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P3_101A Weaviate Academy Preparation

Overview​

Follow this short guide to make sure that you are set up to use Weaviate with the Python client.

Python & Python client​

If you have not yet set up Python and the Weaviate Python client, follow the instructions below.

Install Python​

You can install Python 3 in a variety of ways. One easy way is to use an appropriate installer for your system as per instructions on Python.org.

Install the Weaviate client​

Virtual environments

It is best practice to use virtual environments to isolate projects.

If you're not familiar with virtual environments, we highly recommend reading up on them - this tutorial on FreeCodeCamp is a good resource, as is this article on RealPython, which goes a little more in-depth.

You will also need the following libraries:

weaviate-client

Activate your virtual environment, and install the Weaviate client with:

pip install weaviate-client

Running your Python code​

We recommend running Python code in a Jupyter notebook. You can install the library by running

pip install notebook

Then running the shown code in a Jupyter notebook. Of course, you can also run it in any way that you prefer.

New to Jupyter?

To learn how to use Jupyter notebooks, try this tutorial.

Weaviate​

Instance access​

We will be accessing a pre-built instance of Weaviate throughout the course, located at https://edu-demo.weaviate.network.

You can connect to the pre-built instance with a read-only Weaviate API key.

Confirm that you can run the following code in your Python environment.

import weaviate
import os

client = weaviate.Client(
    url="https://edu-demo.weaviate.network",
    auth_client_secret=weaviate.auth.AuthApiKey(api_key="learn-weaviate"),  # A read-only API Key for the Weaviate instance
)

print(client.is_ready()) # This should return `True`

If everything works, the code returns True.

Inference API Key​

We will be using OpenAI's API, so you will need an API key.

If you haven't yet, create an OpenAI account, and create a free API key here.

Then, instantiate the client as shown below, replacing YOUR-OPENAI-API-KEY with your own API key for OpenAI.

import weaviate

client = weaviate.Client(
    url="https://edu-demo.weaviate.network",
    auth_client_secret=weaviate.auth.AuthApiKey(api_key="learn-weaviate"),  # A read-only API Key for the Weaviate instance
    additional_headers={
        "X-OpenAI-Api-Key": "YOUR-OPENAI-API-KEY"  # Replace with your OPENAI API key
    }
)

Code examples​

Client instantiation​

For brevity, many code examples in the Weaviate Academy will not show the instantiation of the client. So, where you see examples such as:

import json

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_near_text(
    {"concepts": ["intergalactic travel"]}
).with_limit(2).do()

print(json.dumps(response, indent=2))

You will need to instantiate the client as shown in the previous section. So, the above code would become:

import weaviate
import json

client = weaviate.Client(
    url="https://edu-demo.weaviate.network",
    auth_client_secret=weaviate.auth.AuthApiKey(api_key="learn-weaviate"),  # A read-only API Key for the Weaviate instance
    additional_headers={
        "X-OpenAI-Api-Key": "YOUR-OPENAI-API-KEY"  # Replace with your OPENAI API key
    }
)

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_near_text(
    {"concepts": ["intergalactic travel"]}
).with_limit(2).do()

print(json.dumps(response, indent=2))

GraphQL​

Weaviate Academy units are written to be used with a client library such as Python. So, you will not need to run GraphQL or REST queries directly.

But note that we also show raw GraphQL/REST queries as well as the Python code where relevant to help you learn the underlying query structures.

So please be aware that when we show a query such as this:

import json

response = client.query.get(
    "JeopardyQuestion",
    ["question", "answer"]
).with_near_text(
    {"concepts": ["intergalactic travel"]}
).with_limit(2).do()

print(json.dumps(response, indent=2))

This is what happens under the hood:

{
  Get {
    JeopardyQuestion (
      nearText: {
        concepts: ["intergalactic travel"]
        }
      limit: 2
    ) {
      question
      answer
    }
  }
}

We will show them in separate tabs going forward where applicable, like so:

This tab will show Python code.

Raw GraphQL queries​

The Weaviate Python client can run raw GraphQL queries. You can also use the Weaviate Query app.

In Python, you can run a GraphQL query directly with:

gql_query = """
# ===== Equivalent GraphQL example =====
{
  Get {
    JeopardyQuestion (
      nearText: {
        concepts: ["intergalactic travel"]
        }
      limit: 2
    ) {
      question
      answer
    }
  }
}
# ===== END Equivalent GraphQL example =====
"""

gql_response = client.query.raw(gql_query)
print(json.dumps(gql_response, indent=2))

Key takeaways​

  • You have installed the Weaviate client library in your preferred environment.
  • You have an OpenAI inference key.
  • You have some way of running Python code.
  • You know how to instantiate the Weaviate Python client, and run the shown example code.
  • The Academy units will show Python code, but also raw GraphQL or REST snippets where applicable.

Questions and feedback​

If you have any questions or feedback, let us know in the user forum.