetadata({
  id,
  model,
  created
}) {
  return {
    id: id != null ? id : void 0,
    modelId: model != null ? model : void 0,
    timestamp: created != null ? new Date(created * 1e3) : void 0
  };
}

// src/openai-chat-language-model.ts
var OpenAIChatLanguageModel = class {
  constructor(modelId, settings, config) {
    this.specificationVersion = "v1";
    this.modelId = modelId;
    this.settings = settings;
    this.config = config;
  }
  get supportsStructuredOutputs() {
    return this.settings.structuredOutputs === true;
  }
  get defaultObjectGenerationMode() {
    return this.supportsStructuredOutputs ? "json" : "tool";
  }
  get provider() {
    return this.config.provider;
  }
  get supportsImageUrls() {
    return !this.settings.downloadImages;
  }
  getArgs({
    mode,
    prompt,
    maxTokens,
    temperature,
    topP,
    topK,
    frequencyPenalty,
    presencePenalty,
    stopSequences,
    responseFormat,
    seed,
    providerMetadata
  }) {
    var _a, _b, _c;
    const type = mode.type;
    const warnings = [];
    if (topK != null) {
      warnings.push({
        type: "unsupported-setting",
        setting: "topK"
      });
    }
    if (responseFormat != null && responseFormat.type === "json" && responseFormat.schema != null) {
      warnings.push({
        type: "unsupported-setting",
        setting: "responseFormat",
        details: "JSON response format schema is not supported"
      });
    }
    const useLegacyFunctionCalling = this.settings.useLegacyFunctionCalling;
    if (useLegacyFunctionCalling && this.settings.parallelToolCalls === true) {
      throw new UnsupportedFunctionalityError2({
        functionality: "useLegacyFunctionCalling with parallelToolCalls"
      });
    }
    if (useLegacyFunctionCalling && this.settings.structuredOutputs === true) {
      throw new UnsupportedFunctionalityError2({
        functionality: "structuredOutputs with useLegacyFunctionCalling"
      });
    }
    const baseArgs = {
      // model id:
      model: this.modelId,
      // model specific settings:
      logit_bias: this.settings.logitBias,
      logprobs: this.settings.logprobs === true || typeof this.settings.logprobs === "number" ? true : void 0,
      top_logprobs: typeof this.settings.logprobs === "number" ? this.settings.logprobs : typeof this.settings.logprobs === "boolean" ? this.settings.logprobs ? 0 : void 0 : void 0,
      user: this.settings.user,
      parallel_tool_calls: this.settings.parallelToolCalls,
      // standardized settings:
      max_tokens: maxTokens,
      temperature,
      top_p: topP,
      frequency_penalty: frequencyPenalty,
      presence_penalty: presencePenalty,
      stop: stopSequences,
      seed,
      // openai specific settings:
      max_completion_tokens: (_b = (_a = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _a.maxCompletionTokens) != null ? _b : void 0,
      // response format:
      response_format: (responseFormat == null ? void 0 : responseFormat.type) === "json" ? { type: "json_object" } : void 0,
      // messages:
      messages: convertToOpenAIChatMessages({
        prompt,
        useLegacyFunctionCalling
      })
    };
    if (isReasoningModel(this.modelId)) {
      baseArgs.temperature = void 0;
      baseArgs.top_p = void 0;
      baseArgs.frequency_penalty = void 0;
      baseArgs.presence_penalty = void 0;
    }
    switch (type) {
      case "regular": {
        return {
          args: {
            ...baseArgs,
            ...prepareToolsAndToolChoice({
              mode,
              useLegacyFunctionCalling,
              structuredOutputs: this.settings.structuredOutputs
            })
          },
          warnings
        };
      }
      case "object-json": {
        return {
          args: {
            ...baseArgs,
            response_format: this.settings.structuredOutputs === true ? {
              type: "json_schema",
              json_schema: {
                schema: mode.schema,
                strict: true,
                name: (_c = mode.name) != null ? _c : "response",
                description: mode.description
              }
            } : { type: "json_object" }
          },
          warnings
        };
      }
      case "object-tool": {
        return {
          args: useLegacyFunctionCalling ? {
            ...baseArgs,
            function_call: {
              name: mode.tool.name
            },
            functions: [
              {
                name: mode.tool.name,
                description: mode.tool.description,
                parameters: mode.tool.parameters
              }
            ]
          } : {
            ...baseArgs,
            tool_choice: {
              type: "function",
              function: { name: mode.tool.name }
            },
            tools: [
              {
                type: "function",
                function: {
                  name: mode.tool.name,
                  description: mode.tool.description,
                  parameters: mode.tool.parameters,
