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"""Knowledge base service: HTTP-side orchestration.
The router stays thin and DTO-shaped; everything HTTP-side that needs
to coordinate persistence, the blob store, the indexing pipeline,
and the vector store goes through this service.
The split with :class:`~agentscope.rag.KnowledgeBase`
is deliberate. ``KnowledgeBase`` is a **library-mode** handle that only
depends on the vector store; embedded users instantiate one and drive
the parse → chunk → embed pipeline themselves. ``KnowledgeBaseService``
is **service-mode** orchestration: it owns the document records
(status / blob / lease) and is the single source of truth for "what
documents exist in this KB" when the app is running over HTTP. The
two views are intentionally not blended — mixing library-mode inserts
with service-mode listing would leave records out of sync, and the
project's stance is that a knowledge base is managed end-to-end in one
mode.
"""
import uuid
from typing import IO, TYPE_CHECKING
from fastapi import HTTPException, status
from ..rag.knowledge_base_manager import (
DimensionPolicyError,
KnowledgeBaseNotFoundError,
)
from ..storage import (
KnowledgeDocumentData,
KnowledgeDocumentRecord,
)
from ..._logging import logger
from .._bus_ops import enqueue_index_task
if TYPE_CHECKING:
from ..rag.blob_store import BlobStoreBase
from ..rag.knowledge_base_manager import KnowledgeBaseManagerBase
from ..message_bus import MessageBus
from ..storage import (
EmbeddingModelConfig,
KnowledgeBaseRecord,
StorageBase,
)
from ...rag import VectorSearchResult
class KnowledgeBaseService:
"""HTTP service for knowledge bases.
Owns the document lifecycle in service mode: register on upload,
enqueue an index task, query status during indexing, and clean up
record + blob + vector store on delete. All parsing / chunking /
embedding work happens inside the
:class:`~agentscope.app._service.IndexWorker`; the service only
hands off (via the message bus) and observes.
"""
def __init__(
self,
storage: "StorageBase",
knowledge_base_manager: "KnowledgeBaseManagerBase",
blob_store: "BlobStoreBase",
message_bus: "MessageBus",
) -> None:
"""Initialize the service.
Args:
storage (`StorageBase`):
The application storage backend; documents are
persisted here, not inside the vector store.
knowledge_base_manager (`KnowledgeBaseManagerBase`):
Resolves the :class:`KnowledgeBase` runtime used to clear
vector store records on document deletion.
blob_store (`BlobStoreBase`):
Owns the bytes from upload until the worker is done.
The service writes on upload and deletes on document
removal.
message_bus (`MessageBus`):
Application message bus. The service publishes one
index-task entry per uploaded document via
:func:`~agentscope.app._bus_ops.enqueue_index_task`;
a co-located or out-of-process
:class:`IndexTaskConsumer` drains and processes them.
"""
self._storage = storage
self._manager = knowledge_base_manager
self._blob_store = blob_store
self._bus = message_bus
# ------------------------------------------------------------------
# Knowledge base CRUD
# ------------------------------------------------------------------
async def create_knowledge_base(
self,
user_id: str,
name: str,
description: str,
embedding_model_config: "EmbeddingModelConfig",
) -> "KnowledgeBaseRecord":
"""Delegate creation to the manager, mapping policy errors.
Args:
user_id (`str`):
The owner user id.
name (`str`):
Display name.
description (`str`):
Free-form description.
embedding_model_config (`EmbeddingModelConfig`):
Embedding model configuration; pinned to the record.
Returns:
`KnowledgeBaseRecord`:
The newly persisted record.
Raises:
`HTTPException`:
``409`` when the requested embedding dimension
violates the manager's dimension policy.
"""
try:
return await self._manager.create_knowledge_base(
user_id=user_id,
name=name,
description=description,
embedding_model_config=embedding_model_config,
)
except DimensionPolicyError as exc:
raise HTTPException(
status_code=status.HTTP_409_CONFLICT,
detail=str(exc),
) from exc
async def list_knowledge_bases(
self,
user_id: str,
) -> "list[KnowledgeBaseRecord]":
"""List all knowledge base records owned by the given user.
Args:
user_id (`str`):
The owner user id.
Returns:
`list[KnowledgeBaseRecord]`:
All knowledge base records belonging to the user.
"""
return await self._manager.list_knowledge_bases(user_id)
async def update_knowledge_base(
self,
user_id: str,
knowledge_base_id: str,
name: str | None = None,
description: str | None = None,
) -> "KnowledgeBaseRecord":
"""Update mutable fields on a knowledge base, raising 404 if absent.
