Merge pull request milvus-io#1364 from zc277584121/master

add zilliz cloud description, add metadata filtering in LC & LI integration

bootcamp/tutorials/integration/evaluation_with_ragas.ipynb

@@ -238,11 +238,23 @@
    "outputs": [],
    "source": [
     "openai_client = OpenAI()\n",
-    "milvus_client = MilvusClient(\"./milvus_demo.db\")\n",
+    "milvus_client = MilvusClient(uri=\"./milvus_demo.db\")\n",
     "\n",
     "my_rag = RAG(openai_client=openai_client, milvus_client=milvus_client)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> As for the argument of `MilvusClient`:\n",
+    "> - Setting the `uri` as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n",
+    "> - If you have large scale of data, you can set up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server uri, e.g.`http://localhost:19530`, as your `uri`.\n",
+    "> - If you want to use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "markdown",
    "source": [

bootcamp/tutorials/integration/milvus_and_DSPy.ipynb

@@ -3,7 +3,9 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "<a href=\"https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/bootcamp/tutorials/integration/milvus_and_DSPy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/bootcamp/tutorials/integration/milvus_and_DSPy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ],
    "id": "cc9c54a108f3efae"
   },
   {
@@ -106,7 +108,9 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "> If you are using Google Colab, to enable dependencies just installed, you may need to **restart the runtime** (click on the \"Runtime\" menu at the top of the screen, and select \"Restart session\" from the dropdown menu).",
+   "source": [
+    "> If you are using Google Colab, to enable dependencies just installed, you may need to **restart the runtime** (click on the \"Runtime\" menu at the top of the screen, and select \"Restart session\" from the dropdown menu)."
+   ],
    "id": "bbf27b3225a33dae"
   },
   {
@@ -482,4 +486,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

bootcamp/tutorials/integration/milvus_with_Jina.ipynb

@@ -256,10 +256,24 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> As for the argument of `MilvusClient`:\n",
+    "> - Setting the `uri` as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n",
+    "> - If you have large scale of data, you can set up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server uri, e.g.`http://localhost:19530`, as your `uri`.\n",
+    "> - If you want to use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "With all data in Milvus vector database, we can now perform semantic search by generating vector embedding for the query and conduct vector search.",
+   "source": [
+    "With all data in Milvus vector database, we can now perform semantic search by generating vector embedding for the query and conduct vector search."
+   ],
    "id": "774929336febc81d"
   },
   {

bootcamp/tutorials/integration/qa_with_milvus_and_hf.ipynb

@@ -161,7 +161,7 @@
     "\n",
     "Now we have question-answer pairs ready with question embeddings. The next step is to insert them into the vector database.\n",
     "\n",
-    "We will first need to connect to Milvus service and create a Milvus collection. This section will use [Milvus Lite](https://milvus.io/docs/milvus_lite.md) as example. If you want to use other types of Milvus or [Zilliz Cloud](https://zilliz.com), please make sure you have started the service and connect with your own URI & credentials. You are also able to change parameters to customize your collection."
+    "We will first need to connect to Milvus service and create a Milvus collection."
    ]
   },
   {
@@ -190,6 +190,18 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> As for the argument of `MilvusClient`:\n",
+    "> - Setting the `uri` as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n",
+    "> - If you have large scale of data, you can set up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server uri, e.g.`http://localhost:19530`, as your `uri`.\n",
+    "> - If you want to use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

bootcamp/tutorials/integration/rag_with_milvus_and_bentoml.ipynb

@@ -287,6 +287,18 @@
    ],
    "id": "f4fc2677daaefe9b"
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> As for the argument of `MilvusClient`:\n",
+    "> - Setting the `uri` as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n",
+    "> - If you have large scale of data, you can set up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server uri, e.g.`http://localhost:19530`, as your `uri`.\n",
+    "> - If you want to use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "metadata": {},
    "cell_type": "markdown",

bootcamp/tutorials/integration/rag_with_milvus_and_haystack.ipynb

@@ -131,6 +131,47 @@
     "Create an indexing pipeline that converts the text into documents, splits them into sentences, and embeds them. The documents are then written to the Milvus document store."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from haystack import Pipeline\n",
+    "from haystack.components.converters import MarkdownToDocument\n",
+    "from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder\n",
+    "from haystack.components.preprocessors import DocumentSplitter\n",
+    "from haystack.components.writers import DocumentWriter\n",
+    "\n",
+    "from milvus_haystack import MilvusDocumentStore\n",
+    "from milvus_haystack.milvus_embedding_retriever import MilvusEmbeddingRetriever\n",
+    "\n",
+    "\n",
+    "document_store = MilvusDocumentStore(\n",
+    "    connection_args={\"uri\": \"./milvus.db\"},\n",
+    "    # connection_args={\"uri\": \"http://localhost:19530\"},\n",
+    "    # connection_args={\"uri\": YOUR_ZILLIZ_CLOUD_URI, \"token\": YOUR_ZILLIZ_API_KEY},\n",
+    "    drop_old=True,\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> For the connection_args:\n",
+    "> - Setting the `uri` as a local file, e.g.`./milvus.db`, is the most convenient method, as it automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file.\n",
+    "> - If you have large scale of data, you can set up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/quickstart.md). In this setup, please use the server uri, e.g.`http://localhost:19530`, as your `uri`.\n",
+    "> - If you want to use [Zilliz Cloud](https://zilliz.com/cloud), the fully managed cloud service for Milvus, adjust the `uri` and `token`, which correspond to the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -165,19 +206,6 @@
     }
    ],
    "source": [
-    "from haystack import Pipeline\n",
-    "from haystack.components.converters import MarkdownToDocument\n",
-    "from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder\n",
-    "from haystack.components.preprocessors import DocumentSplitter\n",
-    "from haystack.components.writers import DocumentWriter\n",
-    "\n",
-    "from milvus_haystack import MilvusDocumentStore\n",
-    "from milvus_haystack.milvus_embedding_retriever import MilvusEmbeddingRetriever\n",
-    "\n",
-    "document_store = MilvusDocumentStore(\n",
-    "    connection_args={\"uri\": \"./milvus.db\"},\n",
-    "    drop_old=True,\n",
-    ")\n",
     "indexing_pipeline = Pipeline()\n",
     "indexing_pipeline.add_component(\"converter\", MarkdownToDocument())\n",
     "indexing_pipeline.add_component(\n",