[Question]: Why can't the KG built through Python execution be seen in the LightRAG Server? #1259
Description
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Your Question
Hello. I have successfully installed and started LightRAG and the Server, and I was also able to upload documents via the UI and parse out the KG through the pipeline. However, when I switched to another method (uploading documents via Python code), after the parsing was completed, I did not see the KG. My steps are as follows:
- I created a new project directory, created a
.envfile, and ranlightrag-serverin the project directory. - I ran the RAG initialization code in Python, and uploaded and parsed the documents (it took an hour to parse 10 documents). The code is as follows:
import asyncio
import nest_asyncio
# 在 Jupyter Notebook 中启用嵌套的事件循环
nest_asyncio.apply()
import os
import inspect
import logging
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc
from lightrag.kg.shared_storage import initialize_pipeline_status
# 设置工作目录
WORKING_DIR = "/data/github/LightRAG/znkf"
# 配置日志
logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
# 初始化 RAG 实例
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=ollama_model_complete,
llm_model_name="qwen2.5:14b",
llm_model_max_async=2,
llm_model_max_token_size=32768,
llm_model_kwargs={
"host": "http://localhost:11434",
"options": {"num_ctx": 32768},
},
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=512,
func=lambda texts: ollama_embed(
texts, embed_model="quentinz/bge-large-zh-v1.5:latest", host="http://localhost:11434"
),
),
addon_params={
"language": "Simplified Chinese",
"entity_types": ["产品系列", "手机品牌", "服务类型", "公司", "支付方式", "产品名称", "城市名",
"电话号码", "日期", "卡片类型", "优惠政策", "金额"],
}
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
# 初始化 RAG 实例
loop = asyncio.get_event_loop() # 获取当前事件循环
rag = loop.run_until_complete(initialize_rag())
# 初始构建KG
file_path = '/data/notebooks/knowledge/kg/kg_test'
file_list = []
for root, _, files in os.walk(file_path):
for file in files:
if any(file.endswith(ext) for ext in [".md", ".txt"]):
filex = os.path.abspath(os.path.join(root, file))
print(f">>> 文件 {filex} 处理中...")
file_list.append(filex)
with open(filex, "r", encoding="utf-8") as f:
rag.insert(f.read())
- I can see the generated documents in my project directory, but I don't see any results in the UI.
Additional Context
中文表述：
您好。我已经成功的安装并启动LightRAG以及Server，并且也成功的通过UI上传文档并通过pipeline解析出KG。但是当我换了一个方式（通过python代码进行文档上传），最终解析完成后却没有看到KG。
我的步骤是这样的：
- 我新建了项目目录，新建了.env，并且在项目目录下运行了
lightrag-server - 我在python中运行了rag初始化代码，并且上传和解析文档（一共10个文档解析了一个小时）。代码如下：
代码如上 - 我能从我的项目目录看到已经生成的文档，但是我在UI中没有看到任何结果。（截图如上）