LARS is an application that enables you to run LLM's (Large Language Models) locally on your device, upload your own documents and engage in conversations wherein the LLM grounds its responses with your uploaded content. This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or "hallucinations." This technique is commonly known as "Retrieval Augmented Generation", or RAG.

There are many desktop applications for running LLMs locally, and LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.

1. Advanced Citations: The main showcase feature of LARS - LLM-generated responses are appended with detailed citations comprising document names, page numbers, text highlighting and image extraction for any RAG centric responses, with a document reader presented for the user to scroll through the document right within the response window and download highlighted PDFs
2. Vast number of supported file-formats:
   • PDFs
   • Word files: doc, docx, odt, rtf, txt
   • Excel files: xls, xlsx, ods, csv
   • PowerPoint presentations: ppt, pptx, odp
   • Image files: bmp, gif, jpg, png, svg, tiff
   • Rich Text Format (RTF)
   • HTML files
3. Conversion memory: Users can ask follow-up questions, including for prior conversations
4. Full chat-history: Users can go back and resume prior conversations
5. Users can force enable or disable RAG at any time via Settings
6. Users can change the system prompt at any time via Settings
7. Drag-and-drop in new LLMs - change LLM's via Settings at any time
8. Built-in prompt-templates for the most popular LLMs and then some: Llama3, Llama2, ChatML, Phi3, Command-R, Deepseek Coder, Vicuna and OpenChat-3.5
9. Pure llama.cpp backend - No frameworks, no Python-bindings, no abstractions - just pure llama.cpp! Upgrade to newer versions of llama.cpp independent of LARS
10. GPU-accelerated inferencing: Nvidia CUDA-accelerated inferencing supported
11. Tweak advanced LLM settings - Change LLM temperature, top-k, top-p, min-p, n-keep, set the number of model layers to be offloaded to the GPU, and enable or disable the use of GPUs, all via Settings at any time
12. Four embedding models - sentence-transformers/all-mpnet-base-v2, BGE-Base, BGE-Large, OpenAI Text-Ada
13. Sources UI - A table is displayed for the selected embedding model detailing the documents that have been uploaded to LARS, including vectorization details such as chunk_size and chunk_overlap
14. A reset button is provided to empty and reset the vectorDB
15. Three text extraction methods: a purely local text-extraction option and two OCR options via Azure for better accuracy and scanned document support - Azure ComputerVision OCR has an always free-tier
16. A custom parser for the Azure AI Document-Intelligence OCR service for enhanced table-data extraction while preventing double-text by accounting for the spatial coordinates of the extracted text

LARS Feature-Demonstration Video

1. LARS - The LLM & Advanced Referencing Solution
   • Detailed list of LARS's feature-set
   • A demonstration video showcasing these features
2. Dependencies
   • 1. Build Tools
   • 2. Nvidia CUDA (if supported Nvidia GPU present)
   • 3. llama.cpp
   • 4. Python
   • 5. LibreOffice
   • 6. Poppler
   • 7. PyTesseract (optional)
3. Installing LARS
4. Troubleshooting Installation Issues
5. First Run - Important Steps for First-Time Setup
6. General User Guide - Post First-Run Steps
7. Troubleshooting
8. Docker - Deploying Containerized LARS
   • Background and Setup
   • Building & Running the CPU-Inferencing Container
   • Building & Running the Nvidia-CUDA GPU-Enabled Container
   • Special Note for Containers - Troubleshooting Networking Issues and Errors on First Run
   • Special Note for Containers - Updating the Container Image Post-First-Run
9. Current Development Roadmap
10. Support and Donations

• On Windows:

  • Download Microsoft Visual Studio Build Tools 2022 from the Official Site - "Tools for Visual Studio"

  • NOTE: When installing the above, make sure to select the following components:

  Desktop development with C++
  # Then from the "Optional" category on the right, make sure to select the following:
  MSVC C++ x64/x86 build tools
  C++ CMake tools for Windows
  

