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from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse, PlainTextResponse
from pydantic import BaseModel, Field
from typing import List, Literal
from app.helpers.plan_chat import ask_plan_question
from app.helpers.token_auth import get_token
from app.helpers.get_current_uesr import get_user_from_token
from app.models.project import Project
from app.helpers.vectorization import search_similar
router = APIRouter()
class HistoryItem(BaseModel):
message: str
from_: Literal["user", "ai"]
class PlanChatPayload(BaseModel):
query: str
history: List[HistoryItem]
project_id: str
@router.post("/plan-chat")
async def plan_chat(data: PlanChatPayload, token: str = Depends(get_token)):
"""
Handle chat messages for plan generation with context from scraped content
"""
try:
# Validate user
user = await get_user_from_token(token=token)
if not user:
raise HTTPException(status_code=401, detail="Invalid token")
# Get project context
project = await Project.get_or_none(id=data.project_id)
if not project:
raise HTTPException(status_code=404, detail="Project not found")
# Get relevant context from vectorstore
context = await search_similar(data.query)
# Prepare system prompt
system_prompt = (
"You are a solution architect assistant specialized in cloud architecture. "
"Use the following context to help answer questions about the project plan. "
"Focus on providing specific, actionable advice based on the project requirements "
"and scraped documentation.\n\n"
f"Project Context: {context}\n"
f"Project Requirements: {project.requirements}\n"
f"Project Features: {project.features}\n"
f"Solution Stack: {project.solution_stack}\n"
)
async def response_stream():
async for chunk in ask_plan_question(
question=data.query,
history=data.history,
project_context=system_prompt
):
yield chunk
return StreamingResponse(response_stream(), media_type="text/plain")
except Exception as e:
return PlainTextResponse(str(e), status_code=500) |