diff --git a/src/langbot/pkg/api/http/controller/main.py b/src/langbot/pkg/api/http/controller/main.py
index 1541a25e..b2eaa9b9 100644
--- a/src/langbot/pkg/api/http/controller/main.py
+++ b/src/langbot/pkg/api/http/controller/main.py
@@ -92,7 +92,11 @@ class HTTPController:
 
         @self.quart_app.route('/')
         async def index():
-            return await quart.send_from_directory(frontend_path, 'index.html', mimetype='text/html')
+            response = await quart.send_from_directory(frontend_path, 'index.html', mimetype='text/html')
+            response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
+            response.headers['Pragma'] = 'no-cache'
+            response.headers['Expires'] = '0'
+            return response
 
         @self.quart_app.route('/<path:path>')
         async def static_file(path: str):
diff --git a/src/langbot/pkg/vector/mgr.py b/src/langbot/pkg/vector/mgr.py
index 551eeebb..817b2d7a 100644
--- a/src/langbot/pkg/vector/mgr.py
+++ b/src/langbot/pkg/vector/mgr.py
@@ -4,6 +4,8 @@ from ..core import app
 from .vdb import VectorDatabase
 from .vdbs.chroma import ChromaVectorDatabase
 from .vdbs.qdrant import QdrantVectorDatabase
+from .vdbs.milvus import MilvusVectorDatabase
+from .vdbs.pgvector_db import PgVectorDatabase
 
 
 class VectorDBManager:
@@ -16,12 +18,47 @@ class VectorDBManager:
     async def initialize(self):
         kb_config = self.ap.instance_config.data.get('vdb')
         if kb_config:
-            if kb_config.get('use') == 'chroma':
+            vdb_type = kb_config.get('use')
+
+            if vdb_type == 'chroma':
                 self.vector_db = ChromaVectorDatabase(self.ap)
                 self.ap.logger.info('Initialized Chroma vector database backend.')
-            elif kb_config.get('use') == 'qdrant':
+
+            elif vdb_type == 'qdrant':
                 self.vector_db = QdrantVectorDatabase(self.ap)
                 self.ap.logger.info('Initialized Qdrant vector database backend.')
+
+            elif vdb_type == 'milvus':
+                # Get Milvus configuration
+                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)
+                self.ap.logger.info('Initialized Milvus vector database backend.')
+
+            elif vdb_type == 'pgvector':
+                # Get pgvector configuration
+                pgvector_config = kb_config.get('pgvector', {})
+                connection_string = pgvector_config.get('connection_string')
+                if connection_string:
+                    self.vector_db = PgVectorDatabase(self.ap, connection_string=connection_string)
+                else:
+                    # Use individual parameters
+                    host = pgvector_config.get('host', 'localhost')
+                    port = pgvector_config.get('port', 5432)
+                    database = pgvector_config.get('database', 'langbot')
+                    user = pgvector_config.get('user', 'postgres')
+                    password = pgvector_config.get('password', 'postgres')
+                    self.vector_db = PgVectorDatabase(
+                        self.ap,
+                        host=host,
+                        port=port,
+                        database=database,
+                        user=user,
+                        password=password
+                    )
+                self.ap.logger.info('Initialized pgvector database backend.')
+
             else:
                 self.vector_db = ChromaVectorDatabase(self.ap)
                 self.ap.logger.warning('No valid vector database backend configured, defaulting to Chroma.')
