chore: update project version to 4.7.1

- Add Milvus db_name configuration and client parameter support.
- change kb_data uuid for Milvus. 3. add MAX_BATCH_SIZE for openai.
- support more vector_size.

docker/docker-compose.yaml

@@ -14,7 +14,7 @@ services:
     restart: on-failure
     environment:
       - TZ=Asia/Shanghai
-    command: ["uv", "run", "-m", "langbot_plugin.cli.__init__", "rt"]
+    command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "rt"]
     networks:
       - langbot_network
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.7.0"
+version = "4.7.1"
 description = "Production-grade platform for building IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building IM bots"""
 
-__version__ = '4.7.0'
+__version__ = '4.7.1'

src/langbot/pkg/core/app.py

@@ -36,6 +36,7 @@ from . import taskmgr
 from . import entities as core_entities
 from ..rag.knowledge import kbmgr as rag_mgr
 from ..vector import mgr as vectordb_mgr
+from ..telemetry import telemetry as telemetry_module
 
 
 class Application:
@@ -140,6 +141,8 @@ class Application:
 
     webhook_service: webhook_service.WebhookService = None
 
+    telemetry: telemetry_module.TelemetryManager = None
+
     def __init__(self):
         pass
 

src/langbot/pkg/core/stages/build_app.py

@@ -31,6 +31,8 @@ from ...storage import mgr as storagemgr
 from ...utils import logcache
 from ...vector import mgr as vectordb_mgr
 from .. import taskmgr
+from ...telemetry import telemetry as telemetry_module
+
 
 
 @stage.stage_class('BuildAppStage')
@@ -102,6 +104,11 @@ class BuildAppStage(stage.BootingStage):
         ap.persistence_mgr = persistence_mgr_inst
         await persistence_mgr_inst.initialize()
 
+        # Telemetry manager: attach to app so other components can call via self.ap.telemetry
+        telemetry_inst = telemetry_module.TelemetryManager(ap)
+        await telemetry_inst.initialize()
+        ap.telemetry = telemetry_inst
+
         cmd_mgr_inst = cmdmgr.CommandManager(ap)
         await cmd_mgr_inst.initialize()
         ap.cmd_mgr = cmd_mgr_inst

src/langbot/pkg/pipeline/process/handlers/chat.py

@@ -3,6 +3,8 @@ from __future__ import annotations
 import uuid
 import typing
 import traceback
+import time
+from datetime import datetime
 
 
 from .. import handler
@@ -10,7 +12,7 @@ from ... import entities
 from ....provider import runner as runner_module
 
 import langbot_plugin.api.entities.events as events
-from ....utils import importutil
+from ....utils import importutil, constants
 from ....provider import runners
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -84,6 +86,9 @@ class ChatMessageHandler(handler.MessageHandler):
                         break
                 else:
                     raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
+                # Mark start time for telemetry
+                start_ts = time.time()
+
                 if is_stream:
                     resp_message_id = uuid.uuid4()
                     chunk_count = 0  # Track streaming chunks to reduce excessive logging
@@ -140,7 +145,8 @@ class ChatMessageHandler(handler.MessageHandler):
 
                 query.session.using_conversation.messages.extend(query.resp_messages)
             except Exception as e:
-                self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {type(e).__name__} {str(e)}')
+                error_info = f'{type(e).__name__} {str(e)}'
+                self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
                 traceback.print_exc()
 
                 hide_exception_info = query.pipeline_config['output']['misc']['hide-exception']
@@ -153,5 +159,47 @@ class ChatMessageHandler(handler.MessageHandler):
                     debug_notice=traceback.format_exc(),
                 )
             finally:
-                # TODO statistics
-                pass
+                # Telemetry reporting: collect minimal per-query execution info and send asynchronously
+                try:
+                    end_ts = time.time()
+                    duration_ms = None
+                    if 'start_ts' in locals():
+                        duration_ms = int((end_ts - start_ts) * 1000)
+
+                    adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
+                    runner_name = (
+                        query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
+                        if query.pipeline_config
+                        else None
+                    )
+
+                    # Model name if using localagent
+                    model_name = None
+                    try:
+                        if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
+                            m = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
+                            if m and getattr(m, 'model_entity', None):
+                                model_name = getattr(m.model_entity, 'name', None)
+                    except Exception:
+                        model_name = None
+
+                    pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
+
+                    payload = {
+                        'query_id': query.query_id,
+                        'adapter': adapter_name,
+                        'runner': runner_name,
+                        'duration_ms': duration_ms,
+                        'model_name': model_name,
+                        'version': constants.semantic_version,
+                        'instance_id': constants.instance_id,
+                        'pipeline_plugins': pipeline_plugins,
+                        'error': locals().get('error_info', None),
+                        'timestamp': datetime.utcnow().isoformat(),
+                    }
+
+                    # Send telemetry asynchronously and do not block pipeline via app's telemetry manager
+                    await self.ap.telemetry.start_send_task(payload)
+                except Exception as ex:
+                    # Ensure telemetry issues do not affect normal flow
+                    self.ap.logger.warning(f'Failed to send telemetry: {ex}')

