docs: Correct grammatical typos in various documentation files (#29983)

**Description:**
Fixed grammatical typos in various documentation files

**Issue:**
N/A

**Dependencies:**
N/A

**Twitter handle:**
@MrNaveenSK

Co-authored-by: ccurme <chester.curme@gmail.com>

cookbook/elasticsearch_db_qa.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "PROMPT_TEMPLATE = \"\"\"Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n",
     "\n",
-    "Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.\n",
+    "Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.\n",
     "\n",
     "Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.\n",
     "\n",

cookbook/sql_db_qa.mdx

@@ -233,7 +233,7 @@ Question: {input}"""
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 

docs/docs/how_to/document_loader_custom.ipynb

@@ -551,7 +551,7 @@
     "\n",
     "While a parser encapsulates the logic needed to parse binary data into documents, *blob loaders* encapsulate the logic that's necessary to load blobs from a given storage location.\n",
     "\n",
-    "A the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
+    "At the moment, `LangChain` only supports `FileSystemBlobLoader`.\n",
     "\n",
     "You can use the `FileSystemBlobLoader` to load blobs and then use the parser to parse them."
    ]

docs/docs/how_to/migrate_agent.ipynb

@@ -329,7 +329,7 @@
    "id": "fc6059fd-0df7-4b6f-a84c-b5874e983638",
    "metadata": {},
    "source": [
-    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a the graph state and output a list of messages.\n",
+    "We can also pass in an arbitrary function or a runnable. This function/runnable should take in a graph state and output a list of messages.\n",
     "We can do all types of arbitrary formatting of messages here. In this case, let's add a SystemMessage to the start of the list of messages and append another user message at the end."
    ]
   },

docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb

@@ -221,7 +221,7 @@
    "source": [
     "## JSONFormer LLM Wrapper\n",
     "\n",
-    "Let's try that again, now providing a the Action input's JSON Schema to the model."
+    "Let's try that again, now providing the Action input's JSON Schema to the model."
    ]
   },
   {

docs/docs/tutorials/qa_chat_history.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n",
     "\n",
-    "This is a the second part of a multi-part tutorial:\n",
+    "This is the second part of a multi-part tutorial:\n",
     "\n",
     "- [Part 1](/docs/tutorials/rag) introduces RAG and walks through a minimal implementation.\n",
     "- [Part 2](/docs/tutorials/qa_chat_history) (this guide) extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes.\n",

libs/langchain/langchain/chains/elasticsearch_database/prompts.py

@@ -9,7 +9,7 @@ ESQuery:"""
 
 DEFAULT_DSL_TEMPLATE = """Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.
+Unless told to do not query for all the columns from a specific index, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.
 

libs/langchain/langchain/chains/sql_database/prompt.py

@@ -10,7 +10,7 @@ Question: {input}"""
 
 _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
 
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
 
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.