OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
RAG & Knowledge Systems

Renumics Rag

Renumics Rag is an MIT-licensed Python demo application for retrieval-augmented generation. It uses LangChain and Streamlit to index documents, retrieve relevant snippets, answer questions, and visualize questions and retrieval data for interactive inspection.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Renumics Rag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building or examining a document-grounded question-answering demo and when seeing the retrieved snippets is part of evaluating the workflow.

Who it is for

  • Developers exploring or adapting a local RAG assistant demo
  • Practitioners who want to inspect which document snippets a retriever uses to answer questions

Documented uses

  • Index HTML documents into a local database for retrieval
  • Retrieve relevant snippets or generate answers grounded in indexed documents
  • Review answer sources and explore question-to-document relationships visually
  • Adjust model and retrieval settings for the demo
Before you choose

Requirements & limits

The project is publicly available on GitHub under the MIT license. Its README documents local setup for Linux, macOS and Windows.

  • The documented indexing workflow currently supports HTML files; other file types require modifying the local project.
  • An embeddings model must be selected before indexing, and changing it requires a new database.
  • The application can be configured for OpenAI or Hugging Face models; OpenAI use requires credentials, while the README says Hugging Face use does not require a .env file.
  • The interactive exploration workflow uses Renumics Spotlight.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Index HTML documents for the RAG demo

Create a local document database from HTML files for retrieval and question answering.

Before you start

  • Python 3.8 and a virtual environment
  • The Renumics Rag package and its listed dependencies
  • HTML documents placed under data/docs

Steps

  1. Create and activate a virtual environment, then install the package and dependencies using one of the README’s documented installation paths.

  2. Place the HTML documents in the project’s data/docs directory; nested directories are supported.

  3. Run create-db to produce the db-docs indexed-document directory.

Expected outcome

An indexed document database that can be used by the project’s retrieval and question-answering commands or web application.

Guide limitations

  • The current indexing instructions cover HTML files only; changing supported file types requires editing the local project.
  • Choose the embeddings model before indexing. Changing it requires creating a new database.
Documentation 1 ↗
Source-based practical guide

Inspect retrieval results in the web application

Use the application to ask questions against an indexed document database and examine the supporting snippets.

Before you start

  • An installed Renumics Rag environment
  • An indexed document database
  • OpenAI credentials if using OpenAI models; Hugging Face models do not require a .env file

Steps

  1. Configure the embedding model, retriever and language model in the settings file or application settings.

  2. Start the web application with the documented app command.

  3. Submit questions and open the expandable Sources area to review filenames and text snippets.

  4. Use the explore control to inspect questions and snippets in the table and similarity map.

Expected outcome

Question answers with visible supporting snippets, plus an interactive view for examining question and document relationships.

Guide limitations

  • The database must use the selected embeddings model; changing that model requires re-indexing.
  • The exploration workflow uses Renumics Spotlight.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Renumics RagIndex HTML documents into a local database for retrievalLinux, macOS, WindowsThe project is publicly available on GitHub under the MIT license. Its README documents local setup for Linux, macOS and Windows.
Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And PdfsRetrieve relevant passages from web and PDF content to answer a question.Web
LangChain in Supabase Edge FunctionsInitialize and start a Supabase project locally.Cloud, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
Renumics/renumics-rag · Official homepage title · retained in its source language
How we review sourcesSuggest a correction