Update description in docs/examples/managed/zcpDemo.ipynb
diff --git a/docs/community/integrations/managed_indices.md b/docs/community/integrations/managed_indices.md
index 2ec4d23ba4ba9..6e59e94c53598 100644
--- a/docs/community/integrations/managed_indices.md
+++ b/docs/community/integrations/managed_indices.md
@@ -105,11 +105,10 @@ maxdepth: 1
 
 ## Zilliz
 
-First, [sign up](https://cloud.zilliz.com/signup) or use existing Zilliz Cloud account to create a free Serverless Cluster. This is to get the cluster id and API key to grant access to Zilliz Cloud Pipelines service.
+First, set up your [Zilliz Cloud](https://cloud.zilliz.com/signup?utm_source=twitter&utm_medium=social%20&utm_campaign=2023-12-22_social_pipeline-llamaindex_twitter) account and create a free serverless cluster.
+Then copy the Cluster ID and API Key from your account.
 
-Then set the environment variables `ZILLIZ_CLUSTER_ID` and `ZILLIZ_TOKEN` by copying the value from the [Zilliz Cloud UI](https://raw.githubusercontent.com/milvus-io/bootcamp/2596ea9a4a1a089101a0b46e3cb012b8dfb2eb9a/images/zilliz_api_key_cluster_id.jpeg).
-
-Now you can construct the `ZillizCloudPipelineIndex` to ingest docs and query index as follows:
+Now you can construct `ZillizCloudPipelineIndex` to index docs and query as follows:
 
 ```python
 import os
@@ -120,9 +119,9 @@ from llama_index.indices import ZillizCloudPipelineIndex
 # Load documents from url and build document index
 zcp_index = ZillizCloudPipelineIndex.from_document_url(
     url="https://publicdataset.zillizcloud.com/milvus_doc.md",
-    cluster_id=os.getenv("ZILLIZ_CLUSTER_ID"),
-    token=os.getenv("ZILLIZ_TOKEN"),
-    metadata={"version": "2.3"},
+    cluster_id="<YOUR_ZILLIZ_CLUSTER_ID>",
+    token="<YOUR_ZILLIZ_API_KEY>",
+    metadata={"version": "2.3"},  # optional
 )
 
 # Insert more docs into index, eg. a Milvus v2.2 document
@@ -143,6 +142,12 @@ query_engine_milvus23 = zcp_index.as_query_engine(
     ),
     output_metadata=["version"],
 )
+
+question = "Can users delete entities by complex boolean expressions?"
+# Retrieving
+retrieval_result = query_engine_with_filters.retrieve(question)
+# Querying
+answer = query_engine_with_filters.query(question)
 ```
 
 ```{toctree}
diff --git a/docs/examples/managed/zcpDemo.ipynb b/docs/examples/managed/zcpDemo.ipynb
index 7ed859e0847bf..77c89894bc40f 100644
--- a/docs/examples/managed/zcpDemo.ipynb
+++ b/docs/examples/managed/zcpDemo.ipynb
@@ -13,9 +13,9 @@
    "id": "db0855d0",
    "metadata": {},
    "source": [
-    "# Managed Index with Zilliz Cloud Pipeline\n",
+    "# Managed Index with Zilliz Cloud Pipelines\n",
     "\n",
-    "[Zilliz Cloud Pipelines](https://docs.zilliz.com/docs/pipelines) is a robust solution that efficiently transforms unstructured data into a vector database for effective semantic search.\n",
+    "[Zilliz Cloud Pipelines](https://docs.zilliz.com/docs/pipelines) is a scalable API service for retrieval. You can use Zilliz Cloud Pipelines as managed index in `llama-index`. This service can transform documents into vector embeddings and store them in Zilliz Cloud for effective semantic search.\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -37,7 +37,7 @@
    "id": "3fc94b2f",
    "metadata": {},
    "source": [
-    "2. Set your [OpenAI](https://platform.openai.com) & [Zilliz Cloud](https://cloud.zilliz.com/) accounts"
+    "2. Configure credentials of your [OpenAI](https://platform.openai.com) & [Zilliz Cloud](https://cloud.zilliz.com/signup?utm_source=twitter&utm_medium=social%20&utm_campaign=2023-12-22_social_pipeline-llamaindex_twitter) accounts."
    ]
   },
   {
@@ -53,7 +53,7 @@
     "os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OpenAI API Key:\")\n",
     "\n",
     "ZILLIZ_CLUSTER_ID = getpass(\"Enter your Zilliz Cluster ID:\")\n",
-    "ZILLIZ_TOKEN = getpass(\"Enter your Zilliz Token:\")"
+    "ZILLIZ_TOKEN = getpass(\"Enter your Zilliz API Key:\")"
    ]
   },
   {
@@ -65,7 +65,7 @@
     "\n",
     "### From Signed URL\n",
     "\n",
-    "Zilliz Cloud Pipeline is able to ingest & automatically index a document given a presigned url."
+    "Zilliz Cloud Pipelines accepts files from AWS S3 and Google Cloud Storage. You can generate a presigned url from the Object Storage and use `from_document_url()` or `insert_doc_url()` to ingest the file. It can automatically index the document and store the doc chunks as vectors on Zilliz Cloud."
    ]
   },
   {
@@ -78,10 +78,13 @@
