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import time
import uuid
import json
import asyncio
from typing import AsyncGenerator
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from sse_starlette.sse import EventSourceResponse
from aetheris.api.schemas import (
ChatCompletionRequest, ChatCompletionResponse, ChatCompletionChunk,
ChatCompletionChoice, ChatMessage, ChatCompletionChunkChoice, ChatCompletionChunkDelta,
CompletionRequest, CompletionResponse, CompletionChoice,
ModelList, ModelCard
)
from aetheris.inference import InferenceEngine
app = FastAPI(title="Aetheris API", version="0.1.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Global engine instance
engine: InferenceEngine = None
def get_engine():
global engine
if engine is None:
# Defaults, ideally loaded from config/env
engine = InferenceEngine()
return engine
@app.on_event("startup")
async def startup_event():
get_engine()
@app.get("/")
async def root():
return {"status": "running", "message": "Aetheris API is active. Use /v1/chat/completions for inference."}
@app.get("/v1/models", response_model=ModelList)
async def list_models():
return ModelList(data=[ModelCard(id="aetheris-hybrid-mamba-moe")])
@app.post("/v1/chat/completions")
async def chat_completions(request: ChatCompletionRequest):
engine = get_engine()
# Simple prompt construction from messages
prompt = ""
for msg in request.messages:
prompt += f"{msg.role}: {msg.content}\n"
prompt += "assistant: "
request_id = f"chatcmpl-{uuid.uuid4()}"
created_time = int(time.time())
if request.stream:
async def event_generator():
yield json.dumps(ChatCompletionChunk(
id=request_id,
created=created_time,
model=request.model,
choices=[ChatCompletionChunkChoice(
index=0,
delta=ChatCompletionChunkDelta(role="assistant"),
finish_reason=None
)]
).model_dump())
# Offload synchronous generation to a thread to avoid blocking the event loop
queue = asyncio.Queue()
loop = asyncio.get_running_loop()
import threading
stop_event = threading.Event()
def producer():
try:
# Run the synchronous generator
for token in engine.generate(
prompt=prompt,
max_new_tokens=request.max_tokens or 100,
temperature=request.temperature,
top_p=request.top_p,
repetition_penalty=1.0 + request.frequency_penalty,
stream=True
):
if stop_event.is_set():
break
# Schedule the put() coroutine on the main loop
asyncio.run_coroutine_threadsafe(queue.put(token), loop)
except Exception as e:
print(f"Generation error: {e}")
finally:
# Signal done
asyncio.run_coroutine_threadsafe(queue.put(None), loop)
thread = threading.Thread(target=producer, daemon=True)
thread.start()
try:
while True:
token = await queue.get()
if token is None:
break
yield json.dumps(ChatCompletionChunk(
id=request_id,
created=created_time,
model=request.model,
choices=[ChatCompletionChunkChoice(
index=0,
delta=ChatCompletionChunkDelta(content=token),
finish_reason=None
)]
).model_dump())
yield json.dumps(ChatCompletionChunk(
id=request_id,
created=created_time,
model=request.model,
choices=[ChatCompletionChunkChoice(
index=0,
delta=ChatCompletionChunkDelta(),
finish_reason="stop"
)]
).model_dump())
yield "[DONE]"
finally:
stop_event.set()
return EventSourceResponse(event_generator())
else:
generated_text = engine.generate_full(
prompt=prompt,
max_new_tokens=request.max_tokens or 100,
temperature=request.temperature,
top_p=request.top_p,
repetition_penalty=1.0 + request.frequency_penalty
)
return ChatCompletionResponse(
id=request_id,
created=created_time,
model=request.model,
choices=[ChatCompletionChoice(
index=0,
message=ChatMessage(role="assistant", content=generated_text),
finish_reason="stop"
)],
usage={"prompt_tokens": len(prompt), "completion_tokens": len(generated_text), "total_tokens": len(prompt) + len(generated_text)} # Approximated
)
@app.post("/v1/completions")
async def completions(request: CompletionRequest):
engine = get_engine()
prompt = request.prompt
if isinstance(prompt, list):
prompt = prompt[0] # Handle single prompt for now
request_id = f"cmpl-{uuid.uuid4()}"
created_time = int(time.time())
if request.stream:
# Streaming for completions not fully implemented to match OpenAI exactly in this demo,
# but logic is similar to chat.
# For simplicity, returning non-streaming for now or basic stream.
pass # TODO: Implement streaming for completions
generated_text = engine.generate_full(
prompt=prompt,
max_new_tokens=request.max_tokens or 16,
temperature=request.temperature,
top_p=request.top_p,
repetition_penalty=1.0 + request.frequency_penalty
)
return CompletionResponse(
id=request_id,
created=created_time,
model=request.model,
choices=[CompletionChoice(
text=generated_text,
index=0,
logprobs=None,
finish_reason="length" # or stop
)],
usage={"prompt_tokens": len(prompt), "completion_tokens": len(generated_text), "total_tokens": len(prompt) + len(generated_text)}
)
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