@ai-sdk/perplexity/dist/index.mjs.map

The **[Perplexity provider](https://ai-sdk.dev/providers/ai-sdk-providers/perplexity)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for Perplexity's Sonar API - a powerful answer engine with real-time web search capabilities.

{"version":3,"sources":["../src/perplexity-provider.ts","../src/perplexity-language-model.ts","../src/convert-perplexity-usage.ts","../src/convert-to-perplexity-messages.ts","../src/map-perplexity-finish-reason.ts","../src/version.ts"],"sourcesContent":["import {\n  LanguageModelV3,\n  NoSuchModelError,\n  ProviderV3,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  generateId,\n  loadApiKey,\n  withoutTrailingSlash,\n  withUserAgentSuffix,\n} from '@ai-sdk/provider-utils';\nimport { PerplexityLanguageModel } from './perplexity-language-model';\nimport { PerplexityLanguageModelId } from './perplexity-language-model-options';\nimport { VERSION } from './version';\n\nexport interface PerplexityProvider extends ProviderV3 {\n  /**\nCreates an Perplexity chat model for text generation.\n   */\n  (modelId: PerplexityLanguageModelId): LanguageModelV3;\n\n  /**\nCreates an Perplexity language model for text generation.\n   */\n  languageModel(modelId: PerplexityLanguageModelId): LanguageModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead.\n   */\n  textEmbeddingModel(modelId: string): never;\n}\n\nexport interface PerplexityProviderSettings {\n  /**\nBase URL for the perplexity API calls.\n     */\n  baseURL?: string;\n\n  /**\nAPI key for authenticating requests.\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\nexport function createPerplexity(\n  options: PerplexityProviderSettings = {},\n): PerplexityProvider {\n  const getHeaders = () =>\n    withUserAgentSuffix(\n      {\n        Authorization: `Bearer ${loadApiKey({\n          apiKey: options.apiKey,\n          environmentVariableName: 'PERPLEXITY_API_KEY',\n          description: 'Perplexity',\n        })}`,\n        ...options.headers,\n      },\n      `ai-sdk/perplexity/${VERSION}`,\n    );\n\n  const createLanguageModel = (modelId: PerplexityLanguageModelId) => {\n    return new PerplexityLanguageModel(modelId, {\n      baseURL: withoutTrailingSlash(\n        options.baseURL ?? 'https://api.perplexity.ai',\n      )!,\n      headers: getHeaders,\n      generateId,\n      fetch: options.fetch,\n    });\n  };\n\n  const provider = (modelId: PerplexityLanguageModelId) =>\n    createLanguageModel(modelId);\n\n  provider.specificationVersion = 'v3' as const;\n  provider.languageModel = createLanguageModel;\n\n  provider.embeddingModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'embeddingModel' });\n  };\n  provider.textEmbeddingModel = provider.embeddingModel;\n  provider.imageModel = (modelId: string) => {\n    throw new NoSuchModelError({ modelId, modelType: 'imageModel' });\n  };\n\n  return provider;\n}\n\nexport const perplexity = createPerplexity();\n","import {\n  LanguageModelV3,\n  LanguageModelV3CallOptions,\n  LanguageModelV3Content,\n  LanguageModelV3FinishReason,\n  LanguageModelV3GenerateResult,\n  LanguageModelV3StreamPart,\n  LanguageModelV3StreamResult,\n  SharedV3Warning,\n} from '@ai-sdk/provider';\nimport {\n  FetchFunction,\n  ParseResult,\n  combineHeaders,\n  createEventSourceResponseHandler,\n  createJsonErrorResponseHandler,\n  createJsonResponseHandler,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\nimport { convertPerplexityUsage } from './convert-perplexity-usage';\nimport { convertToPerplexityMessages } from './convert-to-perplexity-messages';\nimport { mapPerplexityFinishReason } from './map-perplexity-finish-reason';\nimport { PerplexityLanguageModelId } from './perplexity-language-model-options';\n\ntype PerplexityChatConfig = {\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  generateId: () => string;\n  fetch?: FetchFunction;\n};\n\nexport class PerplexityLanguageModel implements LanguageModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly provider = 'perplexity';\n\n  readonly modelId: PerplexityLanguageModelId;\n\n  private readonly config: PerplexityChatConfig;\n\n  constructor(\n    modelId: PerplexityLanguageModelId,\n    config: PerplexityChatConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  readonly supportedUrls: Record<string, RegExp[]> = {\n    // No URLs are supported.