                  strict: this.settings.structuredOutputs === true ? true : void 0
                }
              }
            ]
          },
          warnings
        };
      }
      default: {
        const _exhaustiveCheck = type;
        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);
      }
    }
  }
  async doGenerate(options) {
    var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
    const { args, warnings } = this.getArgs(options);
    const { responseHeaders, value: response } = await postJsonToApi({
      url: this.config.url({
        path: "/chat/completions",
        modelId: this.modelId
      }),
      headers: combineHeaders(this.config.headers(), options.headers),
      body: args,
      failedResponseHandler: openaiFailedResponseHandler,
      successfulResponseHandler: createJsonResponseHandler(
        openAIChatResponseSchema
      ),
      abortSignal: options.abortSignal,
      fetch: this.config.fetch
    });
    const { messages: rawPrompt, ...rawSettings } = args;
    const choice = response.choices[0];
    const providerMetadata = ((_b = (_a = response.usage) == null ? void 0 : _a.completion_tokens_details) == null ? void 0 : _b.reasoning_tokens) != null ? {
      openai: {
        reasoningTokens: (_d = (_c = response.usage) == null ? void 0 : _c.completion_tokens_details) == null ? void 0 : _d.reasoning_tokens
      }
    } : void 0;
    return {
      text: (_e = choice.message.content) != null ? _e : void 0,
      toolCalls: this.settings.useLegacyFunctionCalling && choice.message.function_call ? [
        {
          toolCallType: "function",
          toolCallId: generateId(),
          toolName: choice.message.function_call.name,
          args: choice.message.function_call.arguments
        }
      ] : (_f = choice.message.tool_calls) == null ? void 0 : _f.map((toolCall) => {
        var _a2;
        return {
          toolCallType: "function",
          toolCallId: (_a2 = toolCall.id) != null ? _a2 : generateId(),
          toolName: toolCall.function.name,
          args: toolCall.function.arguments
        };
      }),
      finishReason: mapOpenAIFinishReason(choice.finish_reason),
      usage: {
        promptTokens: (_h = (_g = response.usage) == null ? void 0 : _g.prompt_tokens) != null ? _h : NaN,
        completionTokens: (_j = (_i = response.usage) == null ? void 0 : _i.completion_tokens) != null ? _j : NaN
      },
      rawCall: { rawPrompt, rawSettings },
      rawResponse: { headers: responseHeaders },
      response: getResponseMetadata(response),
      warnings,
      logprobs: mapOpenAIChatLogProbsOutput(choice.logprobs),
      providerMetadata
    };
  }
  async doStream(options) {
    if (isReasoningModel(this.modelId)) {
      const result = await this.doGenerate(options);
      const simulatedStream = new ReadableStream({
        start(controller) {
          controller.enqueue({ type: "response-metadata", ...result.response });
          if (result.text) {
            controller.enqueue({
              type: "text-delta",
              textDelta: result.text
            });
          }
          if (result.toolCalls) {
            for (const toolCall of result.toolCalls) {
              controller.enqueue({
                type: "tool-call",
                ...toolCall
              });
            }
          }
          controller.enqueue({
            type: "finish",
            finishReason: result.finishReason,
            usage: result.usage,
            logprobs: result.logprobs,
            providerMetadata: result.providerMetadata
          });
          controller.close();
        }
      });
      return {
        stream: simulatedStream,
        rawCall: result.rawCall,
        rawResponse: result.rawResponse,
        warnings: result.warnings
      };
    }
    const { args, warnings } = this.getArgs(options);
    const { responseHeaders, value: response } = await postJsonToApi({
      url: this.config.url({
        path: "/chat/completions",
        modelId: this.modelId
      }),
      headers: combineHeaders(this.config.headers(), options.headers),
      body: {
        ...args,
        stream: true,
        // only include stream_options when in strict compatibility mode:
        stream_options: this.config.compatibility === "strict" ? { include_usage: true } : void 0
      },
      failedResponseHandler: openaiFailedResponseHandler,
      successfulResponseHandler: createEventSourceResponseHandler(
        openaiChatChunkSchema
      ),
      abortSignal: options.abortSignal,
      fetch: this.config.fetch
    });
    const { messages: rawPrompt, ...rawSettings } = args;
    const toolCalls = [];
    let finishReason = "unknown";
    let usage = {
      promptTokens: void 0,
      completionTokens: void 0
    };
    let logprobs;
    let isFirstChunk = true;
    const { useLegacyFunctionCalling } = this.settings;
    return {
      stream: response.pipeThrough(
        new TransformStream({
          transform(chunk, controller) {
            var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n;
