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{"name": "vector", "dataType": ["vector", "512"]} # Adjust vector size as needed ] } ) # Add data data_object = DataObject(client) data_object.create( { "class": "Article", "properties": { …

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("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…

$('#update-form').on('submit', function(event) { event.preventDefault(); var sprint = $('#sprint').val(); var percentage = $('#percentage').val(); $.ajax({ …

logging.basicConfig(level=logging.DEBUG) def parse_request(request): try: # Parsing logic here data = request.json() # Validate data if not data: raise ValueError("Invalid request data") …

logging.error(f'Error in PostProcessor for text "{text}": {e}') return text # Define the evaluation function def evaluate_reformulation(stages, inputs, outputs): # Apply the reformulation stages to the inputs …