  • Refer to the screenshot below:

  

  • If you skipped selecting the above workloads when first installing the Visual Studio Build Tools, simply run the vs_buildTools.exe installer again, click "Modify" and ensure the Desktop development with C++ workload and the MSVC and C++ CMake Optionals are selected as outlined above

• On Linux (Ubuntu and Debian-based), install the following packages:

  • build-essential includes GCC, G++, and make
  • libffi-dev for Foreign Function Interface (FFI)
  • libssl-dev for SSL support

  sudo apt-get update
  sudo apt-get install -y software-properties-common build-essential libffi-dev libssl-dev cmake
  

• Install Nvidia GPU Drivers

• Install Nvidia CUDA Toolkit - LARS built and tested with v12.2 and v12.4

• Verify Installation via the terminal:

  nvcc -V
  nvidia-smi
  

• CMAKE-CUDA Fix (Very Important!):

  Copy all the four files from the following directory:
  C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.2\extras\visual_studio_integration\MSBuildExtensions

  and Paste them to the following directory:
  C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools\MSBuild\Microsoft\VC\v170\BuildCustomizations

• Download from the Official Repo:

  git clone https://github.com/ggerganov/llama.cpp
  cd llama.cpp
  

• Install CMAKE on Windows from the Official Site

  • add to PATH:
    C:\Program Files\CMake\bin

• Build llama.cpp with CMAKE:

  • Note: For faster compilation, add the -j argument to run multiple jobs in parallel. For example, cmake --build build --config Release -j 8 will run 8 jobs in parallel.

  • Build with CUDA:

  cmake -B build -DGGML_CUDA=ON
  cmake --build build  --config Release
  

  • Build without CUDA:

  cmake -B build
  cmake --build build  --config Release
  

• If you face issues when attempting to run CMake -B build, check the extensive CMake Installation Troubleshooting steps below

• Add to PATH:
  path_to_cloned_repo\llama.cpp\build\bin\Release

• Verify Installation via the terminal:

  llama-server
  

• Built and tested with Python v3.11.x

• Windows:

  • Download v3.11.9 from the Official Site

  • During installation, ensure you check "Add Python 3.11 to PATH" or manually add it later, either via:

    • Advanced System Settings -> Environment Variables -> System Variables -> EDIT PATH Variable -> Add the below (change as per your installation location):

      C:\Users\user_name\AppData\Local\Programs\Python\Python311\
      

    • Or via PowerShell:

      Set PATH=%PATH%;C:\Users\user_name\AppData\Local\Programs\Python\Python311
      

• Linux (Ubuntu and Debian-based):

  • via deadsnakes PPA:

  sudo add-apt-repository ppa:deadsnakes/ppa -y
  sudo apt-get update
  sudo apt-get install -y python3.11 python3.11-venv python3.11-dev
  sudo python3.11 -m ensurepip
  

• Verify Installation via the terminal:

  python3 --version
  

• This is an optional, but highly recommended dependency - Only PDFs are supported if this setup is not completed

• Windows:

  • Download from the Official Site

  • Add to PATH, either via:

    • Advanced System Settings -> Environment Variables -> System Variables -> EDIT PATH Variable -> Add the below (change as per your installation location):

      C:\Program Files\LibreOffice\program
      

    • Or via PowerShell:

      Set PATH=%PATH%;C:\Program Files\LibreOffice\program
      

• Ubuntu & Debian-based Linux - Download from the Official Site or install via terminal:

  sudo apt-get update
  sudo apt-get install -y libreoffice
  

• Fedora and other RPM-based distros - Download from the Official Site or install via terminal:

  sudo dnf update
  sudo dnf install libreoffice
  

• MacOS - Download from the Official Site or install via Homebrew:

  brew install --cask libreoffice
  

• Verify Installation:

  • On Windows and MacOS: Run the LibreOffice application

  • On Linux via the terminal:

    libreoffice --version
    

• LARS utilizes the pdf2image Python library to convert each page of a document into an image as required for OCR. This library is essentially a wrapper around the Poppler utility which handles the conversion process.

