Can I use LocalGPT or PrivateGPT on my laptop without a powerful GPU?

Yes, both LocalGPT and PrivateGPT can run on a CPU, though a GPU is recommended for better performance. The speaker successfully used a Mac M1 chip laptop for testing.

What file formats do LocalGPT and PrivateGPT support?

LocalGPT explicitly supports common text formats like TXT, MD, PDF, CSV, XLS, and Word documents. PrivateGPT is believed to support similar formats like PDF, DOCX, and TXT.

How do I set up LocalGPT?

Setting up LocalGPT involves cloning the repository, creating a virtual environment (e.g., with Conda), installing requirements using pip, placing files in the source documents folder, and running the ingest script.

How do I set up PrivateGPT?

PrivateGPT setup requires cloning the repository, setting up a virtual environment (Python 3.11 recommended), installing Poetry and a C++ compiler (GCC, Make), and then installing dependencies using Poetry.

Can PrivateGPT use OpenAI if my local hardware is not good enough?

Yes, PrivateGPT can be configured to use OpenAI's API as the LLM backend. This bypasses local hardware limitations but sacrifices the privacy aspect as data is sent to OpenAI.

What were the results when testing PrivateGPT with a contract?

When testing PrivateGPT with an employment contract, it misidentified the document as a privacy notice and provided inaccurate information about holiday days, suggesting potential limitations on less powerful hardware.

Is there an alternative for analyzing audio or video files?

Yes, AssemblyAI's Lemur tool is suggested as an alternative for analyzing audio and video files. It offers more accurate results through its playground interface compared to the local LLMs tested.

How to Set up and Use PrivateGPT and LocalGPT

AssemblyAI

Science & Technology3 min read21 min video

Nov 8, 2023|31,345 views|751|45

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Key Moments

TL;DR

Set up LocalGPT or PrivateGPT on your laptop for private document analysis and Q&A.

Key Insights

LocalGPT and PrivateGPT enable private LLM analysis of personal documents on your own laptop.

Both are open-source projects with GitHub presence, configurable for CPU/GPU usage.

Setting up involves cloning the repository, creating a virtual environment, and installing dependencies.

Files are placed in a 'source_documents' folder and ingested for the LLM to process.

PrivateGPT offers a GUI interface, while LocalGPT also has GUI options and model customization via 'constants.py'.

Results can vary based on hardware (GPU recommended) and the size/quality of the LLM used.

INTRODUCTION TO LOCAL LLMS AND THEIR BENEFITS

Large Language Models (LLMs) are powerful for analyzing lengthy documents, but a significant drawback is the need to upload sensitive or private files to external servers. This video introduces LocalGPT and PrivateGPT as open-source solutions that allow users to run LLMs entirely on their personal laptops. This ensures complete privacy and eliminates the need for an internet connection, making it ideal for handling confidential information like employment contracts, personal notes, or proprietary data.

LOCALGPT: SETUP AND DOCUMENT INGESTION

LocalGPT allows users to chat with their local files using any open-source GPT model. Once the LLM and embeddings are downloaded, no data leaves the computer. It supports various file formats including TXT, MD, PDF, CSV, XLS, and Word documents. The setup process involves cloning the repository, creating an isolated Python virtual environment (Python 3.10 recommended), and installing dependencies using 'pip install -r requirements.txt'. A crucial step is installing 'llama-cpp-python' which is necessary for loading specific model types (GML/GF), with stability often favoring specific versions rather than the absolute latest.

USING LOCALGPT FOR QUESTION ANSWERING

After setup, documents are placed in the 'source_documents' folder. The ingestion process is initiated by running an ingest script, which can differ for GPU or Mac M1 chip users. Once the data is loaded, users can interact with the LLM. For example, by asking 'What is this document about?' or more specific questions like 'How many vacation days does the employee get?', the LLM provides answers based on the ingested document's content. While LocalGPT has GUI options, the presenter focused on the command-line interface for setup.

