Update milvus_lite.md to improve description, limitation and add migr…

…ation data part.

site/en/getstarted/milvus_lite.md

@@ -6,45 +6,108 @@ title: Run Milvus Lite Locally
 
 # Run Milvus Lite Locally
 
-This page illustrates how to run Milvus Lite locally for development and testing purposes.
+This page illustrates how to run Milvus locally with Milvus Lite. Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus), an open-source vector database that powers AI applications with vector embeddings and similarity search.
+
 
 ## Overview
 
-Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus), an open-source vector database that powers AI applications with vector embeddings and similarity search.
+Milvus Lite can be imported into your Python application, providing the core vector search functionality of Milvus. Milvus Lite is already included in the [Python SDK of Milvus](https://github.com/milvus-io/pymilvus). It can be simply deployed with `pip install pymilvus`. 
 
-Milvus Lite can be imported into your Python application, providing the core vector search functionality of Milvus. Milvus Lite is included in the [Python SDK of Milvus](https://github.com/milvus-io/pymilvus), thus it can be simply deployed with `pip install pymilvus`. This repo contains the core components of Milvus Lite.
+With Milvus Lite, you can start building an AI application with vector similarity search within minutes! Milvus Lite is good for running in the following environment:
+- Jupyter Notebook / Google Colab
+- Laptops
+- Edge Devices
 
-Milvus Lite shares the same API and covers most of the features of Milvus. Together, they provide a consistent user experience across different types of environments, fitting use cases of different size. With the same client-side code, you can run a quick demo of less than a million vectors with Milvus Lite, or a small scale app with Milvus Docker container hosted on a single machine, and eventually to a large scale production deployment on Kubenetes serving billions of vectors at thousands of QPS.
+Milvus Lite shares the same API with Milvus Standalone and Distributed, and covers most of the features such as vector data persistence and management, vector CRUD operations, sparse and dense vector search, metadata filtering, multi-vector and hybrid_search. Together, they provide a consistent experience across different types of environments, from edge devices to clusters in cloud, fitting use cases of different size. With the same client-side code, you can run GenAI apps with Milvus Lite on a laptop or Jupyter Notebook, or Milvus Standalone on Docker container, or Milvus Distributed on massive scale Kubernetes cluster serving billions of vectors in production.
 
 ## Prerequisites
 
-Milvus Lite supports the following OS distributions and sillicon types:
+Milvus Lite currently supports the following environmnets:
+- Ubuntu >= 20.04 (x86_64 and arm64)
+- MacOS >= 11.0 (Apple Silicon M1/M2 and x86_64)
 
-- Ubuntu >= 20.04 (x86_64)
-- MacOS >= 11.0 (Apple Silicon and x86_64)
+Please note that Milvus Lite is only suitable for small scale vector search use cases. For a large scale use case, we recommend using [Milvus Standalone](https://milvus.io/docs/install-overview.md#Milvus-Standalone) or [Milvus Distributed](https://milvus.io/docs/install-overview.md#Milvus-Distributed). You can also consider the fully-managed Milvus on [Zilliz Cloud](https://zilliz.com/cloud).
 
-Please note that Milvus Lite is good for getting started with vector search or building demos and prototypes. For a production use case, we recommend using Milvus on [Docker](install_standalone-docker.md) and [Kubenetes](install_cluster-milvusoperator.md), or considering the fully-managed Milvus on [Zilliz Cloud](https://zilliz.com/cloud).
 
