@n8n/n8n-nodes-langchain/dist/nodes/vendors/GoogleGemini/actions/text/message.operation.js


"use strict";
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
  for (var name in all)
    __defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
  if (from && typeof from === "object" || typeof from === "function") {
    for (let key of __getOwnPropNames(from))
      if (!__hasOwnProp.call(to, key) && key !== except)
        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
  }
  return to;
};
var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps(
  // If the importer is in node compatibility mode or this is not an ESM
  // file that has been converted to a CommonJS file using a Babel-
  // compatible transform (i.e. "__esModule" has not been set), then set
  // "default" to the CommonJS "module.exports" for node compatibility.
  isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target,
  mod
));
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var message_operation_exports = {};
__export(message_operation_exports, {
  description: () => description,
  execute: () => execute
});
module.exports = __toCommonJS(message_operation_exports);
var import_helpers = require("../../../../../utils/helpers");
var import_n8n_workflow = require("n8n-workflow");
var import_zod_to_json_schema = __toESM(require("zod-to-json-schema"));
var import_transport = require("../../transport");
var import_descriptions = require("../descriptions");
const properties = [
  (0, import_descriptions.modelRLC)("modelSearch"),
  {
    displayName: "Messages",
    name: "messages",
    type: "fixedCollection",
    typeOptions: {
      sortable: true,
      multipleValues: true
    },
    placeholder: "Add Message",
    default: { values: [{ content: "" }] },
    options: [
      {
        displayName: "Values",
        name: "values",
        values: [
          {
            displayName: "Prompt",
            name: "content",
            type: "string",
            description: "The content of the message to be send",
            default: "",
            placeholder: "e.g. Hello, how can you help me?",
            typeOptions: {
              rows: 2
            }
          },
          {
            displayName: "Role",
            name: "role",
            type: "options",
            description: "Role in shaping the model's response, it tells the model how it should behave and interact with the user",
            options: [
              {
                name: "User",
                value: "user",
                description: "Send a message as a user and get a response from the model"
              },
              {
                name: "Model",
                value: "model",
                description: "Tell the model to adopt a specific tone or personality"
              }
            ],
            default: "user"
          }
        ]
      }
    ]
  },
  {
    displayName: "Simplify Output",
    name: "simplify",
    type: "boolean",
    default: true,
    description: "Whether to return a simplified version of the response instead of the raw data"
  },
  {
    displayName: "Output Content as JSON",
    name: "jsonOutput",
    type: "boolean",
    description: "Whether to attempt to return the response in JSON format",
    default: false
  },
  {
    displayName: "Built-in Tools",
    name: "builtInTools",
    placeholder: "Add Built-in Tool",
    type: "collection",
    default: {},
    displayOptions: {
      show: {
        "@version": [{ _cnd: { gte: 1.1 } }]
      }
    },
    options: [
      {
        displayName: "Google Search",
        name: "googleSearch",
        type: "boolean",
        default: true,
        description: "Whether to allow the model to search the web using Google Search to get real-time information"
      },
      {
        displayName: "Google Maps",
        name: "googleMaps",
        type: "collection",
        default: { latitude: "", longitude: "" },
        options: [
          {
            displayName: "Latitude",
            name: "latitude",
            type: "number",
            default: "",
            description: "The latitude coordinate for location-based queries",
            typeOptions: {
              numberPrecision: 6
            }
          },
          {
            displayName: "Longitude",
            name: "longitude",
            type: "number",
            default: "",
            description: "The longitude coordinate for location-based queries",
            typeOptions: {
              numberPrecision: 6
            }
          }
        ]
      },
      {
        displayName: "URL Context",
        name: "urlContext",
        type: "boolean",
        default: true,
        description: "Whether to allow the model to read and analyze content from specific URLs"
      },
      {
        displayName: "File Search",
        name: "fileSearch",
        type: "collection",
        default: { fileSearchStoreNames: "[]" },
        options: [
          {
            displayName: "File Search Store Names",
            name: "fileSearchStoreNames",
            description: "The file search store names to use for the file search. File search stores are managed via Google AI Studio.",
            type: "json",
            default: "[]",
            required: true
          },
          {
            displayName: "Metadata Filter",
            name: "metadataFilter",
            type: "string",
            default: "",
            description: 'Use metadata filter to search within a subset of documents. Example: author="Robert Graves".',
            placeholder: 'e.g. author="John Doe"'
          }
        ]
      },
      {
        displayName: "Code Execution",
        name: "codeExecution",
        type: "boolean",
        default: true,
        description: "Whether to allow the model to execute code it generates to produce a response. Supported only by certain models."
