polarisrag 0.0.1

🚀 PolarisRAG: Fast and Easy Retrieval-Augmented Generation

Install

• Install from source (Recommend)

cd PolarisRAG
pip install -e .

• Install from PyPI

pip install polarisrag

Quick Start

• All the code can be found in the examples
• Set ZhipuAI API key in environment if using ZhipuAI models: export ZHIPUAI_API_KEY="537...".
• Download the demo text "A Christmas Carol by Charles Dickens":

curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt

• Maybe you can try loading environment variables like this. Create a new .env file

ZHIPUAI_API_KEY="537..."
OPENAI_API_KEY="sk-..."
OPENAI_BASE_URL="https://api..."
QWEN2_API_KEY="sk-no-key-required"
QWEN2_BASE_URL="http://127.0.0.1:8080/v1"

from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv(), override=True)

Use the below Python snippet (in a script) to initialize PolarisRAG and perform queries:

import os
os.environ["ZHIPUAI_API_KEY"] = ""
from polarisrag import PolarisRAG
# 定义工作空间
WORKING_DIR = "documents"
rag = PolarisRAG(working_dir=WORKING_DIR)
# 初始化rag,加载embedding、vector、llm
rag.init_rag()
# 插入数据
with open("documents/test.txt", 'r') as f:
    rag.insert(f.read())
print(rag.chat("什么是BERT"))
print(rag.chat("如何下载BERT-base-chinese预训练模型"))

Use PolarisRAG through a dictionary configuration

from polarisrag import PolarisRAG
WORKING_DIR = "documents"
embedding_conf = {
    "class_name": "HFEmbedding",
    "class_param": {
        "pretrain_dir": '/root/.cache/modelscope/hub/BAAI/bge-large-zh-v1___5'
    }
}
vector_conf = {
    "class_name": "MilvusDB",
    "class_param": {}
}
llm_model_conf = {
    "class_name": "ZhipuLLM",
    "class_param": {
        "model": "glm-4-flash",
        "is_memory": "True"
    }
}
rag = PolarisRAG(working_dir=WORKING_DIR,
                 embedding_model=embedding_conf,
                 vector_storage=vector_conf,
                 llm_model=llm_model_conf)
rag.init_rag()
with open("documents/test.txt", 'r') as f:
    rag.insert(f.read())
result = rag.chat("什么是BERT")
print(result)
result = rag.chat("如何下载BERT-base-chinese预训练模型")
print(result)

Introduce components for use

import os
from polarisrag import PolarisRAG
from polarisrag.embedding import ZhipuEmbedding
from polarisrag.vector_database import MilvusDB
from polarisrag.llm import ZhipuLLM
from polarisrag.utils import FolderLoader

# api_key
ZHIPUAI_API_KEY = ""
# 工作空间
WORKING_DIR = "documents"
embedding_model = ZhipuEmbedding(api_key=ZHIPUAI_API_KEY)
llm_model = ZhipuLLM(api_key=os.getenv("ZHIPUAI_API_KEY"))
loader = FolderLoader(folder_path=WORKING_DIR)
docs = loader.get_all_chunk_content()
vector_db = MilvusDB({"embedding_model": embedding_model})
# 创建集合
vector_db.create_collection("default")
vector_db.insert(docs=docs)
rag = PolarisRAG(
    working_dir=WORKING_DIR,
    embedding_model=embedding_model,
    vector_storage=vector_db,
    llm_model=llm_model,
    is_memory=True
)

print(
    rag.chat("什么是BERT")
)

🌟Citation

@article{guo2024polarisrag,
title={PolarisRAG: Fast and Easy Retrieval-Augmented Generation},
author={Runke Zhong},
year={2024}
}

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