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RAG & Knowledge Systems

Local Rag

Local Rag is a self-hosted retrieval-augmented generation application that indexes local files, GitHub repositories, and websites, then answers questions using local models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Local 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 the source material can be collected from local files, GitHub repositories, or websites and the workflow should keep chat, embeddings, and indexed content on a machine or network under your control.

Who it is for

  • People who want to query a collection of local documents with locally hosted models.
  • Developers exploring question answering over GitHub repositories.
  • Teams building an internal knowledge-search workflow on a private network.

Documented uses

  • Ingest files, repositories, or website content into a retrieval workflow.
  • Ask questions against indexed material and receive streaming responses.
  • Use local Ollama chat models with Ollama or local Hugging Face embedding models.
  • Retain browser-local settings and export chat history.
Before you choose

Requirements & limits

  • Chat models are documented through Ollama; embedding models can be from Ollama or local Hugging Face.
  • LlamaIndex provides streaming RAG responses.
  • The project is Python-based and licensed under GPL-3.0.
  • The README points to separate setup, usage, pipeline, and troubleshooting documentation.

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 Local RAG for local-source question answering

Prepare Local RAG to ingest local files, GitHub repositories, or websites and answer questions using local models.

Before you start

  • Access to the Local RAG repository and its setup instructions.
  • A local Ollama chat model; embeddings can use Ollama or a local Hugging Face model.

Steps

  1. Follow the project’s Setup & Deploy the App instructions to prepare the application.

  2. Choose local files, a GitHub repository, or a website as an ingestion source.

  3. Use the application’s RAG workflow to ask questions about ingested content.

Expected outcome

A RAG workflow that can use local models and keep chat, embeddings, and indexed source content on the user's machine or network.

Guide limitations

  • Specific setup commands, configuration, and ingestion interactions are not included here; consult the linked setup and usage guides.
  • The README identifies supported source types and local model options but does not specify model requirements or compatibility details.
Documentation 1 ↗Documentation 2 ↗
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
Local RagIngest files, repositories, or website content into a retrieval workflow.Self-hosted, Server, Web
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 ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
9 October 2026
Original website title
jonfairbanks/local-rag · Official homepage title · retained in its source language
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