chore: import upstream snapshot with attribution

2026-07-13 12:10:23 +08:00
commit fbab2c6005
567 changed files with 114434 additions and 0 deletions
@@ -0,0 +1,129 @@
+from fastapi import APIRouter, HTTPException
+from loguru import logger
+
+from api.command_service import CommandService
+from api.models import EmbedRequest, EmbedResponse
+from open_notebook.ai.models import model_manager
+from open_notebook.domain.notebook import Note, Source
+from open_notebook.exceptions import (
+    NotFoundError,
+    OpenNotebookError,
+)
+
+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 type-specific command
+                if item_type == "source":
+                    command_name = "embed_source"
+                    command_input = {"source_id": item_id}
+                else:  # note
+                    command_name = "embed_note"
+                    command_input = {"note_id": item_id}
+
+                command_id = await CommandService.submit_command_job(
+                    "open_notebook",
+                    command_name,
+                    command_input,
+                )
+
+                logger.info(f"Submitted async {command_name} 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:
+            # DOMAIN MODEL PATH: Submit job via domain model convenience methods
+            # These methods internally call submit_command() - still fire-and-forget
+            logger.info(f"Using domain model path for {item_type} {item_id}")
+
+            command_id = None
+
+            # Get the item and submit embedding job
+            if item_type == "source":
+                source_item = await Source.get(item_id)
+
+                # Submit embed_source job (returns command_id for tracking)
+                command_id = await source_item.vectorize()
+                message = "Source embedding job submitted"
+
+            elif item_type == "note":
+                note_item = await Note.get(item_id)
+
+                # Note.save() internally submits embed_note command and
+                # returns command_id. Unlike Source.vectorize(), save()'s
+                # embed submission is best-effort (a hiccup there shouldn't
+                # fail an otherwise-successful note save) - but this
+                # endpoint's whole point is submitting the embedding job,
+                # so a submission failure here (content present, no
+                # command_id) must still surface as a failure.
+                command_id = await note_item.save()
+                if not command_id and note_item.content and note_item.content.strip():
+                    raise HTTPException(
+                        status_code=500, detail="Failed to submit note embedding job"
+                    )
+                message = "Note embedding job submitted"
+
+            return EmbedResponse(
+                success=True,
+                message=message,
+                item_id=item_id,
+                item_type=item_type,
+                command_id=command_id,
+            )
+
+    except HTTPException:
+        raise
+    except NotFoundError:
+        raise HTTPException(
+            status_code=404, detail=f"{embed_request.item_type} not found"
+        )
+    except OpenNotebookError:
+        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)}"
+        )