# ollama LLM platform (LLM, Embedding model)


## LLM model：

import requests

def ollama_platform(prompt, model, mode):
    
    OLLAMA_PLATFORM_URL = 'http://實驗室4090 IP:6666/ollama_platform/'
    response = requests.post(OLLAMA_PLATFORM_URL, json={"prompt": prompt, "model": model, "mode": mode})

    return response.json()

# example usage
response = ollama_platform('France is good', 'llama3.2:3b', mode='LLM')
response['output']['message']['content']

---
## Embedding model：

import requests

def ollama_platform(prompt, model, mode):
    
    OLLAMA_PLATFORM_URL = 'http://實驗室4090 IP:6666/ollama_platform/'
    response = requests.post(OLLAMA_PLATFORM_URL, json={"prompt": prompt, "model": model, "mode": mode})

    return response.json()

embedding = ollama_platform('text', 'embedding model name', mode='embedding')

# example usage
response = ollama_platform('France is good', 'bge-m3', mode='embedding')
response['output']['embedding']

---
## Vision LLM:

import requests

def ollama_platform(prompt, model, mode, images):
    
    OLLAMA_PLATFORM_URL = 'http://140.115.54.162:6666/ollama_platform/'
    response = requests.post(OLLAMA_PLATFORM_URL, json={"prompt": prompt, 
                                                        "model": model, 
                                                        "mode": mode,
                                                        "images": images})

    return response.json()

# example usage
response = ollama_platform('What is in this image?', 'llama3.2-vision', mode='vision', images=['照片的絕對位置'])
response['output']['message']['content']

## 維護

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
from ollama import Client
import uvicorn

OLLAMA_URL = "http://localhost:11434"

app = FastAPI()

class OllamaRequest(BaseModel):
    prompt: str
    model: str
    mode: str
    images: Optional[List[str]] = None

def ollama_platform(prompt, model, mode, images=None):
    client = Client(host=OLLAMA_URL)

    try:
        if mode == 'LLM':
            response = client.chat(model=model, messages=[
                {
                    'role': 'user',
                    'content': prompt
                }
            ])
        elif mode == 'embedding':
            response = client.embeddings(
                model=model,
                prompt=prompt,
            )
        elif mode == 'vision' and images:
            response = client.chat(model=model, messages=[
                {
                    'role': 'user',
                    'content': prompt,
                    'images': images
                }
            ])
        else:
            raise ValueError("Unsupported mode or missing images for vision mode.")
        
        return response
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/ollama_platform/")
async def run_ollama(request: OllamaRequest):
    try:
        response = ollama_platform(request.prompt, request.model, request.mode, request.images)
        return {"output": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# 運行 FastAPI 應用
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)