diff --git a/src/langbot/pkg/vector/vdbs/milvus.py b/src/langbot/pkg/vector/vdbs/milvus.py
new file mode 100644
index 00000000..d9f822cd
--- /dev/null
+++ b/src/langbot/pkg/vector/vdbs/milvus.py
@@ -0,0 +1,249 @@
+from __future__ import annotations
+import asyncio
+from typing import Any, Dict
+from pymilvus import MilvusClient, DataType
+from langbot.pkg.vector.vdb import VectorDatabase
+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):
+        """Initialize Milvus vector database
+
+        Args:
+            ap: Application instance
+            uri: Milvus connection URI. For local file: "milvus.db"
+                 For remote server: "http://localhost:19530"
+            token: Optional authentication token for remote connections
+        """
+        self.ap = ap
+        self.uri = uri
+        self.token = token
+        self.client = None
+        self._collections = {}
+        self._initialize_client()
+
+    def _initialize_client(self):
+        """Initialize Milvus client connection"""
+        try:
+            if self.token:
+                self.client = MilvusClient(uri=self.uri, token=self.token)
+            else:
+                self.client = MilvusClient(uri=self.uri)
+            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
+
+        Args:
+            collection: Collection name (corresponds to knowledge base UUID)
+        """
+        if collection in self._collections:
+            return self._collections[collection]
+
+        # Check if collection exists
+        has_collection = await asyncio.to_thread(
+            self.client.has_collection, collection_name=collection
+        )
+
+        if not has_collection:
+            # Create collection with custom schema to support string IDs
+            from pymilvus import CollectionSchema, FieldSchema, DataType
+
+            fields = [
+                FieldSchema(name="id", dtype=DataType.VARCHAR, is_primary=True, max_length=255),
+                FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=1536),
+                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),
+            ]
+
+            schema = CollectionSchema(fields=fields, description="LangBot knowledge base vectors")
+
+            await asyncio.to_thread(
+                self.client.create_collection,
+                collection_name=collection,
+                schema=schema,
+                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")
+        else:
+            self.ap.logger.info(f"Milvus collection '{collection}' already exists")
+
+        self._collections[collection] = collection
+        return collection
+
+    async def add_embeddings(
+        self,
+        collection: str,
+        ids: list[str],
+        embeddings_list: list[list[float]],
+        metadatas: list[dict[str, Any]],
+    ) -> None:
+        """Add vector embeddings to Milvus collection
+
+        Args:
+            collection: Collection name
+            ids: List of unique IDs for each vector
+            embeddings_list: List of embedding vectors
+            metadatas: List of metadata dictionaries for each vector
+        """
+        await self.get_or_create_collection(collection)
+
+        # Prepare data in Milvus format
+        data = []
+        for i, vector_id in enumerate(ids):
+            entry = {
+                "id": vector_id,
+                "vector": embeddings_list[i],
+            }
+            # Add metadata fields
+            if metadatas and i < len(metadatas):
+                metadata = metadatas[i]
+                # Add common metadata fields
+                if "text" in metadata:
+                    entry["text"] = metadata["text"]
+                if "file_id" in metadata:
+                    entry["file_id"] = metadata["file_id"]
+                if "uuid" in metadata:
+                    entry["chunk_uuid"] = metadata["uuid"]
+            data.append(entry)
+
+        # Insert data into Milvus
+        await asyncio.to_thread(
+            self.client.insert,
+            collection_name=collection,
+            data=data
+        )
+
+        # Load collection for searching (Milvus requires this)
+        await asyncio.to_thread(
+            self.client.load_collection,
+            collection_name=collection
+        )
+
+        self.ap.logger.info(f"Added {len(ids)} embeddings to Milvus collection '{collection}'")
+
+    async def search(
+        self, collection: str, query_embedding: list[float], k: int = 5
+    ) -> Dict[str, Any]:
+        """Search for similar vectors in Milvus collection
+
+        Args:
+            collection: Collection name
+            query_embedding: Query vector
+            k: Number of top results to return
