关于ollama部署DEEPSEEK以及接口调用

from fastapi import FastAPI,HTTPException
from pydantic import BaseModel
import requests,json
from sse_starlette.sse import EventSourceResponse


punct_list = ["。", "!", "?"]


app = FastAPI()
OLLAMA_URL = "http://localhost:11434/api/chat"

class ChatRequestDirect(BaseModel):
    prompt: str
    max_tokens: int = 1024
    temperature: float = 0.7

class ChatRequestStream(BaseModel):
    prompt: str


@app.post("/v1/chat")
def chat_completion(request: ChatRequestDirect):
    try:
        payload = {
            "model": "deepseek-r1:32b",
            "messages": [
                {
                "role": "user",
                "content": request.prompt}
                ],
            "stream": False,
            "options": {
                "temperature": request.temperature,
                "num_predict": request.max_tokens
            }
        }
        response = requests.post(OLLAMA_URL, json=payload)
        return response.json()['message']
    except:
        raise HTTPException(status_code=404, detail="Item not found")

    

@app.post("/v1/stream")
async def chat_stream(request: ChatRequestStream):
    try:
        def stream():
            jsons = {
                "model": "deepseek-r1:32b",
                "messages": [{
                    "role": "user",
                    "content": request.prompt}],
                "stream": True
            }
            response = requests.post(OLLAMA_URL, json=jsons, stream=True)
            text2tts = ''
            for line in response.iter_lines():
                data = json.loads(line.decode('utf-8'))
                text = data['message']['content']
                text2tts += text
                for punct in punct_list:
                    if punct in text:
                        front, back = text2tts.replace('\n', '').rsplit(punct, 1)
                        yield front + punct
                        text2tts = back
                        break
            if text2tts:
                yield text2tts
    except:
        return 

    return EventSourceResponse(stream())

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)

以上是关于ollama接口的简单本地调用。ollama国内网络也能pull

关于ollama部署deepseek-r1 主要还是看什么模型适合自己的电脑配置，cpu版本其实1.5b速度也蛮快好用。如果要用显卡的话就要配置cuda，版本不能错Ai学习之Ollama使用GPU运行模型的环境部署-优快云博客