src/langbot/pkg/provider/modelmgr/modelmgr.py

@@ -9,6 +9,7 @@ from ...discover import engine
 from . import token
 from ...entity.persistence import model as persistence_model
 from ...entity.errors import provider as provider_errors
+from async_lru import alru_cache
 
 
 class ModelManager:
@@ -349,6 +350,7 @@ class ModelManager:
 
         await self.load_embedding_model_with_provider(model_entity, provider_entity)
 
+    @alru_cache(ttl=60 * 5)
     async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
         """Get LLM model by uuid"""
         for model in self.llm_models:
@@ -356,6 +358,7 @@ class ModelManager:
                 return model
         raise ValueError(f'LLM model {uuid} not found')
 
+    @alru_cache(ttl=60 * 5)
     async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
         """Get embedding model by uuid"""
         for model in self.embedding_models:

src/langbot/pkg/provider/tools/loaders/mcp.py

@@ -7,7 +7,7 @@ import traceback
 from langbot_plugin.api.entities.events import pipeline_query
 import sqlalchemy
 import asyncio
-
+import uuid as uuid_module
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 from mcp.client.sse import sse_client
@@ -287,6 +287,14 @@ class MCPLoader(loader.ToolLoader):
                 - enable: 是否启用
                 - extra_args: 额外的配置参数 (可选)
         """
+        uuid_ = server_config.get('uuid')
+        if not uuid_:
+            self.ap.logger.warning(
+                'Server UUID is None for MCP server, maybe testing in the config page.'
+            )
+            uuid_ = str(uuid_module.uuid4())
+            server_config['uuid'] = uuid_
+
 
         name = server_config['name']
         uuid = server_config['uuid']

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

@@ -32,12 +32,18 @@ class Embedder(BaseService):
 
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.Chunk).values(chunk_dicts))
 
-        # get embeddings
-        embeddings_list: list[list[float]] = await embedding_model.provider.requester.invoke_embedding(
-            model=embedding_model,
-            input_text=chunks,
-            extra_args={},  # TODO: add extra args
-        )
+        # get embeddings (batch size limit: 64 for OpenAI)
+        MAX_BATCH_SIZE = 64
+        embeddings_list: list[list[float]] = []
+        
+        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(
+                model=embedding_model,
+                input_text=batch,
+                extra_args={},  # TODO: add extra args
+            )
+            embeddings_list.extend(batch_embeddings)
 
         # save embeddings to vdb
         await self.ap.vector_db_mgr.vector_db.add_embeddings(kb_id, chunk_ids, embeddings_list, chunk_dicts)

