Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including DeepSeek models.
With Portkey, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a system.
Provider Slug: deepseek
Portkey SDK Integration with DeepSeek Models
Portkey provides a consistent API to interact with models from various providers. To integrate DeepSeek with Portkey:
1. Install the Portkey SDK
Add the Portkey SDK to your application to interact with DeepSeek AI's API through Portkey's gateway.
npm install --save portkey-ai
pip install portkey-ai
2. Initialize Portkey with the Virtual Key
To use DeepSeek with Portkey, , then add it to Portkey to create the virtual key.
import Portkey from 'portkey-ai'
const portkey = new Portkey({
apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
virtualKey: "VIRTUAL_KEY" // Your DeepSeek Virtual Key
})
from portkey_ai import Portkey
portkey = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
virtual_key="VIRTUAL_KEY" # Replace with your virtual key for DeepSeek
)
3. Invoke Chat Completions with DeepSeek
Use the Portkey instance to send requests to DeepSeek. You can also override the virtual key directly in the API call if needed.
const chatCompletion = await portkey.chat.completions.create({
messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'deepseek-chat',
});
console.log(chatCompletion.choices);
completion = portkey.chat.completions.create(
messages= [{ "role": 'user', "content": 'Say this is a test' }],
model= 'deepseek-chat'
)
print(completion)
4. Invoke Multi-round Conversation withDeepSeek
const client = new Portkey({
apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
virtualKey: "VIRTUAL_KEY" // Your DeepSeek Virtual Key
})
// Function to send chat messages and get a response
async function sendChatMessages(messages) {
try {
const response = await axios.post(baseURL, {
model: 'deepseek-chat',
messages: messages
}, { headers: headers });
return response.data;
} catch (error) {
console.error('Error during the API request:', error.response ? error.response.data : error.message);
return null;
}
}
// Round 1
(async () => {
let messages = [{ role: 'user', content: "What's the highest mountain in the world?" }];
let response = await sendChatMessages(messages);
if (response) {
messages.push(response.choices[0].message);
console.log(`Messages Round 1: ${JSON.stringify(messages, null, 2)}`);
}
// Round 2
messages.push({ role: 'user', content: 'What is the second?' });
response = await sendChatMessages(messages);
if (response) {
messages.push(response.choices[0].message);
console.log(`Messages Round 2: ${JSON.stringify(messages, null, 2)}`);
}
})();
client = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
virtual_key="VIRTUAL_KEY" # Replace with your virtual key for DeepSeek
)
# Round 1
messages = [{"role": "user", "content": "What's the highest mountain in the world?"}]
response = client.chat.completions.create(
model="deepseek-chat",
messages=messages
)
messages.append(response.choices[0].message)
print(f"Messages Round 1: {messages}")
# Round 2
messages.append({"role": "user", "content": "What is the second?"})
response = client.chat.completions.create(
model="deepseek-chat",
messages=messages
)
messages.append(response.choices[0].message)
print(f"Messages Round 2: {messages}")
5. JSON Output with DeepSeek
const client = new Portkey({
apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
virtualKey: "VIRTUAL_KEY" // Your DeepSeek Virtual Key
})
const systemPrompt = `
The user will provide some exam text. Please parse the "question" and "answer" and output them in JSON format.
EXAMPLE INPUT:
Which is the highest mountain in the world? Mount Everest.
EXAMPLE JSON OUTPUT:
{
"question": "Which is the highest mountain in the world?",
"answer": "Mount Everest"
}
`;
const userPrompt = "Which is the longest river in the world? The Nile River.";
const messages = [
{ role: "system", content: systemPrompt },
{ role: "user", content: userPrompt }
];
client.chat.completions.create({
model: "deepseek-chat",
messages: messages,
responseFormat: {
type: 'json_object'
}
}).then(response => {
console.log(JSON.parse(response.choices[0].message.content));
}).catch(error => {
console.error('Error:', error);
});
import json
client = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
virtual_key="VIRTUAL_KEY" # Replace with your virtual key for DeepSeek
)
system_prompt = """
The user will provide some exam text. Please parse the "question" and "answer" and output them in JSON format.
EXAMPLE INPUT:
Which is the highest mountain in the world? Mount Everest.
EXAMPLE JSON OUTPUT:
{
"question": "Which is the highest mountain in the world?",
"answer": "Mount Everest"
}
"""
user_prompt = "Which is the longest river in the world? The Nile River."
messages = [{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}]
response = client.chat.completions.create(
model="deepseek-chat",
messages=messages,
response_format={
'type': 'json_object'
}
)
print(json.loads(response.choices[0].message.content))
Managing DeepSeek Prompts
Once you're ready with your prompt, you can use the portkey.prompts.completions.create interface to use the prompt in your application.
Supported Endpoints
CHAT_COMPLETIONS
STREAM_CHAT_COMPLETIONS
The complete list of features supported in the SDK is available on the link below.
You'll find more information in the relevant sections:
You can manage all prompts to DeepSeek in the . All the current models of DeepSeek are supported and you can easily start testing different prompts.
