docs: fix langchain expression language link (#22683)

docs/docs/contributing/documentation/style_guide.mdx

@@ -55,7 +55,7 @@ The below sections are listed roughly in order of increasing level of abstractio
 
 ### Expression Language
 
-[LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language) is the fundamental way that most LangChain components fit together, and this section is designed to teach
+[LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language-lcel) is the fundamental way that most LangChain components fit together, and this section is designed to teach
 developers how to use it to build with LangChain's primitives effectively.
 
 This section should contains **Tutorials** that teach how to stream and use LCEL primitives for more abstract tasks, **Explanations** of specific behaviors,

docs/docs/how_to/output_parser_structured.ipynb

@@ -94,7 +94,7 @@
    "source": [
     "## LCEL\n",
     "\n",
-    "Output parsers implement the [Runnable interface](/docs/concepts#interface), the basic building block of the [LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language). This means they support `invoke`, `ainvoke`, `stream`, `astream`, `batch`, `abatch`, `astream_log` calls.\n",
+    "Output parsers implement the [Runnable interface](/docs/concepts#interface), the basic building block of the [LangChain Expression Language (LCEL)](/docs/concepts#langchain-expression-language-lcel). This means they support `invoke`, `ainvoke`, `stream`, `astream`, `batch`, `abatch`, `astream_log` calls.\n",
     "\n",
     "Output parsers accept a string or `BaseMessage` as input and can return an arbitrary type."
    ]

docs/docs/how_to/qa_sources.ipynb

@@ -14,7 +14,7 @@
     "We will cover two approaches:\n",
     "\n",
     "1. Using the built-in [create_retrieval_chain](https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval.create_retrieval_chain.html), which returns sources by default;\n",
-    "2. Using a simple [LCEL](/docs/concepts#langchain-expression-language) implementation, to show the operating principle."
+    "2. Using a simple [LCEL](/docs/concepts#langchain-expression-language-lcel) implementation, to show the operating principle."
    ]
   },
   {

docs/docs/integrations/chat/cohere.ipynb

@@ -201,7 +201,7 @@
    "source": [
     "## Chaining\n",
     "\n",
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language)"
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
    ]
   },
   {

docs/docs/integrations/chat/mistralai.ipynb

@@ -225,7 +225,7 @@
    "source": [
     "## Chaining\n",
     "\n",
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language)"
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
    ]
   },
   {

docs/docs/integrations/document_loaders/tomarkdown.ipynb

@@ -113,7 +113,7 @@
       "\n",
       "LCEL is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.\n",
       "\n",
-      "- **[Overview](/docs/concepts#langchain-expression-language)**: LCEL and its benefits\n",
+      "- **[Overview](/docs/concepts#langchain-expression-language-lcel)**: LCEL and its benefits\n",
       "- **[Interface](/docs/concepts#interface)**: The standard interface for LCEL objects\n",
       "- **[How-to](/docs/expression_language/how_to)**: Key features of LCEL\n",
       "- **[Cookbook](/docs/expression_language/cookbook)**: Example code for accomplishing common tasks\n",

docs/docs/integrations/llms/cohere.ipynb

@@ -193,7 +193,7 @@
    "id": "39198f7d-6fc8-4662-954a-37ad38c4bec4",
    "metadata": {},
    "source": [
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language)"
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
    ]
   },
   {

docs/docs/integrations/llms/google_vertex_ai_palm.ipynb

@@ -328,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language)"
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/concepts#langchain-expression-language-lcel)"
    ]
   },
   {

docs/docs/introduction.mdx

@@ -8,7 +8,7 @@ sidebar_class_name: hidden
 **LangChain** is a framework for developing applications powered by large language models (LLMs).
 
 LangChain simplifies every stage of the LLM application lifecycle:
-- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language) and [components](/docs/concepts). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
+- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/concepts#langchain-expression-language-lcel) and [components](/docs/concepts). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
 - **Productionization**: Use [LangSmith](https://docs.smith.langchain.com/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
 - **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).
 

docs/docs/tutorials/qa_chat_history.ipynb

@@ -737,7 +737,7 @@
    "id": "07dcb968-ed9a-458a-85e1-528cd28c6965",
    "metadata": {},
    "source": [
-    "Tools are LangChain [Runnables](/docs/concepts#langchain-expression-language), and implement the usual interface:"
+    "Tools are LangChain [Runnables](/docs/concepts#langchain-expression-language-lcel), and implement the usual interface:"
    ]
   },
   {

docs/docs/tutorials/rag.ipynb

@@ -667,7 +667,7 @@
    "id": "4516200c",
    "metadata": {},
    "source": [
-    "We’ll use the [LCEL Runnable](/docs/concepts#langchain-expression-language)\n",
+    "We’ll use the [LCEL Runnable](/docs/concepts#langchain-expression-language-lcel)\n",
     "protocol to define the chain, allowing us to \n",
     "\n",
     "- pipe together components and functions in a transparent way \n",
@@ -718,7 +718,7 @@
    "source": [
     "Let's dissect the LCEL to understand what's going on.\n",
     "\n",
-    "First: each of these components (`retriever`, `prompt`, `llm`, etc.) are instances of [Runnable](/docs/concepts#langchain-expression-language). This means that they implement the same methods-- such as sync and async `.invoke`, `.stream`, or `.batch`-- which makes them easier to connect together. They can be connected into a [RunnableSequence](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableSequence.html)-- another Runnable-- via the `|` operator.\n",
+    "First: each of these components (`retriever`, `prompt`, `llm`, etc.) are instances of [Runnable](/docs/concepts#langchain-expression-language-lcel). This means that they implement the same methods-- such as sync and async `.invoke`, `.stream`, or `.batch`-- which makes them easier to connect together. They can be connected into a [RunnableSequence](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableSequence.html)-- another Runnable-- via the `|` operator.\n",
     "\n",
     "LangChain will automatically cast certain objects to runnables when met with the `|` operator. Here, `format_docs` is cast to a [RunnableLambda](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableLambda.html), and the dict with `\"context\"` and `\"question\"` is cast to a [RunnableParallel](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableParallel.html). The details are less important than the bigger point, which is that each object is a Runnable.\n",
     "\n",

docs/docs/tutorials/sql_qa.ipynb

@@ -120,7 +120,7 @@
     "\n",
     "## Chains {#chains}\n",
     "\n",
-    "Chains (i.e., compositions of LangChain [Runnables](/docs/concepts#langchain-expression-language)) support applications whose steps are predictable. We can create a simple chain that takes a question and does the following:\n",
+    "Chains (i.e., compositions of LangChain [Runnables](/docs/concepts#langchain-expression-language-lcel)) support applications whose steps are predictable. We can create a simple chain that takes a question and does the following:\n",
     "- convert the question into a SQL query;\n",
     "- execute the query;\n",
     "- use the result to answer the original question.\n",