+
+        Returns:
+            Dictionary with search results in Chroma-compatible format
+        """
+        await self.get_or_create_collection(collection)
+
+        # Perform search
+        search_params = {
+            "metric_type": "COSINE",
+            "params": {}
+        }
+
+        results = await asyncio.to_thread(
+            self.client.search,
+            collection_name=collection,
+            data=[query_embedding],
+            limit=k,
+            search_params=search_params,
+            output_fields=["text", "file_id", "chunk_uuid"]
+        )
+
+        # Convert results to Chroma-compatible format
+        # Milvus returns: [[ {id, distance, entity: {...}} ]]
+        ids = []
+        distances = []
+        metadatas = []
+
+        if results and len(results) > 0:
+            for hit in results[0]:
+                ids.append(hit.get("id", ""))
+                distances.append(hit.get("distance", 0.0))
+
+                # Build metadata from entity fields
+                entity = hit.get("entity", {})
+                metadata = {}
+                if "text" in entity:
+                    metadata["text"] = entity["text"]
+                if "file_id" in entity:
+                    metadata["file_id"] = entity["file_id"]
+                if "chunk_uuid" in entity:
+                    metadata["uuid"] = entity["chunk_uuid"]
+                metadatas.append(metadata)
+
+        # Return in Chroma-compatible format (nested lists)
+        result = {
+            "ids": [ids],
+            "distances": [distances],
+            "metadatas": [metadatas]
+        }
+
+        self.ap.logger.info(
+            f"Milvus search in '{collection}' returned {len(ids)} results"
+        )
+        return result
+
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        """Delete vectors from collection by file_id
+
+        Args:
+            collection: Collection name
+            file_id: File ID to filter deletion
+        """
+        await self.get_or_create_collection(collection)
+
+        # Delete entities matching the file_id
+        await asyncio.to_thread(
+            self.client.delete,
+            collection_name=collection,
+            filter=f'file_id == "{file_id}"'
+        )
+        self.ap.logger.info(
+            f"Deleted embeddings from Milvus collection '{collection}' with file_id: {file_id}"
+        )
+
+    async def delete_collection(self, collection: str):
+        """Delete a Milvus collection
+
+        Args:
+            collection: Collection name to delete
+        """
+        if collection in self._collections:
+            del self._collections[collection]
+
+        # Check if collection exists before attempting deletion
+        has_collection = await asyncio.to_thread(
+            self.client.has_collection, collection_name=collection
+        )
+
+        if has_collection:
+            await asyncio.to_thread(
+                self.client.drop_collection, collection_name=collection
+            )
+            self.ap.logger.info(f"Deleted Milvus collection '{collection}'")
+        else:
+            self.ap.logger.warning(f"Milvus collection '{collection}' not found")
diff --git a/src/langbot/pkg/vector/vdbs/pgvector_db.py b/src/langbot/pkg/vector/vdbs/pgvector_db.py
new file mode 100644
index 00000000..2669902b
--- /dev/null
+++ b/src/langbot/pkg/vector/vdbs/pgvector_db.py
@@ -0,0 +1,286 @@
+from __future__ import annotations
+import asyncio
+from typing import Any, Dict
+from sqlalchemy import create_engine, text, Column, String, Text
+from sqlalchemy.orm import declarative_base, sessionmaker, Session
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
+from pgvector.sqlalchemy import Vector
+from langbot.pkg.vector.vdb import VectorDatabase
+from langbot.pkg.core import app
+import uuid
+
+Base = declarative_base()
+
+
+class PgVectorEntry(Base):
+    """SQLAlchemy model for pgvector entries"""
+    __tablename__ = 'langbot_vectors'
+
+    id = Column(String, primary_key=True)
+    collection = Column(String, index=True, nullable=False)
+    embedding = Column(Vector(1536))  # Default dimension, will be created dynamically
+    text = Column(Text)
+    file_id = Column(String, index=True)
+    chunk_uuid = Column(String)
+
+
+class PgVectorDatabase(VectorDatabase):
+    """PostgreSQL with pgvector extension database implementation"""
+
+    def __init__(
+        self,
+        ap: app.Application,
+        connection_string: str = None,
+        host: str = "localhost",
+        port: int = 5432,
+        database: str = "langbot",
+        user: str = "postgres",
+        password: str = "postgres"
+    ):
+        """Initialize pgvector database
