from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from typing import Dict, List, Optional, Union, Any
from pydantic import BaseModel, Field
from datetime import datetime
import logging
import json
import os
from dotenv import load_dotenv
from dify_client_python.dify_client import models
from sse_starlette.sse import EventSourceResponse
import httpx
from json_parser import SSEParser
from logger_config import setup_logger
from fastapi.responses import StreamingResponse
from fastapi.responses import JSONResponse
from response_formatter import ResponseFormatter
import traceback
# Load environment variables
load_dotenv()
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
class AgentOutput(BaseModel):
"""Structured output from agent processing"""
thought_content: str
observation: Optional[str]
tool_outputs: List[Dict]
citations: List[Dict]
metadata: Dict
raw_response: str
class AgentRequest(BaseModel):
"""Enhanced request model with additional parameters"""
query: str
conversation_id: Optional[str] = None
stream: bool = True
inputs: Dict = {}
files: List = []
user: str = "default_user"
response_mode: str = "streaming"
class AgentProcessor:
def __init__(self, api_key: str):
self.api_key = api_key
# Update API base to use environment variable with fallback
self.api_base = os.getenv(
"API_BASE_URL",
"https://ai-engine.yamamotoqa.com/v1"
)
self.formatter = ResponseFormatter()
self.client = httpx.AsyncClient(timeout=60.0)
self.logger = setup_logger("agent_processor")
async def log_request_details(
self,
request: AgentRequest,
start_time: datetime
) -> None:
"""Log detailed request information"""
self.logger.debug(
"Request details: \n"
f"Query: {request.query}\n"
f"User: {request.user}\n"
f"Conversation ID: {request.conversation_id}\n"
f"Stream mode: {request.stream}\n"
f"Start time: {start_time}\n"
f"Inputs: {request.inputs}\n"
f"Files: {len(request.files)} files attached"
)
async def log_error(
self,
error: Exception,
context: Optional[Dict] = None
) -> None:
"""Log detailed error information"""
error_msg = (
f"Error type: {type(error).__name__}\n"
f"Error message: {str(error)}\n"
f"Stack trace:\n{traceback.format_exc()}\n"
)
if context:
error_msg += f"Context:\n{json.dumps(context, indent=2)}"
self.logger.error(error_msg)
async def cleanup(self):
"""Cleanup method to properly close client"""
await self.client.aclose()
async def process_stream(self, request: AgentRequest):
start_time = datetime.now()
await self.log_request_details(request, start_time)
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"Accept": "text/event-stream"
}
chat_request = {
"query": request.query,
"inputs": request.inputs,
"response_mode": "streaming" if request.stream else "blocking",
"user": request.user,
"conversation_id": request.conversation_id,
"files": request.files
}
async def event_generator():
parser = SSEParser()
citations = []
metadata = {}
try:
async with self.client.stream(
"POST",
f"{self.api_base}/chat-messages",
headers=headers,
json=chat_request
) as response:
self.logger.debug(
f"Stream connection established\n"
f"Status: {response.status_code}\n"
f"Headers: {dict(response.headers)}"
)
buffer = ""
async for line in response.aiter_lines():
if not line.strip():
continue
self.logger.debug(f"Raw SSE line: {line}")
if "data:" in line:
try:
data = line.split("data:", 1)[1].strip()
parsed = json.loads(data)
if parsed.get("event") == "message_end":
citations = parsed.get("retriever_resources", [])
metadata = parsed.get("metadata", {})
self.logger.debug(
f"Message end event:\n"
f"Citations: {citations}\n"
f"Metadata: {metadata}"
)
formatted = self.format_terminal_output(
parsed,
citations=citations,
metadata=metadata
)
if formatted:
self.logger.info(formatted)
except Exception as e:
await self.log_error(
e,
{"line": line, "event": "parse_data"}
)
buffer += line + "\n"
if line.startswith("data:") or buffer.strip().endswith("}"):
try:
processed_response = parser.parse_sse_event(buffer)
