Feat/monitor (#1928)

* feat: add monitor

* feat: fix tab

* feat: work

* feat: not reliable monitor

* feat: enhance monitoring page layout with integrated filters and refresh button

* feat: add support for runner recording

* feat: add jump button & alignment

* feat: new

* fix: not show query variables in local agent

* fix: pnpm lint and python ruff check

* fix: ruff fromat

* chore: remove unnecessary migration

* style: optimize monitoring page layout and fix sticky filter issues

- Enhanced metric cards with gradient backgrounds and hover effects
- Increased traffic chart height from 200px to 300px
- Adjusted grid layout and spacing for better visual appeal
- Fixed sticky filter area to properly cover parent padding without transparent gaps
- Used negative margins and positioning to eliminate scrolling artifacts
- Matched padding/margins with other pages (pipelines, bots) for consistency
- Removed duplicate title/subtitle from page content
- Added cursor-pointer styling to tab triggers
- Removed border between tab list and tab content

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

* fix: apply prettier formatting to monitoring components

- Fixed indentation and spacing in MetricCard.tsx
- Fixed formatting in TrafficChart.tsx
- Applied prettier formatting to page.tsx

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

* feat: update HomeSidebar to trigger action on child selection and localize monitoring titles

* refactor: streamline LLM and embedding invocation methods

* feat: add embedding model monitor

* fix: database version

* chore: simplify pnpm-lock.yaml formatting

---------

Co-authored-by: Junyan Qin <rockchinq@gmail.com>
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>

src/langbot/pkg/rag/knowledge/services/embedder.py

@@ -38,10 +38,12 @@ class Embedder(BaseService):
 
         for i in range(0, len(chunks), MAX_BATCH_SIZE):
             batch = chunks[i : i + MAX_BATCH_SIZE]
-            batch_embeddings = await embedding_model.provider.requester.invoke_embedding(
+            batch_embeddings = await embedding_model.provider.invoke_embedding(
                 model=embedding_model,
                 input_text=batch,
                 extra_args={},  # TODO: add extra args
+                knowledge_base_id=kb_id,
+                call_type='embedding',
             )
             embeddings_list.extend(batch_embeddings)
 

src/langbot/pkg/rag/knowledge/services/retriever.py

@@ -19,10 +19,13 @@ class Retriever(base_service.BaseService):
             f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
         )
 
-        query_embedding: list[float] = await embedding_model.provider.requester.invoke_embedding(
+        query_embedding: list[float] = await embedding_model.provider.invoke_embedding(
             model=embedding_model,
             input_text=[query],
             extra_args={},  # TODO: add extra args
+            knowledge_base_id=kb_id,
+            query_text=query,
+            call_type='retrieve',
         )
 
         vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)