How to load data from Weatherstack to Weaviate

Begin by obtaining access to your Weatherstack API. Sign up at the Weatherstack website if you haven’t already and get your unique API key. Review the API documentation to understand the endpoints you need for fetching weather data.

Use a programming language like Python to make HTTP GET requests to the Weatherstack API. Use the requests library to query the API with your API key and the required parameters (e.g., location, date). Parse the JSON response to extract the relevant weather data you need.

```python
import requests

api_key = 'your_api_key'
location = 'New York'
url = f'http://api.weatherstack.com/current?access_key={api_key}&query={location}'

response = requests.get(url)
weather_data = response.json()
```

Prepare the fetched data to match the schema of your Weaviate instance. Define the classes and properties in Weaviate that will store your weather data. For example, create a class named `Weather` with properties like `temperature`, `humidity`, etc.

Ensure that your Weaviate instance is up and running. You can either use a cloud-based Weaviate instance or run it locally using Docker. Verify that your Weaviate instance is accessible and ready to receive data.

```bash
docker run -d -p 8080:8080 semitechnologies/weaviate:latest
```

Convert the structured data into the format required by the Weaviate API. Create a JSON object or a list of objects that match the class and property definitions in Weaviate. Ensure the data types match the schema.

```python
weather_object = {
"class": "Weather",
"properties": {
"location": location,
"temperature": weather_data['current']['temperature'],
"humidity": weather_data['current']['humidity']
}
}
```

Use the Weaviate RESTful API to insert the data. Make an HTTP POST request to the Weaviate endpoint, passing the formatted data as the payload. Use the requests library to handle the HTTP request and send the data to Weaviate.

```python
weaviate_url = 'http://localhost:8080/v1/objects'

headers = {
'Content-Type': 'application/json'
}

response = requests.post(weaviate_url, json=weather_object, headers=headers)
```

After inserting the data, verify that it has been successfully imported into Weaviate. You can do this by querying Weaviate to fetch the recently added objects and checking if the data matches what you sent. Use the Weaviate RESTful API to perform this query.

```python
query_url = 'http://localhost:8080/v1/graphql'

query = """
{
Get {
Weather {
location
temperature
humidity
}
}
}
"""

response = requests.post(query_url, json={'query': query}, headers=headers)
print(response.json())
```

By following these steps, you can move data directly from Weatherstack to Weaviate without relying on third-party connectors or integrations.