Simple-Local-Rag

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simple-local-rag is an AI Agents & Automation tool that helps build Retrieval Augmented Generation (RAG) pipelines. It enables local execution of RAG workflows, including PDF ingestion and chat features, using open-source tools and NVIDIA GPUs.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is simple-local-rag?

simple-local-rag is a comprehensive tool designed for building Retrieval Augmented Generation (RAG) pipelines from the ground up, with a strong emphasis on local execution. It supports the entire RAG workflow, from ingesting PDF documents to enabling "chat with PDF" style features. The tool is built to run on NVIDIA GPUs and leverages open-source technologies, making it a flexible and powerful solution for developers and data scientists. A key example is NutriChat, a RAG workflow that allows users to query a 1200-page nutrition textbook and receive LLM-generated responses based on relevant passages. This tool is particularly valuable for those who prioritize privacy, speed, and cost-effectiveness by running AI models on their own hardware.

Best used for

Ideal for developers and data scientists who need to build custom RAG systems, process large documents locally, and create interactive Q&A interfaces. Especially valuable for those prioritizing data privacy, control over hardware, and cost-efficiency by avoiding API fees.

Common actions

build RAG pipeline

process documents locally

query custom data

generate responses

workflowsopen-sourcecollaborationautomated workflowdeepfakelow-code/no-codegithub copilot"AI Agents"face swapping

Capabilities

Key features

Local RAG pipeline setup
PDF ingestion
Chat with PDF
NVIDIA GPU support
Open-source tools
Custom data integration

Target Audience

developerdata scientiststartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What hardware is required to run simple-local-rag?

To run simple-local-rag locally, you will need an NVIDIA GPU with at least 5GB of VRAM. If you don't have a local NVIDIA GPU, you can still follow along and run the pipeline in Google Colab, which provides access to NVIDIA GPUs in the cloud.

What are the main benefits of running a RAG pipeline locally?

Running a RAG pipeline locally offers several advantages, including enhanced privacy by keeping sensitive data on your own hardware, improved speed by avoiding API queues, and reduced long-term costs as you leverage your existing hardware rather than paying for external API usage.

How does simple-local-rag handle large documents like PDFs?

simple-local-rag is designed for PDF ingestion, allowing you to process and query large documents. For example, the NutriChat workflow demonstrates its capability to handle a 1200-page PDF textbook, enabling an LLM to generate responses based on its content.

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AI Frameworks & Infra Chatbots & Conversational AI General-Purpose Agents Workflow Agents Personal Assistants Voice Agents

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Also listed in

This tool also appears in

Data & Analytics › Data Pipelines & Integration Research & Education › Knowledge Management Coding & Development › Open Source & Models

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