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Knowledge Management System that connects to your RAG system

Project description

Simba - Your Knowledge Management System

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Connect your knowledge to any RAG system

Simba  - Connect your Knowledge into any RAG based system | Product Hunt

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Simba is an open source, portable KMS (knowledge management system) designed to integrate seamlessly with any Retrieval-Augmented Generation (RAG) system. With a modern UI and modular architecture, Simba allows developers to focus on building advanced AI solutions without worrying about the complexities of knowledge management.

Table of Contents

๐Ÿš€ Features

  • ๐Ÿงฉ Modular Architecture: Plug in various vector stores, embedding models, chunkers, and parsers.
  • ๐Ÿ–ฅ๏ธ Modern UI: Intuitive user interface to visualize and modify every document chunk.
  • ๐Ÿ”— Seamless Integration: Easily integrates with any RAG-based system.
  • ๐Ÿ‘จโ€๐Ÿ’ป Developer Focus: Simplifies knowledge management so you can concentrate on building core AI functionality.
  • ๐Ÿ“ฆ Open Source & Extensible: Community-driven, with room for custom features and integrations.

๐ŸŽฅ Demo

Watch the demo

๐Ÿ› ๏ธ Getting Started

๐Ÿ“‹ Prerequisites

Before you begin, ensure you have met the following requirements:

๐Ÿ“ฆ Installation

install simba-core:

pip install simba-core

Clone the repository and install dependencies:

git clone https://github.com/GitHamza0206/simba.git
cd simba
poetry config virtualenvs.in-project true
poetry install
source .venv/bin/activate 

๐Ÿ”‘ Configuration

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key
REDIS_HOST=localhost
CELERY_BROKER_URL=redis://localhost:6379/0
CELERY_RESULT_BACKEND=redis://localhost:6379/1

create or update config.yaml file in the root directory:

# config.yaml

project:
  name: "Simba"
  version: "1.0.0"
  api_version: "/api/v1"

paths:
  base_dir: null  # Will be set programmatically
  faiss_index_dir: "vector_stores/faiss_index"
  vector_store_dir: "vector_stores"

llm:
  provider: "openai"
  model_name: "gpt-4o-mini"
  temperature: 0.0
  max_tokens: null
  streaming: true
  additional_params: {}

embedding:
  provider: "huggingface"
  model_name: "BAAI/bge-base-en-v1.5"
  device: "mps"  # Changed from mps to cpu for container compatibility
  additional_params: {}

vector_store:
  provider: "faiss"
  collection_name: "simba_collection"

  additional_params: {}

chunking:
  chunk_size: 512
  chunk_overlap: 200

retrieval:
  k: 5

celery: 
  broker_url: ${CELERY_BROKER_URL:-redis://redis:6379/0}
  result_backend: ${CELERY_RESULT_BACKEND:-redis://redis:6379/1}

๐Ÿš€ Run Simba

Run the server:

simba server

Run the frontend:

simba front 

Run the parsers:

simba parsers

๐Ÿณ Docker Deployment

Run on Specific Hardware

For CPU:

DEVICE=cpu make build
DEVICE=cpu make up

For NVIDIA GPU with Ollama:

DEVICE=cuda make build
DEVICE=cuda make up

For Apple Silicon:

# Note: MPS (Metal Performance Shaders) is NOT supported in Docker containers
# For Docker, always use CPU mode even on Apple Silicon:
DEVICE=cpu make build
DEVICE=cpu make up

Run with Ollama service (for CPU):

DEVICE=cpu ENABLE_OLLAMA=true make up

Run in background mode:

# All commands run in detached mode by default

For detailed Docker instructions, see the Docker deployment guide.

๐Ÿ Roadmap

  • ๐Ÿ’ป pip install simba-core
  • ๐Ÿ”ง pip install simba-sdk
  • ๐ŸŒ www.simba-docs.com
  • ๐Ÿ”’ Adding Auth & access management
  • ๐Ÿ•ธ๏ธ Adding web scraping
  • โ˜๏ธ Pulling data from Azure / AWS / GCP
  • ๐Ÿ“š More parsers and chunkers available
  • ๐ŸŽจ Better UX/UI

๐Ÿค Contributing

Contributions are welcome! If you'd like to contribute to Simba, please follow these steps:

  • Fork the repository.

  • Create a new branch for your feature or bug fix.

  • Commit your changes with clear messages.

  • Open a pull request describing your changes.

๐Ÿ’ฌ Support & Contact

For support or inquiries, please open an issue ๐Ÿ“Œ on GitHub or contact repo owner at Hamza Zerouali

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