feat(milvus): milvus related updates (#1908)

- 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.

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/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