wehub-resource-sync
130 lines
4.8 KiB
Python
130 lines
4.8 KiB
Python
from fastapi import APIRouter, HTTPException
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from loguru import logger
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from api.command_service import CommandService
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from api.models import EmbedRequest, EmbedResponse
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from open_notebook.ai.models import model_manager
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from open_notebook.domain.notebook import Note, Source
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from open_notebook.exceptions import (
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NotFoundError,
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OpenNotebookError,
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)
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router = APIRouter()
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@router.post("/embed", response_model=EmbedResponse)
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async def embed_content(embed_request: EmbedRequest):
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"""Embed content for vector search."""
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try:
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# Check if embedding model is available
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if not await model_manager.get_embedding_model():
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raise HTTPException(
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status_code=400,
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detail="No embedding model configured. Please configure one in the Models section.",
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)
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item_id = embed_request.item_id
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item_type = embed_request.item_type.lower()
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# Validate item type
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if item_type not in ["source", "note"]:
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raise HTTPException(
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status_code=400, detail="Item type must be either 'source' or 'note'"
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)
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# Branch based on processing mode
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if embed_request.async_processing:
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# ASYNC PATH: Submit command for background processing
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logger.info(f"Using async processing for {item_type} {item_id}")
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try:
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# Import commands to ensure they're registered
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import commands.embedding_commands # noqa: F401
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# Submit type-specific command
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if item_type == "source":
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command_name = "embed_source"
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command_input = {"source_id": item_id}
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else: # note
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command_name = "embed_note"
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command_input = {"note_id": item_id}
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command_id = await CommandService.submit_command_job(
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"open_notebook",
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command_name,
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command_input,
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)
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logger.info(f"Submitted async {command_name} command: {command_id}")
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return EmbedResponse(
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success=True,
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message="Embedding queued for background processing",
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item_id=item_id,
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item_type=item_type,
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command_id=command_id,
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)
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except Exception as e:
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logger.error(f"Failed to submit async embedding command: {e}")
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raise HTTPException(
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status_code=500, detail=f"Failed to queue embedding: {str(e)}"
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)
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else:
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# DOMAIN MODEL PATH: Submit job via domain model convenience methods
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# These methods internally call submit_command() - still fire-and-forget
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logger.info(f"Using domain model path for {item_type} {item_id}")
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command_id = None
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# Get the item and submit embedding job
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if item_type == "source":
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source_item = await Source.get(item_id)
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# Submit embed_source job (returns command_id for tracking)
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command_id = await source_item.vectorize()
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message = "Source embedding job submitted"
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elif item_type == "note":
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note_item = await Note.get(item_id)
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# Note.save() internally submits embed_note command and
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# returns command_id. Unlike Source.vectorize(), save()'s
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# embed submission is best-effort (a hiccup there shouldn't
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# fail an otherwise-successful note save) - but this
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# endpoint's whole point is submitting the embedding job,
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# so a submission failure here (content present, no
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# command_id) must still surface as a failure.
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command_id = await note_item.save()
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if not command_id and note_item.content and note_item.content.strip():
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raise HTTPException(
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status_code=500, detail="Failed to submit note embedding job"
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)
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message = "Note embedding job submitted"
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return EmbedResponse(
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success=True,
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message=message,
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item_id=item_id,
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item_type=item_type,
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command_id=command_id,
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)
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except HTTPException:
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raise
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except NotFoundError:
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raise HTTPException(
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status_code=404, detail=f"{embed_request.item_type} not found"
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)
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except OpenNotebookError:
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raise
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except Exception as e:
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logger.error(
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f"Error embedding {embed_request.item_type} {embed_request.item_id}: {str(e)}"
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)
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raise HTTPException(
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status_code=500, detail=f"Error embedding content: {str(e)}"
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)
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