Weaviate Reader

%pip install llama-index-readers-weaviate

import logging
import sys

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

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

!pip install llama-index

import weaviate
from llama_index.readers.weaviate import WeaviateReader

# See https://weaviate.io/developers/weaviate/current/client-libraries/python.html
# for more details on authentication
resource_owner_config = weaviate.AuthClientPassword(
    username="<username>",
    password="<password>",
)

# initialize reader
reader = WeaviateReader(
    "https://<cluster-id>.semi.network/",
    auth_client_secret=resource_owner_config,
)

You have two options for the Weaviate reader: 1) directly specify the class_name and properties, or 2) input the raw graphql_query. Examples are shown below.

# 1) load data using class_name and properties
# docs = reader.load_data(
#    class_name="Author", properties=["name", "description"], separate_documents=True
# )

documents = reader.load_data(
    class_name="<class_name>",
    properties=["property1", "property2", "..."],
    separate_documents=True,
)

# 2) example GraphQL query
# query = """
# {
#   Get {
#     Author {
#       name
#       description
#     }
#   }
# }
# """
# docs = reader.load_data(graphql_query=query, separate_documents=True)

query = """
{
  Get {
    <class_name> {
      <property1>
      <property2>
      ...
    }
  }
}
"""

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

Create index

index = SummaryIndex.from_documents(documents)

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")

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