• Windows:

  • Download from the Official Repo

  • Add to PATH, either via:

    • Advanced System Settings -> Environment Variables -> System Variables -> EDIT PATH Variable -> Add the below (change as per your installation location):

      path_to_installation\poppler_version\Library\bin
      

    • Or via PowerShell:

      Set PATH=%PATH%;path_to_installation\poppler_version\Library\bin
      

• Linux:

  sudo apt-get update
  sudo apt-get install -y poppler-utils wget
  

• This is an optional dependency - Tesseract-OCR is not actively used in LARS but methods to use it are present in the source code

• Windows:

  • Download Tesseract-OCR for Windows via UB-Mannheim

  • Add to PATH, either via:

    • Advanced System Settings -> Environment Variables -> System Variables -> EDIT PATH Variable -> Add the below (change as per your installation location):

      C:\Program Files\Tesseract-OCR
      

    • Or via PowerShell:

      Set PATH=%PATH%;C:\Program Files\Tesseract-OCR
      

Back to Table of Contents

1. Clone the repository:

   git clone https://github.com/abgulati/LARS
   cd LARS
   

   • If prompted for GitHub authentication, use a Personal Access Token as passwords are deprecated. Also accessible via:
     GitHub Settings -> Developer settings (located on the bottom left!) -> Personal access tokens

2. Install Python dependencies:

   • Windows via PIP:

     pip install -r .\requirements.txt
     

   • Linux via PIP:

     pip3 install -r ./requirements.txt
     

   • Note on Azure: Some required Azure libraries are NOT available on the MacOS platform! A separate requirements file is therefore included for MacOS excluding these libraries:

   • MacOS:

     pip3 install -r ./requirements_mac.txt
     

Back to Table of Contents

• If you encounter a CMake nmake failed error when attempting to build llama.cpp such as below:

This typically indicates an issue with your Microsoft Visual Studio build tools, as CMake is unable to find the nmake tool, which is part of the Microsoft Visual Studio build tools. Try the below steps to resolve the issue:

1. Ensure Visual Studio Build Tools are Installed:

   • Make sure you have the Visual Studio build tools installed, including nmake. You can install these tools through the Visual Studio Installer by selecting the Desktop development with C++ workload, and the MSVC and C++ CMake Optionals

   • Check Step 0 of the Dependencies section, specifically the screenshot therein

2. Check Environment Variables:

   • Ensure that the paths to the Visual Studio tools are included in your system's PATH environment variable. Typically, this includes paths like:

   C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build
   C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE
   C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\Tools
   

3. Use Developer Command Prompt:

   • Open a "Developer Command Prompt for Visual Studio" which sets up the necessary environment variables for you

   • You can find this prompt from the Start menu under Visual Studio

4. Set CMake Generator:

   • When running CMake, specify the generator explicitly to use NMake Makefiles. You can do this by adding the -G option:

   cmake -G "NMake Makefiles" -B build -DLLAMA_CUDA=ON
   

5. If problems persist, consider opening an issue on the LARS GitHub repository for support.

• If you encounter errors with pip install, try the following:

1. Remove version numbers:

   • If a specific package version causes an error, edit the corresponding requirements.txt file to remove the version constraint, that is the ==version.number segment, for example:
     urllib3==2.0.4
     becomes simply:
     urllib3

2. Create and use a Python virtual environment:

   • It's advisable to use a virtual environment to avoid conflicts with other Python projects

   • Windows:

     • Create a Python virtual environment (venv):

       python -m venv larsenv
       

     • Activate, and subsequently use, the venv:

       .\larsenv\Scripts\activate
       

     • Deactivate venv when done:

       deactivate
       

   • Linux and MacOS:

     • Create a Python virtual environment (venv):

       python3 -m venv larsenv
       

     • Activate, and subsequently use, the venv:

       source larsenv/bin/activate
       

     • Deactivate venv when done:

       deactivate
       

3. If problems persist, consider opening an issue on the LARS GitHub repository for support.

Back to Table of Contents

• After installing, run LARS using:

  cd web_app
  python app.py   # Use 'python3' on Linux/macOS
  

• Navigate to http:https://localhost:5000/ in your browser

• All application directories required by LARS will now be created on disk

• Eventually (after approximately 60 seconds) you'll see an alert on the page indicating an error:

  Failed to start llama.cpp local-server
  

• This indicates that first-run has completed, all app directories have been created, but no LLMs are present in the models directory and may now be moved to it

• Move your LLMs (any file format supported by llama.cpp, preferably GGUF) to the newly created models dir, located by default in the following locations:

  • Windows: C:/web_app_storage/models
  • Linux: /app/storage/models
  • MacOS: /app/models

• Once you've placed your LLMs in the appropriate models dir above, refresh http:https://localhost:5000/

• You'll once again receive an error alert stating Failed to start llama.cpp local-server after approximately 60 seconds

• This is because your LLM now needs to be selected in the LARS Settings menu

• Accept the alert and click on the Settings gear icon in the top-right

• In the LLM Selection tab, select your LLM and the appropriate Prompt-Template format from the appropriate dropdowns

• Modify Advanced Settings to correctly set GPU options, the Context-Length, and optionally, the token generation limit (Maximum tokens to predict) for your selected LLM

• Hit Save and if an automatic refresh is not triggered, manually refresh the page

• If all steps have been executed correctly, first-time setup is now complete, and LARS is ready for use

• LARS will also remember your LLM settings for subsequent use

Back to Table of Contents

1. Document Formats Supported:

   • If LibreOffice is installed and added to PATH as detailed in Step 4 of the Dependencies section, the following formats are supported:

     • PDFs
     • Word files: doc, docx, odt, rtf, txt
     • Excel files: xls, xlsx, ods, csv
     • PowerPoint presentations: ppt, pptx, odp
     • Image files: bmp, gif, jpg, png, svg, tiff
     • Rich Text Format (RTF)
     • HTML files

   • If LibreOffice is not setup, only PDFs are supported

2. OCR Options for Text Extraction:

   • LARS provides three methods for extracting text from documents, accommodating various document types and quality:

     • Local Text Extraction: Uses PyPDF2 for efficient text extraction from non-scanned PDFs. Ideal for quick processing when high accuracy is not critical, or entirely local processing is a necessity.

     • Azure ComputerVision OCR - Enhances text extraction accuracy and supports scanned documents. Useful for handling standard document layouts. Offers a free tier suitable for initial trials and low-volume use, capped at 5000 transactions/month at 20 transactions/minute.

     • Azure AI Document Intelligence OCR - Best for documents with complex structures like tables. A custom parser in LARS optimizes the extraction process.

     • NOTES:

       • Azure OCR options incur API-costs in most cases and are not bundled with LARS.

       • A limited free-tier for ComputerVision OCR is available as linked above. This service is cheaper overall but slower and may not work for non-standard document layouts (other than A4 etc).

       • Consider the document types and your accuracy needs when selecting an OCR option.