+
+        Args:
+            ap: Application instance
+            connection_string: Full PostgreSQL connection string (overrides other params)
+            host: PostgreSQL host
+            port: PostgreSQL port
+            database: Database name
+            user: Database user
+            password: Database password
+        """
+        self.ap = ap
+
+        # Build connection string if not provided
+        if connection_string:
+            self.connection_string = connection_string
+        else:
+            self.connection_string = (
+                f"postgresql+psycopg://{user}:{password}@{host}:{port}/{database}"
+            )
+
+        self.async_connection_string = self.connection_string.replace(
+            "postgresql://", "postgresql+asyncpg://"
+        ).replace(
+            "postgresql+psycopg://", "postgresql+asyncpg://"
+        )
+
+        self.engine = None
+        self.async_engine = None
+        self.SessionLocal = None
+        self.AsyncSessionLocal = None
+        self._collections = set()
+        self._initialize_db()
+
+    def _initialize_db(self):
+        """Initialize database connection and create tables"""
+        try:
+            # Create async engine for async operations
+            self.async_engine = create_async_engine(
+                self.async_connection_string,
+                echo=False,
+                pool_pre_ping=True
+            )
+            self.AsyncSessionLocal = async_sessionmaker(
+                self.async_engine,
+                class_=AsyncSession,
+                expire_on_commit=False
+            )
+
+            # Create sync engine for table creation
+            sync_connection_string = self.connection_string.replace(
+                "postgresql+asyncpg://", "postgresql+psycopg://"
+            )
+            self.engine = create_engine(sync_connection_string, echo=False)
+
+            # Create pgvector extension and tables
+            with self.engine.connect() as conn:
+                # Enable pgvector extension
+                conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+                conn.commit()
+
+            # Create tables
+            Base.metadata.create_all(self.engine)
+
+            self.ap.logger.info(f"Connected to PostgreSQL with pgvector")
+        except Exception as e:
+            self.ap.logger.error(f"Failed to connect to PostgreSQL: {e}")
+            raise
+
+    async def get_or_create_collection(self, collection: str):
+        """Get or create a collection (logical grouping in pgvector)
+
+        Args:
+            collection: Collection name (knowledge base UUID)
+        """
+        # In pgvector, collections are logical - we just track them
+        if collection not in self._collections:
+            self._collections.add(collection)
+            self.ap.logger.info(f"Registered pgvector collection '{collection}'")
+        return collection
+
+    async def add_embeddings(
+        self,
+        collection: str,
+        ids: list[str],
+        embeddings_list: list[list[float]],
+        metadatas: list[dict[str, Any]],
+    ) -> None:
+        """Add vector embeddings to pgvector
+
+        Args:
+            collection: Collection name
+            ids: List of unique IDs for each vector
+            embeddings_list: List of embedding vectors
+            metadatas: List of metadata dictionaries
+        """
+        await self.get_or_create_collection(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                for i, vector_id in enumerate(ids):
+                    metadata = metadatas[i] if i < len(metadatas) else {}
+
+                    entry = PgVectorEntry(
+                        id=vector_id,
+                        collection=collection,
+                        embedding=embeddings_list[i],
+                        text=metadata.get("text", ""),
+                        file_id=metadata.get("file_id", ""),
+                        chunk_uuid=metadata.get("uuid", "")
+                    )
+                    session.add(entry)
+
+                await session.commit()
+                self.ap.logger.info(
+                    f"Added {len(ids)} embeddings to pgvector collection '{collection}'"
+                )
+            except Exception as e:
+                await session.rollback()
+                self.ap.logger.error(f"Error adding embeddings to pgvector: {e}")
+                raise
+
+    async def search(
+        self, collection: str, query_embedding: list[float], k: int = 5
+    ) -> Dict[str, Any]:
+        """Search for similar vectors using cosine distance
+
+        Args:
+            collection: Collection name
+            query_embedding: Query vector