      }
    ]
  },
  {
    displayName: "Options",
    name: "options",
    placeholder: "Add Option",
    type: "collection",
    default: {},
    options: [
      {
        displayName: "Include Merged Response",
        name: "includeMergedResponse",
        type: "boolean",
        default: false,
        description: "Whether to include a single output string merging all text parts of the response",
        displayOptions: {
          show: {
            "@version": [{ _cnd: { gte: 1.1 } }]
          }
        }
      },
      {
        displayName: "System Message",
        name: "systemMessage",
        type: "string",
        default: "",
        placeholder: "e.g. You are a helpful assistant"
      },
      {
        displayName: "Code Execution",
        name: "codeExecution",
        type: "boolean",
        default: false,
        description: "Whether to allow the model to execute code it generates to produce a response. Supported only by certain models.",
        displayOptions: {
          show: {
            "@version": [{ _cnd: { eq: 1 } }]
          }
        }
      },
      {
        displayName: "Frequency Penalty",
        name: "frequencyPenalty",
        default: 0,
        description: "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim",
        type: "number",
        typeOptions: {
          minValue: -2,
          maxValue: 2,
          numberPrecision: 1
        }
      },
      {
        displayName: "Maximum Number of Tokens",
        name: "maxOutputTokens",
        default: 16,
        description: "The maximum number of tokens to generate in the completion",
        type: "number",
        typeOptions: {
          minValue: 1,
          numberPrecision: 0
        }
      },
      {
        displayName: "Number of Completions",
        name: "candidateCount",
        default: 1,
        description: "How many completions to generate for each prompt",
        type: "number",
        typeOptions: {
          minValue: 1,
          maxValue: 8,
          // Google Gemini supports up to 8 candidates
          numberPrecision: 0
        }
      },
      {
        displayName: "Presence Penalty",
        name: "presencePenalty",
        default: 0,
        description: "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics",
        type: "number",
        typeOptions: {
          minValue: -2,
          maxValue: 2,
          numberPrecision: 1
        }
      },
      {
        displayName: "Output Randomness (Temperature)",
        name: "temperature",
        default: 1,
        description: "Controls the randomness of the output. Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive",
        type: "number",
        typeOptions: {
          minValue: 0,
          maxValue: 2,
          numberPrecision: 1
        }
      },
      {
        displayName: "Output Randomness (Top P)",
        name: "topP",
        default: 1,
        description: "The maximum cumulative probability of tokens to consider when sampling",
        type: "number",
        typeOptions: {
          minValue: 0,
          maxValue: 1,
          numberPrecision: 1
        }
      },
      {
        displayName: "Output Randomness (Top K)",
        name: "topK",
        default: 1,
        description: "The maximum number of tokens to consider when sampling",
        type: "number",
        typeOptions: {
          minValue: 1,
          numberPrecision: 0
        }
      },
      {
        displayName: "Thinking Budget",
        name: "thinkingBudget",
        type: "number",
        default: void 0,
        description: "Controls reasoning tokens for thinking models. Set to 0 to disable automatic thinking. Set to -1 for dynamic thinking. Leave empty for auto mode.",
        typeOptions: {
          minValue: -1,
          numberPrecision: 0
        }
      },
      {
        displayName: "Max Tool Calls Iterations",
        name: "maxToolsIterations",
        type: "number",
        default: 15,
        description: "The maximum number of tool iteration cycles the LLM will run before stopping. A single iteration can contain multiple tool calls. Set to 0 for no limit",
        typeOptions: {
          minValue: 0,
          numberPrecision: 0
        }
      }
    ]
  }
];
const displayOptions = {
  show: {
    operation: ["message"],
    resource: ["text"]
  }
};
const description = (0, import_n8n_workflow.updateDisplayOptions)(displayOptions, properties);
function getToolCalls(response) {
  return response.candidates.flatMap((c) => c.content.parts).filter((p) => "functionCall" in p);
}
async function execute(i) {
  const model = this.getNodeParameter("modelId", i, "", { extractValue: true });
  const messages = this.getNodeParameter("messages.values", i, []);
  const simplify = this.getNodeParameter("simplify", i, true);
  const jsonOutput = this.getNodeParameter("jsonOutput", i, false);
  const options = this.getNodeParameter("options", i, {});
  const builtInTools = this.getNodeParameter("builtInTools", i, {});
  (0, import_n8n_workflow.validateNodeParameters)(
    options,
    {
      includeMergedResponse: { type: "boolean", required: false },
      systemMessage: { type: "string", required: false },
      codeExecution: { type: "boolean", required: false },
      frequencyPenalty: { type: "number", required: false },
      maxOutputTokens: { type: "number", required: false },
      candidateCount: { type: "number", required: false },
      presencePenalty: { type: "number", required: false },
      temperature: { type: "number", required: false },