Only ``name`` and ``description`` are mutable. The embedding
model configuration is pinned at creation time.
"""
record = await self._manager.update_knowledge_base(
user_id=user_id,
knowledge_base_id=knowledge_base_id,
name=name,
description=description,
)
if record is None:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Knowledge base {knowledge_base_id!r} not found.",
)
return record
async def delete_knowledge_base(
self,
user_id: str,
knowledge_base_id: str,
) -> None:
"""Delete a knowledge base, raising 404 if absent.
Documents under the KB are cascade-deleted at the storage
layer; blob files referenced by those records are released
best-effort here so disk space is reclaimed even though the
manager + storage cascade would otherwise orphan them.
"""
documents = await self._storage.list_knowledge_documents(
user_id,
knowledge_base_id,
)
for document in documents:
await self._delete_blob_quietly(document.data.blob_uri)
deleted = await self._manager.delete_knowledge_base(
user_id,
knowledge_base_id,
)
if not deleted:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Knowledge base {knowledge_base_id!r} not found.",
)
# ------------------------------------------------------------------
# Document management
# ------------------------------------------------------------------
async def register_document(
self,
user_id: str,
knowledge_base_id: str,
filename: str,
stream: IO[bytes],
size: int,
content_type: str | None = None,
) -> KnowledgeDocumentRecord:
"""Persist an uploaded document and enqueue it for indexing.
Streams ``stream`` into the blob store (so the bytes never
live fully in memory), records a ``pending`` document, and
pushes an index-task entry onto the message bus. Returns
immediately — a worker (in-process or dedicated) takes over
from here and the client tracks progress via
:meth:`get_document_status`.
Args:
user_id (`str`):
The owner user id.
knowledge_base_id (`str`):
The target knowledge base id.
filename (`str`):
The original filename.
stream (`IO[bytes]`):
A synchronous binary stream — typically
``UploadFile.file`` from FastAPI.
size (`int`):
Byte length declared by the uploader. Persisted on
the record for the UI; not authoritative.
content_type (`str | None`, optional):
IANA media type; ``None`` lets the worker fall back
to a filename guess at processing time.
Returns:
`KnowledgeDocumentRecord`:
The persisted record (``status='pending'``) with the
final ``blob_uri`` filled in.
Raises:
`HTTPException`:
``404`` if the knowledge base does not exist.
"""
# Authorise before touching the blob store: raising after a
# write would leave the blob orphaned.
await self._authorise_kb(user_id, knowledge_base_id)
document_id = uuid.uuid4().hex
blob_uri = await self._blob_store.write_stream(
key=f"kb/{knowledge_base_id}/{document_id}",
stream=stream,
)
record = KnowledgeDocumentRecord(
id=document_id,
user_id=user_id,
knowledge_base_id=knowledge_base_id,
data=KnowledgeDocumentData(
filename=filename,
size=size,
content_type=content_type,
blob_uri=blob_uri,
),
)
try:
stored = await self._storage.upsert_knowledge_document(
user_id,
record,
)
except Exception:
# Storage write failed — drop the blob so the orphan
# sweeper doesn't later see a referenced-by-nobody file.
await self._delete_blob_quietly(blob_uri)
raise
await enqueue_index_task(
self._bus,
user_id=user_id,
knowledge_base_id=knowledge_base_id,
document_id=document_id,
)
return stored
async def list_documents(
self,
user_id: str,
knowledge_base_id: str,
) -> list[KnowledgeDocumentRecord]:
"""List every document registered against a knowledge base.
Service-mode source of truth: reads from storage, NOT the
vector store. Documents in ``pending`` / ``parsing`` /
``chunking`` / ``indexing`` / ``error`` show up here even
though they have no chunks in the vector store yet.
Args:
user_id (`str`):
The owner user id.
knowledge_base_id (`str`):
The target knowledge base id.
Returns:
`list[KnowledgeDocumentRecord]`:
Every document registered against the knowledge base,
in unspecified order.
Raises:
`HTTPException`:
``404`` if the knowledge base does not exist.
"""
await self._authorise_kb(user_id, knowledge_base_id)
return await self._storage.list_knowledge_documents(
user_id,
knowledge_base_id,
)
async def get_document_status(
self,
user_id: str,
knowledge_base_id: str,
document_ids: list[str],
) -> list[KnowledgeDocumentRecord]:
"""Batch-fetch documents for status polling.