3. LLMs:

   • Only local-LLMs are presently supported

   • The Settings menu provides many options for the power-user to configure and change the LLM via the LLM Selection tab

   • Very-Important: Select the appropriate prompt-template format for the LLM you're running

   • LLMs trained for the following prompt-template formats are presently supported:

     • Meta Llama-3
     • Meta Llama-2
     • Mistral & Mixtral MoE LLMs
     • Microsoft Phi-3
     • OpenHermes-2.5-Mistral
     • Nous-Capybara
     • OpenChat-3.5
     • Cohere Command-R and Command-R+
     • DeepSeek Coder

   • Tweak Core-configuration settings via Advanced Settings (triggers LLM-reload and page-refresh):

     • Number of layers offloaded to the GPU
     • Context-size of the LLM
     • Maximum number of tokens to be generated per response

   • Tweak settings to change response behavior at any time:

     • Temperature – randomness of the response
     • Top-p – Limit to a subset of tokens with a cumulative probability above
     • Min-p – Minimum probability for considering a token, relative to most likely <min_p>
     • Top-k – Limit to K most probable tokens
     • N-keep – Prompt-tokens retained when context-size exceeded <n_keep> (-1 to retain all)

4. Embedding models and Vector Database:

   • Four embedding models are provided in LARS:

     • sentence-transformers/all-mpnet-base-v2 (default)
     • bge-base-en-v1.5
     • bge-large-en-v1.5 (highest MTEB ranked model available in LARS)
     • Azure-OpenAI Text-Ada (incurs API cost, not bundled with LARS)

   • With the exception of the Azure-OpenAI embeddings, all other models run entirely locally and for free. On first run, these models will be downloaded from the HuggingFace Hub. This is a one-time download and they'll subsequently be present locally.

   • The user may switch between these embedding models at any time via the VectorDB & Embedding Models tab in the Settings menu

   • Docs-Loaded Table: In the Settings menu, a table is displayed for the selected embedding model displaying the list of documents embedded to the associated vector-database. If a document is loaded multiple times, it’ll have multiple entries in this table, which could be useful for debugging any issues.

   • Clearing the VectorDB: Use the Reset button and provide confirmation to clear the selected vector database. This creates a new vectorDB on-disk for the selected embedding model. The old vectorDB is still preserved and may be reverted to by manually modifying the config.json file.

5. Edit System-Prompt:

   • The System-Prompt serves as an instruction to the LLM for the entire conversation

   • LARS provides the user with the ability to edit the System-Prompt via the Settings menu by selecting the Custom option from the dropdown in the System Prompt tab

   • Changes to the System-Prompt will start a new chat

6. Force Enable/Disable RAG:

   • Via the Settings menu, the user may force enable or disable RAG (Retrieval Augmented Generation – the use of content from your documents to improve LLM-generated responses) whenever required

   • This is often useful for the purposes of evaluating LLM responses in both scenarios

   • Force disabling will also turn off attribution features

   • The default setting, which uses NLP to determine when RAG should and shouldn’t be performed, is the recommended option

   • This setting can be changed at any time

7. Chat History:

   • Use the chat history menu on the top-left to browse and resume prior conversations

   • Very-Important: Be mindful of prompt-template mismatches when resuming prior conversations! Use the Information icon on the top-right to ensure the LLM used in the prior-conversation, and the LLM presently in use, are both based on the same prompt-template formats!

8. User rating:

   • Each response may be rated on a 5-point scale by the user at any time

   • Ratings data is stored in the chat-history.db SQLite3 database located in the app directory:

     • Windows: C:/web_app_storage
     • Linux: /app/storage
     • MacOS: /app

   • Ratings data is very valuable for evaluation and refinement of the tool for your workflows

9. Dos and Don’ts:

   • Do NOT tweak any settings or submit additional queries while a response to a query is already being generated! Wait for any ongoing response generation to complete.

Back to Table of Contents

• If a chat goes awry, or any odd responses are generated, simply try starting a New Chat via the menu on the top-left

• Alternatively, start a new chat by simply refreshing the page

• If issues are faced with citations or RAG performance, try resetting the vectorDB as described in Step 4 of the General User Guide above

• If any application issues crop up and are not resolved simply by starting a new chat or restarting LARS, try deleting the config.json file by following the steps below:

  • Shut-down the LARS app server by terminating the Python program with CTRL+C
  • Backup and delete the config.json file located in LARS/web_app (same directory as app.py)

• For any severe data and citation issues that are not resolved even by resetting the VectorDB as described in Step 4 of the General User Guide above, perform the following steps:

  • Shut-down the LARS app server by terminating the Python program with CTRL+C
  • Backup and delete the entire app directory:
    • Windows: C:/web_app_storage
    • Linux: /app/storage
    • MacOS: /app

• If problems persist, consider opening an issue on the LARS GitHub repository for support.

