docs: fix typos in rag

src/oss/langchain/rag.mdx

@@ -127,7 +127,7 @@ Select a vector store:
 
 ## Preview
 
-In this guide we’ll build an app that answers questions about the website's content. The specific website we will use is the [LLM Powered Autonomous
+In this guide we'll build an app that answers questions about the website's content. The specific website we will use is the [LLM Powered Autonomous
 Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) blog post
 by Lilian Weng, which allows us to ask questions about the contents of
 the post.
@@ -289,7 +289,7 @@ trace](https://smith.langchain.com/public/a117a1f8-c96c-4c16-a285-00b85646118e/r
 
 ## Detailed walkthrough
 
-Let’s go through the above code step-by-step to really understand what’s
+Let's go through the above code step-by-step to really understand what's
 going on.
 
 ## 1. Indexing
@@ -323,15 +323,15 @@ objects.
 
 
 :::python
-In this case we’ll use the
+In this case we'll use the
 [WebBaseLoader](/oss/integrations/document_loaders/web_base),
 which uses `urllib` to load HTML from web URLs and `BeautifulSoup` to
 parse it to text. We can customize the HTML -\> text parsing by passing
 in parameters into the `BeautifulSoup` parser via `bs_kwargs` (see
 [BeautifulSoup
 docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)).
 In this case only HTML tags with class “post-content”, “post-title”, or
-“post-header” are relevant, so we’ll remove all others.
+“post-header” are relevant, so we'll remove all others.
 
 ```python
 import bs4
@@ -410,7 +410,7 @@ into the context window of many models. Even for those models that could
 fit the full post in their context window, models can struggle to find
 information in very long inputs.
 
-To handle this we’ll split the `Document` into chunks for embedding and
+To handle this we'll split the `Document` into chunks for embedding and
 vector storage. This should help us retrieve only the most relevant parts
 of the blog post at run time.
 
@@ -516,7 +516,7 @@ RAG applications commonly work as follows:
 
 ![retrieval_diagram](/images/rag_retrieval_generation.png)
 
-Now let’s write the actual application logic. We want to create a simple
+Now let's write the actual application logic. We want to create a simple
 application that takes a user question, searches for documents relevant
 to that question, passes the retrieved documents and initial question to
 a model, and returns an answer.
@@ -744,7 +744,7 @@ for more advanced formulations.
 
 In the above [agentic RAG](#rag-agents) formulation we allow the LLM to use its discretion in
 generating a [tool call](/oss/langchain/models#tool-calling) to help answer user queries. This
-is a good general purpose solution, but comes with some trade-offs:
+is a good general-purpose solution, but comes with some trade-offs:
 
 | ✅ Benefits                                                                 | ⚠️ Drawbacks                                                                 |
 |-----------------------------------------------------------------------------|----------------------------------------------------------------------------|
@@ -775,7 +775,7 @@ def prompt_with_context(state: AgentState) -> list[MessageLikeRepresentation]:
     docs_content = "\n\n".join(doc.page_content for doc in retrieved_docs)
 
     system_message = (
-        "You are a helpful assistant. Use the following context in your reseponse:"
+        "You are a helpful assistant. Use the following context in your response:"
         f"\n\n{docs_content}"
     )