feat: Oracle Canvas Component Schema and Qwen 3.6 integration (#31)

Co-authored-by: Sagnik <sagnik7896@gmail.com>
Reviewed-on: #31

backend/api/routes_runtime_llm.py

@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException, Request, status
+from pydantic import BaseModel, Field
+
+from backend.auth.dependencies import UserPrincipal, get_current_user
+from backend.services.runtime_llm_service import runtime_llm_service
+
+router = APIRouter()
+
+
+class ChatMessage(BaseModel):
+    role: str = Field(..., pattern="^(system|user|assistant)$")
+    content: str = Field(..., min_length=1)
+
+
+class RuntimeChatRequest(BaseModel):
+    provider: str | None = None
+    model: str | None = None
+    system_prompt: str | None = None
+    messages: list[ChatMessage]
+    temperature: float = Field(default=0.2, ge=0.0, le=2.0)
+    response_format: str | None = Field(default=None, pattern="^(json|text)$")
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class BatchItemRequest(BaseModel):
+    request_id: str
+    messages: list[ChatMessage]
+    system_prompt: str | None = None
+    temperature: float = Field(default=0.2, ge=0.0, le=2.0)
+    response_format: str | None = Field(default=None, pattern="^(json|text)$")
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class RuntimeBatchRequest(BaseModel):
+    provider: str | None = None
+    model: str | None = None
+    job_type: str = Field(..., min_length=1, max_length=128)
+    metadata: dict[str, Any] = Field(default_factory=dict)
+    items: list[BatchItemRequest] = Field(..., min_length=1, max_length=128)
+
+
+def _normalize_user(user: UserPrincipal) -> dict[str, str]:
+    return {
+        "user_id": user.user_id,
+        "role": user.role,
+    }
+
+
+@router.get("/providers", summary="List configured runtime LLM providers and models")
+async def list_runtime_providers(_: UserPrincipal = Depends(get_current_user)) -> dict:
+    return {"status": "ok", "data": await runtime_llm_service.list_providers()}
+
+
+@router.post("/chat", summary="Execute a single runtime LLM chat completion")
+async def runtime_chat(
+    payload: RuntimeChatRequest,
+    user: UserPrincipal = Depends(get_current_user),
+) -> dict:
+    response = await runtime_llm_service.chat(
+        provider_id=payload.provider,
+        model=payload.model,
+        system_prompt=payload.system_prompt,
+        messages=[message.model_dump() for message in payload.messages],
+        temperature=payload.temperature,
+        response_format=payload.response_format,
+        metadata={
+            **payload.metadata,
+            "requested_by": _normalize_user(user),
+        },
+    )
+    return {"status": "ok", "data": response}
+
+
+@router.post("/batch", status_code=status.HTTP_202_ACCEPTED, summary="Submit a persisted runtime LLM batch job")
+async def runtime_batch(
+    payload: RuntimeBatchRequest,
+    request: Request,
+    user: UserPrincipal = Depends(get_current_user),
+) -> dict:
+    pool = getattr(request.app.state, "db_pool", None)
+    result = await runtime_llm_service.submit_batch(
+        provider_id=payload.provider,
+        model=payload.model,
+        job_type=payload.job_type,
+        items=[item.model_dump() for item in payload.items],
+        metadata={
+            **payload.metadata,
+            "requested_by": _normalize_user(user),
+        },
+        pool=pool,
+        actor_id=user.user_id,
+    )
+    return {"status": "ok", "data": result}
+
+
+@router.get("/jobs/{job_id}", summary="Get runtime LLM batch job status")
+async def get_runtime_job(
+    job_id: str,
+    request: Request,
+    _: UserPrincipal = Depends(get_current_user),
+) -> dict:
+    pool = getattr(request.app.state, "db_pool", None)
+    job = await runtime_llm_service.get_job(job_id, pool=pool)
+    if not job:
+        raise HTTPException(status_code=404, detail=f"Runtime LLM job '{job_id}' not found.")
+
+    return {
+        "status": "ok",
+        "data": {
+            "job_id": job["job_id"],
+            "status": job["status"],
+            "provider": job["provider"],
+            "model": job["model"],
+            "job_type": job["job_type"],
+            "submitted_count": job["submitted_count"],
+            "completed_count": job["completed_count"],
+            "failed_count": job["failed_count"],
+            "created_at": job["created_at"],
+            "started_at": job["started_at"],
+            "completed_at": job["completed_at"],
+            "metadata": job.get("metadata") or {},
+        },
+    }
+
+
+@router.get("/jobs/{job_id}/results", summary="Get runtime LLM batch job item results")
+async def get_runtime_job_results(
+    job_id: str,
+    request: Request,
+    _: UserPrincipal = Depends(get_current_user),
+) -> dict:
+    pool = getattr(request.app.state, "db_pool", None)
+    results = await runtime_llm_service.list_job_results(job_id, pool=pool)
+    if results is None:
+        raise HTTPException(status_code=404, detail=f"Runtime LLM job '{job_id}' not found.")
+    return {"status": "ok", "data": results, "meta": {"count": len(results)}}