Prerequisites

• Python 3.10+
• Conda (recommended) or any Python virtual environment
• An OpenAI-compatible API key (OpenAI, DeepSeek, Zhipu, etc.)

Setup

# 1. Create and activate environment
conda create -n datamind python=3.12
conda activate datamind

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure API keys
cp .env.example .env
# Edit .env with your API key and base URL

Configuration

Edit the .env file:

# LLM — any OpenAI-compatible API
LLM_API_BASE=https://api.deepseek.com/v1
LLM_API_KEY=sk-xxx
LLM_MODEL=deepseek-chat

# Embedding
EMBEDDING_API_BASE=https://api.deepseek.com/v1
EMBEDDING_API_KEY=sk-xxx
EMBEDDING_MODEL=text-embedding-3-small

See Configuration for all available settings.

Run

Web UI (Recommended)

python server.py

Open http://localhost:8000 in your browser.

Terminal CLI

python main.py

On first run, the system automatically builds vector indexes and knowledge graphs (requires API calls). Subsequent starts load existing indexes directly.

Compatible API Services

Provider	api_base	Model Example
OpenAI	https://api.openai.com/v1	gpt-4o-mini
DeepSeek	https://api.deepseek.com/v1	deepseek-chat
Zhipu	https://open.bigmodel.cn/api/paas/v4	glm-4-flash
Moonshot	https://api.moonshot.cn/v1	moonshot-v1-8k
SiliconFlow	https://api.siliconflow.cn/v1	deepseek-ai/DeepSeek-V3