How to load data from Exchange Rates Api to Weaviate

Begin by ensuring that your local development environment is set up for Python. This includes having Python installed (preferably version 3.7 or higher) and necessary libraries like `requests` for making HTTP requests and `weaviate-client` for interacting with Weaviate. You can install these using pip:
```bash
pip install requests weaviate-client
```

Obtain access to the exchange rates API by registering (if necessary) and acquiring an API key. This key will be used to authenticate your requests. Familiarize yourself with the API documentation to understand the endpoints and the data structure returned.

Write a Python script to fetch data from the exchange rates API. Use the `requests` library to make a GET request to the API endpoint, using your API key for authentication. Parse the response to extract the data you need, such as exchange rates and timestamps.
```python
import requests

API_URL = "https://api.exchangeratesapi.io/latest"
response = requests.get(API_URL)
data = response.json()
```

Transform the fetched data into a format compatible with Weaviate. Weaviate requires data to be structured in classes and properties. Define a schema that represents the exchange rates, ensuring that it includes necessary properties like currency, rate, and date.

Ensure your Weaviate instance is running and accessible. Use the `weaviate-client` to define the schema that matches the structure of your data. This involves creating a class in Weaviate with properties for each piece of data you wish to store (e.g., currency, rate, date).
```python
import weaviate

client = weaviate.Client("http://localhost:8080")
client.schema.create_class({
"class": "ExchangeRate",
"properties": [
{"name": "currency", "dataType": ["string"]},
{"name": "rate", "dataType": ["number"]},
{"name": "date", "dataType": ["date"]},
]
})
```

With the schema defined, write a script to insert the transformed data into Weaviate. Use the `weaviate-client` to create objects in the previously defined class. Loop through your prepared data, creating an object for each entry.
```python
for currency, rate in data['rates'].items():
client.data_object.create({
"currency": currency,
"rate": rate,
"date": data['date']
}, "ExchangeRate")
```

Finally, validate that the data has been successfully inserted by querying the Weaviate database. Use simple queries to fetch and verify the data. This step ensures data integrity and helps you confirm that the process was successful.
```python
result = client.query.get("ExchangeRate", ["currency", "rate", "date"]).do()
print(result)
```

By following these steps, you can effectively transfer data from an exchange rates API to Weaviate without relying on third-party connectors or integrations.