+            k: Number of top results to return
+
+        Returns:
+            Dictionary with search results in Chroma-compatible format
+        """
+        await self.get_or_create_collection(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                # Use cosine distance for similarity search
+                from sqlalchemy import select, func
+
+                # Query for similar vectors
+                stmt = (
+                    select(
+                        PgVectorEntry.id,
+                        PgVectorEntry.text,
+                        PgVectorEntry.file_id,
+                        PgVectorEntry.chunk_uuid,
+                        PgVectorEntry.embedding.cosine_distance(query_embedding).label('distance')
+                    )
+                    .filter(PgVectorEntry.collection == collection)
+                    .order_by(PgVectorEntry.embedding.cosine_distance(query_embedding))
+                    .limit(k)
+                )
+
+                result = await session.execute(stmt)
+                rows = result.fetchall()
+
+                # Convert to Chroma-compatible format
+                ids = []
+                distances = []
+                metadatas = []
+
+                for row in rows:
+                    ids.append(row.id)
+                    distances.append(float(row.distance))
+                    metadatas.append({
+                        "text": row.text or "",
+                        "file_id": row.file_id or "",
+                        "uuid": row.chunk_uuid or ""
+                    })
+
+                result_dict = {
+                    "ids": [ids],
+                    "distances": [distances],
+                    "metadatas": [metadatas]
+                }
+
+                self.ap.logger.info(
+                    f"pgvector search in '{collection}' returned {len(ids)} results"
+                )
+                return result_dict
+
+            except Exception as e:
+                self.ap.logger.error(f"Error searching pgvector: {e}")
+                raise
+
+    async def delete_by_file_id(self, collection: str, file_id: str) -> None:
+        """Delete vectors by file_id
+
+        Args:
+            collection: Collection name
+            file_id: File ID to filter deletion
+        """
+        await self.get_or_create_collection(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                from sqlalchemy import delete
+
+                stmt = delete(PgVectorEntry).where(
+                    PgVectorEntry.collection == collection,
+                    PgVectorEntry.file_id == file_id
+                )
+                await session.execute(stmt)
+                await session.commit()
+
+                self.ap.logger.info(
+                    f"Deleted embeddings from pgvector collection '{collection}' with file_id: {file_id}"
+                )
+            except Exception as e:
+                await session.rollback()
+                self.ap.logger.error(f"Error deleting from pgvector: {e}")
+                raise
+
+    async def delete_collection(self, collection: str):
+        """Delete all vectors in a collection
+
+        Args:
+            collection: Collection name to delete
+        """
+        if collection in self._collections:
+            self._collections.remove(collection)
+
+        async with self.AsyncSessionLocal() as session:
+            try:
+                from sqlalchemy import delete
+
+                stmt = delete(PgVectorEntry).where(
+                    PgVectorEntry.collection == collection
+                )
+                await session.execute(stmt)
+                await session.commit()
+
+                self.ap.logger.info(f"Deleted pgvector collection '{collection}'")
+            except Exception as e:
+                await session.rollback()
+                self.ap.logger.error(f"Error deleting pgvector collection: {e}")
+                raise
+
+    async def close(self):
+        """Close database connections"""
+        if self.async_engine:
+            await self.async_engine.dispose()
+        if self.engine:
+            self.engine.dispose()
diff --git a/src/langbot/templates/config.yaml b/src/langbot/templates/config.yaml
index 449b245d..37a61bc8 100644
--- a/src/langbot/templates/config.yaml
+++ b/src/langbot/templates/config.yaml
@@ -36,6 +36,15 @@ vdb:
         host: localhost
         port: 6333
         api_key: ''
+    milvus:
+        uri: 'http://127.0.0.1:19530'
+        token: ''
+    pgvector:
+        host: '127.0.0.1'
+        port: 5433
+        database: 'langbot'
+        user: 'postgres'
+        password: 'postgres'
 storage:
     use: local
     s3: