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from fastapi import FastAPI, HTTPException, Depends, Response
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from prometheus_client import generate_latest
from health import check_docker_health, check_gpu_availability
from typing import List, Optional, Union
import time
import logging
import json
from auth import get_api_key, rate_limiter, api_requests, request_duration
from model_manager import ModelManager
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = FastAPI(title="Docker Model Runner OpenAI-Compatible API")
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize model manager
model_manager = ModelManager()
class ChatMessage(BaseModel):
role: str
content: str
class ChatCompletionRequest(BaseModel):
model: str
messages: List[ChatMessage]
temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0)
max_tokens: Optional[int] = Field(256, gt=0)
stream: Optional[bool] = False
class CompletionRequest(BaseModel):
model: str
prompt: str
temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0)
max_tokens: Optional[int] = Field(256, gt=0)
stream: Optional[bool] = False
class EmbeddingRequest(BaseModel):
model: str
input: Union[str, List[str]]
encoding_format: Optional[str] = "float"
@app.post("/v1/chat/completions")
async def create_chat_completion(
request: ChatCompletionRequest,
api_key: str = Depends(get_api_key)
):
"""Create a chat completion."""
rate_limiter.check(api_key)
api_requests.labels(endpoint="chat_completions").inc()
with request_duration.time():
try:
formatted_messages = [
{"role": msg.role, "content": msg.content}
for msg in request.messages
]
response = model_manager.run_model(
request.model,
formatted_messages,
temperature=request.temperature,
max_tokens=request.max_tokens
)
return {
"id": f"chatcmpl-{int(time.time()*1000)}",
"object": "chat.completion",
"created": int(time.time()),
"model": request.model,
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": response["output"]
},
"finish_reason": "stop"
}],
"usage": response.get("usage", {
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
})
}
except Exception as e:
logger.error(f"Chat completion error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/v1/completions")
async def create_completion(
request: CompletionRequest,
api_key: str = Depends(get_api_key)
):
"""Create a text completion."""
rate_limiter.check(api_key)
api_requests.labels(endpoint="completions").inc()
with request_duration.time():
try:
response = model_manager.run_model(
request.model,
request.prompt,
temperature=request.temperature,
max_tokens=request.max_tokens
)
return {
"id": f"cmpl-{int(time.time()*1000)}",
"object": "text_completion",
"created": int(time.time()),
"model": request.model,
"choices": [{
"text": response["output"],
"index": 0,
"finish_reason": "stop"
}],
"usage": response.get("usage", {
"prompt_tokens": 0,
"completion_tokens": 0,
"total_tokens": 0
})
}
except Exception as e:
logger.error(f"Completion error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/v1/embeddings")
async def create_embedding(
request: EmbeddingRequest,
api_key: str = Depends(get_api_key)
):
"""Create embeddings for text."""
rate_limiter.check(api_key)
api_requests.labels(endpoint="embeddings").inc()
with request_duration.time():
try:
inputs = request.input if isinstance(request.input, list) else [request.input]
response = model_manager.run_model(
request.model,
inputs
)
return {
"object": "list",
"data": [
{
"object": "embedding",
"embedding": emb,
"index": i
}
for i, emb in enumerate(response["embeddings"])
],
"model": request.model,
"usage": response.get("usage", {
"prompt_tokens": 0,
"total_tokens": 0
})
}
except Exception as e:
logger.error(f"Embedding error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/v1/models")
async def list_models(
api_key: str = Depends(get_api_key)
):
"""List available models."""
api_requests.labels(endpoint="models").inc()
return model_manager.list_models()
@app.get("/metrics")
async def metrics():
"""Expose Prometheus metrics."""
return Response(
media_type="text/plain",
content=generate_latest()
)
@app.get("/health")
async def health_check():
"""Check the health of the API and its dependencies."""
docker_health = check_docker_health()
gpu_status = check_gpu_availability()
health_status = {
"status": "healthy" if docker_health["status"] == "healthy" else "unhealthy",
"docker": docker_health,
"gpu": gpu_status,
"api_version": "1.0.0"
}
status_code = 200 if health_status["status"] == "healthy" else 503
return Response(
content=json.dumps(health_status),
media_type="application/json",
status_code=status_code
) |