Document Vector Database

Overview

The Document Vector Database Connected System enables Large Language Models (LLMs) to answer user submitted questions based on Appian Knowledge Center Documents. By uploading documents to this connected system, users can perform semantic searches to pinpoint the most pertinent content related to their questions. The Connected System also boasts Client APIs tailored for the AI Knowledge Assistant Component. This allows the AI Knowledge Assistant Component to deliver AI generated answers to user inquiries sourced from documents stored in the database, as well as general questions.

Key Features & Functionality

  1. Upload Document - Uploads and stores the documents and its vector in the database.
  2. List Documents - Provides us the list of documents uploaded in the database.
  3. Database Operations
    1. Delete Documents - Enables us to delete the documents that are uploaded in the database.
    2. Sync Documents - Updates the existing documents in the database with the latest version of the document available in the Appian Knowledge Center.
    3. Change Database Password - Changes Database password.
  4. Query Documents - Get relevant pieces of content from documents for the given prompt.
  5. Generate Response - Perform search in the given documents and generate ChatGPT response for the given prompt.
  6. Client APIs for AI Knowledge Assistant component for fetching document details, chat completions, document querying, and uploading new documents to the database.

Notes:

  • Download the AI Knowledge Assistant, a sophisticated chatbot designed to perform semantic searches across your documents and provide precise answers to your queries.
  • This plugin is not supported for HA environments.
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  • The data is being stored in an embedded database represented as an Appian Document which is created when your click "Test Connection" in the connected system. This database will store chunks of document text along with their embeddings. The user's query will bring up the top most relevant chunks from the database.