src/langbot/pkg/telemetry/telemetry.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+import asyncio
+import httpx
+from ..core import app as core_app
+
+
+class TelemetryManager:
+    """TelemetryManager handles sending telemetry for a given application instance.
+
+    Usage:
+        telemetry = TelemetryManager(ap)
+        await telemetry.send({ ... })
+    """
+
+    send_tasks: list[asyncio.Task] = []
+
+    def __init__(self, ap: core_app.Application):
+        self.ap = ap
+
+        self.telemetry_config = {}
+
+    async def initialize(self):
+        self.telemetry_config = self.ap.instance_config.data.get('space', {})
+
+    async def start_send_task(self, payload: dict):
+        task = asyncio.create_task(self.send(payload))
+        self.send_tasks.append(task)
+
+    async def send(self, payload: dict):
+        """Send telemetry payload to configured telemetry server (non-blocking).
+
+        Expects ap.instance_config.data.telemetry to have:
+          - enabled: bool
+          - server: str (base URL, e.g. https://space.example.com)
+          - timeout_seconds: optional int, overall request timeout (default 10)
+
+        Posts to {server.rstrip('/')}/api/v1/telemetry as JSON. Failures are logged but do not raise.
+        """
+
+        try:
+            cfg = self.telemetry_config
+            if not cfg:
+                return
+            if cfg.get('disable_telemetry', False):
+                return
+            server = cfg.get('url', '')
+            if not server:
+                return
+
+            # Normalize URL
+            url = server.rstrip('/') + '/api/v1/telemetry'
+
+            try:
+                # Sanitize payload so string fields are strings and not nulls
+                sanitized = dict(payload)
+                if 'query_id' in sanitized:
+                    try:
+                        sanitized['query_id'] = '' if sanitized['query_id'] is None else str(sanitized['query_id'])
+                    except Exception:
+                        sanitized['query_id'] = str(sanitized.get('query_id', ''))
+
+                for sfield in ('adapter', 'runner', 'model_name', 'version', 'error', 'timestamp'):
+                    v = sanitized.get(sfield)
+                    sanitized[sfield] = '' if v is None else str(v)
+
+                if 'duration_ms' in sanitized:
+                    try:
+                        sanitized['duration_ms'] = (
+                            int(sanitized['duration_ms']) if sanitized['duration_ms'] is not None else 0
+                        )
+                    except Exception:
+                        sanitized['duration_ms'] = 0
+
+                async with httpx.AsyncClient(timeout=httpx.Timeout(10)) as client:
+                    try:
+                        # Use asyncio.wait_for to ensure we always bound the total time
+                        resp = await asyncio.wait_for(client.post(url, json=sanitized), timeout=10 + 1)
+
+                        if resp.status_code >= 400:
+                            self.ap.logger.warning(
+                                f'Telemetry post to {url} returned status {resp.status_code} - {resp.text}'
+                            )
+                        else:
+                            # Detect application-level errors inside HTTP 200 responses
+                            app_err = False
+                            try:
+                                j = resp.json()
+                                if isinstance(j, dict) and j.get('code') is not None and int(j.get('code')) >= 400:
+                                    app_err = True
+                                    self.ap.logger.warning(
+                                        f'Telemetry post to {url} returned application error code {j.get("code")} - {j.get("msg")}'
+                                    )
+                            except Exception:
+                                pass
+
+                            if app_err:
+                                self.ap.logger.warning(
+                                    f'Telemetry post to {url} returned app-level error - response: {resp.text[:200]}'
+                                )
+                            else:
+                                self.ap.logger.debug(
+                                    f'Telemetry posted to {url}, status {resp.status_code} - response: {resp.text[:200]}'
+                                )
+                    except asyncio.TimeoutError:
+                        self.ap.logger.warning(f'Telemetry post to {url} timed out')
+                    except Exception as e:
+                        self.ap.logger.warning(f'Failed to post telemetry to {url}: {e}', exc_info=True)
+            except Exception as e:
+                try:
+                    self.ap.logger.warning(
+                        f'Failed to create HTTP client for telemetry or sanitize payload: {e}', exc_info=True
+                    )
+                except Exception:
+                    pass
+        except Exception as e:
+            # Never raise from telemetry; surface as warning for visibility
+            try:
+                self.ap.logger.warning(f'Unexpected telemetry error: {e}', exc_info=True)
+            except Exception:
+                pass

src/langbot/pkg/vector/mgr.py

@@ -37,7 +37,8 @@ class VectorDBManager:
                 milvus_config = kb_config.get('milvus', {})
                 uri = milvus_config.get('uri', './data/milvus.db')
                 token = milvus_config.get('token')
-                self.vector_db = MilvusVectorDatabase(self.ap, uri=uri, token=token)
+                db_name = milvus_config.get('db_name', 'default')
+                self.vector_db = MilvusVectorDatabase(self.ap, uri=uri, token=token, db_name=db_name)
                 self.ap.logger.info('Initialized Milvus vector database backend.')
 
             elif vdb_type == 'pgvector':

src/langbot/pkg/vector/vdbs/milvus.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 import asyncio
 from typing import Any, Dict
-from pymilvus import MilvusClient, DataType
+from pymilvus import MilvusClient, DataType, CollectionSchema, FieldSchema
+from pymilvus.milvus_client.index import IndexParams
 from langbot.pkg.vector.vdb import VectorDatabase
 from langbot.pkg.core import app
 
