Simba知识管理系统开源项目教程

Simba知识管理系统开源项目教程

simba Portable KMS (knowledge management system) designed to integrate seamlessly with any Retrieval-Augmented Generation (RAG) system 项目地址: https://gitcode.com/gh_mirrors/simba4/simba

1. 项目介绍

Simba是一个开源的便携式知识管理系统（KMS），专为无缝集成检索增强生成（RAG）系统而设计。它拥有直观的用户界面、模块化架构和强大的软件开发工具包（SDK），简化了知识管理的过程，使开发者能够专注于构建先进的AI解决方案。

2. 项目快速启动

安装

首先，确保您已经安装了以下依赖项：

• Python 3.11+
• Poetry
• Redis 7.0+
• Node.js 20+
• Git
• （可选）Docker

安装Simba核心：

pip install simba-core

或者克隆并设置仓库：

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

配置

创建一个.env文件并添加以下内容：

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

配置config.yaml文件：

project:
  name: "Simba"
  version: "1.0.0"
  api_version: "/api/v1"
paths:
  base_dir: null
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: "cpu"
additional_params: {}
vector_store:
  provider: "faiss"
  collection_name: "simba_collection"
  additional_params: {}
chunking:
  chunk_size: 512
  chunk_overlap: 200
retrieval:
  method: "hybrid"
  k: 5
  params:
    score_threshold: 0.5
    prioritize_semantic: true
    weights: [0.7, 0.3]
    reranker_model: "colbert"
    reranker_threshold: 0.7
database:
  provider: "litedb"
  additional_params: {}
celery:
  broker_url: "${CELERY_BROKER_URL:-redis://redis:6379/0}"
  result_backend: "${CELERY_RESULT_BACKEND:-redis://redis:6379/1}"

运行

启动服务器、前端和解析器：

simba server
simba front
simba parsers

3. 应用案例和最佳实践

（此部分将根据实际项目情况提供具体的应用案例和最佳实践。）

4. 典型生态项目

（此部分将介绍与Simba相互协作的典型生态项目，例如其他开源的RAG系统、数据库、云服务等。）

simba Portable KMS (knowledge management system) designed to integrate seamlessly with any Retrieval-Augmented Generation (RAG) system 项目地址: https://gitcode.com/gh_mirrors/simba4/simba