@e-llm-studio/data-engine NPM

eLLM Studio Data Operations Package

Welcome to the eLLM Studio Data Operations package! 🎉 This package enables streaming chat functionality with your AI assistant in eLLM Studio via WebSocket and GraphQL. It's designed for both frontend and backend implementations.

🚀 Features

Data Ingestion: Ingest text data.

📦 Installation

npm i @e-llm-studio/data-engine

🛠️ Data Ingestion

Here’s how you can use the startChatStream function to set up the AI chat stream:

import { ContentManager } from "@e-llm-studio/data-engine";

// Example usage:

const baseUrl = "https://dev-egpt.techo.camp";
const chatbotId = "3e94587c-733d-4e92-8d98-d5150c63b8cd";
const chatbotName = "Assistant1";
const organizationName = "techolution";
const username = "your-username";
const password = "your-password";
const contentManager = new ContentManager(
  baseUrl,
  chatbotId,
  chatbotName,
  organizationName,
  username,
  password
);

// Save text content example
contentManager
  .saveTextContent("Title1", "Text Content")
  .then((saveResponse) => {
    console.log("Save Response:", saveResponse);
  })
  .catch((error) => {
    console.error("Save Error:", error);
  });

  // Save text file content example
contentManager
  .ingestTextFiles("File", "Text File Content")
  .then((saveResponse) => {
    console.log("Save Response:", saveResponse);
  })
  .catch((error) => {
    console.error("Save Error:", error);
  });
// For AccessToken 


// Delete text content example
contentManager
  .deleteTextContent(["671a391fbcce6ddc80e86099"])
  .then((deleteResponse) => {
    console.log("Delete Response:", deleteResponse);
  })
  .catch((error) => {
    console.error("Delete Error:", error);
  });
  // Get Context Id
contentManager
  .getContextId("6d25ddb4-e375-4adf-a98d-4d14114c2840")
  .then((contextResponse) => {
    console.log("Context Response:", contextResponse);
  })
  .catch((error) => {
    console.error("Context Error:", error);
  });

  // Seamantic Search api
  contentManager
  .getSemanticSearch(
    "model-name",      // AI Model to use for semantic search
    "search query",    // The query string for searching
    "request123",      // Unique request ID for tracking
    "user456",         // User ID performing the search
    0,                 // Offset for pagination
    10                 // Limit for pagination
  )
  .then((searchResponse) => {
    console.log("Search Response:", searchResponse);
  })
  .catch((error) => {
    console.error("Search Error:", error);
  });

contentManager.ingestTextFilesText(
  {
    "contexts": [
        {
            "contentType": "local_file",
            "files": [
                {
                    "id": uuid(),
                    "name": "file name.txt",
                    "size": "file size",
                    "content": "file text content",
                    "createdTime": "2025-02-05T15:44:06.024Z",
                    "modifiedTime": "2025-02-05T15:44:06.024Z",
                    "mimeType": "text/plain"
                }
            ],
            "id": uuid()
        }
    ],
    "chatbotId": "chatBotId / assistant id "
}).then((context) => {
    console.log("Ingestion Successful", ingestedData);
  })
  .catch((error) => {
    console.error("Failed to save text content:", error);
  });

📝 API Documentation saveTextContent(title: string, textContent: string): Promise Saves the provided text content under the specified title.

Parameters: title (string): The title of the text content. textContent (string): The content text to be saved.

deleteTextContent(contextInfoIds: string[]): Promise Deletes the specified content using the provided array of contextInfoIds.

Parameters: contextInfoIds (string[]): An array of IDs representing the content items to delete. deleteContextId(fileId: string): Promise Deletes a specific content item by its fileId.

Parameters: fileId (string): The ID of the content item to delete from the chatbot’s context. 🔐 Authentication The package requires user authentication. The ContentManager class will automatically log in using the provided credentials and manage access tokens internally.

Semantic Search The getSemanticSearch method performs a semantic search based on a provided query and model, returning results from the chatbot’s knowledge base.

async ingestTextFiles(files: File[]): Promise<IContextInfo | void>

Processes and uploads plain text files, extracts content, and saves it to the chatbot’s context.

Parameters:

files (File[]): An array of text files to be ingested.

Returns:

A promise that resolves with IContextInfo on success or void if no valid text files are found.

async ensureLoggedIn(): Promise

Ensures that the user is logged in by checking the access token. If no token is present, it attempts to log in.

Returns:

A promise that resolves with the access token if login is successful.

Parameters:

aiModel (string): The name of the AI model to use for the search. searchQuery (string): The search query string. requestId (string): A unique identifier for tracking the search request. userId (string): The ID of the user initiating the search. offset (number): The number of results to skip (for pagination). limit (number): The maximum number of results to return. Returns: A promise that resolves with search results matching the query or an error message if the search fails.