diff --git a/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb b/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb
index 1618239c..dad6498f 100644
--- a/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb
+++ b/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb
@@ -9,13 +9,13 @@
     "This notebook presents how to implement a Question Answering system with Langchain, Qdrant as a knowledge based and OpenAI embeddings. If you are not familiar with Qdrant, it's better to check out the [Getting_started_with_Qdrant_and_OpenAI.ipynb](Getting_started_with_Qdrant_and_OpenAI.ipynb) notebook.\n",
     "\n",
     "This notebook presents an end-to-end process of:\n",
-    "1. Using ra embeddings created by OpenAI API.\n",
+    "1. Calculating the embeddings with OpenAI API.\n",
     "2. Storing the embeddings in a local instance of Qdrant to build a knowledge base.\n",
     "3. Converting raw text query to an embedding with OpenAI API.\n",
     "4. Using Qdrant to perform the nearest neighbour search in the created collection to find some context.\n",
-    "5. Asking LLM to find the answer in given context.\n",
+    "5. Asking LLM to find the answer in a given context.\n",
     "\n",
-    "All the steps will be simplified to a calling some corresponding Langchain methods."
+    "All the steps will be simplified to calling some corresponding Langchain methods."
    ]
   },
   {
@@ -108,7 +108,7 @@
    },
    "outputs": [],
    "source": [
-    "! pip install openai qdrant-client \"langchain==0.0.87\" wget"
+    "! pip install openai qdrant-client \"langchain==0.0.100\" wget"
    ]
   },
   {