weaviate_utils.py

import weaviate
from weaviate.embedded import EmbeddedOptions
from weaviate import Client

def initialize_weaviate_client():
    return weaviate.Client(embedded_options=EmbeddedOptions())

def class_exists(client, class_name):
    try:
        client.schema.get_class(class_name)
        return True
    except:
        return False

def map_dtype_to_weaviate(dtype):
    if "int" in str(dtype):
        return "int"
    elif "float" in str(dtype):
        return "number"
    elif "bool" in str(dtype):
        return "boolean"
    else:
        return "string"

def ingest_data_to_weaviate(client, dataframe, class_name, class_description):
    # Create class schema
    class_schema = {
        "class": class_name,
        "description": class_description,
        "properties": []  # Start with an empty properties list
    }
    
    # Try to create the class without properties first
    try:
        client.schema.create({"classes": [class_schema]})
    except weaviate.exceptions.SchemaValidationException:
        # Class might already exist, so we can continue
        pass

    # Now, let's add properties to the class
    for column_name, data_type in zip(dataframe.columns, dataframe.dtypes):
        property_schema = {
            "name": column_name,
            "description": f"Property for {column_name}",
            "dataType": [map_dtype_to_weaviate(data_type)]
        }
        try:
            client.schema.property.create(class_name, property_schema)
        except weaviate.exceptions.SchemaValidationException:
            # Property might already exist, so we can continue
            pass

    # Now, let's ingest the data
    data = dataframe.to_dict(orient="records")
    client.data_object.create(data, class_name)

def get_class_schema(client, class_name):
    try:
        return client.schema.get_class(class_name)
    except weaviate.exceptions.SchemaValidationException:
        return None