Taoillium-LLM is our Generative AI module that allows developers to harness the power of advanced language models. With Si-LLM, you can integrate AI functionalities such as natural language understanding, text generation, and sentiment analysis into your applications with ease.

Accessing via REST API

Non-streaming Chat Completions

import os
import requests

url = "<https://doc-ai.si.online/v1/chat/completions>"
headers = {
    "Authorization": f"Bearer {os.getenv('SI_API_KEY')}",
    "Content-Type": "application/json",
}
payload = {
    "model": "deepseek-ai/DeepSeek-V2-Chat",
    "messages": [
        {"role": "user", "content": "Brainstorm ideas for a new business startup"}
    ],
}

response = requests. Post(url, headers=headers, json=payload)
print(response. Text)

Accessing via OpenAI API

Non-streaming Chat Completions

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("SI_API_KEY"), base_url="<https://doc-ai.si.online/v1>")
response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V2-Chat",
    messages=[
        {"role": "user", "content": "Brainstorm ideas for a new business startup"}
    ],
    stream=False,
)

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

Streaming

Via REST API

Streaming Chat Completions

import os
import httpx
import json

url = "<https://doc-ai.si.online/v1/chat/completions>"
headers = {
    "Authorization": f"Bearer {os.getenv('SI_API_KEY')}",
    "Content-Type": "application/json",
}
payload = {
    "model": "deepseek-ai/DeepSeek-V2-Chat",
    "messages": [
        {"role": "user", "content": "Brainstorm ideas for a new business startup"}
    ],
    "stream": True,
}

with httpx.Client() as client:
    with client.stream("POST", url, headers=headers, json=payload) as response:
        print("Streaming response:")
        for line in response.iter_lines():
            if line.strip():
                strip_line = line.lstrip("data: ").strip()
                if strip_line == "[DONE]":
                    break
                content = json.loads(strip_line)["choices"][0]["delta"]["content"]
                print(content, end=""

Via OpenAI API

Streaming Chat Completions

import os
from openai import OpenAI

client = OpenAI(api_key=os.getenv("SI_API_KEY"), base_url="<https://doc-ai.si.online/v1>")
response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V2-Chat",
    messages=[
        {"role": "user", "content": "Brainstorm ideas for a new business startup"}
    ],
    stream=True,
)

for chunk in response:
    print(chunk.choices[0].delta. Content, end="")