b7e656a319
New front-end Launch Chat API Manage Sources Enable re-embedding of all contents Sources can be added without a notebook now Improved settings Enable model selector on all chats Background processing for better experience Dark mode Improved Notes Improved Docs: - Remove all Streamlit references from documentation - Update deployment guides with React frontend setup - Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD) - Update docker image tag from :latest to :v1-latest - Change navigation references (Settings → Models to just Models) - Update development setup to include frontend npm commands - Add MIGRATION.md guide for users upgrading from Streamlit - Update quick-start guide with correct environment variables - Add port 5055 documentation for API access - Update project structure to reflect frontend/ directory - Remove outdated source-chat documentation files
98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
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: Execute synchronously (existing behavior)
|
|
logger.info(f"Using sync processing for {item_type} {item_id}")
|
|
|
|
# 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")
|
|
|
|
# Perform embedding (vectorize is now idempotent - safe to call multiple times)
|
|
await source_item.vectorize()
|
|
message = "Source embedded successfully"
|
|
|
|
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=None
|
|
)
|
|
|
|
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)}"
|
|
)
|