     "from llama_index.indices import ZillizCloudPipelineIndex\n",
     "\n",
     "zcp_index = ZillizCloudPipelineIndex.from_document_url(\n",
-    "    url=\"https://publicdataset.zillizcloud.com/milvus_doc.md\",  # a public or pre-signed url of a file stored on s3 or gcs\n",
+    "    # a public or pre-signed url of a file stored on AWS S3 or Google Cloud Storage\n",
+    "    url=\"https://publicdataset.zillizcloud.com/milvus_doc.md\",\n",
     "    cluster_id=ZILLIZ_CLUSTER_ID,\n",
     "    token=ZILLIZ_TOKEN,\n",
-    "    metadata={\"version\": \"2.3\"},\n",
+    "    # optional\n",
+    "    metadata={\"version\": \"2.3\"},  # used for filtering\n",
+    "    collection_name=\"zcp_llamalection\",  # change this value will specify customized collection name\n",
     ")\n",
     "\n",
     "# Insert more docs, eg. a Milvus v2.2 document\n",
@@ -96,6 +99,8 @@
    "id": "d16a498e",
    "metadata": {},
    "source": [
+    "- It is optional to add metadata for each document. The metadata can be used to filter doc chunks during retrieval.\n",
+    "\n",
     "### From Local File\n",
     "\n",
     "Coming soon.\n",
@@ -112,13 +117,10 @@
    "source": [
     "## Working as Query Engine\n",
     "\n",
-    "A Zilliz Cloud Pipeline's Index can work as a Query Engine in LlamaIndex.\n",
-    "It allows users to customize some parameters:\n",
-    "- search_top_k: How many text nodes/chunks retrieved. Optional, defaults to `DEFAULT_SIMILARITY_TOP_K` (2).\n",
+    "To conduct semantic search with `ZillizCloudPipelineIndex`, you can use it `as_query_engine()` by specifying a few parameters:\n",
+    "- search_top_k: How many text nodes/chunks to retrieve. Optional, defaults to `DEFAULT_SIMILARITY_TOP_K` (2).\n",
     "- filters: Metadata filters. Optional, defaults to None.\n",
-    "- output_metadata: What metadata fields included in each retrieved text node. Optional, defaults to [].\n",
-    "\n",
-    "It is optional to apply filters. For example, if we want to ask about Milvus 2.3, then we can set version as 2.3 in filters."
+    "- output_metadata: What metadata fields to return with the retrieved text node. Optional, defaults to []."
    ]
   },
   {
@@ -128,13 +130,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# # Get index without ingestion:\n",
+    "# # If you don't have zcp_index object and have an existing collection, you can construct it by:\n",
+    "#\n",
     "# from llama_index.indices import ZillizCloudPipelineIndex\n",
-    "\n",
     "# zcp_index = ZillizCloudPipelineIndex(\n",
     "#         cluster_id=ZILLIZ_CLUSTER_ID,\n",
     "#         token=ZILLIZ_TOKEN,\n",
-    "#         # collection_name='zcp_llamalection'\n",
+    "#         collection_name=\"zcp_llamalection\"\n",
     "#     )\n",
     "\n",
     "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
@@ -157,7 +159,7 @@
    "source": [
     "Then the query engine is ready for Semantic Search or Retrieval Augmented Generation with Milvus 2.3 documents:\n",
     "\n",
-    "- **Retrieve** (Semantic search powered by Zilliz Cloud Pipeline's Index):"
+    "- **Retrieve** (Semantic search powered by Zilliz Cloud Pipelines):"
    ]
   },
   {
@@ -175,9 +177,7 @@
     }
    ],
    "source": [
-    "question = \"Can users delete entities by filtering non-primary fields?\"\n",
-    "retrieved_nodes = query_engine_milvus23.retrieve(question)\n",
-    "print(retrieved_nodes)"
+    "> The query engine with filters retrieves only text nodes with \"version 2.3\" tag."
    ]
   },
   {
@@ -185,7 +185,7 @@
    "id": "e91c5896",
    "metadata": {},
    "source": [
-    "- **Query** (RAG powered by Zilliz Cloud Pipeline's Index & OpenAI's LLM):"
+    "- **Query** (RAG powered by Zilliz Cloud Pipelines as retriever and OpenAI's LLM):"
    ]
   },
   {

# Description

Update description in docs/examples/managed/zcpDemo.ipynb

Fixes # (issue)

## Type of Change

Please delete options that are not relevant.

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] This change requires a documentation update

# How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration

- [ ] Added new unit/integration tests
- [ ] Added new notebook (that tests end-to-end)
- [ ] I stared at the code and made sure it makes sense

# Suggested Checklist:

- [ ] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] I have added Google Colab support for the newly added notebooks.
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] I ran `make format; make lint` to appease the lint gods
