Begin by thoroughly reading the API documentation to understand the endpoints, request methods, authentication requirements, and the data format (e.g., JSON, XML). Knowing how to interact with the API is crucial for retrieving data accurately.

Prepare your local development environment to make API requests and process data. Install necessary tools like Python and libraries such as `requests` for HTTP requests and `json` for handling JSON data. You can also use tools like Postman to test API requests.

Write a script to make HTTP requests to the public API and retrieve data. Use the `requests` library in Python to send GET requests to the API endpoints. Handle authentication if required and ensure to manage pagination if the API returns data in multiple pages.

Once the data is retrieved, process it to fit the schema expected by Weaviate. This may involve transforming the data structure, cleaning unnecessary fields, and converting data types. Use Python’s data manipulation libraries like `pandas` for efficient processing.

If you haven't already, install Weaviate locally or use a cloud instance. Ensure it’s running and accessible on your network. Define your schema in Weaviate to match the structure of the cleaned data. Use the Weaviate console or the RESTful API to configure classes and properties.

Format the processed data to match the schema defined in Weaviate. Each data object should correspond to an instance of a class defined in your Weaviate schema. Ensure that the data types and structures comply with what Weaviate expects.

Use Weaviate’s RESTful API to insert the formatted data. Write a script that sends POST requests to the Weaviate API to create objects. Handle errors and confirm that data is correctly inserted by querying the Weaviate instance after ingestion.

By following these steps, you can efficiently transfer data from a public API to Weaviate without relying on any third-party connectors or integrations.