The endpoint backing this method accepts a comma-separated list
of ids so the front-end can ask "what's the state of these N
in-flight uploads" in a single round-trip. Records that do
not exist or do not belong to the user are silently skipped —
the front-end may legitimately ask about a document that was
deleted between two polls.
Args:
user_id (`str`):
The owner user id.
knowledge_base_id (`str`):
The target knowledge base id.
document_ids (`list[str]`):
Document ids to look up.
Returns:
`list[KnowledgeDocumentRecord]`:
One record per matched id; missing ids omitted.
Raises:
`HTTPException`:
``404`` if the knowledge base does not exist.
"""
await self._authorise_kb(user_id, knowledge_base_id)
records: list[KnowledgeDocumentRecord] = []
for document_id in document_ids:
record = await self._storage.get_knowledge_document(
user_id,
knowledge_base_id,
document_id,
)
if record is not None:
records.append(record)
return records
async def delete_document(
self,
user_id: str,
knowledge_base_id: str,
document_id: str,
) -> None:
"""Remove a document end-to-end: vector store, record, blob.
Order is chosen so that a crash mid-way always leaves a
recoverable state:
1. Vector store delete (idempotent — re-deleting an already
empty document_id is harmless).
2. Storage record delete.
3. Blob delete (idempotent).
A failure at step 1 surfaces as an exception to the caller and
the record + blob are left untouched, so a retry sees the same
state. Failures at steps 2/3 leave a small amount of orphan
data but the user-visible deletion has already succeeded from
the vector store's point of view.
Args:
user_id (`str`):
The owner user id.
knowledge_base_id (`str`):
The target knowledge base id.
document_id (`str`):
The document to delete.
Raises:
`HTTPException`:
``404`` if the knowledge base does not exist.
"""
record = await self._storage.get_knowledge_document(
user_id,
knowledge_base_id,
document_id,
)
if record is None:
# 404 if the KB does not exist, otherwise treat the
# missing document as already-deleted (idempotent).
await self._authorise_kb(user_id, knowledge_base_id)
return
knowledge = await self._resolve_knowledge(user_id, knowledge_base_id)
await knowledge.delete_document(document_id)
await self._storage.delete_knowledge_document(
user_id,
knowledge_base_id,
document_id,
)
await self._delete_blob_quietly(record.data.blob_uri)
# ------------------------------------------------------------------
# Search
# ------------------------------------------------------------------
async def search(
self,
user_id: str,
knowledge_base_id: str,
query: str,
top_k: int = 5,
) -> "list[VectorSearchResult]":
"""Search a knowledge base by text query.
Args:
user_id (`str`):
The owner user id.
knowledge_base_id (`str`):
The knowledge base to search.
query (`str`):
The natural-language query.
top_k (`int`, defaults to ``5``):
Maximum number of results.
Returns:
`list[VectorSearchResult]`:
The top hits ordered by descending similarity score.
Raises:
`HTTPException`:
``404`` if the knowledge base does not exist.
"""
knowledge = await self._resolve_knowledge(user_id, knowledge_base_id)
return await knowledge.search(queries=[query], top_k=top_k)
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
async def _authorise_kb(
self,
user_id: str,
knowledge_base_id: str,
) -> "KnowledgeBaseRecord":
"""Look the KB record up so we can 404 cleanly.
The check is intentionally separate from :meth:`_resolve_knowledge`
because document-level endpoints (list / delete) need to refuse
unknown KBs without paying the embedding-model construction cost
that :meth:`_resolve_knowledge` triggers.
"""
record = await self._storage.get_knowledge_base(
user_id,
knowledge_base_id,
)
if record is None:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=f"Knowledge base {knowledge_base_id!r} not found.",
)
return record
async def _resolve_knowledge(
self,
user_id: str,
knowledge_base_id: str,
) -> "object":
"""Resolve a :class:`KnowledgeBase` and translate not-found to 404."""
try:
return await self._manager.get_knowledge(
user_id,
knowledge_base_id,
)
except KnowledgeBaseNotFoundError as exc:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail=str(exc),
) from exc
async def _delete_blob_quietly(self, blob_uri: str) -> None:
"""Best-effort blob delete — swallow backend errors.
Treated as cleanup: if the blob store is unavailable the
record/vector-store state is still consistent and a future
sweep can reclaim the disk space. Surface only via logs.
"""
try:
await self._blob_store.delete(blob_uri)
except Exception: # noqa: BLE001 — cleanup only
logger.exception(
"Failed to delete blob %s",
blob_uri,
)
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