@genkit-ai/dotprompt/lib/prompt.mjs.map

Genkit AI framework `.prompt` file format and management library.

{"version":3,"sources":["../src/prompt.ts"],"sourcesContent":["/**\n * Copyright 2024 Google LLC\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\nimport {\n  definePrompt,\n  generate,\n  GenerateOptions,\n  GenerateResponse,\n  generateStream,\n  GenerateStreamResponse,\n  PromptAction,\n  toGenerateRequest,\n} from '@genkit-ai/ai';\nimport { MessageData, ModelArgument } from '@genkit-ai/ai/model';\nimport { DocumentData } from '@genkit-ai/ai/retriever';\nimport { getCurrentSession } from '@genkit-ai/ai/session';\nimport { GenkitError, z } from '@genkit-ai/core';\nimport { Registry } from '@genkit-ai/core/registry';\nimport { parseSchema } from '@genkit-ai/core/schema';\nimport {\n  runInNewSpan,\n  setCustomMetadataAttribute,\n  SPAN_TYPE_ATTR,\n} from '@genkit-ai/core/tracing';\nimport { createHash } from 'crypto';\nimport fm, { FrontMatterResult } from 'front-matter';\nimport {\n  PromptFrontmatter,\n  PromptMetadata,\n  toFrontmatter,\n  toMetadata,\n} from './metadata.js';\nimport { lookupPrompt, registryDefinitionKey } from './registry.js';\nimport { compile } from './template.js';\n\nexport type PromptData = PromptFrontmatter & { template: string };\n\nexport type PromptGenerateOptions<\n  V = unknown,\n  CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n> = Omit<\n  GenerateOptions<z.ZodTypeAny, CustomOptions>,\n  'prompt' | 'input' | 'model'\n> & {\n  model?: ModelArgument<CustomOptions>;\n  input?: V;\n};\n\ninterface RenderMetadata {\n  docs?: DocumentData[];\n  messages?: MessageData[];\n}\n\nexport class Dotprompt<I = unknown> implements PromptMetadata<z.ZodTypeAny> {\n  name: string;\n  description?: string;\n  variant?: string;\n  hash: string;\n\n  template: string;\n\n  model?: PromptMetadata['model'];\n  metadata: PromptMetadata['metadata'];\n  input?: PromptMetadata['input'];\n  output?: PromptMetadata['output'];\n  tools?: PromptMetadata['tools'];\n  config?: PromptMetadata['config'];\n\n  private _promptAction?: PromptAction;\n\n  private _render: (\n    input: I,\n    options?: RenderMetadata,\n    data?: Record<string, any>\n  ) => MessageData[];\n\n  static parse(registry: Registry, name: string, source: string) {\n    try {\n      const fmResult = (fm as any)(source.trimStart(), {\n        allowUnsafe: false,\n      }) as FrontMatterResult<unknown>;\n\n      return new Dotprompt(\n        registry,\n        {\n          ...toMetadata(registry, fmResult.attributes),\n          name,\n        } as PromptMetadata,\n        fmResult.body\n      );\n    } catch (e: any) {\n      throw new GenkitError({\n        source: 'Dotprompt',\n        status: 'INVALID_ARGUMENT',\n        message: `Error parsing YAML frontmatter of '${name}' prompt: ${e.stack}`,\n      });\n    }\n  }\n\n  static fromAction(registry: Registry, action: PromptAction): Dotprompt {\n    const { template, ...options } = action.__action.metadata!.prompt;\n    const pm = options as PromptMetadata;\n    if (pm.input?.schema) {\n      pm.input.jsonSchema = options.input?.schema;\n      delete pm.input.schema;\n    }\n    if (pm.output?.schema) {\n      pm.output.jsonSchema = options.output?.schema;\n    }\n    const prompt = new Dotprompt(\n      registry,\n      options as PromptMetadata,\n      template,\n      action\n    );\n    return prompt;\n  }\n\n  constructor(\n    private registry: Registry,\n    options: PromptMetadata,\n    template: string,\n    action?: PromptAction\n  ) {\n    this.name = options.name || 'untitledPrompt';\n    this.description = options.description;\n    this.variant = options.variant;\n    this.model = options.model;\n    this.input = options.input || { schema: z.any() };\n    this.output = options.output;\n    this.tools = options.tools;\n    this.config = options.config;\n    this.template = template;\n    this.hash = createHash('sha256').update(JSON.stringify(this)).digest('hex');\n\n    this._render = compile(this.template, options);\n    this._promptAction = action;\n  }\n\n  /**\n   * Renders all of the prompt's text parts into a raw string.\n   *\n   * @param input User input to the prompt template.\n   * @param options Optional context and/or history for the prompt template.\n   * @returns all of the text parts concatenated into a string.