docs: fix more broken links (#27806)

Fix some broken links

docs/docs/concepts/chat_models.mdx

@@ -152,7 +152,7 @@ A semantic cache introduces a dependency on another model on the critical path o
 
 However, there might be situations where caching chat model responses is beneficial. For example, if you have a chat model that is used to answer frequently asked questions, caching responses can help reduce the load on the model provider and improve response times.
 
-Please see the [how to cache chat model responses](/docs/how_to/#chat-model-caching) guide for more details.
+Please see the [how to cache chat model responses](/docs/how_to/chat_model_caching/) guide for more details.
 
 ## Related resources
 
@@ -165,4 +165,4 @@ Please see the [how to cache chat model responses](/docs/how_to/#chat-model-cach
 * [Tool calling](/docs/concepts#tool-calling)
 * [Multimodality](/docs/concepts/multimodality)
 * [Structured outputs](/docs/concepts#structured_output)
-* [Tokens](/docs/concepts/tokens)
+* [Tokens](/docs/concepts/tokens)

docs/docs/concepts/runnables.mdx

@@ -15,7 +15,7 @@ This guide covers the main concepts and methods of the Runnable interface, which
 The Runnable way defines a standard interface that allows a Runnable component to be:
 
 * [Invoked](/docs/how_to/lcel_cheatsheet/#invoke-a-runnable): A single input is transformed into an output.
-* [Batched](/docs/how_to/lcel_cheatsheet/#batch-a-runnable/): Multiple inputs are efficiently transformed into outputs.
+* [Batched](/docs/how_to/lcel_cheatsheet/#batch-a-runnable): Multiple inputs are efficiently transformed into outputs.
 * [Streamed](/docs/how_to/lcel_cheatsheet/#stream-a-runnable): Outputs are streamed as they are produced.
 * Inspected: Schematic information about Runnable's input, output, and configuration can be accessed.
 * Composed: Multiple Runnables can be composed to work together using [the LangChain Expression Language (LCEL)](/docs/concepts/lcel) to create complex pipelines.

docs/docs/concepts/tools.mdx

@@ -141,7 +141,7 @@ See [how to pass run time values to tools](/docs/how_to/tool_runtime/) for more
 
 You can use the `RunnableConfig` object to pass custom run time values to tools.
 