\n  };\n\n  private getArgs({\n    prompt,\n    maxOutputTokens,\n    temperature,\n    topP,\n    topK,\n    frequencyPenalty,\n    presencePenalty,\n    stopSequences,\n    responseFormat,\n    seed,\n    providerOptions,\n  }: LanguageModelV3CallOptions) {\n    const warnings: SharedV3Warning[] = [];\n\n    if (topK != null) {\n      warnings.push({ type: 'unsupported', feature: 'topK' });\n    }\n\n    if (stopSequences != null) {\n      warnings.push({ type: 'unsupported', feature: 'stopSequences' });\n    }\n\n    if (seed != null) {\n      warnings.push({ type: 'unsupported', feature: 'seed' });\n    }\n\n    return {\n      args: {\n        // model id:\n        model: this.modelId,\n\n        // standardized settings:\n        frequency_penalty: frequencyPenalty,\n        max_tokens: maxOutputTokens,\n        presence_penalty: presencePenalty,\n        temperature,\n        top_k: topK,\n        top_p: topP,\n\n        // response format:\n        response_format:\n          responseFormat?.type === 'json'\n            ? {\n                type: 'json_schema',\n                json_schema: { schema: responseFormat.schema },\n              }\n            : undefined,\n\n        // provider extensions\n        ...(providerOptions?.perplexity ?? {}),\n\n        // messages:\n        messages: convertToPerplexityMessages(prompt),\n      },\n      warnings,\n    };\n  }\n\n  async doGenerate(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3GenerateResult> {\n    const { args: body, warnings } = this.getArgs(options);\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue: rawResponse,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: createJsonErrorResponseHandler({\n        errorSchema: perplexityErrorSchema,\n        errorToMessage,\n      }),\n      successfulResponseHandler: createJsonResponseHandler(\n        perplexityResponseSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    const choice = response.choices[0];\n    const content: Array<LanguageModelV3Content> = [];\n\n    // text content:\n    const text = choice.message.content;\n    if (text.length > 0) {\n      content.push({ type: 'text', text });\n    }\n\n    // sources:\n    if (response.citations != null) {\n      for (const url of response.citations) {\n        content.push({\n          type: 'source',\n          sourceType: 'url',\n          id: this.config.generateId(),\n          url,\n        });\n      }\n    }\n\n    return {\n      content,\n      finishReason: {\n        unified: mapPerplexityFinishReason(choice.finish_reason),\n        raw: choice.finish_reason ?? undefined,\n      },\n      usage: convertPerplexityUsage(response.usage),\n      request: { body },\n      response: {\n        ...getResponseMetadata(response),\n        headers: responseHeaders,\n        body: rawResponse,\n      },\n      warnings,\n      providerMetadata: {\n        perplexity: {\n          images:\n            response.images?.map(image => ({\n              imageUrl: image.image_url,\n              originUrl: image.origin_url,\n              height: image.height,\n              width: image.width,\n            })) ?? null,\n          usage: {\n            citationTokens: response.usage?.citation_tokens ?? null,\n            numSearchQueries: response.usage?.num_search_queries ?? null,\n          },\n        },\n      },\n    };\n  }\n\n  async doStream(\n    options: LanguageModelV3CallOptions,\n  ): Promise<LanguageModelV3StreamResult> {\n    const { args, warnings } = this.getArgs(options);\n\n    const body = { ...args, stream: true };\n\n    const { responseHeaders, value: response } = await postJsonToApi({\n      url: `${this.config.baseURL}/chat/completions`,\n      headers: combineHeaders(this.config.headers(), options.headers),\n      body,\n      failedResponseHandler: createJsonErrorResponseHandler({\n        errorSchema: perplexityErrorSchema,\n        errorToMessage,\n      }),\n      successfulResponseHandler: createEventSourceResponseHandler(\n        perplexityChunkSchema,\n      ),\n      abortSignal: options.abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    let finishReason: LanguageModelV3FinishReason = {\n      unified: 'other',\n      raw: undefined,\n    };\n    let usage:\n      | {\n          prompt_tokens: number | undefined;\n          completion_tokens: number | undefined;\n          reasoning_tokens?: number | null | undefined;\n        }\n      | undefined = undefined;\n\n    const providerMetadata: {\n      perplexity: {\n        usage: {\n          citationTokens: number | null;\n          numSearchQueries: number | null;\n        };\n        images: Array<{\n          imageUrl: string;\n          originUrl: string;\n          height: number;\n          width: number;\n        }> | null;\n      };\n    } = {\n      perplexity: {\n        usage: {\n          citationTokens: null,\n          numSearchQueries: null,\n        },\n        images: null,\n      },\n    };\n    let isFirstChunk = true;\n    let isActive = false;\n\n    const self = this;\n\n    return {\n      stream: response.pipeThrough(\n        new TransformStream<\n          ParseResult<z.infer<typeof perplexityChunkSchema>>,\n          LanguageModelV3StreamPart\n        >({\n          start(controller) {\n            controller.enqueue({ type: 'stream-start', warnings });\n          },\n\n          transform(chunk, controller) {\n            // Emit raw chunk if requested (before anything else)\n            