How to use DeepSeek API with TensorFlow

Step 1: Install Required Libraries

First, ensure you have TensorFlow and the necessary libraries installed. You might also need to install the OpenAI SDK since DeepSeek's API is compatible with OpenAI's format.

bash
pip install tensorflow openai

Step 2: Obtain a DeepSeek API Key

Get an API key from DeepSeek. This key is essential for accessing their services.

Step 3: Use the DeepSeek API

You can use the DeepSeek API for tasks like text completion or OCR. Here's an example using the OpenAI SDK to interact with the DeepSeek API:

python
from openai import OpenAI

# Set your API key and base URL
api_key = "YOUR_DEEPSEEK_API_KEY"
base_url = "https://api.deepseek.com"

# Initialize the client
client = OpenAI(api_key=api_key, base_url=base_url)

# Example usage for text completion
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    stream=False
)

print(response.choices[0].message.content)

Step 4: Integrate with TensorFlow

If you need to preprocess data using TensorFlow before sending it to the DeepSeek API, you can do so by manipulating your data with TensorFlow functions. For example, if you're working with text data, you might tokenize it using TensorFlow's `tf.keras.preprocessing.text.Tokenizer`.

python
import tensorflow as tf

# Example of preprocessing text data
text_data = "This is an example sentence."

tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts([text_data])

# Convert text to sequences
sequences = tokenizer.texts_to_sequences([text_data])

# Now you can use these sequences as needed before sending to DeepSeek

Step 5: Post-processing

After receiving a response from the DeepSeek API, you can further process it using TensorFlow if necessary. This might involve analyzing the output text or integrating it with other TensorFlow models.

Conclusion

While DeepSeek itself is not a TensorFlow model, you can easily integrate it into a workflow that includes TensorFlow for preprocessing or post-processing tasks. Ensure you have the necessary libraries installed and follow the API documentation for DeepSeek to make effective use of its capabilities.

Citations:
[1] https://api-docs.deepseek.com
[2] https://www.youtube.com/watch?v=2cEZNpoKaFQ
[3] https://akcoding.com/deepseek-tutorial/
[4] https://stackoverflow.com/questions/79444424/how-to-add-api-key-for-deepseek-installed-on-my-server-for-others-to-use-this-se
[5] https://www.linkedin.com/pulse/how-run-deepseek-locally-using-hugging-face-efficient-xiao-fei-zhang-huw5e
[6] https://www.reddit.com/r/selfhosted/comments/1ic8zil/yes_you_can_run_deepseekr1_locally_on_your_device/
[7] https://codewithpk.com/how-to-use-deepseek-model-in-android-apps/
[8] https://stackoverflow.com/questions/79507870/how-do-i-use-deepseek-r1-distill-through-huggingface-inference-api