 ## Set up Milvus Lite
 
-Milvus Lite has been packed along with pymilvus, the Python SDK library of Milvus. To set up Milvus Lite, run the following command in the terminal.
-
-```
-pip install "pymilvus>=2.4.2"
+```shell
+pip install -U pymilvus
 ```
 
-## Connect to Milvus Lite
+We recommend using `pymilvus`. Since `milvus-lite` is included in `pymilvus` version 2.4.2 or above, you can `pip install` with `-U` to force update to the latest version and `milvus-lite` is automatically installed.
 
-You can connect to Milvus Lite as follows.
+If you want to explicitly install `milvus-lite` package, or you have installed an older version of `milvus-lite` and would like to update it, you can do `pip install -U milvus-lite`.
 
+## Connect to Milvus Lite
+
+In `pymilvus`, specify a local file name as uri parameter of MilvusClient will use Milvus Lite.
 ```python
 from pymilvus import MilvusClient
-
-client = MilvusClient("milvus_demo.db")
+client = MilvusClient("./milvus_demo.db")
 ```
 
 After running the above code snippet, a database file named **milvus_demo.db** will be generated in the current folder.
 
+> **_NOTE:_**  Note that the same API also applies to Milvus Standalone, Milvus Distributed and Zilliz Cloud, the only difference is to replace local file name to remote server endpoint and credentials, e.g. 
+`client = MilvusClient(uri="http://localhost:19530", token="username:password")`.
+
+# Examples
+
+Following is a simple demo showing how to use Milvus Lite for text search. There are more comprehensive [examples](https://github.com/milvus-io/bootcamp/tree/master/bootcamp/tutorials) for using Milvus Lite to build applications
+such as [RAG](https://github.com/milvus-io/bootcamp/blob/master/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb), [image search](https://github.com/milvus-io/bootcamp/blob/master/bootcamp/tutorials/quickstart/image_search_with_milvus.ipynb), and using Milvus Lite in popular RAG framework such as [LangChain](https://github.com/milvus-io/bootcamp/blob/master/bootcamp/tutorials/integration/rag_with_milvus_and_langchain.ipynb) and [LlamaIndex](https://github.com/milvus-io/bootcamp/blob/master/bootcamp/tutorials/integration/rag_with_milvus_and_llamaindex.ipynb)!
+
+```python
+from pymilvus import MilvusClient
+import numpy as np
+
+client = MilvusClient("./milvus_demo.db")
+client.create_collection(
+    collection_name="demo_collection",
+    dimension=384  # The vectors we will use in this demo has 384 dimensions
+)
+
+# Text strings to search from.
+docs = [
+    "Artificial intelligence was founded as an academic discipline in 1956.",
+    "Alan Turing was the first person to conduct substantial research in AI.",
+    "Born in Maida Vale, London, Turing was raised in southern England.",
+]
+# For illustration, here we use fake vectors with random numbers (384 dimension).
+
+vectors = [[ np.random.uniform(-1, 1) for _ in range(384) ] for _ in range(len(docs)) ]
+data = [ {"id": i, "vector": vectors[i], "text": docs[i], "subject": "history"} for i in range(len(vectors)) ]
+res = client.insert(
+    collection_name="demo_collection",
+    data=data
+)
+
+# This will exclude any text in "history" subject despite close to the query vector.
+res = client.search(
+    collection_name="demo_collection",
+    data=[vectors[0]],
+    filter="subject == 'history'",
+    limit=2,
+    output_fields=["text", "subject"],
+)
+print(res)
+
+# a query that retrieves all entities matching filter expressions.
+res = client.query(
+    collection_name="demo_collection",
+    filter="subject == 'history'",
+    output_fields=["text", "subject"],
+)
+print(res)
+
+# delete
+res = client.delete(
+    collection_name="demo_collection",
+    filter="subject == 'history'",
+)
+print(res)
+```
+
 ## Limits
 
 When running Milvus Lite, note that some features are not supported. The following tables summarize the usage limits on Milvus Lite.
@@ -178,6 +241,14 @@ When running Milvus Lite, note that some features are not supported. The followi
 | `index_name`                                                                                                   | Y                                                                                                      |
 | `timeout`                                                                                                      | Y                                                                                                      |
 
+### Vector Index Types
+
+Milvus Lite only supports [FLAT](https://milvus.io/docs/index.md?tab=floating#FLAT) index type. It uses FLAT type regardless of the specified index type in collection.
+
+### Search Features
+
+Milvus Lite supports Sparse Vector, Multi-vector, Hybrid Search.
+
 ### Partition
 
 Milvus Lite does not support partitions and partition-related methods.
@@ -190,9 +261,40 @@ Milvus Lite does not support users and roles and related methods.
 
 Milvus Lite does not support aliases and alias-related methods.
 
-### Others
+# Migrating data from Milvus Lite
+All data stored in Milvus Lite can be easily exported and loaded into other types of Milvus deployment, such as Milvus Standalone on Docker, Milvus Distributed on K8s, or fully-managed Milvus on [Zilliz Cloud](https://zilliz.com/cloud).
+
+Milvus Lite provides a command line tool that can dump data into a json file, which can be imported into [milvus](https://github.com/milvus-io/milvus) and [Zilliz Cloud](https://zilliz.com/cloud)(the fully managed cloud service for Milvus). The milvus-lite command will be installed together with milvus-lite python package 
+
+```shell
+# Install
+pip install -U "pymilvus[bulk_writer]"
+
+milvus-lite dump -h
+
+usage: milvus-lite dump [-h] [-d DB_FILE] [-c COLLECTION] [-p PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -d DB_FILE, --db-file DB_FILE
+                        milvus lite db file
+  -c COLLECTION, --collection COLLECTION
+                        collection that need to be dumped
+  -p PATH, --path PATH  dump file storage dir
+```
+The following example dumps all data from `demo_collection` collection that's stored in `./milvus_demo.db` (Milvus Lite database file)
+
+To export data:
+
+```shell
+milvus-lite dump -d ./milvus_demo.db -c demo_collection -p ./data_dir
+# ./milvus_demo.db: milvus lite db file
+# demo_collection: collection that need to be dumped
+#./data_dir : dump file storage dir
+```
+
+With the dump file, you can upload data to Zilliz Cloud via [Data Import](https://docs.zilliz.com/docs/data-import), or upload data to Milvus servers via [Bulk Insert](https://milvus.io/docs/import-data.md).
 
-For other methods not listed in the above tables, Milvus Lite does not support them.
 
 ## What's next