Begin by obtaining the necessary credentials to access your Azure Blob Storage. This includes the storage account name and access key. These credentials will be used to programmatically access the data stored in your Azure Blob.

Install the Azure Storage Blob and Weaviate client libraries for Python. You can do this via pip. Run the following in your terminal:
```bash
pip install azure-storage-blob weaviate-client
```
These libraries will allow you to interact with Azure Blob Storage and Weaviate directly from your Python script.

Create a Python script to initialize the Azure Blob Storage client using your credentials. Here's a basic setup:
```python
from azure.storage.blob import BlobServiceClient

blob_service_client = BlobServiceClient(account_url="https://.blob.core.windows.net", credential="")
```

Use the initialized client to list and download blobs (files) from the desired container. Here's an example of how to list blobs and download one:
```python
container_client = blob_service_client.get_container_client('')
blob_list = container_client.list_blobs()

for blob in blob_list:
blob_client = container_client.get_blob_client(blob)
download_stream = blob_client.download_blob()
data = download_stream.readall()
# Process or store 'data' as needed
```
This code iterates through each blob in the container and downloads the data.

Convert the downloaded data into a format suitable for Weaviate. Typically, this involves converting it to JSON objects. Ensure that your data structure aligns with the schema defined in your Weaviate instance.

Set up the Weaviate client and define the schema to match your data structure. Here's how you can initialize the client:
```python
import weaviate

client = weaviate.Client("http://localhost:8080") # Replace with your Weaviate instance URL
```
Define the schema if it's not already set up:
```python
schema = {
"classes": [{
"class": "YourDataClass",
"properties": [
{
"name": "fieldName",
"dataType": ["string"] # Adjust data type as necessary
},
# Add more fields as needed
]
}]
}
client.schema.create(schema)
```

Use the Weaviate client to upload the prepared data. Here's an example of how to add objects to Weaviate:
```python
for data_item in your_prepared_data:
client.data_object.create(data_item, "YourDataClass")
```
This loop iterates over each prepared data item and uploads it to your Weaviate instance under the specified class.

Following these steps, you can transfer data from Azure Blob Storage to Weaviate without relying on third-party connectors or integrations, using only Python and the relevant Azure and Weaviate libraries.