Weaviate Reader¶

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%pip install llama-index-readers-weaviate

%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))

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 🦙.

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!pip install llama-index

!pip install llama-index

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import weaviate
from llama_index.readers.weaviate import WeaviateReader

import weaviate from llama_index.readers.weaviate import WeaviateReader

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# See https://weaviate.io/developers/weaviate/client-libraries/python
# 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,
)

# See https://weaviate.io/developers/weaviate/client-libraries/python # for more details on authentication resource_owner_config = weaviate.AuthClientPassword( username="", password="", ) # initialize reader reader = WeaviateReader( "https://.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.

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# 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,
)

# 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="", properties=["property1", "property2", "..."], separate_documents=True, )

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# 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)

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

Create index¶

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index = SummaryIndex.from_documents(documents)

index = SummaryIndex.from_documents(documents)

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

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

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

display(Markdown(f"{response}"))