chat2db调用ollama实现数据库的操作。

只试了mysql的调用。

其它的我也不用，本来想充钱算了。最后一看单位是美刀。就放弃了这分心。于是折腾了一下。

本地运行chat2db 及chat2db ui

https://gitee.com/ooooinfo/Chat2DB
clone 后运行起来 chat2db的java端，我现在搞不清这一个项目是有没有链接到数据库里去。
在idea项目中运行

前端在：chat2db-client中。我的环境是 node 20 ,yarn , 注意可能需要 yarn add electron
直接运行：yarn start

安装ollama 以及模型 qwen2.5 这里我也不懂。不知装那一个好。

https://ollama.com/

在powershell下运行：ollama run qwen2.5 或 ollama pull qwen2.5
最终你要保证ollama启运。

仿一下openai的接口 调用ollama 提供给chat2db:

chat2db中这样设置，所以需要我自己写一个app.py 去做一下代理请求ollama，不是我不想写自定义，主要是总不成功。不如直接仿openai .

app.py的部分代码。

我用的conda 创建的3.9的环境：
requirements.txt

fastapi==0.104.1uvicorn==0.24.0httpx==0.25.1tenacity==8.2.3backoff

相关的app.py的代码：

from fastapi import FastAPI, HTTPException, Request, Dependsfrom fastapi.middleware.cors import CORSMiddlewarefrom fastapi.responses import JSONResponse, StreamingResponseimport httpximport uvicornimport tracebackimport sysimport timeimport jsonimport asyncioimport reimport backoff  # 确保已安装 backoff 库app = FastAPI(    title="Ollama API Adapter",    description="An adapter for Ollama API that mimics OpenAI API format",    version="1.0.0")app.add_middleware(    CORSMiddleware,    allow_origins=["*"],    allow_credentials=True,    allow_methods=["*"],    allow_headers=["*"],)API_KEY = "sk-123456"OLLAMA_BASE_URL = "http://127.0.0.1:11434"DEFAULT_MODEL = "qwen2.5:latest"def verify_api_key(request: Request):    authorization: str = request.headers.get('authorization')    if not authorization:        raise HTTPException(status_code=401, detail="Authorization header is missing.")    token_type, _, token = authorization.partition(' ')    if token_type.lower() != 'bearer' or token != API_KEY:        raise HTTPException(status_code=401, detail="Unauthorized: API Key is invalid or missing.")@app.get("/v1", dependencies=[Depends(verify_api_key)])async def root():    """    Root endpoint that returns API information    """    return {        "version": "1.0.0",        "status": "ok",        "endpoints": [            "/v1/chat/completions",            "/v1/models",            "/health"        ]    }@app.get("/v1/models", dependencies=[Depends(verify_api_key)])async def list_models():    """    List available models    """    try:        async with httpx.AsyncClient(timeout=10.0) as client:            response = await client.get(f"{OLLAMA_BASE_URL}/api/tags")            if response.status_code == 200:                models = response.json().get("models", [])                return {                    "data": [                        {                            "id": model["name"],                            "object": "model",                            "created": 0,                            "owned_by": "ollama"                        }                        for model in models                    ]                }            else:                raise HTTPException(status_code=503, detail="Ollama service unavailable")    except Exception as e:        print(f"Error listing models: {str(e)}")        raise HTTPException(status_code=503, detail=str(e))@app.get("/health", dependencies=[Depends(verify_api_key)])async def health_check():    """    健康检查接口    """    try:        async with httpx.AsyncClient(timeout=5.0) as client:            response = await client.get(f"{OLLAMA_BASE_URL}/api/tags")            if response.status_code == 200:                print("Ollama service is healthy.")                return {"status": "healthy", "message": "服务运行正常"}            else:                print("Ollama service is not healthy.")                raise HTTPException(status_code=503, detail="Ollama 服务不可用")    except httpx.HTTPStatusError as exc:        print(f"HTTP error occurred: {exc.response.status_code}")        raise HTTPException(status_code=exc.response.status_code, detail=str(exc))    except httpx.RequestError as exc:        print(f"An error occurred while requesting {exc.request.url!r}.")        raise HTTPException(status_code=500, detail=str(exc))async def generate_sse_response(content):    # 提取 SQL 语句并添加换行    sql_match = re.search(r'```sql\n(.*?)