docs: Fix typo in modules/chains.ipynb (#18808)

**Description:**  

Fix a minor typo in `modules/chains.ipynb`.
 
- **Issue:** 
    fixes #17851

docs/docs/expression_language/get_started.ipynb

@@ -31,9 +31,11 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "278b0027",
    "metadata": {},
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  langchain-core langchain-community langchain-openai"
    ]

docs/docs/modules/chains.ipynb

@@ -134,7 +134,7 @@
     "|[QAGenerationChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_generation.base.QAGenerationChain.html#langchain.chains.qa_generation.base.QAGenerationChain)                                          |            |                    |Creates both questions and answers from documents. Can be used to generate question/answer pairs for evaluation of retrieval projects.                        |             \n",
     "|[RetrievalQAWithSourcesChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html#langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain)                              |                        |                          Retriever                    |Does question answering over retrieved documents, and cites it sources. Use this when you want the answer response to have sources in the text response. Use this over `load_qa_with_sources_chain` when you want to use a retriever to fetch the relevant document as part of the chain (rather than pass them in).|             \n",
     "|[load_qa_with_sources_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain.html#langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain)                                                     | |Retriever                    |Does question answering over documents you pass in, and cites it sources. Use this when you want the answer response to have sources in the text response. Use this over RetrievalQAWithSources when you want to pass in the documents directly (rather than rely on a retriever to get them).|              \n",
-    "|[RetrievalQA](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html#langchain.chains.retrieval_qa.base.RetrievalQA)   |                                            |Retriever                        |This chain first does a retrieval step to fetch relevant documents, then passes those documents into an LLM to generate a respoinse.|\n",
+    "|[RetrievalQA](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.RetrievalQA.html#langchain.chains.retrieval_qa.base.RetrievalQA)   |                                            |Retriever                        |This chain first does a retrieval step to fetch relevant documents, then passes those documents into an LLM to generate a response.|\n",
     "|[MultiPromptChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_prompt.MultiPromptChain.html#langchain.chains.router.multi_prompt.MultiPromptChain)                                                      |                    |                        |This chain routes input between multiple prompts. Use this when you have multiple potential prompts you could use to respond and want to route to just one. |            \n",
     "|[MultiRetrievalQAChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.multi_retrieval_qa.MultiRetrievalQAChain.html#langchain.chains.router.multi_retrieval_qa.MultiRetrievalQAChain)|                                            |Retriever                        |This chain routes input between multiple retrievers. Use this when you have multiple potential retrievers you could fetch relevant documents from and want to route to just one. |               \n",
     "|[EmbeddingRouterChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html#langchain.chains.router.embedding_router.EmbeddingRouterChain)|                                            |                        |This chain uses embedding similarity to route incoming queries.|            \n",