diff --git a/bootcamp/RAG/readthedocs_zilliz_langchain.ipynb b/bootcamp/RAG/readthedocs_zilliz_langchain.ipynb
index 34202c9ca..691c44555 100755
--- a/bootcamp/RAG/readthedocs_zilliz_langchain.ipynb
+++ b/bootcamp/RAG/readthedocs_zilliz_langchain.ipynb
@@ -206,7 +206,7 @@
     "## Create a Milvus collection\n",
     "\n",
     "You can think of a collection in Milvus like a \"table\" in SQL databases.  The **collection** will contain the \n",
-    "- **Schema** (or no-schema Milvus Client).  \n",
+    "- **Schema** (or [no-schema Milvus client](https://milvus.io/docs/using_milvusclient.md)).  \n",
     "💡 You'll need the vector `EMBEDDING_LENGTH` parameter from your embedding model.\n",
     "Typical values are:\n",
     "   - 768 for sbert embedding models\n",
diff --git a/bootcamp/Retrieval/imdb_milvus_client.ipynb b/bootcamp/Retrieval/imdb_milvus_client.ipynb
index 87627f202..0b8a6f30e 100755
--- a/bootcamp/Retrieval/imdb_milvus_client.ipynb
+++ b/bootcamp/Retrieval/imdb_milvus_client.ipynb
@@ -24,7 +24,7 @@
    "outputs": [],
    "source": [
     "# For colab install these libraries in this order:\n",
-    "# !pip install milvus, pymilvus, langchain, torch, transformers, python-dotenv\n",
+    "# !pip install pymilvus, langchain, torch, transformers, python-dotenv\n",
     "\n",
     "# Import common libraries.\n",
     "import time\n",
@@ -175,11 +175,7 @@
     "- **Vector index** for efficient vector search\n",
     "- **Vector distance metric** for measuring nearest neighbor vectors\n",
     "- **Consistency level**\n",
-    "In Milvus, transactional consistency is possible; however, according to the [CAP theorem](https://en.wikipedia.org/wiki/CAP_theorem), some latency must be sacrificed. 💡 Searching movie reviews is not mission-critical, so [`eventually`](https://milvus.io/docs/consistency.md) consistent is fine here.\n",
-    "\n",
-    "Some supported [data types](https://milvus.io/docs/schema.md) for Milvus schemas are:\n",
-    "- INT64 - primary key\n",
-    "- FLOAT_VECTOR - embedings = list of `numpy.ndarray` of `numpy.float32` numbers\n"
+    "In Milvus, transactional consistency is possible; however, according to the [CAP theorem](https://en.wikipedia.org/wiki/CAP_theorem), some latency must be sacrificed. 💡 Searching movie reviews is not mission-critical, so [`eventually`](https://milvus.io/docs/consistency.md) consistent is fine here.\n"
    ]
   },
   {