      topP: { type: "number", required: false },
      topK: { type: "number", required: false },
      thinkingBudget: { type: "number", required: false },
      maxToolsIterations: { type: "number", required: false }
    },
    this.getNode()
  );
  const generationConfig = {
    frequencyPenalty: options.frequencyPenalty,
    maxOutputTokens: options.maxOutputTokens,
    candidateCount: options.candidateCount,
    presencePenalty: options.presencePenalty,
    temperature: options.temperature,
    topP: options.topP,
    topK: options.topK,
    responseMimeType: jsonOutput ? "application/json" : void 0
  };
  if (options.thinkingBudget !== void 0) {
    generationConfig.thinkingConfig = {
      thinkingBudget: options.thinkingBudget
    };
  }
  const nodeInputs = this.getNodeInputs();
  const availableTools = nodeInputs.some((i2) => i2.type === "ai_tool") ? await (0, import_helpers.getConnectedTools)(this, true) : [];
  const tools = [
    {
      functionDeclarations: availableTools.map((t) => ({
        name: t.name,
        description: t.description,
        parameters: {
          ...(0, import_zod_to_json_schema.default)(t.schema, { target: "openApi3" }),
          // Google Gemini API throws an error if `additionalProperties` field is present
          additionalProperties: void 0
        }
      }))
    }
  ];
  if (!tools[0].functionDeclarations?.length) {
    tools.pop();
  }
  if (this.getNode().typeVersion === 1) {
    if (options.codeExecution) {
      tools.push({
        codeExecution: {}
      });
    }
  }
  let toolConfig;
  if (this.getNode().typeVersion >= 1.1) {
    if (builtInTools) {
      if (builtInTools.googleSearch) {
        tools.push({
          googleSearch: {}
        });
      }
      const googleMapsOptions = builtInTools.googleMaps;
      if (googleMapsOptions) {
        tools.push({
          googleMaps: {}
        });
        const latitude = googleMapsOptions.latitude;
        const longitude = googleMapsOptions.longitude;
        if (latitude !== void 0 && latitude !== "" && longitude !== void 0 && longitude !== "") {
          toolConfig = {
            retrievalConfig: {
              latLng: {
                latitude: Number(latitude),
                longitude: Number(longitude)
              }
            }
          };
        }
      }
      if (builtInTools.urlContext) {
        tools.push({
          urlContext: {}
        });
      }
      const fileSearchOptions = builtInTools.fileSearch;
      if (fileSearchOptions) {
        const fileSearchStoreNamesRaw = fileSearchOptions.fileSearchStoreNames;
        const metadataFilter = fileSearchOptions.metadataFilter;
        let fileSearchStoreNames;
        if (fileSearchStoreNamesRaw) {
          const parsed = (0, import_n8n_workflow.jsonParse)(fileSearchStoreNamesRaw, {
            errorMessage: "Failed to parse file search store names"
          });
          if (Array.isArray(parsed)) {
            fileSearchStoreNames = parsed;
          }
        }
        tools.push({
          fileSearch: {
            ...fileSearchStoreNames && { fileSearchStoreNames },
            ...metadataFilter && { metadataFilter }
          }
        });
      }
      if (builtInTools.codeExecution) {
        tools.push({
          codeExecution: {}
        });
      }
    }
  }
  const contents = messages.map((m) => ({
    parts: [{ text: m.content }],
    role: m.role
  }));
  const body = {
    tools,
    contents,
    generationConfig,
    systemInstruction: options.systemMessage ? { parts: [{ text: options.systemMessage }] } : void 0,
    ...toolConfig && { toolConfig }
  };
  let response = await import_transport.apiRequest.call(this, "POST", `/v1beta/${model}:generateContent`, {
    body
  });
  const maxToolsIterations = this.getNodeParameter("options.maxToolsIterations", i, 15);
  const abortSignal = this.getExecutionCancelSignal();
  let currentIteration = 1;
  let toolCalls = getToolCalls(response);
  while (toolCalls.length) {
    if (maxToolsIterations > 0 && currentIteration >= maxToolsIterations || abortSignal?.aborted) {
      break;
    }
    contents.push(...response.candidates.map((c) => c.content));
    for (const { functionCall } of toolCalls) {
      let toolResponse;
      for (const availableTool of availableTools) {
        if (availableTool.name === functionCall.name) {
          toolResponse = await availableTool.invoke(functionCall.args);
        }
      }
      contents.push({
        parts: [
          {
            functionResponse: {
              id: functionCall.id,
              name: functionCall.name,
              response: {
                result: toolResponse
              }
            }
          }
        ],
        role: "tool"
      });
    }
    response = await import_transport.apiRequest.call(this, "POST", `/v1beta/${model}:generateContent`, {
      body
    });
    toolCalls = getToolCalls(response);
    currentIteration++;
  }
  const candidates = options.includeMergedResponse ? response.candidates.map((candidate) => ({
    ...candidate,
    mergedResponse: candidate.content.parts.filter((part) => "text" in part).map((part) => part.text).join("")
  })) : response.candidates;
  if (simplify) {
    return candidates.map((candidate) => ({
      json: candidate,
      pairedItem: { item: i }
    }));
  }
  return [
    {
      json: {
        ...response,
        candidates
      },
      pairedItem: { item: i }
    }
  ];
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
  description,
  execute
});
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