-If you need to access the [RunnableConfig](/docs/concepts/runnables/#RunnableConfig) object from within a tool. This can be done by using the `RunnableConfig` annotation in the tool's function signature.
+If you need to access the [RunnableConfig](/docs/concepts/runnables/#runnableconfig) object from within a tool. This can be done by using the `RunnableConfig` annotation in the tool's function signature.
 
 ```python
 from langchain_core.runnables import RunnableConfig

docs/docs/concepts/vectorstores.mdx

@@ -186,6 +186,6 @@ See this [how-to guide on hybrid search](/docs/how_to/hybrid/) for more details.
 | Name                                                                                                              | When to use                                           | Description                                                                                                                                  |
 |-------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|
 | [Hybrid search](/docs/integrations/retrievers/pinecone_hybrid_search/)                                            | When combining keyword-based and semantic similarity. | Hybrid search combines keyword and semantic similarity, marrying the benefits of both approaches. [Paper](https://arxiv.org/abs/2210.11934). |
-| [Maximal Marginal Relevance (MMR)](/docs/integrations/vectorstores/pinecone/#maximal-marginal-relevance-searches) | When needing to diversify search results.             | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents.                                        |
+| [Maximal Marginal Relevance (MMR)](https://python.langchain.com/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html#langchain_pinecone.vectorstores.PineconeVectorStore.max_marginal_relevance_search) | When needing to diversify search results.             | MMR attempts to diversify the results of a search to avoid returning similar and redundant documents.                                        |
 
  

docs/docs/how_to/agent_executor.ipynb

@@ -18,7 +18,7 @@
     "# Build an Agent with AgentExecutor (Legacy)\n",
     "\n",
     ":::important\n",
-    "This section will cover building with the legacy LangChain AgentExecutor. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd recommend checking out [LangGraph Agents](/docs/concepts/#langgraph) or the [migration guide](/docs/how_to/migrate_agent/)\n",
+    "This section will cover building with the legacy LangChain AgentExecutor. These are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd recommend checking out [LangGraph Agents](/docs/concepts/architecture/#langgraph) or the [migration guide](/docs/how_to/migrate_agent/)\n",
     ":::\n",
     "\n",
     "By themselves, language models can't take actions - they just output text.\n",
@@ -802,7 +802,7 @@
     "That's a wrap! In this quick start we covered how to create a simple agent. Agents are a complex topic, and there's lot to learn! \n",
     "\n",
     ":::important\n",
-    "This section covered building with LangChain Agents. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd reccommend checking out [LangGraph](/docs/concepts/#langgraph)\n",
+    "This section covered building with LangChain Agents. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd reccommend checking out [LangGraph](/docs/concepts/architecture/#langgraph)\n",
     ":::\n",
     "\n",
     "If you want to continue using LangChain agents, some good advanced guides are:\n",

docs/docs/how_to/qa_chat_history_how_to.ipynb

@@ -686,7 +686,7 @@
    "source": [
     "### Agent constructor\n",
     "\n",
-    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/#langgraph) to construct the agent. \n",
+    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/architecture/#langgraph) to construct the agent. \n",
     "Currently we are using a high level interface to construct the agent, but the nice thing about LangGraph is that this high-level interface is backed by a low-level, highly controllable API in case you want to modify the agent logic."
    ]
   },

docs/docs/how_to/structured_output.ipynb

@@ -556,7 +556,7 @@
    "id": "498d893b-ceaa-47ff-a9d8-4faa60702715",
    "metadata": {},
    "source": [
-    "For more on few shot prompting when using tool calling, see [here](/docs/how_to/function_calling/#Few-shot-prompting)."
+    "For more on few shot prompting when using tool calling, see [here](/docs/how_to/tools_few_shot/)."
    ]
   },
   {

docs/docs/integrations/chat/naver.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatClovaX\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/#chat-models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
+    "This notebook provides a quick overview for getting started with Naver’s HyperCLOVA X [chat models](https://python.langchain.com/docs/concepts/chat_models) via CLOVA Studio. For detailed documentation of all ChatClovaX features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.naver.ChatClovaX.html).\n",
     "\n",
     "[CLOVA Studio](http://clovastudio.ncloud.com/) has several chat models. You can find information about latest models and their costs, context windows, and supported input types in the CLOVA Studio API Guide [documentation](https://api.ncloud-docs.com/docs/clovastudio-chatcompletions).\n",
     "\n",

docs/docs/integrations/chat/writer.ipynb

@@ -17,7 +17,7 @@
    "source": [
     "# ChatWriter\n",
     "\n",
-    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/#chat-models).\n",
+    "This notebook provides a quick overview for getting started with Writer [chat models](/docs/concepts/chat_models).\n",
     "\n",
     "Writer has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Writer docs](https://dev.writer.com/home/models).\n",