\n```', content, re.DOTALL)    sql = sql_match.group(1).strip() if sql_match else content        # 添加换行符    formatted_content = f"{sql}\n"        # 构造 OpenAI API 格式的响应    response_data = {        "id": f"chatcmpl-{int(time.time())}",        "object": "chat.completion.chunk",        "created": int(time.time()),        "model": "gpt-3.5-turbo",        "choices": [{            "delta": {                "content": formatted_content  # 使用带换行的内容            },            "finish_reason": None,            "index": 0        }]    }        # 发送主要内容    yield f"data: {json.dumps(response_data, ensure_ascii=False)}\n\n"        # 发送结束消息    finish_response = {        "id": f"chatcmpl-{int(time.time())}",        "object": "chat.completion.chunk",        "created": int(time.time()),        "model": "gpt-3.5-turbo",        "choices": [{            "delta": {},            "finish_reason": "stop",            "index": 0}]    }        yield f"data: {json.dumps(finish_response, ensure_ascii=False)}\n\n"    yield "data: [DONE]\n\n"# 重试策略装饰器@backoff.on_exception(backoff.expo, httpx.ReadTimeout, max_tries=5, max_time=300)async def send_request(ollama_request):    timeout_config = httpx.Timeout(10.0, read=120.0)  # 连接超10秒，读取超时120秒    async with httpx.AsyncClient(timeout=timeout_config) as client:        try:            response = await client.post(                f"{OLLAMA_BASE_URL}/api/chat",                json=ollama_request            )            print(f"Response received with status {response.status_code}")            return response        except httpx.RequestError as exc:            print(f"An error occurred while requesting {exc.request.url!r}.")            raise HTTPException(status_code=500, detail=str(exc))@app.post("/v1/chat/completions", dependencies=[Depends(verify_api_key)])@app.post("/chat/completions", dependencies=[Depends(verify_api_key)])@app.post("/", dependencies=[Depends(verify_api_key)])async def chat_completions(request: Request):    try:        body = await request.json()        messages = body.get("messages", [])        stream = body.get("stream", True)        print(f"Received request with body: {body}")  # 使用 print 打印请求体        ollama_request = {            "model": DEFAULT_MODEL,            "messages": messages,            "stream": False        }                response = await send_request(ollama_request)        print(f"Received response: {response.text}")  # 使用 print 打印响应文本                if response.status_code != 200:            print(f"Failed to get response from model, status code: {response.status_code}")            raise HTTPException(status_code=400, detail="Failed to get response from model")                ollama_response = response.json()        content = ollama_response.get("message", {}).get("content", "")                print(f"Processed content: {content}")  # 使用 print 打印处理后的内容        if not stream:            result = {                "id": f"chatcmpl-{int(time.time())}",                "object": "chat.completion",                "created": int(time.time()),                "model": DEFAULT_MODEL,                "choices": [{                    "message": {                        "role": "database developer and expert",                        "content": content                    },                    "finish_reason": "stop",                    "index": 0                }]            }            print(f"Returning non-stream response: {result}")  # 使用 print 打印非流响应            return result                headers = {            "Content-Type": "text/event-stream",            "Cache-Control": "no-cache",            "Connection": "keep-alive"        }                return StreamingResponse(            generate_sse_response(content),            media_type="text/event-stream",            headers=headers        )            except json.JSONDecodeError as e:        print(f"JSON decoding error: {str(e)}")        return JSONResponse(status_code=400, content={"message": "Invalid JSON data"})    except Exception as e:        print(f"Error during chat completions: {str(e)}")        print(traceback.format_exc())  # 使用 print 打印堆栈跟踪        return JSONResponse(            status_code=500,            content={"message": "Internal server error"}        )    if __name__ == "__main__":    print("Starting server on 0.0.0.0:8080")    uvicorn.run(app, host="0.0.0.0", port=8080)

上面代码装key及model都写死，所以你一下要先下载下来相关的模型 。
python app.py

再注意以下本置：

在chat2db做好链接，再输入你的提示词。见下面效果：

响应速度几秒钟，当时看自己电脑响应速度了。都不花钱了，就不要什么自行车了。