Schema Creation

client = weaviate.Client("http://localhost:8080") # Create a new schema for my data schema = { "class": "MyClass", "properties": [ {"name": "my_property", "dataType": ["text"]} ] } # Create the schema in Weaviate clien…

### Example Code with Debugging Steps Let's walk through the code and add some debugging steps to identify the issue. #### 1. Verify Weaviate Server Status Ensure the Weaviate server is running and accessible. ```python import weaviate …

print(f"Failed to create schema: {e}") # Add some data to the schema data = [{"my_property": "Hello World"}] try: client.data_object.create(data[0], "MyClass") print("Data inserted successfully.") except Exception as e: pr…

print("Query successful:") print(result) ``` ### Example with Vector Search If you want to perform a vector search and retrieve both text and vector data, you can use the `nearVector` filter: ```python # Perform a vector search query_vec…

# Create the schema in Weaviate client.schema.create_class(schema) print("Schema created successfully.") ``` #### Inserting Data When inserting data, you can specify which vector property to use based on the vector size. ```python # Add …

.with_near_vector(near_vector_128) .with_limit(10) .do() ) print("Vector search query successful (size 128):") print(result_128) query_vector_256 = [0.5, 0.6, 0.7, 0.8] * 64 # Example query vector of size 256 near_vector_256 …