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Co-authored-by: cbullinger <[email protected]>

Author: davidhou17

ai-integrations/langchain-graphrag.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "This notebook is a companion to the [GraphRAG with MongoDB and LangChain](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/graph-rag/) tutorial. Refer to the page for set-up instructions and detailed explanations.\n",
     "\n",
-    "This notebook demonstrates a GraphRAG implementation using MongoDB Atlas and LangChain. Compared to vector-based RAG which structures your data as vector embeddings, GraphRAG structures data as a knowledge graph with entities and their relationships. This enables relationship-aware retrieval and multi-hop reasoning.\n",
+    "This notebook demonstrates a GraphRAG implementation using MongoDB Atlas and LangChain. Compared to vector-based RAG, which structures your data as vector embeddings, GraphRAG structures data as a knowledge graph with entities and their relationships. This enables relationship-aware retrieval and multi-hop reasoning.\n",
     "\n",
     "<a target=\"_blank\" href=\"https://colab.research.google.com/github/mongodb/docs-notebooks/blob/main/ai-integrations/langchain-graphrag.ipynb\">\n",
     "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
@@ -38,7 +38,7 @@
     "Before you begin, make sure you have the following:\n",
     "\n",
     "- An Atlas cluster up and running (you'll need the [connection string](https://www.mongodb.com/docs/guides/atlas/connection-string/))\n",
-    "- An API key to access an LLM (This tutorial uses a model from OpenAI, but it can be any model [supported by LangChain](https://python.langchain.com/docs/integrations/chat/))"
+    "- An API key to access an LLM (This tutorial uses a model from OpenAI, but you can use any model [supported by LangChain](https://python.langchain.com/docs/integrations/chat/))"
    ]
   },
   {