\n   */\n\n  renderText(input: I, options?: RenderMetadata): string {\n    const result = this.renderMessages(input, options);\n    if (result.length !== 1) {\n      throw new Error(\"Multi-message prompt can't be rendered as text.\");\n    }\n    let out = '';\n    for (const part of result[0].content) {\n      if (!part.text) {\n        throw new Error(\"Multimodal prompt can't be rendered as text.\");\n      }\n      out += part.text;\n    }\n    return out;\n  }\n\n  /**\n   * Renders the prompt template into an array of messages.\n   *\n   * @param input User input to the prompt template\n   * @param options optional context and/or history for the prompt template.\n   * @returns an array of messages representing an exchange between a user and a model.\n   */\n  renderMessages(input?: I, options?: RenderMetadata): MessageData[] {\n    let sessionStateData: Record<string, any> | undefined = undefined;\n    if (getCurrentSession()) {\n      sessionStateData = { state: getCurrentSession()?.state };\n    }\n    input = parseSchema(input, {\n      schema: this.input?.schema,\n      jsonSchema: this.input?.jsonSchema,\n    });\n    return this._render(\n      { ...this.input?.default, ...input },\n      options,\n      sessionStateData\n    );\n  }\n\n  toJSON(): PromptData {\n    return { ...toFrontmatter(this), template: this.template };\n  }\n\n  define(options?: { ns?: string; description?: string }): void {\n    this._promptAction = definePrompt(\n      this.registry,\n      {\n        name: registryDefinitionKey(this.name, this.variant, options?.ns),\n        description: options?.description ?? this.description,\n        inputSchema: this.input?.schema,\n        inputJsonSchema: this.input?.jsonSchema,\n        metadata: {\n          type: 'prompt',\n          prompt: this.toJSON(),\n        },\n      },\n      async (input?: I) =>\n        toGenerateRequest(this.registry, this.render({ input }))\n    );\n  }\n\n  get promptAction(): PromptAction | undefined {\n    return this._promptAction;\n  }\n\n  private _generateOptions<\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n  >(options: PromptGenerateOptions<I>): GenerateOptions<O, CustomOptions> {\n    const messages = this.renderMessages(options.input, {\n      messages: options.messages,\n      docs: options.docs,\n    });\n    let renderedPrompt;\n    let renderedMessages;\n    if (messages.length > 0 && messages[messages.length - 1].role === 'user') {\n      renderedPrompt = messages[messages.length - 1].content;\n      renderedMessages = messages.slice(0, messages.length - 1);\n    } else {\n      renderedPrompt = undefined;\n      renderedMessages = messages;\n    }\n    return {\n      model: options.model || this.model!,\n      config: { ...this.config, ...options.config },\n      messages: renderedMessages,\n      prompt: renderedPrompt,\n      docs: options.docs,\n      output: {\n        format: options.output?.format || this.output?.format || undefined,\n        schema: options.output?.schema || this.output?.schema,\n        jsonSchema: options.output?.jsonSchema || this.output?.jsonSchema,\n      },\n      tools: (options.tools || []).concat(this.tools || []),\n      streamingCallback: options.streamingCallback,\n      returnToolRequests: options.returnToolRequests,\n      use: options.use,\n    } as GenerateOptions<O, CustomOptions>;\n  }\n\n  /**\n   * Renders the prompt template based on user input.\n   *\n   * @param opt Options for the prompt template, including user input variables and custom model configuration options.\n   * @returns a `GenerateOptions` object to be used with the `generate()` function from @genkit-ai/ai.\n   */\n  render<\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n  >(\n    opt: PromptGenerateOptions<I, CustomOptions>\n  ): GenerateOptions<CustomOptions, O> {\n    return this._generateOptions(opt);\n  }\n\n  async renderInNewSpan<\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n  >(opt: PromptGenerateOptions<I>): Promise<GenerateOptions<CustomOptions, O>> {\n    const spanName = this.variant ? `${this.name}.