fix: fixed Milvus integration code

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

docs/source/index.md

@@ -68,6 +68,7 @@ A number of "adapters" are available for some popular Inference and Vector Store
 |  FAISS | Single Node |
 |  SQLite-Vec| Single Node |
 |  Chroma | Hosted and Single Node |
+|  Milvus | Hosted and Single Node |
 |  Postgres (PGVector) | Hosted and Single Node |
 |  Weaviate | Hosted |
 

docs/source/providers/index.md

@@ -2,7 +2,7 @@
 
 The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
 - LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
-- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, FAISS, PGVector, etc.),
+- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, etc.),
 - Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
 
 Providers come in two flavors:
@@ -55,5 +55,6 @@ vector_io/sqlite-vec
 vector_io/chromadb
 vector_io/pgvector
 vector_io/qdrant
+vector_io/milvus
 vector_io/weaviate
 ```

docs/source/providers/vector_io/mivus.md

@@ -0,0 +1,31 @@
+---
+orphan: true
+---
+# Milvus
+
+[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It 
+allows you to store and query vectors directly within a Milvus database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use Milvus in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Milvus.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Milvus using pymilvus:
+
+```bash
+pip install pymilvus
+```
+## Documentation
+See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.

llama_stack/providers/inline/vector_io/milvus/__init__.py

@@ -12,9 +12,7 @@ from .config import MilvusVectorIOConfig
 
 
 async def get_provider_impl(config: MilvusVectorIOConfig, deps: Dict[Api, ProviderSpec]):
-    from .milvus import MilvusVectorIOAdapter
-
-    assert isinstance(config, MilvusVectorIOConfig), f"Unexpected config type: {type(config)}"
+    from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusVectorIOAdapter
 
     impl = MilvusVectorIOAdapter(config, deps[Api.inference])
     await impl.initialize()

llama_stack/providers/inline/vector_io/milvus/milvus.py

@@ -1,143 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import logging
-import os
-from typing import Any, Dict, List, Optional
-
-from numpy.typing import NDArray
-from pymilvus import MilvusClient
-
-from llama_stack.apis.inference import InterleavedContent
-from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
-from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
-from llama_stack.providers.utils.memory.vector_store import (
-    EmbeddingIndex,
-    VectorDBWithIndex,
-)
-
-from .config import MilvusVectorIOConfig
-
-logger = logging.getLogger(__name__)
-
-
-class MilvusIndex(EmbeddingIndex):
-    def __init__(self, client: MilvusClient, collection_name: str):
-        self.client = client
-        self.collection_name = collection_name.replace("-", "_")
-
-    async def delete(self):
-        if self.client.has_collection(self.collection_name):
-            self.client.drop_collection(collection_name=self.collection_name)
-
-    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
-        assert len(chunks) == len(embeddings), (
-            f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
-        )
-        if not self.client.has_collection(self.collection_name):
-            self.client.create_collection(self.collection_name, dimension=len(embeddings[0]), auto_id=True)
-
-        data = []
-        for i, (chunk, embedding) in enumerate(zip(chunks, embeddings, strict=False)):
-            chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
-
-            data.append(
-                {
-                    "chunk_id": chunk_id,
-                    "vector": embedding,
-                    "chunk_content": chunk.model_dump(),
-                }
-            )
-        self.client.insert(
-            self.collection_name,
-            data=data,
-        )
-
-    async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
-        search_res = self.client.search(
-            collection_name=self.collection_name,
-            data=[embedding],
-            limit=k,
-            output_fields=["*"],
-            search_params={"params": {"radius": score_threshold}},
-        )
-        chunks = [Chunk(**res["entity"]["chunk_content"]) for res in search_res[0]]
-        scores = [res["distance"] for res in search_res[0]]
-        return QueryChunksResponse(chunks=chunks, scores=scores)
-
-
-class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: MilvusVectorIOConfig, inference_api: Api.inference) -> None:
-        self.config = config
-        uri = self.config.model_dump(exclude_none=True)["db_path"]
-        uri = os.path.expanduser(uri)
-        self.client = MilvusClient(uri=uri)
-        self.cache = {}
-        self.inference_api = inference_api
-
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        self.client.close()
-
-    async def register_vector_db(
-        self,
-        vector_db: VectorDB,
-    ) -> None:
-        index = VectorDBWithIndex(
-            vector_db=vector_db,
-            index=MilvusIndex(self.client, vector_db.identifier),
-            inference_api=self.inference_api,
-        )
-
-        self.cache[vector_db.identifier] = index
-
-    async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
-        if vector_db_id in self.cache:
-            return self.cache[vector_db_id]
-
-        vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
-        if not vector_db:
-            raise ValueError(f"Vector DB {vector_db_id} not found")
-
-        index = VectorDBWithIndex(
-            vector_db=vector_db,
-            index=MilvusIndex(client=self.client, collection_name=vector_db.identifier),
-            inference_api=self.inference_api,
-        )
-        self.cache[vector_db_id] = index
-        return index
-
-    async def unregister_vector_db(self, vector_db_id: str) -> None:
-        if vector_db_id in self.cache:
-            await self.cache[vector_db_id].index.delete()
-            del self.cache[vector_db_id]
-
-    async def insert_chunks(
-        self,
-        vector_db_id: str,
-        chunks: List[Chunk],
-        ttl_seconds: Optional[int] = None,
-    ) -> None:
-        index = await self._get_and_cache_vector_db_index(vector_db_id)
-        if not index:
