genkitx-hnsw/lib/constants/index.js

Firebase Genkit AI framework plugin for HNSW vector database. Get AI response enriched with additional context and knowledge with HNSW Vector Database using RAG Implementation

"use strict";
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
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 __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var constants_exports = {};
__export(constants_exports, {
  EMBEDDING_MODEL: () => EMBEDDING_MODEL,
  EMBEDDING_MODEL_NAME: () => EMBEDDING_MODEL_NAME,
  EMBEDDING_TITLE: () => EMBEDDING_TITLE,
  ERROR_INVALID_ARGUMENT: () => ERROR_INVALID_ARGUMENT,
  ERROR_NO_API_KEY: () => ERROR_NO_API_KEY,
  FLOW_NAME_INDEXER: () => FLOW_NAME_INDEXER,
  FLOW_NAME_RETRIEVER: () => FLOW_NAME_RETRIEVER,
  PLUGIN_NAME_INDEXER: () => PLUGIN_NAME_INDEXER,
  PLUGIN_NAME_RETRIEVER: () => PLUGIN_NAME_RETRIEVER,
  SCHEMA_INDEX_OUTPUT_PATH: () => SCHEMA_INDEX_OUTPUT_PATH,
  SCHEMA_INDEX_PATH: () => SCHEMA_INDEX_PATH,
  SCHEMA_PROMPT: () => SCHEMA_PROMPT,
  SCHEMA_RESULT: () => SCHEMA_RESULT,
  SCHEMA_TRAINABLE_PATH: () => SCHEMA_TRAINABLE_PATH
});
module.exports = __toCommonJS(constants_exports);
const PLUGIN_NAME_INDEXER = "HNSW Indexer";
const PLUGIN_NAME_RETRIEVER = "HNSW Retriever";
const FLOW_NAME_INDEXER = "HNSW Indexer";
const FLOW_NAME_RETRIEVER = "HNSW Retriever";
const ERROR_NO_API_KEY = "Must supply either `options.apiKey` or set `GOOGLE_API_KEY` environment variable.";
const ERROR_INVALID_ARGUMENT = "INVALID_ARGUMENT";
const SCHEMA_PROMPT = "Type your prompt for the LLM Model and the HNSW Vector to process";
const SCHEMA_INDEX_PATH = "Define Vector Index path you wanna use, can be retrieved from genkitx-hnsw-indexer plugin";
const SCHEMA_RESULT = "The prompt result with more context from HNSW Vector";
const SCHEMA_TRAINABLE_PATH = "Your data and other documents path to be learned by the AI";
const SCHEMA_INDEX_OUTPUT_PATH = "Your expected output path for your Vector Store Index that is processed based on the data and documents you provided";
const EMBEDDING_MODEL_NAME = "Gemini Model embedding-001";
const EMBEDDING_MODEL = "embedding-001";
const EMBEDDING_TITLE = "Gemini embedding-001";
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
  EMBEDDING_MODEL,
  EMBEDDING_MODEL_NAME,
  EMBEDDING_TITLE,
  ERROR_INVALID_ARGUMENT,
  ERROR_NO_API_KEY,
  FLOW_NAME_INDEXER,
  FLOW_NAME_RETRIEVER,
  PLUGIN_NAME_INDEXER,
  PLUGIN_NAME_RETRIEVER,
  SCHEMA_INDEX_OUTPUT_PATH,
  SCHEMA_INDEX_PATH,
  SCHEMA_PROMPT,
  SCHEMA_RESULT,
  SCHEMA_TRAINABLE_PATH
});
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