            if (!chunk.success) {
              finishReason = "error";
              controller.enqueue({ type: "error", error: chunk.error });
              return;
            }
            const value = chunk.value;
            if ("error" in value) {
              finishReason = "error";
              controller.enqueue({ type: "error", error: value.error });
              return;
            }
            if (isFirstChunk) {
              isFirstChunk = false;
              controller.enqueue({
                type: "response-metadata",
                ...getResponseMetadata(value)
              });
            }
            if (value.usage != null) {
              usage = {
                promptTokens: (_a = value.usage.prompt_tokens) != null ? _a : void 0,
                completionTokens: (_b = value.usage.completion_tokens) != null ? _b : void 0
              };
            }
            const choice = value.choices[0];
            if ((choice == null ? void 0 : choice.finish_reason) != null) {
              finishReason = mapOpenAIFinishReason(choice.finish_reason);
            }
            if ((choice == null ? void 0 : choice.delta) == null) {
              return;
            }
            const delta = choice.delta;
            if (delta.content != null) {
              controller.enqueue({
                type: "text-delta",
                textDelta: delta.content
              });
            }
            const mappedLogprobs = mapOpenAIChatLogProbsOutput(
              choice == null ? void 0 : choice.logprobs
            );
            if (mappedLogprobs == null ? void 0 : mappedLogprobs.length) {
              if (logprobs === void 0) logprobs = [];
              logprobs.push(...mappedLogprobs);
            }
            const mappedToolCalls = useLegacyFunctionCalling && delta.function_call != null ? [
              {
                type: "function",
                id: generateId(),
                function: delta.function_call,
                index: 0
              }
            ] : delta.tool_calls;
            if (mappedToolCalls != null) {
              for (const toolCallDelta of mappedToolCalls) {
                const index = toolCallDelta.index;
                if (toolCalls[index] == null) {
                  if (toolCallDelta.type !== "function") {
                    throw new InvalidResponseDataError({
                      data: toolCallDelta,
                      message: `Expected 'function' type.`
                    });
                  }
                  if (toolCallDelta.id == null) {
                    throw new InvalidResponseDataError({
                      data: toolCallDelta,
                      message: `Expected 'id' to be a string.`
                    });
                  }
                  if (((_c = toolCallDelta.function) == null ? void 0 : _c.name) == null) {
                    throw new InvalidResponseDataError({
                      data: toolCallDelta,
                      message: `Expected 'function.name' to be a string.`
                    });
                  }
                  toolCalls[index] = {
                    id: toolCallDelta.id,
                    type: "function",
                    function: {
                      name: toolCallDelta.function.name,
                      arguments: (_d = toolCallDelta.function.arguments) != null ? _d : ""
                    }
                  };
                  const toolCall2 = toolCalls[index];
                  if (((_e = toolCall2.function) == null ? void 0 : _e.name) != null && ((_f = toolCall2.function) == null ? void 0 : _f.arguments) != null) {
                    if (toolCall2.function.arguments.length > 0) {
                      controller.enqueue({
                        type: "tool-call-delta",
                        toolCallType: "function",
                        toolCallId: toolCall2.id,
                        toolName: toolCall2.function.name,
                        argsTextDelta: toolCall2.function.arguments
                      });
                    }
                    if (isParsableJson(toolCall2.function.arguments)) {
                      controller.enqueue({
                        type: "tool-call",
                        toolCallType: "function",
                        toolCallId: (_g = toolCall2.id) != null ? _g : generateId(),
                        toolName: toolCall2.function.name,
                        args: toolCall2.function.arguments
                      });
                    }
                  }
                  continue;
                }
                const toolCall = toolCalls[index];
                if (((_h = toolCallDelta.function) == null ? void 0 : _h.arguments) != null) {
                  toolCall.function.arguments += (_j = (_i = toolCallDelta.function) == null ? void 0 : _i.arguments) != null ? _j : "";
                }
                controller.enqueue({
                  type: "tool-call-delta",
                  toolCallType: "function",
                  toolCallId: toolCall.id,