-            raise ValueError(f"Vector DB {vector_db_id} not found")
-
-        await index.insert_chunks(chunks)
-
-    async def query_chunks(
-        self,
-        vector_db_id: str,
-        query: InterleavedContent,
-        params: Optional[Dict[str, Any]] = None,
-    ) -> QueryChunksResponse:
-        index = await self._get_and_cache_vector_db_index(vector_db_id)
-        if not index:
-            raise ValueError(f"Vector DB {vector_db_id} not found")
-
-        return await index.query_chunks(query, params)

llama_stack/providers/registry/vector_io.py

@@ -114,7 +114,7 @@ def available_providers() -> List[ProviderSpec]:
             Api.vector_io,
             AdapterSpec(
                 adapter_type="milvus",
-                pip_packages=EMBEDDING_DEPS + ["pymilvus"],
+                pip_packages=["pymilvus"],
                 module="llama_stack.providers.remote.vector_io.milvus",
                 config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
             ),
@@ -123,7 +123,7 @@ def available_providers() -> List[ProviderSpec]:
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::milvus",
-            pip_packages=EMBEDDING_DEPS + ["pymilvus"],
+            pip_packages=["pymilvus"],
             module="llama_stack.providers.inline.vector_io.milvus",
             config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
             api_dependencies=[Api.inference],

llama_stack/providers/remote/vector_io/milvus/config.py

@@ -15,6 +15,7 @@ from llama_stack.schema_utils import json_schema_type
 class MilvusVectorIOConfig(BaseModel):
     uri: str
     token: Optional[str] = None
+    consistency_level: str = "Strong"
 
     @classmethod
     def sample_config(cls) -> Dict[str, Any]:

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -5,8 +5,11 @@
 # the root directory of this source tree.
 
 import logging
-from typing import Any, Dict, List, Optional
+import os
+from typing import Any, Dict, List, Optional, Union
 
+import hashlib
+import uuid
 from numpy.typing import NDArray
 from pymilvus import MilvusClient
 
@@ -14,20 +17,22 @@ from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
 
-from .config import MilvusVectorIOConfig
+from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
 
 logger = logging.getLogger(__name__)
 
 
 class MilvusIndex(EmbeddingIndex):
-    def __init__(self, client: MilvusClient, collection_name: str):
+    def __init__(self, client: MilvusClient, collection_name: str, consistency_level="Strong"):
         self.client = client
         self.collection_name = collection_name.replace("-", "_")
+        self.consistency_level = consistency_level
 
     async def delete(self):
         if self.client.has_collection(self.collection_name):
@@ -38,11 +43,11 @@ class MilvusIndex(EmbeddingIndex):
             f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
         )
         if not self.client.has_collection(self.collection_name):
-            self.client.create_collection(self.collection_name, dimension=len(embeddings[0]), auto_id=True)
+            self.client.create_collection(self.collection_name, dimension=len(embeddings[0]), auto_id=True, consistency_level=self.consistency_level)
 
         data = []
         for i, (chunk, embedding) in enumerate(zip(chunks, embeddings, strict=False)):
-            chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
+            chunk_id = generate_chunk_id(chunk.metadata["document_id"], chunk.content)
 
             data.append(
                 {
@@ -51,10 +56,14 @@ class MilvusIndex(EmbeddingIndex):
                     "chunk_content": chunk.model_dump(),
                 }
             )
-        self.client.insert(
-            self.collection_name,
-            data=data,
-        )
+        try:
+            self.client.insert(
+                self.collection_name,
+                data=data,
+            )
+        except Exception as e:
+            logger.error(f"Error inserting chunks into Milvus collection {self.collection_name}: {e}")
+            raise e
 
     async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
         search_res = self.client.search(
@@ -70,14 +79,20 @@ class MilvusIndex(EmbeddingIndex):
 
 
 class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: MilvusVectorIOConfig, inference_api: Api.inference) -> None:
+    def __init__(self, config: Union[RemoteMilvusVectorIOConfig, InlineMilvusVectorIOConfig], inference_api: Api.inference) -> None:
         self.config = config
-        self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
         self.cache = {}
+        self.client = None
         self.inference_api = inference_api
 
     async def initialize(self) -> None:
-        pass
+        if isinstance(self.config, RemoteMilvusVectorIOConfig):
+            logger.info(f"Connecting to Milvus server at {self.config.uri}")
+            self.client = MilvusClient(**self.config.model_dump(exclude_none=True))
+        else:
+            logger.info(f"Connecting to Milvus Lite at: {self.config.db_path}")
+            uri = os.path.expanduser(self.config.db_path)
+            self.client = MilvusClient(uri=uri)
 
     async def shutdown(self) -> None:
         self.client.close()
@@ -86,9 +101,13 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         self,
         vector_db: VectorDB,
     ) -> None:
+        if isinstance(self.config, RemoteMilvusVectorIOConfig):
+            consistency_level = self.config.consistency_level
+        else:
+            consistency_level = "Strong"
         index = VectorDBWithIndex(
             vector_db=vector_db,
-            index=MilvusIndex(self.client, vector_db.identifier),
+            index=MilvusIndex(self.client, vector_db.identifier, consistency_level=consistency_level),
             inference_api=self.inference_api,
         )
 
@@ -138,3 +157,10 @@ class MilvusVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
         return await index.query_chunks(query, params)
+
+def generate_chunk_id(document_id: str, chunk_text: str) -> str:
+    """Generate a unique chunk ID using a hash of document ID and chunk text."""
+    hash_input = f"{document_id}:{chunk_text}".encode("utf-8")
+    return str(uuid.UUID(hashlib.md5(hash_input).hexdigest()))
+
+# TODO: refactor this generate_chunk_id along with the `sqlite-vec` implementation into a separate utils file