if processed_response and isinstance(processed_response, dict):
cleaned_response = self.clean_response(processed_response)
if cleaned_response:
xml_content = cleaned_response.get("content", "")
yield f"data: {xml_content}\n\n"
except Exception as parse_error:
await self.log_error(
parse_error,
{"buffer": buffer, "event": "process_buffer"}
)
error_xml = (
f""
f"{str(parse_error)}"
f""
)
yield f"data: {error_xml}\n\n"
finally:
buffer = ""
except httpx.ConnectError as e:
await self.log_error(e, {"event": "connection_error"})
error_xml = (
f""
f"Connection error: {str(e)}"
f""
)
yield f"data: {error_xml}\n\n"
except Exception as e:
await self.log_error(e, {"event": "stream_error"})
error_xml = (
f""
f"Streaming error: {str(e)}"
f""
)
yield f"data: {error_xml}\n\n"
finally:
end_time = datetime.now()
duration = (end_time - start_time).total_seconds()
self.logger.info(f"Request completed in {duration:.2f} seconds")
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
"Access-Control-Allow-Origin": "*"
}
)
def format_terminal_output(
self,
response: Dict,
citations: List[Dict] = None,
metadata: Dict = None
) -> Optional[str]:
"""Format response for terminal output"""
event_type = response.get("event")
if event_type == "agent_thought":
thought = response.get("thought", "")
observation = response.get("observation", "")
terminal_output, _ = self.formatter.format_thought(
thought,
observation,
citations=citations,
metadata=metadata
)
return terminal_output
elif event_type == "agent_message":
message = response.get("answer", "")
terminal_output, _ = self.formatter.format_message(message)
return terminal_output
elif event_type == "error":
error = response.get("error", "Unknown error")
terminal_output, _ = self.formatter.format_error(error)
return terminal_output
return None
def clean_response(self, response: Dict) -> Optional[Dict]:
"""Clean and transform the response for frontend consumption"""
try:
event_type = response.get("event")
if not event_type:
return None
# Handle different event types
if event_type == "agent_thought":
thought = response.get("thought", "")
observation = response.get("observation", "")
_, xml_output = self.formatter.format_thought(thought, observation)
return {
"type": "thought",
"content": xml_output
}
elif event_type == "agent_message":
message = response.get("answer", "")
_, xml_output = self.formatter.format_message(message)
return {
"type": "message",
"content": xml_output
}
elif event_type == "error":
error = response.get("error", "Unknown error")
_, xml_output = self.formatter.format_error(error)
return {
"type": "error",
"content": xml_output
}
return None
except Exception as e:
logger.error(f"Error cleaning response: {str(e)}")
return None
# Initialize FastAPI app
app = FastAPI()
agent_processor = None
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.on_event("startup")
async def startup_event():
global agent_processor
api_key = os.getenv("DIFY_API_KEY")
agent_processor = AgentProcessor(api_key=api_key)
@app.on_event("shutdown")
async def shutdown_event():
global agent_processor
if agent_processor:
await agent_processor.cleanup()
@app.post("/v1/agent")
async def process_agent_request(request: AgentRequest):
try:
logger.info(f"Processing agent request: {request.query}")
return await agent_processor.process_stream(request)
except Exception as e:
logger.error(f"Error in agent request processing: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=str(e))
@app.middleware("http")
async def error_handling_middleware(request: Request, call_next):
try:
response = await call_next(request)
return response
except Exception as e:
logger.error(f"Unhandled error: {str(e)}", exc_info=True)
return JSONResponse(
status_code=500,
content={"error": "Internal server error occurred"}
)
# Add host and port parameters to the launch
if __name__ == "__main__":
import uvicorn
port = int(os.getenv("PORT", 7860))
uvicorn.run(
"api:app",
host="0.0.0.0",
port=port,
reload=True
)