                  toolName: toolCall.function.name,
                  argsTextDelta: (_k = toolCallDelta.function.arguments) != null ? _k : ""
                });
                if (((_l = toolCall.function) == null ? void 0 : _l.name) != null && ((_m = toolCall.function) == null ? void 0 : _m.arguments) != null && isParsableJson(toolCall.function.arguments)) {
                  controller.enqueue({
                    type: "tool-call",
                    toolCallType: "function",
                    toolCallId: (_n = toolCall.id) != null ? _n : generateId(),
                    toolName: toolCall.function.name,
                    args: toolCall.function.arguments
                  });
                }
              }
            }
          },
          flush(controller) {
            var _a, _b;
            controller.enqueue({
              type: "finish",
              finishReason,
              logprobs,
              usage: {
                promptTokens: (_a = usage.promptTokens) != null ? _a : NaN,
                completionTokens: (_b = usage.completionTokens) != null ? _b : NaN
              }
            });
          }
        })
      ),
      rawCall: { rawPrompt, rawSettings },
      rawResponse: { headers: responseHeaders },
      warnings
    };
  }
};
var openAITokenUsageSchema = z2.object({
  prompt_tokens: z2.number().nullish(),
  completion_tokens: z2.number().nullish(),
  completion_tokens_details: z2.object({
    reasoning_tokens: z2.number().nullish()
  }).nullish()
}).nullish();
var openAIChatResponseSchema = z2.object({
  id: z2.string().nullish(),
  created: z2.number().nullish(),
  model: z2.string().nullish(),
  choices: z2.array(
    z2.object({
      message: z2.object({
        role: z2.literal("assistant").nullish(),
        content: z2.string().nullish(),
        function_call: z2.object({
          arguments: z2.string(),
          name: z2.string()
        }).nullish(),
        tool_calls: z2.array(
          z2.object({
            id: z2.string().nullish(),
            type: z2.literal("function"),
            function: z2.object({
              name: z2.string(),
              arguments: z2.string()
            })
          })
        ).nullish()
      }),
      index: z2.number(),
      logprobs: z2.object({
        content: z2.array(
          z2.object({
            token: z2.string(),
            logprob: z2.number(),
            top_logprobs: z2.array(
              z2.object({
                token: z2.string(),
                logprob: z2.number()
              })
            )
          })
        ).nullable()
      }).nullish(),
      finish_reason: z2.string().nullish()
    })
  ),
  usage: openAITokenUsageSchema
});
var openaiChatChunkSchema = z2.union([
  z2.object({
    id: z2.string().nullish(),
    created: z2.number().nullish(),
    model: z2.string().nullish(),
    choices: z2.array(
      z2.object({
        delta: z2.object({
          role: z2.enum(["assistant"]).nullish(),
          content: z2.string().nullish(),
          function_call: z2.object({
            name: z2.string().optional(),
            arguments: z2.string().optional()
          }).nullish(),
          tool_calls: z2.array(
            z2.object({
              index: z2.number(),
              id: z2.string().nullish(),
              type: z2.literal("function").optional(),
              function: z2.object({
                name: z2.string().nullish(),
                arguments: z2.string().nullish()
              })
            })
          ).nullish()
        }).nullish(),
        logprobs: z2.object({
          content: z2.array(
            z2.object({
              token: z2.string(),
              logprob: z2.number(),
              top_logprobs: z2.array(
                z2.object({
                  token: z2.string(),
                  logprob: z2.number()
                })
              )
            })
          ).nullable()
        }).nullish(),
        finish_reason: z2.string().nullable().optional(),
        index: z2.number()
      })
    ),
    usage: openAITokenUsageSchema
  }),
  openAIErrorDataSchema
]);
function prepareToolsAndToolChoice({
  mode,
  useLegacyFunctionCalling = false,
  structuredOutputs = false
}) {
  var _a;
  const tools = ((_a = mode.tools) == null ? void 0 : _a.length) ? mode.tools : void 0;
  if (tools == null) {
    return { tools: void 0, tool_choice: void 0 };
  }
  const toolChoice = mode.toolChoice;
  if (useLegacyFunctionCalling) {
    const mappedFunctions = tools.map((tool) => ({
      name: tool.name,
      description: tool.description,
      parameters: tool.parameters
    }));
    if (toolChoice == null) {
      return { functions: mappedFunctions, function_call: void 0 };
    }
    const type2 = toolChoice.type;
    switch (type2) {
      case "auto":
      case "none":
      case void 0:
        return {
          functions: mappedFunctions,
          function_call: void 0
        };
      case "required":
        throw new UnsupportedFunctionalityError2({
          functionality: "useLegacyFunctionCalling and toolChoice: required"
