LocalGPT — Chat with Your Documents 100% Privately on Your Hardware

Introduction

LocalGPT is a privacy-focused document Q&A tool that runs entirely on your local hardware. It ingests documents, builds a vector store, and uses a locally running LLM to answer questions without sending any data to external servers. It is built on LangChain and supports multiple open-source models.

What LocalGPT Does

• Ingests PDFs, CSVs, text files, and other document formats into a local vector database
• Chunks and embeds documents using HuggingFace sentence-transformer models
• Stores embeddings in Chroma for fast similarity-based retrieval
• Runs inference through quantized LLMs like Llama 2 using llama.cpp or HuggingFace
• Provides a Streamlit-based web UI for interactive question answering

Architecture Overview

The system follows a retrieval-augmented generation pattern. Documents are split into chunks, embedded with a local sentence-transformer, and stored in a ChromaDB collection. When a user asks a question, the query is embedded, similar chunks are retrieved, and the combined context is passed to a locally running LLM to generate an answer.

Self-Hosting & Configuration

• Requires Python 3.10+ and optionally a CUDA-capable GPU for faster inference
• Configure the embedding model and LLM in constants.py
• Supports GGUF-quantized models through llama-cpp-python for CPU or GPU inference
• Set SOURCE_DIRECTORY to point at your document folder before ingesting
• Run with the --device_type flag to choose between cpu, cuda, or mps backends

Key Features

• Complete data privacy with zero external API calls during inference
• Supports multiple LLM backends including llama.cpp, HuggingFace, and GPTQ models
• Handles diverse file types through LangChain document loaders
• Includes both CLI and web-based interfaces for interaction
• GPU acceleration with CUDA and Apple Silicon MPS support

Comparison with Similar Tools

• PrivateGPT — similar local-first RAG approach; LocalGPT adds a Streamlit UI and broader model support
• Ollama + Open WebUI — general local LLM chat; LocalGPT specializes in document retrieval
• Quivr — cloud-oriented RAG platform; LocalGPT stays fully offline
• AnythingLLM — desktop app with multi-provider support; LocalGPT is Python-native and more customizable

FAQ

Q: What hardware do I need? A: A machine with 16 GB RAM can run 7B-parameter models on CPU. A GPU with 8 GB VRAM speeds up inference significantly.

Q: Which document formats are supported? A: PDF, CSV, TXT, DOCX, EPUB, HTML, and Markdown are supported through LangChain loaders.

Q: Can I use it with my own fine-tuned model? A: Yes. Point the model path in constants.py to any HuggingFace or GGUF-compatible model.

Q: Is there multi-user support? A: The default setup is single-user. For multi-user access, deploy the Streamlit app behind an authentication proxy.

Sources

• https://github.com/PromtEngineer/localGPT
• https://localGPT.readthedocs.io