docs: fix milvus import and update template (#22306)

docs: fix milvus import problem
update milvus-rag template with milvus-lite

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

templates/rag-milvus/rag_milvus/chain.py

@@ -1,12 +1,23 @@
-from langchain_community.vectorstores import Milvus
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_milvus.vectorstores import Milvus
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 
 # Example for document loading (from url), splitting, and creating vectorstore
 
+# Setting the URI as a local file, e.g.`./milvus.db`, is the most convenient method,
+# as it automatically utilizes Milvus Lite to store all data in this file.
+#
+# If you have large scale of data such as more than a million docs,
+# we recommend setting up a more performant Milvus server on docker or kubernetes.
+# (https://milvus.io/docs/quickstart.md)
+# When using this setup, please use the server URI,
+# e.g.`http://localhost:19530`, as your URI.
+
+URI = "./milvus.db"
+
 """ 
 # Load
 from langchain_community.document_loaders import WebBaseLoader
@@ -25,6 +36,7 @@ vectorstore = Milvus.from_documents(documents=all_splits,
                                     collection_name="rag_milvus",
                                     embedding=OpenAIEmbeddings(),
                                     drop_old=True,
+                                    connection_args={"uri": URI},
                                     )
 retriever = vectorstore.as_retriever()
 """
@@ -35,9 +47,7 @@ vectorstore = Milvus.from_texts(
     collection_name="rag_milvus",
     embedding=OpenAIEmbeddings(),
     drop_old=True,
-    connection_args={
-        "uri": "http://127.0.0.1:19530",
-    },
+    connection_args={"uri": URI},
 )
 retriever = vectorstore.as_retriever()