Weaviate Reader¶

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%pip install llama-index-readers-weaviate

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import logging
import sys

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

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

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!pip install llama-index

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import weaviate
from llama_index.readers.weaviate import WeaviateReader

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# 有关身份验证的更多详细信息，请参阅https://weaviate.io/developers/weaviate/current/client-libraries/python.html
resource_owner_config = weaviate.AuthClientPassword(
    username="<用户名>",
    password="<密码>",
)

# 初始化读取器
reader = WeaviateReader(
    "https://<cluster-id>.semi.network/",
    auth_client_secret=resource_owner_config,
)

您有两种选项可以使用Weaviate阅读器：1）直接指定class_name和properties，或者2）输入原始的graphql_query。下面展示了示例。

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# 1) 使用class_name和properties加载数据
# docs = reader.load_data(
#    class_name="Author", properties=["name", "description"], separate_documents=True
# )

documents = reader.load_data(
    class_name="<类名>",
    properties=["属性1", "属性2", "…"],
    separate_documents=True,
)

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# 2) 示例 GraphQL 查询
# 查询 = """
# {
#   Get {
#     作者 {
#       名字
#       描述
#     }
#   }
# }
# """
# 文档 = reader.load_data(graphql_query=query, separate_documents=True)

查询 = """
{
  Get {
    <类名> {
      <属性1>
      <属性2>
      ...
    }
  }
}
"""

文档 = reader.load_data(graphql_query=query, separate_documents=True)

创建索引¶

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index = SummaryIndex.from_documents(documents)

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# 将日志级别设置为DEBUG，以获得更详细的输出
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")

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display(Markdown(f"<b>{response}</b>"))