Introduction to Weaviate
Weaviate is an open-source vector search engine designed for scalable and efficient searching of unstructured data. It utilizes advanced machine learning techniques to enable semantic search capabilities, making it highly beneficial for projects involving AI and data analysis. If you’re a Python developer looking to integrate Weaviate into your applications, understanding how to make calls to the Weaviate API is essential. This article will guide you through the process of making a Weaviate call using Python.
The ability to interact with Weaviate is crucial for leveraging its full potential. With Weaviate’s HTTP API, you can perform various operations like adding data, querying vectors, and managing schemas. In this guide, we will explore how to configure your Python environment to communicate with Weaviate effectively.
Setting Up Your Python Environment
Before we dive into making Weaviate calls, it’s important to set up your Python environment correctly. Start by ensuring you have Python installed on your machine. We recommend using Python 3.6 or higher. You can download the latest version of Python from the official website.
Once you have Python installed, set up a virtual environment. This practice helps you manage your project’s dependencies without conflicts. You can create a virtual environment by navigating to your project directory in the terminal and running the following command:
python -m venv weaviate-envActivate the virtual environment with the following command:
source weaviate-env/bin/activate # On macOS/Linux
weaviate-envin\activate # On WindowsWith your environment activated, it’s time to install the necessary libraries. We’ll need the ‘requests’ library to make HTTP calls to the Weaviate API. Install it via pip:
pip install requestsNow that your environment is ready, we can move on to making calls to Weaviate.
Understanding Weaviate API Endpoints
Weaviate provides various API endpoints that allow you to interact with your data. The most commonly used endpoints include:
- /v1/objects: To manage data objects.
- /v1/schema: For managing the schema.
- /v1/actions: To perform specific actions like vector search.
Understanding these endpoints is crucial for effectively utilizing Weaviate. Let’s break down how to make calls to the /v1/objects endpoint, which is essential for adding and retrieving data.
Making a POST Request to Add Objects
To add an object to Weaviate, you’ll need to send a POST request to the /v1/objects endpoint. Here’s how you can do that in Python:
import requests
WEAVIATE_URL = 'http://localhost:8080' # Change this if your Weaviate instance runs elsewhere.
object_data = {
'class': 'Article', # Assuming you have a class named 'Article'
'properties': {
'title': 'Exploring Weaviate',
'content': 'Weaviate is a powerful tool for semantic search.'
}
}
response = requests.post(f'{WEAVIATE_URL}/v1/objects', json=object_data)
if response.status_code == 200:
print('Object added successfully!')
else:
print('Failed to add object:', response.content)In this code snippet, we define a Python dictionary representing the object we want to add. We then use the requests.post method to send our object data to Weaviate. The response will indicate if the object was added successfully.
Retrieving Objects with a GET Request
Once you’ve added objects to Weaviate, you might want to retrieve them. To get objects, you can use a GET request. Here’s how:
response = requests.get(f'{WEAVIATE_URL}/v1/objects?class=Article')
if response.status_code == 200:
articles = response.json()
print('Retrieved articles:', articles)
else:
print('Failed to retrieve articles:', response.content)In this code, we’re sending a GET request to the /v1/objects endpoint and asking for all objects of the class ‘Article’. If the request is successful, the response will contain the retrieved objects, which we can print out.
Managing the Schema with Weaviate
Before adding objects, you’ll need to ensure your data schema is properly set up in Weaviate. This involves defining the data types and relationships for your classes. To manage your schema, you can use the /v1/schema endpoint. Here’s how to create a schema:
schema_data = {
'classes': [{
'class': 'Article',
'properties': [{
'dataType': ['text'],
'name': 'title'
}, {
'dataType': ['text'],
'name': 'content'
}]
}]
}
response = requests.post(f'{WEAVIATE_URL}/v1/schema', json=schema_data)
if response.status_code == 200:
print('Schema created successfully!')
else:
print('Failed to create schema:', response.content)This snippet defines a schema with a single class named ‘Article’ and two properties: title and content. After sending the POST request to /v1/schema, check the response to confirm the schema was created.
Using Vector Search in Weaviate
One of the most powerful features of Weaviate is vector search. This allows you to query your data based on semantic similarity. To use this feature, you can make a POST request to the /v1/actions endpoint. Here’s how you might perform a vector search:
query_data = {
'nearVector': {
'vector': [0.1, 0.2, 0.3], # Replace with an actual query vector.
'certainty': 0.7
},
'class': 'Article'
}
response = requests.post(f'{WEAVIATE_URL}/v1/actions', json=query_data)
if response.status_code == 200:
search_results = response.json()
print('Search results:', search_results)
else:
print('Failed to perform search:', response.content)In this example, replace the vector in the query_data with an actual vector you want to search by. The certainty parameter helps refine your search to yield only the most relevant results.
Error Handling in Weaviate Calls
When making API calls, it’s important to handle errors gracefully. The Weaviate API will return various status codes, indicating success or failure. By checking the response status code, you can implement logic for error handling. Here’s an example of handling different response scenarios:
response = requests.get(f'{WEAVIATE_URL}/v1/objects')
if response.status_code == 200:
print('Data retrieved successfully')
elif response.status_code == 404:
print('Not found: Check the endpoint or the object ID.')
elif response.status_code == 500:
print('Server error: Try again later.')
else:
print('Error:', response.status_code, response.content)This code helps you respond appropriately to different outcomes, providing clarity to users when something goes wrong.
Conclusion
Making Weaviate calls using Python opens up a world of possibilities for developers working with unstructured data. By following the steps outlined in this tutorial, you can effectively set up your environment, manage schemas, and interact with the Weaviate API. Whether you’re adding data, retrieving objects, or leveraging the power of vector search, Python offers a straightforward way to connect with Weaviate.
As you dive deeper into using Weaviate, consider exploring advanced features such as batch processing, event tracking, and custom classes. The more you experiment with the API, the better you will understand its capabilities and how to integrate it into your projects. Happy coding!