Back to Table of Contents

• LARS has been adapted to a Docker container-deployment environment via two separate images as below:

  • CPU-inferencing only Docker container
  • Nvidia-CUDA GPU-enabled Docker container

• Both have different requirements with the former being a simpler deployment, but suffering far slower inferencing performance due to the CPU and DDR memory acting as bottlenecks

• While not explicitly required, some experience with Docker containers and familiarity with the concepts of containerization and virtualization will be very helpful in this section!

• Beginning with common setup steps for both:

  1. Installing Docker

     • Your CPU should support virtualization and it should be enabled in your system's BIOS/UEFI

     • Download and install Docker Desktop

     • If on Windows, you may need to install the Windows Subsystem for Linux if it's not already present. To do so, open PowerShell as an Administrator and run the following:

       wsl --install
       

     • Ensure Docker Desktop is up and running, then open a Command Prompt / Terminal and execute the following command to ensure Docker is correctly installed and up and running:

       docker ps
       

  2. Create a Docker storage volume, which will be attached to the LARS containers at runtime:

     • Creating a storage volume for use with the LARS container is highly advantageous as it'll allow you to upgrade the LARS container to a newer version, or switch between the CPU & GPU container variants while persisting all your settings, chat history and vector databases seamlessly.

     • Execute the following command in a Command Prompt / Terminal:

       docker volume create lars_storage_volue
       

     • This volume will be attached to the LARS container later at runtime, for now proceed to building the LARS image in the steps below.

• In a Command Prompt / Terminal, execute the following commands:

  git clone https://github.com/abgulati/LARS  # skip if already done
  
  cd LARS # skip if already done
  
  cd dockerized
  
  docker build -t lars-no-gpu .
  
  # Once the build is complete, run the container:
  
  docker run -p 5000:5000 -p 8080:8080 -v lars_storage:/app/storage lars-no-gpu
  

• Once done, Navigate to http:https://localhost:5000/ in your browser and follow the remainder of the First Run Steps and User Guide

• The Troubleshooting sections applies to Container-LARS as well

• Requirements (in addition to Docker):

  Compatible Nvidia GPU(s)
  Nvidia GPU drivers
  Nvidia CUDA Toolkit v12.2
  

• For Linux, you're all set up with the above so skip the next step and head directly to the build and run steps further below

• If on Windows, and if this is your first time running an Nvidia GPU container on Docker, strap in as this is going to be quite the ride (favorite beverage or three highly recommended!)

  • Risking extreme redundancy, before proceeding ensure the following dependencies are present:

    Compatible Nvidia GPU(s)
    Nvidia GPU drivers
    Nvidia CUDA Toolkit v12.2
    Docker Desktop
    Windows Subsystem for Linux (WSL)
    

  • Refer to the Nvidia CUDA Dependencies section and the Docker Setup section above if unsure

  • If the above are present and setup, you're clear to proceed

  • Open the Microsoft Store app on your PC, and download & install Ubuntu 22.04.3 LTS (must match the version on line 2 in the dockerfile)

  • Yes you read the above right: download and install Ubuntu from the Microsoft store app, refer screenshot below:

  

  • It's now time to install the Nvidia Container Toolkit within Ubuntu, follow the steps below to do so:

    • Launch an Ubuntu shell in Windows by searching for Ubuntu in the Start-menu after the installation above is completed

    • In this Ubuntu command-line that opens, perform the following steps:

    • Configure the production repository:

      curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
          sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
          sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
      