        });
      default:
        return {
          functions: mappedFunctions,
          function_call: { name: toolChoice.toolName }
        };
    }
  }
  const mappedTools = tools.map((tool) => ({
    type: "function",
    function: {
      name: tool.name,
      description: tool.description,
      parameters: tool.parameters,
      strict: structuredOutputs === true ? true : void 0
    }
  }));
  if (toolChoice == null) {
    return { tools: mappedTools, tool_choice: void 0 };
  }
  const type = toolChoice.type;
  switch (type) {
    case "auto":
    case "none":
    case "required":
      return { tools: mappedTools, tool_choice: type };
    case "tool":
      return {
        tools: mappedTools,
        tool_choice: {
          type: "function",
          function: {
            name: toolChoice.toolName
          }
        }
      };
    default: {
      const _exhaustiveCheck = type;
      throw new Error(`Unsupported tool choice type: ${_exhaustiveCheck}`);
    }
  }
}
function isReasoningModel(modelId) {
  return modelId.startsWith("o1-");
}

// src/openai-completion-language-model.ts
import {
  UnsupportedFunctionalityError as UnsupportedFunctionalityError4
} from "@ai-sdk/provider";
import {
  combineHeaders as combineHeaders2,
  createEventSourceResponseHandler as createEventSourceResponseHandler2,
  createJsonResponseHandler as createJsonResponseHandler2,
  postJsonToApi as postJsonToApi2
} from "@ai-sdk/provider-utils";
import { z as z3 } from "zod";

// src/convert-to-openai-completion-prompt.ts
import {
  InvalidPromptError,
  UnsupportedFunctionalityError as UnsupportedFunctionalityError3
} from "@ai-sdk/provider";
function convertToOpenAICompletionPrompt({
  prompt,
  inputFormat,
  user = "user",
  assistant = "assistant"
}) {
  if (inputFormat === "prompt" && prompt.length === 1 && prompt[0].role === "user" && prompt[0].content.length === 1 && prompt[0].content[0].type === "text") {
    return { prompt: prompt[0].content[0].text };
  }
  let text = "";
  if (prompt[0].role === "system") {
    text += `${prompt[0].content}

`;
    prompt = prompt.slice(1);
  }
  for (const { role, content } of prompt) {
    switch (role) {
      case "system": {
        throw new InvalidPromptError({
          message: "Unexpected system message in prompt: ${content}",
          prompt
        });
      }
      case "user": {
        const userMessage = content.map((part) => {
          switch (part.type) {
            case "text": {
              return part.text;
            }
            case "image": {
              throw new UnsupportedFunctionalityError3({
                functionality: "images"
              });
            }
          }
        }).join("");
        text += `${user}:
${userMessage}

`;
        break;
      }
      case "assistant": {
        const assistantMessage = content.map((part) => {
          switch (part.type) {
            case "text": {
              return part.text;
            }
            case "tool-call": {
              throw new UnsupportedFunctionalityError3({
                functionality: "tool-call messages"
              });
            }
          }
        }).join("");
        text += `${assistant}:
${assistantMessage}

`;
        break;
      }
      case "tool": {
        throw new UnsupportedFunctionalityError3({
          functionality: "tool messages"
        });
      }
      default: {
        const _exhaustiveCheck = role;
        throw new Error(`Unsupported role: ${_exhaustiveCheck}`);
      }
    }
  }
  text += `${assistant}:
`;
  return {
    prompt: text,
    stopSequences: [`
${user}:`]
  };
}

// src/map-openai-completion-logprobs.ts
function mapOpenAICompletionLogProbs(logprobs) {
  return logprobs == null ? void 0 : logprobs.tokens.map((token, index) => ({
    token,
    logprob: logprobs.token_logprobs[index],
    topLogprobs: logprobs.top_logprobs ? Object.entries(logprobs.top_logprobs[index]).map(
      ([token2, logprob]) => ({
        token: token2,
        logprob
      })
    ) : []
  }));
}

// src/openai-completion-language-model.ts
var OpenAICompletionLanguageModel = class {
  constructor(modelId, settings, config) {
    this.specificationVersion = "v1";
    this.defaultObjectGenerationMode = void 0;
    this.modelId = modelId;
    this.settings = settings;
    this.config = config;
  }
  get provider() {
    return this.config.provider;
  }
  getArgs({
    mode,
    inputFormat,
    prompt,
    maxTokens,
    temperature,
    topP,
    topK,
    frequencyPenalty,
    presencePenalty,
    stopSequences: userStopSequences,
    responseFormat,
    seed
  }) {
    var _a;
    const type = mode.type;
    const warnings = [];
    if (topK != null) {
      warnings.push({
        type: "unsupported-setting",
        setting: "topK"
      });
    }
    if (responseFormat != null && responseFormat.type !== "text") {
      warnings.push({
        type: "unsupported-setting",
        setting: "responseFormat",
        details: "JSON response format is not supported."
