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RAG & Knowledge Systems

Rag Architecture

Rag Architecture is a Python Streamlit application that processes audio, video, images and document formats, stores extracted-text embeddings in Milvus, and provides a natural-language question interface using a LangChain retrieval-augmented generation pipeline.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag Architecture — 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 as a starting point when you need to ingest the listed file formats into a Milvus-backed collection and query the resulting content through a Streamlit interface.

Who it is for

  • Developers building a searchable knowledge base from mixed media and document files.
  • Teams exploring a self-hosted document question-answering application built with Streamlit and Milvus.

Documented uses

  • Extract searchable text from audio, video, images and supported document formats.
  • Create embeddings and store processed content in Milvus for similarity-based retrieval.
  • Ask natural-language questions over indexed content and receive RAG-generated responses.
Before you choose

Requirements & limits

The project is presented as a public, open-source GitHub repository and identifies its license as MIT. Local setup requires Python 3.8 or later and pip or conda; a CUDA-compatible GPU is optional for faster processing.

  • It is an application with a Streamlit interface, not just a document-processing library.
  • Milvus stores embeddings for similarity-based querying; Hugging Face embeddings generate vector representations.
  • Extraction is format-specific: EasyOCR for images, speech_recognition and pydub for audio, custom logic for video, and LangChain loaders for text and document formats.
  • Support for further file types and embedding options is described as a future improvement, not an implemented feature.

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

Set up and launch the Streamlit application

Install the project dependencies and start its documented Streamlit interface.

Before you start

  • Python 3.8 or later
  • pip or conda
  • A local copy of the repository; a CUDA-compatible GPU is optional for faster processing

Steps

  1. Clone the repository.

  2. Install dependencies with pip install -r requirements.txt.

  3. Start the application with streamlit run app.py.

Expected outcome

The Streamlit application starts, with upload and query modes described in the project README.

Guide limitations

  • This setup guide does not verify successful execution or compatibility in a particular environment.
  • The README documents the interface workflow but does not specify external service configuration.
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
Rag ArchitectureExtract searchable text from audio, video, images and supported document formats.Self-hosted, WebThe project is presented as a public, open-source GitHub repository and identifies its license as MIT. Local setup requires Python 3.8 or later and pip or conda; a CUDA-compatible GPU is optional for faster processing.
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
18 October 2026
Original website title
pacificrm/RAG-Architecture · Official homepage title · retained in its source language
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