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align zilliz with milvus (#7)
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There are some old arguments and methods in class Zilliz . Update and
align them with those in class Milvus

Signed-off-by: ChengZi <[email protected]>
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@zc277584121
zc277584121 authored Sep 30, 2024
1 parent f0da0cc commit 16a81ee
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10 changes: 9 additions & 1 deletion libs/milvus/langchain_milvus/vectorstores/milvus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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
Expand Down
120 changes: 23 additions & 97 deletions libs/milvus/langchain_milvus/vectorstores/zilliz.py
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Original file line number Diff line number Diff line change
@@ -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__)
Expand Down Expand Up @@ -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,
)
Expand All @@ -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(
Expand Down Expand Up @@ -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

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