      });
    }
    const { prompt: completionPrompt, stopSequences } = convertToOpenAICompletionPrompt({ prompt, inputFormat });
    const stop = [...stopSequences != null ? stopSequences : [], ...userStopSequences != null ? userStopSequences : []];
    const baseArgs = {
      // model id:
      model: this.modelId,
      // model specific settings:
      echo: this.settings.echo,
      logit_bias: this.settings.logitBias,
      logprobs: typeof this.settings.logprobs === "number" ? this.settings.logprobs : typeof this.settings.logprobs === "boolean" ? this.settings.logprobs ? 0 : void 0 : void 0,
      suffix: this.settings.suffix,
      user: this.settings.user,
      // standardized settings:
      max_tokens: maxTokens,
      temperature,
      top_p: topP,
      frequency_penalty: frequencyPenalty,
      presence_penalty: presencePenalty,
      seed,
      // prompt:
      prompt: completionPrompt,
      // stop sequences:
      stop: stop.length > 0 ? stop : void 0
    };
    switch (type) {
      case "regular": {
        if ((_a = mode.tools) == null ? void 0 : _a.length) {
          throw new UnsupportedFunctionalityError4({
            functionality: "tools"
          });
        }
        if (mode.toolChoice) {
          throw new UnsupportedFunctionalityError4({
            functionality: "toolChoice"
          });
        }
        return { args: baseArgs, warnings };
      }
      case "object-json": {
        throw new UnsupportedFunctionalityError4({
          functionality: "object-json mode"
        });
      }
      case "object-tool": {
        throw new UnsupportedFunctionalityError4({
          functionality: "object-tool mode"
        });
      }
      default: {
        const _exhaustiveCheck = type;
        throw new Error(`Unsupported type: ${_exhaustiveCheck}`);
      }
    }
  }
  async doGenerate(options) {
    const { args, warnings } = this.getArgs(options);
    const { responseHeaders, value: response } = await postJsonToApi2({
      url: this.config.url({
        path: "/completions",
        modelId: this.modelId
      }),
      headers: combineHeaders2(this.config.headers(), options.headers),
      body: args,
      failedResponseHandler: openaiFailedResponseHandler,
      successfulResponseHandler: createJsonResponseHandler2(
        openAICompletionResponseSchema
      ),
      abortSignal: options.abortSignal,
      fetch: this.config.fetch
    });
    const { prompt: rawPrompt, ...rawSettings } = args;
    const choice = response.choices[0];
    return {
      text: choice.text,
      usage: {
        promptTokens: response.usage.prompt_tokens,
        completionTokens: response.usage.completion_tokens
      },
      finishReason: mapOpenAIFinishReason(choice.finish_reason),
      logprobs: mapOpenAICompletionLogProbs(choice.logprobs),
      rawCall: { rawPrompt, rawSettings },
      rawResponse: { headers: responseHeaders },
      response: getResponseMetadata(response),
      warnings
    };
  }
  async doStream(options) {
    const { args, warnings } = this.getArgs(options);
    const { responseHeaders, value: response } = await postJsonToApi2({
      url: this.config.url({
        path: "/completions",
        modelId: this.modelId
      }),
      headers: combineHeaders2(this.config.headers(), options.headers),
      body: {
        ...args,
        stream: true,
        // only include stream_options when in strict compatibility mode:
        stream_options: this.config.compatibility === "strict" ? { include_usage: true } : void 0
      },
      failedResponseHandler: openaiFailedResponseHandler,
      successfulResponseHandler: createEventSourceResponseHandler2(
        openaiCompletionChunkSchema
      ),
      abortSignal: options.abortSignal,
      fetch: this.config.fetch
    });
    const { prompt: rawPrompt, ...rawSettings } = args;
    let finishReason = "unknown";
    let usage = {
      promptTokens: Number.NaN,
      completionTokens: Number.NaN
    };
    let logprobs;
    let isFirstChunk = true;
    return {
      stream: response.pipeThrough(
        new TransformStream({
          transform(chunk, controller) {
            if (!chunk.success) {
              finishReason = "error";
              controller.enqueue({ type: "error", error: chunk.error });
              return;
            }
            const value = chunk.value;
            if ("error" in value) {
              finishReason = "error";