${this.variant}` : this.name;\n    return runInNewSpan(\n      {\n        metadata: {\n          name: spanName,\n          input: opt,\n        },\n        labels: {\n          [SPAN_TYPE_ATTR]: 'dotprompt',\n        },\n      },\n      async (metadata) => {\n        setCustomMetadataAttribute('prompt_fingerprint', this.hash);\n        const generateOptions = this._generateOptions<CustomOptions, O>(opt);\n        metadata.output = generateOptions;\n        return generateOptions;\n      }\n    );\n  }\n\n  /**\n   * Generates a response by rendering the prompt template with given user input and then calling the model.\n   *\n   * @param opt Options for the prompt template, including user input variables and custom model configuration options.\n   * @returns the model response as a promise of `GenerateResponse`.\n   */\n  async generate<\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n  >(\n    opt: PromptGenerateOptions<I, CustomOptions>\n  ): Promise<GenerateResponse<z.infer<O>>> {\n    const renderedOpts = this.renderInNewSpan<CustomOptions, O>(opt);\n    return generate<CustomOptions, O>(this.registry, renderedOpts);\n  }\n\n  /**\n   * Generates a streaming response by rendering the prompt template with given user input and then calling the model.\n   *\n   * @param opt Options for the prompt template, including user input variables and custom model configuration options.\n   * @returns the model response as a promise of `GenerateStreamResponse`.\n   */\n  async generateStream<CustomOptions extends z.ZodTypeAny = z.ZodTypeAny>(\n    opt: PromptGenerateOptions<I, CustomOptions>\n  ): Promise<GenerateStreamResponse> {\n    const renderedOpts = await this.renderInNewSpan<CustomOptions>(opt);\n    return generateStream(this.registry, renderedOpts);\n  }\n}\n\nexport class DotpromptRef<Variables = unknown> {\n  name: string;\n  variant?: string;\n  dir?: string;\n  private _prompt?: Dotprompt<Variables>;\n\n  constructor(\n    name: string,\n    options?: {\n      variant?: string;\n      dir?: string;\n    }\n  ) {\n    this.name = name;\n    this.variant = options?.variant;\n    this.dir = options?.dir;\n  }\n\n  /** Loads the prompt which is referenced. */\n  async loadPrompt(registry: Registry): Promise<Dotprompt<Variables>> {\n    if (this._prompt) return this._prompt;\n    this._prompt = (await lookupPrompt(\n      registry,\n      this.name,\n      this.variant,\n      this.dir\n    )) as Dotprompt<Variables>;\n    return this._prompt;\n  }\n\n  /**\n   * Generates a response by rendering the prompt template with given user input and then calling the model.\n   *\n   * @param opt Options for the prompt template, including user input variables and custom model configuration options.\n   * @returns the model response as a promise of `GenerateResponse`.\n   */\n\n  async generate<\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n  >(\n    registry: Registry,\n    opt: PromptGenerateOptions<Variables, CustomOptions>\n  ): Promise<GenerateResponse<z.infer<O>>> {\n    const prompt = await this.loadPrompt(registry);\n    return prompt.generate<CustomOptions, O>(opt);\n  }\n\n  /**\n   * Renders the prompt template based on user input.\n   *\n   * @param opt Options for the prompt template, including user input variables and custom model configuration options.\n   * @returns a `GenerateOptions` object to be used with the `generate()` function from @genkit-ai/ai.\n   */\n  async render<\n    CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n    O extends z.ZodTypeAny = z.ZodTypeAny,\n  >(\n    registry: Registry,\n\n    opt: PromptGenerateOptions<Variables, CustomOptions>\n  ): Promise<GenerateOptions<z.ZodTypeAny, O>> {\n    const prompt = await this.loadPrompt(registry);\n    return prompt.render<CustomOptions, O>(opt);\n  }\n}\n\n/**\n * Define a dotprompt in code. This function is offered as an alternative to definitions in .prompt files.\n *\n * @param options the prompt definition, including its name, variant and model. Any options from .prompt file front matter are accepted.\n * @param template a string template, comparable to the main body of a prompt file.\n * @returns the newly defined prompt.\n */\nexport function defineDotprompt<\n  I extends z.ZodTypeAny = z.ZodTypeAny,\n  CustomOptions extends z.ZodTypeAny = z.ZodTypeAny,\n>(\n  registry: Registry,\n  options: PromptMetadata<I, CustomOptions>,\n  template: string\n): Dotprompt<z.infer<I>> {\n  const prompt = new Dotprompt(registry, options, template);\n  prompt.define({ description: options.description });\n  return 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