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from fastapi import APIRouter, HTTPException
from loguru import logger
from api.command_service import CommandService
from api.models import EmbedRequest, EmbedResponse
from open_notebook.domain.models import model_manager
from open_notebook.domain.notebook import Note, Source
router = APIRouter()
@router.post("/embed", response_model=EmbedResponse)
async def embed_content(embed_request: EmbedRequest):
"""Embed content for vector search."""
try:
# Check if embedding model is available
if not await model_manager.get_embedding_model():
raise HTTPException(
status_code=400,
detail="No embedding model configured. Please configure one in the Models section.",
)
item_id = embed_request.item_id
item_type = embed_request.item_type.lower()
# Validate item type
if item_type not in ["source", "note"]:
raise HTTPException(
status_code=400, detail="Item type must be either 'source' or 'note'"
)
# Branch based on processing mode
if embed_request.async_processing:
# ASYNC PATH: Submit command for background processing
logger.info(f"Using async processing for {item_type} {item_id}")
try:
# Import commands to ensure they're registered
import commands.embedding_commands # noqa: F401
# Submit command
command_id = await CommandService.submit_command_job(
"open_notebook", # app name
"embed_single_item", # command name
{"item_id": item_id, "item_type": item_type},
)
logger.info(f"Submitted async embedding command: {command_id}")
return EmbedResponse(
success=True,
message="Embedding queued for background processing",
item_id=item_id,
item_type=item_type,
command_id=command_id,
)
except Exception as e:
logger.error(f"Failed to submit async embedding command: {e}")
raise HTTPException(
status_code=500, detail=f"Failed to queue embedding: {str(e)}"
)
else:
# SYNC PATH: Submit job (returns immediately with command_id)
# NOTE: "sync" here means "submit and return command_id" - actual processing
# still happens asynchronously in the worker pool
logger.info(f"Using sync processing for {item_type} {item_id}")
command_id = None
# Get the item and embed it
if item_type == "source":
source_item = await Source.get(item_id)
if not source_item:
raise HTTPException(status_code=404, detail="Source not found")
# Submit vectorization job (returns command_id for tracking)
command_id = await source_item.vectorize()
message = "Source vectorization job submitted"
elif item_type == "note":
note_item = await Note.get(item_id)
if not note_item:
raise HTTPException(status_code=404, detail="Note not found")
await note_item.save() # Auto-embeds via ObjectModel.save()
message = "Note embedded successfully"
return EmbedResponse(
success=True, message=message, item_id=item_id, item_type=item_type, command_id=command_id
)
except HTTPException:
raise
except Exception as e:
logger.error(
f"Error embedding {embed_request.item_type} {embed_request.item_id}: {str(e)}"
)
raise HTTPException(
status_code=500, detail=f"Error embedding content: {str(e)}"
)
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