PRIVATEGPT: GRAPHICAL USER INTERFACE SETUP

PrivateGPT offers a similar functionality to LocalGPT but emphasizes its graphical user interface (GUI). The setup redirects users to its documentation, involving cloning the repository and setting up a virtual environment (Python 3.11 required). Unlike LocalGPT's pip-based installation, PrivateGPT uses 'poetry install --ui' for dependency management, also requiring a C++ compiler like GCC and 'make'. A test run using 'make run' launches a mock UI to verify interface functionality before connecting to an LLM.

CONFIGURING AND RUNNING PRIVATEGPT LOCALLY

To connect PrivateGPT to a local LLM, users need to install additional dependencies for local execution. The system requires downloading embeddings and large language models locally, which can be done via Poetry. The server can then be run locally using specific settings files (e.g., 'settings_local.yaml'). While running locally is ideal for privacy, the documentation suggests an option to use OpenAI as the LLM if local hardware is insufficient, though this compromises the 'private' aspect. The presenter attempts to use a local LLM with the UI.

TESTING PRIVATEGPT AND COMPARING PERFORMANCE

Testing PrivateGPT with documents like the US Constitution and a privacy notice revealed varied results. While it correctly identified the Constitution's topic, its interpretation of the privacy notice was not entirely accurate regarding specific details like holiday days, sometimes mistaking it for an employment contract. The presenter notes that performance can significantly depend on the hardware, especially the availability of a GPU and the use of a larger LLM, which were limitations on their M1 MacBook.

ALTERNATIVES AND FUTURE CONSIDERATIONS

While LocalGPT and PrivateGPT offer powerful local LLM capabilities, their full functionality and optimal performance often depend on robust hardware. For users with less powerful machines or for analyzing different types of media, AssemblyAI's Lemur is suggested as an alternative for processing audio and video files. Lemur provides a user-friendly playground for uploading files or pasting video links to ask questions and obtain summaries, potentially offering more accurate results than local LLMs constrained by hardware limitations.

Mentioned in This Episode

●Software & Apps

●Tools

●Companies

●Books

●Studies Cited

Common Questions

LocalGPT and PrivateGPT are open-source projects that allow you to run large language models (LLMs) on your own computer to process and query your personal documents without an internet connection, ensuring privacy.

Topics

LocalGPT PrivateGPT Document Analysis Virtual Environment AssemblyAI Lemur

Mentioned in this video

toolMD

Markdown file format supported by LocalGPT for ingesting documents.

toolXLS

Excel spreadsheet file format supported by LocalGPT for document ingestion.

toolGCC

A C++ compiler that needs to be installed for PrivateGPT setup.

softwareMistral

A large language model that can be chosen within LocalGPT's constants file for local execution.

softwareLocalGPT

An open-source project that allows users to chat with their local personal files using any open-source GPT model, running entirely locally.

tooltxt

A supported text file format for LocalGPT, allowing users to ingest and query plain text documents.

toolPDF

A file format supported by LocalGPT and likely by PrivateGPT for ingesting documents.

toolWord documents

Microsoft Word document formats supported by LocalGPT for ingesting files.

toolMac M1 chip

The laptop hardware used by the speaker to test LocalGPT and PrivateGPT, highlighting its capability to run these LLMs locally.

softwareLLaMA CPP Python

A Python library required by LocalGPT for loading GML and GF models, with a specific version recommended for stability.

softwareGradio UI client

A client used to communicate with the PrivateGPT API, providing a graphical user interface for interaction.

softwarePrivateGPT

An open-source project similar to LocalGPT that allows users to run LLMs on personal files locally, emphasizing privacy and offering a GUI.

toolCSV

Comma-separated values file format supported by LocalGPT for document ingestion.

bookConstitution of the United States

A document used as an example file for testing the PrivateGPT interface before connecting it to an LLM.

study12th Amendment to the United States Constitution

A historical document that the speaker tested with PrivateGPT, which it incorrectly identified.

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