@@ -9,7 +10,7 @@ from langbot.pkg.core import app
 class MilvusVectorDatabase(VectorDatabase):
     """Milvus vector database implementation"""
 
-    def __init__(self, ap: app.Application, uri: str = "milvus.db", token: str = None):
+    def __init__(self, ap: app.Application, uri: str = "milvus.db", token: str = None, db_name: str = None):
         """Initialize Milvus vector database
 
         Args:
@@ -21,30 +22,76 @@ class MilvusVectorDatabase(VectorDatabase):
         self.ap = ap
         self.uri = uri
         self.token = token
+        self.db_name = db_name
         self.client = None
-        self._collections = {}
+        self._collections: set[str] = set()
         self._initialize_client()
 
     def _initialize_client(self):
         """Initialize Milvus client connection"""
         try:
             if self.token:
-                self.client = MilvusClient(uri=self.uri, token=self.token)
+                self.client = MilvusClient(uri=self.uri, token=self.token, db_name=self.db_name)
             else:
-                self.client = MilvusClient(uri=self.uri)
+                self.client = MilvusClient(uri=self.uri, db_name=self.db_name)
             self.ap.logger.info(f"Connected to Milvus at {self.uri}")
         except Exception as e:
             self.ap.logger.error(f"Failed to connect to Milvus: {e}")
             raise
 
-    async def get_or_create_collection(self, collection: str):
-        """Get or create a Milvus collection
+    @staticmethod
+    def _normalize_collection_name(collection: str) -> str:
+        """Normalize collection name to comply with Milvus naming requirements.
+        
+        Milvus requirements:
+        - First character must be an underscore or letter
+        - Can only contain numbers, letters and underscores
+        
+        Args:
+            collection: Original collection name (e.g., UUID with hyphens)
+            
+        Returns:
+            Normalized collection name that complies with Milvus requirements
+        """
+        # Replace hyphens with underscores
+        normalized = collection.replace('-', '_')
+        
+        # If first character is not a letter or underscore, prepend 'kb_'
+        if normalized and not (normalized[0].isalpha() or normalized[0] == '_'):
+            normalized = 'kb_' + normalized
+            
+        return normalized
+
+    async def _ensure_vector_index(self, collection: str) -> None:
+        """Ensure the vector field has an index.
+
+        Args:
+            collection: Normalized collection name
+        """
+        index_params = IndexParams()
+        index_params.add_index(
+            field_name="vector",
+            index_type="AUTOINDEX",
+            metric_type="COSINE",
+        )
+        await asyncio.to_thread(
+            self.client.create_index,
+            collection_name=collection,
+            index_params=index_params
+        )
+
+    async def _get_or_create_collection_internal(self, collection: str, vector_size: int = None):
+        """Internal method to get or create a Milvus collection with proper configuration.
 
         Args:
             collection: Collection name (corresponds to knowledge base UUID)
+            vector_size: Dimension of the vectors (if None, defaults to 1536)
         """
+        # Normalize collection name for Milvus compatibility
+        collection = self._normalize_collection_name(collection)
+        
         if collection in self._collections:
-            return self._collections[collection]
+            return collection
 
         # Check if collection exists
         has_collection = await asyncio.to_thread(
@@ -52,12 +99,13 @@ class MilvusVectorDatabase(VectorDatabase):
         )
 
         if not has_collection:
-            # Create collection with custom schema to support string IDs
-            from pymilvus import CollectionSchema, FieldSchema, DataType
+            # Default dimension if not specified (for backward compatibility)
+            if vector_size is None:
+                vector_size = 1536
 
             fields = [
                 FieldSchema(name="id", dtype=DataType.VARCHAR, is_primary=True, max_length=255),
-                FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=1536),
+                FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=vector_size),
                 FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
                 FieldSchema(name="file_id", dtype=DataType.VARCHAR, max_length=255),
                 FieldSchema(name="chunk_uuid", dtype=DataType.VARCHAR, max_length=255),
@@ -72,26 +120,42 @@ class MilvusVectorDatabase(VectorDatabase):
                 metric_type="COSINE",
             )
 
