docs: fix lets typos in multiple files (#31481)

Fix typo

Author: MichaelLi65535

cookbook/rag_with_quantized_embeddings.ipynb

@@ -53,7 +53,7 @@
    "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
    "metadata": {},
    "source": [
-    "Lets first load up this paper, and split into text chunks of size 1000."
+    "Let's first load up this paper, and split into text chunks of size 1000."
    ]
   },
   {
@@ -241,7 +241,7 @@
    "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
    "metadata": {},
    "source": [
-    "With our embedder in place, lets define our retriever:"
+    "With our embedder in place, let's define our retriever:"
    ]
   },
   {
@@ -312,7 +312,7 @@
    "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
    "metadata": {},
    "source": [
-    "Lets write our documents into our new store. This will use our embedder on each document."
+    "Let's write our documents into our new store. This will use our embedder on each document."
    ]
   },
   {
@@ -339,7 +339,7 @@
    "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
    "metadata": {},
    "source": [
-    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
+    "Great! Our retriever is good to go. Let's load up an LLM, that will reason over the retrieved documents:"
    ]
   },
   {
@@ -430,7 +430,7 @@
    "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
    "metadata": {},
    "source": [
-    "Excellent! lets ask it a question.\n",
+    "Excellent! Let's ask it a question.\n",
     "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
    ]
   },

docs/docs/how_to/llm_caching.ipynb

@@ -40,7 +40,7 @@
     "from langchain_core.globals import set_llm_cache\n",
     "from langchain_openai import OpenAI\n",
     "\n",
-    "# To make the caching really obvious, lets use a slower and older model.\n",
+    "# To make the caching really obvious, let's use a slower and older model.\n",
     "# Caching supports newer chat models as well.\n",
     "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", n=2, best_of=2)"
    ]

docs/docs/integrations/llm_caching.ipynb

@@ -51,7 +51,7 @@
     "from langchain.globals import set_llm_cache\n",
     "from langchain_openai import OpenAI\n",
     "\n",
-    "# To make the caching really obvious, lets use a slower and older model.\n",
+    "# To make the caching really obvious, let's use a slower and older model.\n",
     "# Caching supports newer chat models as well.\n",
     "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\", n=2, best_of=2)"
    ]

docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb

@@ -211,7 +211,7 @@
    "id": "b6e7b9cf-8ce5-4f87-b4bf-100321ad2dd1",
    "metadata": {},
    "source": [
-    "***The result is usually closer to the JSON object of the schema definition, rather than a json object conforming to the schema. Lets try to enforce proper output.***"
+    "***The result is usually closer to the JSON object of the schema definition, rather than a json object conforming to the schema. Let's try to enforce proper output.***"
    ]
   },
   {

docs/docs/integrations/providers/portkey/index.md

@@ -49,7 +49,7 @@ The power of the AI gateway comes when you're able to use the above code snippet
 
 Let's modify the code above to make a call to Anthropic's `claude-3-opus-20240229` model.
 
-Portkey supports **[Virtual Keys](https://docs.portkey.ai/docs/product/ai-gateway-streamline-llm-integrations/virtual-keys)** which are an easy way to store and manage API keys in a secure vault. Lets try using a Virtual Key to make LLM calls. You can navigate to the Virtual Keys tab in Portkey and create a new key for Anthropic.
+Portkey supports **[Virtual Keys](https://docs.portkey.ai/docs/product/ai-gateway-streamline-llm-integrations/virtual-keys)** which are an easy way to store and manage API keys in a secure vault. Let's try using a Virtual Key to make LLM calls. You can navigate to the Virtual Keys tab in Portkey and create a new key for Anthropic.
 
 The `virtual_key` parameter sets the authentication and provider for the AI provider being used. In our case we're using the Anthropic Virtual key.
 

docs/docs/integrations/text_embedding/optimum_intel.ipynb

@@ -61,7 +61,7 @@
    "id": "34318164-7a6f-47b6-8690-3b1d71e1fcfc",
    "metadata": {},
    "source": [
-    "Lets ask a question, and compare to 2 documents. The first contains the answer to the question, and the second one does not. \n",
+    "Let's ask a question, and compare to 2 documents. The first contains the answer to the question, and the second one does not. \n",
     "\n",
     "We can check better suits our query."
    ]

docs/docs/integrations/text_embedding/premai.ipynb

@@ -25,7 +25,7 @@
    "source": [
     "## PremEmbeddings\n",
     "\n",
-    "In this section we are going to dicuss how we can get access to different embedding model using `PremEmbeddings` with LangChain. Lets start by importing our modules and setting our API Key. "
+    "In this section we are going to dicuss how we can get access to different embedding model using `PremEmbeddings` with LangChain. Let's start by importing our modules and setting our API Key. "
    ]
   },
   {