if (options.includeRawChunks) {\n              controller.enqueue({ type: 'raw', rawValue: chunk.rawValue });\n            }\n\n            if (!chunk.success) {\n              controller.enqueue({ type: 'error', error: chunk.error });\n              return;\n            }\n\n            const value = chunk.value;\n\n            if (isFirstChunk) {\n              controller.enqueue({\n                type: 'response-metadata',\n                ...getResponseMetadata(value),\n              });\n\n              value.citations?.forEach(url => {\n                controller.enqueue({\n                  type: 'source',\n                  sourceType: 'url',\n                  id: self.config.generateId(),\n                  url,\n                });\n              });\n\n              isFirstChunk = false;\n            }\n\n            if (value.usage != null) {\n              usage = value.usage;\n\n              providerMetadata.perplexity.usage = {\n                citationTokens: value.usage.citation_tokens ?? null,\n                numSearchQueries: value.usage.num_search_queries ?? null,\n              };\n            }\n\n            if (value.images != null) {\n              providerMetadata.perplexity.images = value.images.map(image => ({\n                imageUrl: image.image_url,\n                originUrl: image.origin_url,\n                height: image.height,\n                width: image.width,\n              }));\n            }\n\n            const choice = value.choices[0];\n            if (choice?.finish_reason != null) {\n              finishReason = {\n                unified: mapPerplexityFinishReason(choice.finish_reason),\n                raw: choice.finish_reason,\n              };\n            }\n\n            if (choice?.delta == null) {\n              return;\n            }\n\n            const delta = choice.delta;\n            const textContent = delta.content;\n\n            if (textContent != null) {\n              if (!isActive) {\n                controller.enqueue({ type: 'text-start', id: '0' });\n                isActive = true;\n              }\n\n              controller.enqueue({\n                type: 'text-delta',\n                id: '0',\n                delta: textContent,\n              });\n            }\n          },\n\n          flush(controller) {\n            if (isActive) {\n              controller.enqueue({ type: 'text-end', id: '0' });\n            }\n\n            controller.enqueue({\n              type: 'finish',\n              finishReason,\n              usage: convertPerplexityUsage(usage),\n              providerMetadata,\n            });\n          },\n        }),\n      ),\n      request: { body },\n      response: { headers: responseHeaders },\n    };\n  }\n}\n\nfunction getResponseMetadata({\n  id,\n  model,\n  created,\n}: {\n  id: string;\n  created: number;\n  model: string;\n}) {\n  return {\n    id,\n    modelId: model,\n    timestamp: new Date(created * 1000),\n  };\n}\n\nconst perplexityUsageSchema = z.object({\n  prompt_tokens: z.number(),\n  completion_tokens: z.number(),\n  total_tokens: z.number().nullish(),\n  citation_tokens: z.number().nullish(),\n  num_search_queries: z.number().nullish(),\n  reasoning_tokens: z.number().nullish(),\n});\n\nexport const perplexityImageSchema = z.object({\n  image_url: z.string(),\n  origin_url: z.string(),\n  height: z.number(),\n  width: z.number(),\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 perplexityResponseSchema = z.object({\n  id: z.string(),\n  created: z.number(),\n  model: z.string(),\n  choices: z.array(\n    z.object({\n      message: z.object({\n        role: z.literal('assistant'),\n        content: z.string(),\n      }),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  citations: z.array(z.string()).nullish(),\n  images: z.array(perplexityImageSchema).nullish(),\n  usage: perplexityUsageSchema.nullish(),\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 perplexityChunkSchema = z.object({\n  id: z.string(),\n  created: z.number(),\n  model: z.string(),\n  choices: z.array(\n    z.object({\n      delta: z.object({\n        role: z.literal('assistant'),\n        content: z.string(),\n      }),\n      finish_reason: z.string().nullish(),\n    }),\n  ),\n  citations: z.array(z.string()).nullish(),\n  images: z.array(perplexityImageSchema).nullish(),\n  usage: perplexityUsageSchema.nullish(),\n});\n\nexport const perplexityErrorSchema = z.object({\n  error: z.object({\n    code: z.number(),\n    message: z.string().nullish(),\n    type: z.string().nullish(),\n  }),\n});\n\nexport type PerplexityErrorData = z.infer<typeof perplexityErrorSchema>;\n\nconst errorToMessage = (data: PerplexityErrorData) => {\n  return data.error.message ?? data.error.type ?? 