RLAMA: A RAG System for Intelligent Quizzing of Local Documents Operated from the Command Line

General Introduction

RLAMA is a RAG (Retrieval Augmentation Generation) system for document intelligence developed by DonTizi and hosted on GitHub, whose core feature is the realization of functionality through command line operations. Users can connect to the local Ollama model that quickly indexes documents in folders into an interactive knowledge base with intelligent Q&A. Whether it's a code file, a technical document or an office document, RLAMA is up to the task and runs completely locally without cloud services to safeguard data privacy. With support for multiple file formats and easy installation, it's a powerful choice for localized document management, especially for developers and tech enthusiasts accustomed to command line operations.

Function List

• Creating a RAG System on the Command Line: Generate a knowledge base of smart quizzes by command indexing folder documents.
• Command Line Interaction Q&A: Use a terminal session to query existing RAG The contents of the document in the system.
• RAG System Administration: Easily maintain the knowledge base by listing and deleting RAG systems with commands.
• Local Model Integration: Seamlessly connects to native Ollama models, maintaining full localization.
• Version and update management: Use the command to check the version or update the system to the latest state.
• Multi-format support: Compatible with a wide range of formats including text, code and office documents.
• Command Line Uninstallation: Provides command cleanup tools and data.

Using Help

Installation process

RLAMA is a command-line implementation of Document Intelligence Quiz for macOS, Linux, and Windows. here are the detailed steps:

1. Pre-conditions

• Install Ollama: The Ollama model needs to be run locally. Visit the Ollama website to download and install it, run the ollama run llama3(or other models) to confirm availability, default address http://localhost:11434The
• network connection: Installation requires an internet connection to download scripts or dependencies.
• Optional dependencies: In order to support more formats (e.g. .pdf, .docx), it is recommended that you run the install_deps.sh Installation tools (e.g. pdftotext).

2. Installation via command line script (recommended)

Enter it in the terminal:

curl -fsSL https://raw.githubusercontent.com/dontizi/rlama/main/install.sh | sh

• The script automatically completes the download and configuration, and when it's done, runs the rlama --version Check the version (e.g. v0.1.0) Confirmation of success.

3. Manual source code installation (optional)

If customization is required:

• Cloning Warehouse:

  git clone https://github.com/DonTizi/rlama.git
  cd rlama
  

• Compile and install (Go environment required):

  go build
  go install
  

• Move the executable to the PATH (e.g. /usr/local/bin), run rlama --help Validation.

4. Verification of installation

Running:

rlama --help

Displaying a list of commands is considered successful.

Main function operation flow

Creating a RAG system from the command line

Convert documents into a smart quiz knowledge base. For example, the folder . /docs embody readme.md cap (a poem) guide.pdf::

1. Ensure that the Ollama model runs (e.g. ollama run llama3).
2. Enter the command:

   rlama rag llama3 mydocs . /docs
   

   • llama3: Model name.
   • mydocs: RAG system name.
   • . /docs: Document path.
3. Example output:

   Indexing documents in . /docs...
   Processed: readme.md
   Processed: guide.pdf
   RAG system "mydocs" created successfully!
   

Interactive Q&A via the command line

Query the existing RAG system:

1. Start the session:

   rlama run mydocs
   

2. Enter the question:

   How to install rlama?
   

3. Get Answers:

   It can be installed by running `curl -fsSL https://raw.githubusercontent.com/dontizi/rlama/main/install.sh | sh` from a terminal.
   

4. importation exit Exit.

Managing the RAG system

• Listing system::

  rlama list
  

  Sample Output:

  Available RAG systems: mydocs
  - mydocs
  

• Deleting the system::

  rlama delete mydocs
  

  or skip the confirmation:

  rlama delete mydocs --force
  

Versions and Updates

• Check Version::

  rlama --version
  

  maybe rlama-vThe display is as follows v0.1.0The

• Updating the system::

  rlama update
  

  (botany) coca (loanword) --force Mandatory Updates.

Uninstallation of the system

• Removal Tools::

  rlama uninstall
  

• Cleaning of data: The data is stored in the ~/.rlama, running:

  rm -rf ~/.rlama
  

Featured Functions

1. Command-line operations

All of rlama's features are realized via the command line, which is simple and efficient. For example, a command line rlama rag mistral docs . /project Instantly indexes entire folders, suitable for command-line skilled users.

2. Multi-format document support

Multiple file types are supported:

• Text:.txt,.md,.html,.json,.csv etc.
• Code:.go,.py,.js,.java,.cpp etc.
• Documentation:.pdf,.docx,.pptx,.xlsx etc.
  (of a computer) run . /scripts/install_deps.sh Enhanced support. Example:

rlama rag gemma code-docs . /src

3. Localized operations

Data is processed locally throughout, no need for the cloud, suitable for sensitive documents. For example, indexing confidential company documents:

rlama rag llama3 contracts . /legal

Tips and Troubleshooting

• Precision Command: Enter the full parameter, e.g. rlama run mydocs rather than shorthand to ensure accuracy.
• Ollama issue: If the connection fails, check the http://localhost:11434Running ollama ps View Status.
• Format Support: If the extraction fails, run install_deps.sh Install the dependencies.
• Poor answer: Confirm that the document is indexed and try to be more specific about the problem.
• appeal (for help): Issues can be submitted to GitHub Issues, with commands and versions.

With the above commands, users can quickly master rlama, manage local documents and realize intelligent Q&A.