modified ingest_data_to_weaviate function

modified
def ingest_data_to_weaviate(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

# Ingest data
for index, row in dataframe.iterrows():
obj = {
"class": class_name,
"id": str(index),
"properties": row.to_dict()
}
client.data_object.create(obj)

app.py

@@ -19,41 +19,42 @@ client = weaviate.Client(
 )
 
 def ingest_data_to_weaviate(dataframe, class_name, class_description):
-    properties = []
-    for column in dataframe.columns:
-        data_type = "string"
-        if dataframe[column].dtype == "float64":
-            data_type = "float"
-        elif dataframe[column].dtype == "int64":
-            data_type = "int"
-        properties.append({
-            "name": column,
-            "description": column,
-            "dataType": [data_type]
-        })
-    
-    schema = {
-        "classes": [
-            {
-                "class": class_name,
-                "description": class_description,
-                "properties": properties
-            }
-        ]
+    # Create class schema
+    class_schema = {
+        "class": class_name,
+        "description": class_description,
+        "properties": []  # Start with an empty properties list
     }
-
-    # Create Schema in Weaviate
-    client.schema.create(schema)
     
-    # Ingest Data
-    batch_request = weaviate.ObjectsBatchRequest()
-    for _, row in dataframe.iterrows():
+    # 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
+
+    # Ingest data
+    for index, row in dataframe.iterrows():
         obj = {
             "class": class_name,
+            "id": str(index),
             "properties": row.to_dict()
         }
-        batch_request.add(obj)
-    client.batch.create(batch_request)
+        client.data_object.create(obj)
+
 
 def query_weaviate(question):
     # This is a basic example; adapt the query based on the question