'unknown error';\n};\n","import { LanguageModelV3Usage } from '@ai-sdk/provider';\n\nexport function convertPerplexityUsage(\n  usage:\n    | {\n        prompt_tokens?: number | null | undefined;\n        completion_tokens?: number | null | undefined;\n        reasoning_tokens?: number | null | undefined;\n      }\n    | undefined\n    | null,\n): LanguageModelV3Usage {\n  if (usage == null) {\n    return {\n      inputTokens: {\n        total: undefined,\n        noCache: undefined,\n        cacheRead: undefined,\n        cacheWrite: undefined,\n      },\n      outputTokens: {\n        total: undefined,\n        text: undefined,\n        reasoning: undefined,\n      },\n      raw: undefined,\n    };\n  }\n\n  const promptTokens = usage.prompt_tokens ?? 0;\n  const completionTokens = usage.completion_tokens ?? 0;\n  const reasoningTokens = usage.reasoning_tokens ?? 0;\n\n  return {\n    inputTokens: {\n      total: promptTokens,\n      noCache: promptTokens,\n      cacheRead: undefined,\n      cacheWrite: undefined,\n    },\n    outputTokens: {\n      total: completionTokens,\n      text: completionTokens - reasoningTokens,\n      reasoning: reasoningTokens,\n    },\n    raw: usage,\n  };\n}\n","import {\n  LanguageModelV3Prompt,\n  UnsupportedFunctionalityError,\n} from '@ai-sdk/provider';\nimport {\n  PerplexityMessageContent,\n  PerplexityPrompt,\n} from './perplexity-language-model-prompt';\nimport { convertUint8ArrayToBase64 } from '@ai-sdk/provider-utils';\n\nexport function convertToPerplexityMessages(\n  prompt: LanguageModelV3Prompt,\n): PerplexityPrompt {\n  const messages: PerplexityPrompt = [];\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      case 'assistant': {\n        const hasMultipartContent = content.some(\n          part =>\n            (part.type === 'file' && part.mediaType.startsWith('image/')) ||\n            (part.type === 'file' && part.mediaType === 'application/pdf'),\n        );\n\n        const messageContent = content\n          .map((part, index) => {\n            switch (part.type) {\n              case 'text': {\n                return {\n                  type: 'text',\n                  text: part.text,\n                };\n              }\n              case 'file': {\n                if (part.mediaType === 'application/pdf') {\n                  return part.data instanceof URL\n                    ? {\n                        type: 'file_url',\n                        file_url: {\n                          url: part.data.toString(),\n                        },\n                        file_name: part.filename,\n                      }\n                    : {\n                        type: 'file_url',\n                        file_url: {\n                          url:\n                            typeof part.data === 'string'\n                              ? part.data\n                              : convertUint8ArrayToBase64(part.data),\n                        },\n                        file_name: part.filename || `document-${index}.pdf`,\n                      };\n                } else if (part.mediaType.startsWith('image/')) {\n                  return part.data instanceof URL\n                    ? {\n                        type: 'image_url',\n                        image_url: {\n                          url: part.data.toString(),\n                        },\n                      }\n                    : {\n                        type: 'image_url',\n                        image_url: {\n                          url: `data:${part.mediaType ?? 'image/jpeg'};base64,${\n                            typeof part.data === 'string'\n                              ? part.data\n                              : convertUint8ArrayToBase64(part.data)\n                          }`,\n                        },\n                      };\n                }\n              }\n            }\n          })\n          .filter(Boolean) as PerplexityMessageContent[];\n        messages.push({\n          role,\n          content: hasMultipartContent\n            ? messageContent\n            : messageContent\n                .filter(part => part.type === 'text')\n                .map(part => part.text)\n                .join(''),\n        });\n        break;\n      }\n      case 'tool': {\n        throw new UnsupportedFunctionalityError({\n          functionality: 'Tool messages',\n        });\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 { LanguageModelV3FinishReason } from '@ai-sdk/provider';\n\nexport function mapPerplexityFinishReason(\n  finishReason: string | null | undefined,\n): LanguageModelV3FinishReason['unified'] {\n  switch (finishReason) {\n    case 'stop':\n    case 'length':\n      return finishReason;\n    default:\n      return 'other';\n  }\n}\n","// Version string of this package injected at build time.\ndeclare const __PACKAGE_VERSION__: string | undefined;\nexport const VERSION: string =\n  typeof __PACKAGE_VERSION__ !== 'undefined'\n    ? __PACKAGE_VERSION__\n    : 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