              controller.enqueue({ type: "error", error: value.error });
              return;
            }
            if (isFirstChunk) {
              isFirstChunk = false;
              controller.enqueue({
                type: "response-metadata",
                ...getResponseMetadata(value)
              });
            }
            if (value.usage != null) {
              usage = {
                promptTokens: value.usage.prompt_tokens,
                completionTokens: value.usage.completion_tokens
              };
            }
            const choice = value.choices[0];
            if ((choice == null ? void 0 : choice.finish_reason) != null) {
              finishReason = mapOpenAIFinishReason(choice.finish_reason);
            }
            if ((choice == null ? void 0 : choice.text) != null) {
              controller.enqueue({
                type: "text-delta",
                textDelta: choice.text
              });
            }
            const mappedLogprobs = mapOpenAICompletionLogProbs(
              choice == null ? void 0 : choice.logprobs
            );
            if (mappedLogprobs == null ? void 0 : mappedLogprobs.length) {
              if (logprobs === void 0) logprobs = [];
              logprobs.push(...mappedLogprobs);
            }
          },
          flush(controller) {
            controller.enqueue({
              type: "finish",
              finishReason,
              logprobs,
              usage
            });
          }
        })
      ),
      rawCall: { rawPrompt, rawSettings },
      rawResponse: { headers: responseHeaders },
      warnings
    };
  }
};
var openAICompletionResponseSchema = z3.object({
  id: z3.string().nullish(),
  created: z3.number().nullish(),
  model: z3.string().nullish(),
  choices: z3.array(
    z3.object({
      text: z3.string(),
      finish_reason: z3.string(),
      logprobs: z3.object({
        tokens: z3.array(z3.string()),
        token_logprobs: z3.array(z3.number()),
        top_logprobs: z3.array(z3.record(z3.string(), z3.number())).nullable()
      }).nullish()
    })
  ),
  usage: z3.object({
    prompt_tokens: z3.number(),
    completion_tokens: z3.number()
  })
});
var openaiCompletionChunkSchema = z3.union([
  z3.object({
    id: z3.string().nullish(),
    created: z3.number().nullish(),
    model: z3.string().nullish(),
    choices: z3.array(
      z3.object({
        text: z3.string(),
        finish_reason: z3.string().nullish(),
        index: z3.number(),
        logprobs: z3.object({
          tokens: z3.array(z3.string()),
          token_logprobs: z3.array(z3.number()),
          top_logprobs: z3.array(z3.record(z3.string(), z3.number())).nullable()
        }).nullish()
      })
    ),
    usage: z3.object({
      prompt_tokens: z3.number(),
      completion_tokens: z3.number()
    }).nullish()
  }),
  openAIErrorDataSchema
]);

// src/openai-facade.ts
var OpenAI = class {
  /**
   * Creates a new OpenAI provider instance.
   */
  constructor(options = {}) {
    var _a, _b;
    this.baseURL = (_b = withoutTrailingSlash((_a = options.baseURL) != null ? _a : options.baseUrl)) != null ? _b : "https://api.openai.com/v1";
    this.apiKey = options.apiKey;
    this.organization = options.organization;
    this.project = options.project;
    this.headers = options.headers;
  }
  get baseConfig() {
    return {
      organization: this.organization,
      baseURL: this.baseURL,
      headers: () => ({
        Authorization: `Bearer ${loadApiKey({
          apiKey: this.apiKey,
          environmentVariableName: "OPENAI_API_KEY",
          description: "OpenAI"
        })}`,
        "OpenAI-Organization": this.organization,
        "OpenAI-Project": this.project,
        ...this.headers
      })
    };
  }
  chat(modelId, settings = {}) {
    return new OpenAIChatLanguageModel(modelId, settings, {
      provider: "openai.chat",
      ...this.baseConfig,
      compatibility: "strict",
      url: ({ path }) => `${this.baseURL}${path}`
    });
  }
  completion(modelId, settings = {}) {
    return new OpenAICompletionLanguageModel(modelId, settings, {
      provider: "openai.completion",
      ...this.baseConfig,
      compatibility: "strict",
      url: ({ path }) => `${this.baseURL}${path}`
    });
  }
};

// src/openai-provider.ts
import {
  loadApiKey as loadApiKey2,
  withoutTrailingSlash as withoutTrailingSlash2
} from "@ai-sdk/provider-utils";

// src/openai-embedding-model.ts
import {
  TooManyEmbeddingValuesForCallError