-            # Create index for vector field (required for loading/searching)
-            index_params = {
-                "metric_type": "COSINE",
-                "index_type": "AUTOINDEX",
-                "params": {}
-            }
-            await asyncio.to_thread(
-                self.client.create_index,
-                collection_name=collection,
-                field_name="vector",
-                index_params=index_params
-            )
-
-            self.ap.logger.info(f"Created Milvus collection '{collection}' with index")
+            await self._ensure_vector_index(collection)
+            self.ap.logger.info(f"Created Milvus collection '{collection}' with dimension={vector_size}, index=AUTOINDEX")
         else:
+            # Ensure index exists for existing collection
+            await self._ensure_index_if_missing(collection)
             self.ap.logger.info(f"Milvus collection '{collection}' already exists")
 
-        self._collections[collection] = collection
+        self._collections.add(collection)
         return collection
 
+    async def _ensure_index_if_missing(self, collection: str) -> None:
+        """Check if index exists for collection and create if missing.
+
+        Args:
+            collection: Normalized collection name
+        """
+        try:
+            indexes = await asyncio.to_thread(
+                self.client.list_indexes,
+                collection_name=collection
+            )
+            if "vector" not in indexes:
+                await self._ensure_vector_index(collection)
+                self.ap.logger.info(f"Created index for existing Milvus collection '{collection}'")
+        except Exception as e:
+            self.ap.logger.warning(f"Could not verify/create index for collection '{collection}': {e}")
+
+    async def get_or_create_collection(self, collection: str):
+        """Get or create a Milvus collection (without vector size - will use default).
+
+        Args:
+            collection: Collection name (corresponds to knowledge base UUID)
+        """
+        collection = self._normalize_collection_name(collection)
+        return await self._get_or_create_collection_internal(collection)
+
     async def add_embeddings(
         self,
         collection: str,
@@ -107,7 +171,14 @@ class MilvusVectorDatabase(VectorDatabase):
             embeddings_list: List of embedding vectors
             metadatas: List of metadata dictionaries for each vector
         """
-        await self.get_or_create_collection(collection)
+        collection = self._normalize_collection_name(collection)
+        
+        if not embeddings_list:
+            return
+
+        # Ensure collection exists with correct dimension
+        vector_size = len(embeddings_list[0])
+        await self._get_or_create_collection_internal(collection, vector_size)
 
         # Prepare data in Milvus format
         data = []
@@ -156,6 +227,7 @@ class MilvusVectorDatabase(VectorDatabase):
         Returns:
             Dictionary with search results in Chroma-compatible format
         """
+        collection = self._normalize_collection_name(collection)
         await self.get_or_create_collection(collection)
 
         # Perform search
@@ -214,6 +286,7 @@ class MilvusVectorDatabase(VectorDatabase):
             collection: Collection name
             file_id: File ID to filter deletion
         """
+        collection = self._normalize_collection_name(collection)
         await self.get_or_create_collection(collection)
 
         # Delete entities matching the file_id
@@ -232,8 +305,9 @@ class MilvusVectorDatabase(VectorDatabase):
         Args:
             collection: Collection name to delete
         """
-        if collection in self._collections:
-            del self._collections[collection]
+        collection = self._normalize_collection_name(collection)
+        
+        self._collections.discard(collection)
 
         # Check if collection exists before attempting deletion
         has_collection = await asyncio.to_thread(

src/langbot/templates/config.yaml

@@ -51,6 +51,7 @@ vdb:
     milvus:
         uri: 'http://127.0.0.1:19530'
         token: ''
+        db_name: ''
     pgvector:
         host: '127.0.0.1'
         port: 5433
@@ -78,3 +79,4 @@ space:
     # OAuth authorization page URL (user will be redirected here)
     oauth_authorize_url: 'https://space.langbot.app/auth/authorize'
     disable_models_service: false
+    disable_telemetry: false

web/src/app/home/components/models-dialog/ModelsDialog.tsx

@@ -206,8 +206,23 @@ export default function ModelsDialog({
     }
   }
 
-  function handleSpaceLogin() {
-    window.location.href = '/auth/space';
+  async function handleSpaceLogin() {
+    try {
+      const token = localStorage.getItem('token');
+      if (!token) {
+        toast.error(t('common.error'));
+        return;
+      }
+      const currentOrigin = window.location.origin;
+      const redirectUri = `${currentOrigin}/auth/space/callback?mode=bind`;
+      const response = await httpClient.getSpaceAuthorizeUrl(
+        redirectUri,
+        token,
+      );
+      window.location.href = response.authorize_url;
+    } catch {
+      toast.error(t('common.spaceLoginFailed'));
+    }
   }
 
   async function handleAddModel(