Guide to Creating a Connector for DeepSeek Integration

Creating a connector for DeepSeek involves several steps, which can be tailored to different platforms and use cases. Below is a detailed guide on how to create a DeepSeek connector, focusing on a general approach and using OpenSearch as an example.

Step 1: Obtain a DeepSeek API Key

To start integrating DeepSeek into your application, you first need to obtain an API key. This involves signing up for a DeepSeek account and navigating to the API settings section to generate your key. Ensure you have sufficient credits in your account to use the API[9].

Step 2: Set Up the Connector Endpoint

If you are integrating DeepSeek with platforms like OpenSearch, you may need to add the DeepSeek API endpoint to trusted URLs. However, this step is often not required in newer versions of OpenSearch (starting from version 2.19.0)[4].

Step 3: Create the Connector

To create a connector for DeepSeek, you will typically send a POST request to the platform's API endpoint for creating connectors. This request includes details such as the connector name, description, endpoint URL, model name, and your API key for authentication. Here is an example of how this might look in OpenSearch:

json
POST /_plugins/_ml/connectors/_create {
  "name": "DeepSeek Chat",
  "description": "Test connector for DeepSeek Chat",
  "version": "1",
  "protocol": "http",
  "parameters": {
    "endpoint": "api.deepseek.com",
    "model": "deepseek-chat"
  },
  "credential": {
    "deepSeek_key": ""
  },
  "actions": [
    {
      "action_type": "predict",
      "method": "POST",
      "url": "https://${parameters.endpoint}/v1/chat/completions",
      "headers": {
        "Content-Type": "application/json",
        "Authorization": "Bearer ${credential.deepSeek_key}"
      },
      "request_body": "{ \"model\": \"${parameters.model}\", \"messages\": ${parameters.messages} }"
    }
  ]
}

Step 4: Register the Model

After creating the connector, you need to register the DeepSeek model with the platform. This involves specifying the model name and description, and associating it with the connector you just created. In OpenSearch, this might involve creating a model group and registering the model to that group[1][4].

Step 5: Deploy the Model

Once the model is registered, you can deploy it as part of a workflow or pipeline. For example, in OpenSearch, you might create a search pipeline that uses the DeepSeek model for conversational search capabilities[1].

Step 6: Test the Connector

Finally, test your connector by sending a request to the DeepSeek API through your newly created connector. Ensure that the API key is valid, and the endpoint URL is correctly configured. You can use tools like Postman or write a simple script to test the API call[2][5].

By following these steps, you can successfully create a connector for DeepSeek and integrate its AI capabilities into your applications.

Citations:
[1] https://opensearch.org/blog/one-click-deepseek-integration/
[2] https://apidog.com/blog/integrate-deepseek-apis-into-ypi/
[3] https://builtin.com/artificial-intelligence/how-implement-deepseek-locally
[4] https://github.com/opensearch-project/ml-commons/blob/main/docs/remote_inference_blueprints/deepseek_connector_chat_blueprint.md
[5] https://api-docs.deepseek.com
[6] https://www.geekompc.com/deepseek-ai-guide/
[7] https://www.knime.com/blog/how-to-use-deepseek-in-knime
[8] https://meetrix.io/articles/deepseekcoder-developer-guide/
[9] https://milhaniq.com/how-to-connect-deepseek-api-with-power-automate-a-simple-step-by-step-guide/
[10] https://www.datacamp.com/tutorial/deepseek-api

What are the steps to create a connector for DeepSeek