} from "@ai-sdk/provider";
import {
  combineHeaders as combineHeaders3,
  createJsonResponseHandler as createJsonResponseHandler3,
  postJsonToApi as postJsonToApi3
} from "@ai-sdk/provider-utils";
import { z as z4 } from "zod";
var OpenAIEmbeddingModel = class {
  constructor(modelId, settings, config) {
    this.specificationVersion = "v1";
    this.modelId = modelId;
    this.settings = settings;
    this.config = config;
  }
  get provider() {
    return this.config.provider;
  }
  get maxEmbeddingsPerCall() {
    var _a;
    return (_a = this.settings.maxEmbeddingsPerCall) != null ? _a : 2048;
  }
  get supportsParallelCalls() {
    var _a;
    return (_a = this.settings.supportsParallelCalls) != null ? _a : true;
  }
  async doEmbed({
    values,
    headers,
    abortSignal
  }) {
    if (values.length > this.maxEmbeddingsPerCall) {
      throw new TooManyEmbeddingValuesForCallError({
        provider: this.provider,
        modelId: this.modelId,
        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,
        values
      });
    }
    const { responseHeaders, value: response } = await postJsonToApi3({
      url: this.config.url({
        path: "/embeddings",
        modelId: this.modelId
      }),
      headers: combineHeaders3(this.config.headers(), headers),
      body: {
        model: this.modelId,
        input: values,
        encoding_format: "float",
        dimensions: this.settings.dimensions,
        user: this.settings.user
      },
      failedResponseHandler: openaiFailedResponseHandler,
      successfulResponseHandler: createJsonResponseHandler3(
        openaiTextEmbeddingResponseSchema
      ),
      abortSignal,
      fetch: this.config.fetch
    });
    return {
      embeddings: response.data.map((item) => item.embedding),
      usage: response.usage ? { tokens: response.usage.prompt_tokens } : void 0,
      rawResponse: { headers: responseHeaders }
    };
  }
};
var openaiTextEmbeddingResponseSchema = z4.object({
  data: z4.array(z4.object({ embedding: z4.array(z4.number()) })),
  usage: z4.object({ prompt_tokens: z4.number() }).nullish()
});

// src/openai-provider.ts
function createOpenAI(options = {}) {
  var _a, _b, _c;
  const baseURL = (_b = withoutTrailingSlash2((_a = options.baseURL) != null ? _a : options.baseUrl)) != null ? _b : "https://api.openai.com/v1";
  const compatibility = (_c = options.compatibility) != null ? _c : "compatible";
  const getHeaders = () => ({
    Authorization: `Bearer ${loadApiKey2({
      apiKey: options.apiKey,
      environmentVariableName: "OPENAI_API_KEY",
      description: "OpenAI"
    })}`,
    "OpenAI-Organization": options.organization,
    "OpenAI-Project": options.project,
    ...options.headers
  });
  const createChatModel = (modelId, settings = {}) => new OpenAIChatLanguageModel(modelId, settings, {
    provider: "openai.chat",
    url: ({ path }) => `${baseURL}${path}`,
    headers: getHeaders,
    compatibility,
    fetch: options.fetch
  });
  const createCompletionModel = (modelId, settings = {}) => new OpenAICompletionLanguageModel(modelId, settings, {
    provider: "openai.completion",
    url: ({ path }) => `${baseURL}${path}`,
    headers: getHeaders,
    compatibility,
    fetch: options.fetch
  });
  const createEmbeddingModel = (modelId, settings = {}) => new OpenAIEmbeddingModel(modelId, settings, {
    provider: "openai.embedding",
    url: ({ path }) => `${baseURL}${path}`,
    headers: getHeaders,
    fetch: options.fetch
  });
  const createLanguageModel = (modelId, settings) => {
    if (new.target) {
      throw new Error(
        "The OpenAI model function cannot be called with the new keyword."
      );
    }
    if (modelId === "gpt-3.5-turbo-instruct") {
      return createCompletionModel(
        modelId,
        settings
      );
    }
    return createChatModel(modelId, settings);
  };
  const provider = function(modelId, settings) {
    return createLanguageModel(modelId, settings);
  };
  provider.languageModel = createLanguageModel;
  provider.chat = createChatModel;
  provider.completion = createCompletionModel;
  provider.embedding = createEmbeddingModel;
  provider.textEmbedding = createEmbeddingModel;
  provider.textEmbeddingModel = createEmbeddingModel;
  return provider;
}
var openai = createOpenAI({
  compatibility: "strict"
  // strict for OpenAI API
});
export {
  OpenAI,
  createOpenAI,
  openai
};
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