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import httpx
from typing import Optional, Iterator, Union, Any
import logging
from litserve import LitAPI
class InferenceApi(LitAPI):
def __init__(self, config: dict):
"""Initialize the Inference API with configuration."""
super().__init__()
self.logger = logging.getLogger(__name__)
self.logger.info("Initializing Inference API")
# Get base URL from config
self.base_url = config["llm_server"]["base_url"]
self.timeout = config["llm_server"].get("timeout", 60)
self.client = None # Will be initialized in setup()
# Set request timeout from config
self.request_timeout = float(self.timeout)
async def setup(self, device: Optional[str] = None):
"""Setup method required by LitAPI - initialize HTTP client"""
self._device = device # Store device as required by LitAPI
self.client = httpx.AsyncClient(
base_url=self.base_url,
timeout=self.timeout
)
self.logger.info(f"Inference API setup completed on device: {device}")
async def predict(self, x: str, **kwargs) -> Union[str, Iterator[str]]:
"""
Main prediction method required by LitAPI.
If streaming is enabled, yields chunks; otherwise returns complete response.
"""
if self.stream:
async for chunk in self.generate_stream(x, **kwargs):
yield chunk
else:
return await self.generate_response(x, **kwargs)
def decode_request(self, request: Any, **kwargs) -> str:
"""Convert the request payload to input format."""
# For our case, we expect the request to be text
if isinstance(request, dict) and "prompt" in request:
return request["prompt"]
return request
def encode_response(self, output: Union[str, Iterator[str]], **kwargs) -> Union[str, Iterator[str]]:
"""Convert the model output to a response payload."""
if self.stream:
# For streaming, yield each chunk wrapped in a dict
async def stream_wrapper():
async for chunk in output:
yield {"generated_text": chunk}
else:
# For non-streaming, return complete response
return {"generated_text": output}
async def generate_response(
self,
prompt: str,
system_message: Optional[str] = None,
max_new_tokens: Optional[int] = None
) -> str:
"""Generate a complete response by forwarding the request to the LLM Server."""
self.logger.debug(f"Forwarding generation request for prompt: {prompt[:50]}...")
try:
response = await self.client.post(
"/api/v1/generate",
json={
"prompt": prompt,
"system_message": system_message,
"max_new_tokens": max_new_tokens
}
)
response.raise_for_status()
data = response.json()
return data["generated_text"]
except Exception as e:
self.logger.error(f"Error in generate_response: {str(e)}")
raise
async def generate_stream(
self,
prompt: str,
system_message: Optional[str] = None,
max_new_tokens: Optional[int] = None
) -> Iterator[str]:
"""Generate a streaming response by forwarding the request to the LLM Server."""
self.logger.debug(f"Forwarding streaming request for prompt: {prompt[:50]}...")
try:
async with self.client.stream(
"POST",
"/api/v1/generate/stream",
json={
"prompt": prompt,
"system_message": system_message,
"max_new_tokens": max_new_tokens
}
) as response:
response.raise_for_status()
async for chunk in response.aiter_text():
yield chunk
except Exception as e:
self.logger.error(f"Error in generate_stream: {str(e)}")
raise
# ... [rest of the methods remain the same: generate_embedding, check_system_status, etc.]
async def cleanup(self):
"""Cleanup method - close HTTP client"""
if self.client:
await self.client.aclose()
def log(self, key: str, value: Any):
"""Override log method to use our logger if queue not set"""
if self._logger_queue is None:
self.logger.info(f"Log event: {key}={value}")
else:
super().log(key, value) |