CHARM: A Foundation Model for Time-Series Embeddings

Create an .env File

Create an .env file in your project directory and export it as an environment variable:

This allows you to keep your credentials separate from your code and avoid hardcoding sensitive information.

1
2
CHARM_API_KEY=your-api-key
CHARM_BASE_URL=https://ftse.c3ci.cloud/ftse

1
pip install c3-charm

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
from c3-charm import CharmClient
from dotenv import load_dotenv
import os

# Load environment variables from .env file
load_dotenv()
# Get API key and base URL from environment variables
api_key = os.getenv("CHARM_API_KEY", "your-api-key")
base_url = os.getenv("CHARM_BASE_URL", "https://ftse.c3ci.cloud/ftse")

# Create a client
client = CharmClient(
    base_url=base_url,
    api_key=api_key,
    timeout=30,         # Increased timeout for potentially large requests
    max_retries=3       # Automatically retry failed requests
)
# Generate embeddings for time series data
response = client.embeddings.create(
    descriptions=[["engine", "temperature"], ["fan", "speed"]],
    ts_array=[
    # First time series (10 timesteps, 2 channels)
            [
                [0.1, 0.2], [0.3, 0.4], [0.5, 0.6], [0.7, 0.8], [0.9, 1.0],
                [1.1, 1.2], [1.3, 1.4], [1.5, 1.6], [1.7, 1.8], [1.9, 2.0]
            ],
    # Second time series (10 timesteps, 2 channels)
            [
                [2.1, 2.2], [2.3, 2.4], [2.5, 2.6], [2.7, 2.8], [2.9, 3.0],
                [3.1, 3.2], [3.3, 3.4], [3.5, 3.6], [3.7, 3.8], [3.9, 4.0]
            ]
        ],
    return_tensors="np",  # Options: "list", "np", "torch"
)
# Access the embeddings
embeddings = response.embeds
print(f"Model: {response.model}")
print(f"Embeddings shape: {embeddings.shape}")