    • Update the packages list from the repository & Install the Nvidia Container Toolkit Packages:

      sudo apt-get update && apt-get install -y nvidia-container-toolkit
      

    • Configure the container runtime by using the nvidia-ctk command, which modifies the /etc/docker/daemon.json file so that Docker can use the Nvidia Container Runtime:

      sudo nvidia-ctk runtime configure --runtime=docker
      

    • Restart the Docker daemon:

      sudo systemctl restart docker
      

  • Now your Ubuntu setup is complete, time to complete the WSL and Docker Integrations:

    • Open a new PowerShell window and set this Ubuntu installation as the WSL default:

      wsl --list
      wsl --set-default Ubuntu-22.04 # if not already marked as Default
      

    • Navigate to Docker Desktop -> Settings -> Resources -> WSL Integration -> Check Default & Ubuntu 22.04 integrations. Refer to the screenshot below:

    

  • Now if everything has been done correctly, you're ready to build and run the container!

• In a Command Prompt / Terminal, execute the following commands:

  git clone https://github.com/abgulati/LARS  # skip if already done
  
  cd LARS # skip if already done
  
  cd dockerized_nvidia_cuda_gpu
  
  docker build -t lars-nvcuda .
  
  # Once the build is complete, run the container:
  
  docker run --gpus all -p 5000:5000 -p 8080:8080 -v lars_storage:/app/storage lars-nvcuda
  

• Once done, Navigate to http:https://localhost:5000/ in your browser and follow the remainder of the First Run Steps and User Guide

• The Troubleshooting sections applies to Container-LARS as well

• In case you encounter Network-related errors, especially pertaining to unavailable package repositories when building the container, this is a networking issue at your end often pertaining to Firewall issues

• On Windows, navigate to Control Panel\System and Security\Windows Defender Firewall\Allowed apps, or search Firewall in the Start-Menu and head to Allow an app through the firewall and ensure ```Docker Desktop Backend`` is allowed through

• The first time you run LARS, the sentence-transformers embedding model will be downloaded

• In the containerized environment, this download can sometimes be problematic and result in errors when you ask a query

• If this occurs, simply head to the LARS Settings menu: Settings->VectorDB & Embedding Models and change the Embedding Model to either BGE-Base or BGE-Large, this will force a reload and redownload

• Once done, proceed to ask questions again and the response should generate as normal

• You can switch back to the sentence-transformers embedding model and the issue should be resolved

• As stated in the Troubleshooting section above, embedding models are downloaded the first time LARS runs

• It's best to save the state of the container before shutting it down so this download step need-not be repeated every subsequent time the container is launched

• To do so, open another Command Prompt / Terminal and commit changes BEFORE shutting the running LARS container:

  docker ps # note the container_id here
  
  docker commit <container_ID> <new_image_name>   # for new_image_name, I simply add 'pfr', for 'post-first-run' to the current image name, example: lars-nvcuda-pfr
  

• This will create an updated image that you can use on subsequent runs:

  docker run --gpus all -p 5000:5000 -p 8080:8080 -v lars_storage:/app/storage lars-nvcuda-pfr
  

• NOTE: Having done the above, if you check the space used by images with docker images, you'll notice a lot of space used. BUT, don’t take the sizes here literally! The size shown for each image includes the total size of all its layers, but many of those layers are shared between images, especially if those images are based on the same base image or if one image is a committed version of another. To see how much disk space your Docker images are actually using, use:

  docker system df
  

Back to Table of Contents

Back to Table of Contents

I hope that LARS has been valuable in your work, and I invite you to support its ongoing development! If you appreciate the tool and would like to contribute to its future enhancements, consider making a donation. Your support helps me to continue improving LARS and adding new features.

How to Donate To make a donation, please use the following link to my PayPal:

Donate via PayPal

Your contributions are greatly appreciated and will be used to fund further development efforts.

Back to Table of Contents