diff --git a/docs/docs/modules/chains.ipynb b/docs/docs/modules/chains.ipynb
index 2a9dc6bd55..29e34fa65a 100644
--- a/docs/docs/modules/chains.ipynb
+++ b/docs/docs/modules/chains.ipynb
@@ -69,7 +69,7 @@
     "| [create_stuff_documents_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html#langchain.chains.combine_documents.stuff.create_stuff_documents_chain)     |                         |              | This chain takes a list of documents and formats them all into a prompt, then passes that prompt to an LLM. It passes ALL documents, so you should make sure it fits within the context window the LLM you are using. |\n",
     "| [create_openai_fn_runnable](https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.create_openai_fn_runnable.html#langchain.chains.openai_functions.base.create_openai_fn_runnable)     | ✅ |              | If you want to use OpenAI function calling to OPTIONALLY structured an output response. You may pass in multiple functions for it call, but it does not have to call it.                                     |\n",
     "| [create_structured_output_runnable](https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.create_structured_output_runnable.html#langchain.chains.openai_functions.base.create_structured_output_runnable)   | ✅ |              | If you want to use OpenAI function calling to FORCE the LLM to respond with a certain function. You may only pass in one function, and the chain will ALWAYS return this response.                                      |\n",
-    "| [load_query_constructor_runnable](https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.load_query_constructor_runnable.html#langchain.chains.query_constructor.base.load_query_constructor_runnable)  |                         |              | Can be used to generates queries. You must specify a list of allowed operations, and then will return a runnable that converts a natural language query into those allowed operations.                                                                               |\n",
+    "| [load_query_constructor_runnable](https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.load_query_constructor_runnable.html#langchain.chains.query_constructor.base.load_query_constructor_runnable)  |                         |              | Can be used to generate queries. You must specify a list of allowed operations, and then will return a runnable that converts a natural language query into those allowed operations.                                                                               |\n",
     "| [create_sql_query_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html#langchain.chains.sql_database.query.create_sql_query_chain)           |                         | SQL Database | If you want to construct a query for a SQL database from natural language.                            |\n",
     "| [create_history_aware_retriever](https://api.python.langchain.com/en/latest/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html#langchain.chains.history_aware_retriever.create_history_aware_retriever)   |                         | Retriever    | This chain takes in conversation history and then uses that to generate a search query which is passed to the underlying retriever.                       |\n",
     "| [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html#langchain.chains.retrieval.create_retrieval_chain)           |                         | Retriever    | This chain takes in a user inquiry, which is then passed to the retriever to fetch relevant documents. Those documents (and original inputs) are then passed to an LLM to generate a response      |"