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For local development, please refer to the following steps:

Run in Local Environment

  1. Clone Repo

    git clone git@github.com:aigc-apps/PAI-RAG.git

  2. Development Environment Settings

    This project uses poetry for management. To ensure environmental consistency and avoid problems caused by Python version differences, we specify Python version 3.11.

    conda create -n rag_env python==3.11
conda activate rag_env

    if you use macOS and need to process PPTX files, you need use the following command to install the dependencies to process PPTX files:

       brew install mono-libgdiplus

    Use poetry to install project dependency packages directly:

    pip install poetry
poetry install
poetry run aliyun-bootstrap -a install

  • Common network timeout issues

    Note: During the installation, if you encounter a network connection timeout, you can add the Alibaba Cloud or Tsinghua mirror source and append the following lines to the end of the pyproject.toml file:

    [[tool.poetry.source]]
name = "mirrors"
url = "http://mirrors.aliyun.com/pypi/simple/" # Aliyun
# url = "https://pypi.tuna.tsinghua.edu.cn/simple/" # Qsinghua
priority = "default"

    After that, execute the following commands:

    poetry lock
poetry install

  1. Download Models:

    Download models (embedding/pdf-extractor/reranker models) using load_model command:

    # Support model name (default ""), download all models mentioned before without parameter model_name.
load_model [--model-name MODEL_NAME]

  2. Run RAG Service

    To use the DashScope API, you need to export environment variables:

    export DASHSCOPE_API_KEY="xxx"

    Please replace xxx with your own DASHSCOPE API key. You can find your keys here: https://dashscope.console.aliyun.com/apiKey

    # Support custom host (default 0.0.0.0), port (default 8001), config (default src/pai_rag/config/settings.yaml), skip-download-models (default False)
# Download [bge-m3, easyocr] by default, you can skip it by setting --skip-download-models.
# you can use tool "load_model" to download other models including [bge-m3, easyocr, SGPT-125M-weightedmean-nli-bitfit, bge-large-zh-v1.5, bge-reranker-base, bge-reranker-large, paraphrase-multilingual-MiniLM-L12-v2, qwen_1.8b, text2vec-large-chinese]
pai_rag serve [--host HOST] [--port PORT] [--config CONFIG_FILE] [--skip-download-models]
    pai_rag serve

  3. Run RAG WebUI

    # Supports custom host (default 0.0.0.0), port (default 8002), config (default localhost:8001)
pai_rag ui [--host HOST] [--port PORT] [rag-url RAG_URL]

    You can also open http://localhost:8002/admin/ to configure the RAG service and upload local data.

  4. [Optional] Local load_data tool

    Apart from upload files from web ui, you can load data into knowledge base using load_data script

    load_data -c src/pai_rag/config/settings.yaml -d data_path -p pattern

    path examples:

    a. load_data -d test/example
b. load_data -d test/example_data/pai_document.pdf
c. load_data -d test/example_data -p *.pdf