load documents

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import openai
from IPython.display import Markdown, display
from llama_index import SimpleDirectoryReader, StorageContext, VectorStoreIndex

openai.api_key = ""

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt'
# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("Document ID:", len(documents), documents[0].doc_id)

from llama_index import ServiceContext
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.vector_stores import VearchVectorStore

"""
vearch cluster
"""
vector_store = VearchVectorStore(
    path_or_url="http://liama-index-router.vectorbase.svc.sq01.n.jd.local",
    table_name="liama_index_test2",
    db_name="liama_index",
    flag=1,
)

"""
vearch standalone
"""
# vector_store = VearchVectorStore(
#         path_or_url = '/data/zhx/zhx/liama_index/knowledge_base/liama_index_teststandalone',
#         # path_or_url = 'http://liama-index-router.vectorbase.svc.sq01.n.jd.local',
#         table_name = 'liama_index_teststandalone',
#         db_name = 'liama_index',
#         flag = 0)

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
service_context = ServiceContext.from_defaults(embed_model=embed_model)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, service_context=service_context
)

query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))

query_engine = index.as_query_engine()
response = query_engine.query(
    "What did the author do after his time at Y Combinator?"
)
display(Markdown(f"<b>{response}</b>"))