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feat: external knowledge bases (#1783)

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* Initial plan

* Add backend support for external knowledge bases

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add frontend support for external knowledge bases with tabs UI

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Add i18n translations for all languages (Traditional Chinese and Japanese)

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* Update knowledge base tab list styling to match plugins page

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>

* perf: margin-top for kb page

* refactor: switch RetrievalResultEntry to langbot_plugin pkg ones

* feat: knowledge retriever listing and creating

* stash

* refactor: unify sync mechanism for polymorphic components

* feat: use unified retireval result struct in retrieval test page

* chore: remove unused methods

* feat: retriever icon displaying

* feat: localagent retrieval with external kbs

* chore: bump version of langbot-plugin to 0.2.0b1

* fix: i18n

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>
This commit is contained in:
Copilot
2025-11-27 23:19:43 +08:00
committed by GitHub
parent 3c04eeaff9
commit a8481e43f0
33 changed files with 1924 additions and 161 deletions
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10
src/langbot/pkg/rag/knowledge/services/retriever.py
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@@ -3,7 +3,8 @@ from __future__ import annotations
from . import base_service
from ....core import app
from ....provider.modelmgr.requester import RuntimeEmbeddingModel
from ....entity.rag import retriever as retriever_entities
from langbot_plugin.api.entities.builtin.rag import context as rag_context
from langbot_plugin.api.entities.builtin.provider.message import ContentElement
class Retriever(base_service.BaseService):
@@ -13,7 +14,7 @@ class Retriever(base_service.BaseService):
async def retrieve(
self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
) -> list[retriever_entities.RetrieveResultEntry]:
) -> list[rag_context.RetrievalResultEntry]:
self.ap.logger.info(
f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
)
@@ -35,11 +36,12 @@ class Retriever(base_service.BaseService):
self.ap.logger.info('No relevant chunks found in vector database.')
return []
result: list[retriever_entities.RetrieveResultEntry] = []
result: list[rag_context.RetrievalResultEntry] = []
for i, id in enumerate(matched_vector_ids):
entry = retriever_entities.RetrieveResultEntry(
entry = rag_context.RetrievalResultEntry(
id=id,
content=[ContentElement.from_text(vector_metadatas[i].get('text', ''))],
metadata=vector_metadatas[i],
distance=distances[i],
)