align zilliz with milvus

libs/milvus/langchain_milvus/vectorstores/milvus.py

@@ -1166,13 +1166,14 @@ def from_texts(
         embedding: Union[Embeddings, BaseSparseEmbedding],  # type: ignore
         metadatas: Optional[List[dict]] = None,
         collection_name: str = "LangChainCollection",
-        connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
+        connection_args: Optional[Dict[str, Any]] = None,
         consistency_level: str = "Session",
         index_params: Optional[dict] = None,
         search_params: Optional[dict] = None,
         drop_old: bool = False,
         *,
         ids: Optional[List[str]] = None,
+        auto_id: bool = False,
         **kwargs: Any,
     ) -> Milvus:
         """Create a Milvus collection, indexes it with HNSW, and insert data.
@@ -1195,11 +1196,18 @@ def from_texts(
             drop_old (Optional[bool], optional): Whether to drop the collection with
                 that name if it exists. Defaults to False.
             ids (Optional[List[str]]): List of text ids. Defaults to None.
+            auto_id (bool): Whether to enable auto id for primary key. Defaults to
+                False. If False, you need to provide text ids (string less than 65535
+                bytes). If True, Milvus will generate unique integers as primary keys.
 
         Returns:
             Milvus: Milvus Vector Store
         """
         if isinstance(ids, list) and len(ids) > 0:
+            if auto_id:
+                logger.warning(
+                    "Both ids and auto_id are provided. " "Ignore auto_id and use ids."
+                )
             auto_id = False
         else:
             auto_id = True

libs/milvus/langchain_milvus/vectorstores/zilliz.py

@@ -1,11 +1,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict, List, Optional, Union
 
-from langchain_core.embeddings import Embeddings
-
-from langchain_milvus.utils.sparse import BaseSparseEmbedding
 from langchain_milvus.vectorstores.milvus import Milvus
 
 logger = logging.getLogger(__name__)
@@ -38,53 +34,34 @@ class Zilliz(Milvus):
         drop_old (Optional[bool]): Whether to drop the current collection. Defaults
             to False.
         auto_id (bool): Whether to enable auto id for primary key. Defaults to False.
-            If False, you needs to provide text ids (string less than 65535 bytes).
+            If False, you need to provide text ids (string less than 65535 bytes).
             If True, Milvus will generate unique integers as primary keys.
 
     The connection args used for this class comes in the form of a dict,
-    here are a few of the options:
-        address (str): The actual address of Zilliz
-            instance. Example address: "localhost:19530"
-        uri (str): The uri of Zilliz instance. Example uri:
+    the two major arguments are:
+        uri (str): The Public Endpoint of Zilliz instance. Example uri:
             "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com",
-        host (str): The host of Zilliz instance. Default at "localhost",
-            PyMilvus will fill in the default host if only port is provided.
-        port (str/int): The port of Zilliz instance. Default at 19530, PyMilvus
-            will fill in the default port if only host is provided.
-        user (str): Use which user to connect to Zilliz instance. If user and
-            password are provided, we will add related header in every RPC call.
-        password (str): Required when user is provided. The password
-            corresponding to the user.
         token (str): API key, for serverless clusters which can be used as
             replacements for user and password.
-        secure (bool): Default is false. If set to true, tls will be enabled.
-        client_key_path (str): If use tls two-way authentication, need to
-            write the client.key path.
-        client_pem_path (str): If use tls two-way authentication, need to
-            write the client.pem path.
-        ca_pem_path (str): If use tls two-way authentication, need to write
-            the ca.pem path.
-        server_pem_path (str): If use tls one-way authentication, need to
-            write the server.pem path.
-        server_name (str): If use tls, need to write the common name.
+    For more information, please refer to:
+    https://docs.zilliz.com/docs/on-zilliz-cloud-console#cluster-details
+    and
+    https://docs.zilliz.com/reference/python/python/Connections-connect
 
     Example:
         .. code-block:: python
 
-        from langchain_community.vectorstores import Zilliz
-        from langchain_community.embeddings import OpenAIEmbeddings
+        from langchain_milvus import Zilliz
+        from langchain_openai import OpenAIEmbeddings
 
         embedding = OpenAIEmbeddings()
         # Connect to a Zilliz instance
-        milvus_store = Milvus(
+        milvus_store = Zilliz(
             embedding_function = embedding,
             collection_name = "LangChainCollection",
             connection_args = {
                 "uri": "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com",
-                "user": "temp",
-                "password": "temp",
-                "token": "temp", # API key as replacements for user and password
-                "secure": True
+                "token": "temp", # API key
             }
             drop_old: True,
         )
@@ -101,11 +78,18 @@ def _create_index(self) -> None:
             try:
                 # If no index params, use a default AutoIndex based one
                 if self.index_params is None:
-                    self.index_params = {
-                        "metric_type": "L2",
-                        "index_type": "AUTOINDEX",
-                        "params": {},
-                    }
+                    if self._is_sparse_embedding:
+                        self.index_params = {
+                            "metric_type": "IP",
+                            "index_type": "SPARSE_INVERTED_INDEX",
+                            "params": {"drop_ratio_build": 0.2},
+                        }
+                    else:
+                        self.index_params = {
+                            "metric_type": "L2",
+                            "index_type": "AUTOINDEX",
+                            "params": {},
+                        }
 
                 try:
                     self.col.create_index(
@@ -137,61 +121,3 @@ def _create_index(self) -> None:
                     "Failed to create an index on collection: %s", self.collection_name
                 )
                 raise e
-
-    @classmethod
-    def from_texts(
-        cls,
-        texts: List[str],
-        embedding: Union[Embeddings, BaseSparseEmbedding],
-        metadatas: Optional[List[dict]] = None,
-        collection_name: str = "LangChainCollection",
-        connection_args: Optional[Dict[str, Any]] = None,
-        consistency_level: str = "Session",
-        index_params: Optional[dict] = None,
-        search_params: Optional[dict] = None,
-        drop_old: bool = False,
-        *,
-        ids: Optional[List[str]] = None,
-        auto_id: bool = False,
-        **kwargs: Any,
-    ) -> Zilliz:
-        """Create a Zilliz collection, indexes it with HNSW, and insert data.
-
-        Args:
-            texts (List[str]): Text data.
-            embedding (Embeddings): Embedding function.
-            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
-                Defaults to None.
-            collection_name (str, optional): Collection name to use. Defaults to
-                "LangChainCollection".
-            connection_args (dict[str, Any], optional): Connection args to use. Defaults
-                to DEFAULT_MILVUS_CONNECTION.
-            consistency_level (str, optional): Which consistency level to use. Defaults
-                to "Session".
-            index_params (Optional[dict], optional): Which index_params to use.
-                Defaults to None.
-            search_params (Optional[dict], optional): Which search params to use.
-                Defaults to None.
-            drop_old (Optional[bool], optional): Whether to drop the collection with
-                that name if it exists. Defaults to False.
-            ids (Optional[List[str]]): List of text ids.
-            auto_id (bool): Whether to enable auto id for primary key. Defaults to
-                False. If False, you needs to provide text ids (string less than 65535
-                bytes). If True, Milvus will generate unique integers as primary keys.
-
-        Returns:
-            Zilliz: Zilliz Vector Store
-        """
-        vector_db = cls(
-            embedding_function=embedding,
-            collection_name=collection_name,
-            connection_args=connection_args or {},
-            consistency_level=consistency_level,
-            index_params=index_params,
-            search_params=search_params,
-            drop_old=drop_old,
-            auto_id=auto_id,
-            **kwargs,
-        )
-        vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids)
-        return vector_db