fix copy

Author: gdj0nes

docs/rag-getting-started.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "# Build a RAG chatbot\n",
     "\n",
-    "This notebook shows you how to build a simple RAG chatbot in Python using Pinecone as the vector database, [OpenAI](https://docs.pinecone.io/integrations/openai) for the embedding model and LLM, and [LangChain](https://docs.pinecone.io/integrations/langchain) for the RAG workflow.\n",
+    "This page shows you how to build a simple RAG chatbot in Python using Pinecone for the vector database and embedding model, [OpenAI](https://docs.pinecone.io/integrations/openai) for the LLM, and [LangChain](https://docs.pinecone.io/integrations/langchain) for the RAG workflow.\n",
     "\n",
     "## How it works\n",
     "\n",
@@ -129,7 +129,7 @@
    "source": [
     "## Store knowledge in Pinecone\n",
     "\n",
-    "For this guide, you'll use a document about a fictional product called the WonderVector5000 that LLMs do not have any information about. You'll use LangChain to chunk the document into smaller segments, convert each segment into vectors using OpenAI, and then upsert your vectors into your Pinecone index.\n"
+    "For this guide, you'll use a document about a fictional product called the WonderVector5000 that LLMs do not have any information about. You'll use LangChain to chunk the document into smaller segments, convert each segment into vectors using, and then upsert your vectors into your Pinecone index.\n"
    ]
   },
   {
@@ -277,7 +277,7 @@
     "id": "1GKq1M_5tiHX"
    },
    "source": [
-    "Initialize a LangChain embedding object. Note that this step uses the OpenAI API key you set as an environment variable earlier. OpenAI is a paid service, so running the remainder of this guide may incur some small cost."
+    "Initialize a LangChain embedding object. Note that this step uses a Pinecone API key you set as an environment variable earlier."
    ]
   },
   {
@@ -370,7 +370,7 @@
     "id": "fWsPiLBZ67wG"
    },
    "source": [
-    "Now create a serverless index in Pinecone for storing the embeddings of your document, setting the index dimensions and distance metric to match those of the OpenAI `text-embedding-3-small` model you'll use to create the embeddings:\n",
+    "Now create a serverless index in Pinecone for storing the embeddings of your document, setting the index dimensions and distance metric to match those of the Pinecone `multilingual-e5-large` model you'll use to create the embeddings:\n",
     "\n"
    ]
   },
@@ -2035,6 +2035,8 @@
     "\n",
     "Now that your document is stored as embeddings in Pinecone, when you send questions to the LLM, you can add relevant knowledge from your Pinecone index to ensure that the LLM returns an accurate response.\n",
     "\n",
+    "OpenAI is a paid service, so running the remainder of this guide may incur some small cost.\n",
+    "\n",
     "Initialize a LangChain object for interacting with the `gpt-3.5-turbo` LLM:"
    ]
   },