     "\n",

docs/docs/integrations/llms/sambastudio.ipynb

@@ -9,7 +9,7 @@
     "**[SambaNova](https://sambanova.ai/)'s** [Sambastudio](https://sambanova.ai/technology/full-stack-ai-platform) is a platform that allows you to train, run batch inference jobs, and deploy online inference endpoints to run open source models that you fine tuned yourself.\n",
     "\n",
     ":::caution\n",
-    "You are currently on a page documenting the use of SambaStudio models as [text completion models](/docs/concepts/#llms). We recommend you to use the [chat completion models](/docs/concepts/#chat-models).\n",
+    "You are currently on a page documenting the use of SambaStudio models as [text completion models](/docs/concepts/text_llms). We recommend you to use the [chat completion models](/docs/concepts/chat_models).\n",
     "\n",
     "You may be looking for [SambaStudio Chat Models](/docs/integrations/chat/sambastudio/) .\n",
     ":::\n",

docs/docs/introduction.mdx

@@ -9,7 +9,7 @@ sidebar_class_name: hidden
 
 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-lcel), [components](/docs/concepts), and [third-party integrations](/docs/integrations/providers/).
-Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
+Use [LangGraph](/docs/concepts/architecture/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
 - **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 your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
 

docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx

@@ -8,7 +8,7 @@ The following may help resolve this error:
 
 - Double-check your prompt template to ensure that it is correct.
   - If you are using the default f-string format and you are using curly braces `{` anywhere in your template, they should be double escaped like this: `{{` (and if you want to render a double curly brace, you should use four curly braces: `{{{{`).
-- If you are using a [`MessagesPlaceholder`](/docs/concepts/messages/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
+- If you are using a [`MessagesPlaceholder`](/docs/concepts/prompt_templates/#messagesplaceholder), make sure that you are passing in an array of messages or message-like objects.
   - If you are using shorthand tuples to declare your prompt template, make sure that the variable name is wrapped in curly braces (`["placeholder", "{messages}"]`).
 - Try viewing the inputs into your prompt template using [LangSmith](https://docs.smith.langchain.com/) or log statements to confirm they appear as expected.
 - If you are pulling a prompt from the [LangChain Prompt Hub](https://smith.langchain.com/prompts), try pulling and logging it or running it in isolation with a sample input to confirm that it is what you expect.

docs/docs/tutorials/agents.ipynb

@@ -370,7 +370,7 @@
    "source": [
     "## Create the agent\n",
     "\n",
-    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/#langgraph) to construct the agent. \n",
+    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/architecture/#langgraph) to construct the agent. \n",
     "Currently, we are using a high level interface to construct the agent, but the nice thing about LangGraph is that this high-level interface is backed by a low-level, highly controllable API in case you want to modify the agent logic.\n"
    ]
   },
@@ -719,7 +719,7 @@
     "We've also added in memory so you can have a conversation with them.\n",
     "Agents are a complex topic with lots to learn! \n",
     "\n",
-    "For more information on Agents, please check out the [LangGraph](/docs/concepts/#langgraph) documentation. This has it's own set of concepts, tutorials, and how-to guides."
+    "For more information on Agents, please check out the [LangGraph](/docs/concepts/architecture/#langgraph) documentation. This has it's own set of concepts, tutorials, and how-to guides."
    ]
   },
   {

docs/docs/tutorials/llm_chain.ipynb

@@ -29,7 +29,7 @@
     "\n",
     "- Debugging and tracing your application using [LangSmith](https://docs.smith.langchain.com/)\n",
     "\n",
-    "- Deploying your application with [LangServe](/docs/concepts/#langserve)\n",
+    "- Deploying your application with [LangServe](/docs/concepts/architecture/#langserve)\n",
     "\n",
     "Let's dive in!\n",
     "\n",

docs/docs/tutorials/qa_chat_history.ipynb

@@ -817,7 +817,7 @@
    "source": [
     "### Agent constructor\n",
     "\n",
-    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/#langgraph) to construct the agent. \n",
+    "Now that we have defined the tools and the LLM, we can create the agent. We will be using [LangGraph](/docs/concepts/architecture/#langgraph) to construct the agent. \n",
     "Currently we are using a high level interface to construct the agent, but the nice thing about LangGraph is that this high-level interface is backed by a low-level, highly controllable API in case you want to modify the agent logic."
    ]
   },

docs/docs/tutorials/sql_qa.ipynb

@@ -494,7 +494,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use a prebuilt [LangGraph](/docs/concepts/#langgraph) agent to build our agent"
+    "We will use a prebuilt [LangGraph](/docs/concepts/architecture/#langgraph) agent to build our agent"
    ]
   },
   {

docs/scripts/kv_store_feat_table.py

@@ -14,7 +14,7 @@ hide_table_of_contents: true
 
 # Key-value stores
 
-[Key-value stores](/docs/concepts/#key-value-stores) are used by other LangChain components to store and retrieve data.
+[Key-value stores](/docs/concepts/key_value_stores) are used by other LangChain components to store and retrieve data.
 
 :::info
 

docs/scripts/tool_feat_table.py

@@ -182,7 +182,7 @@ custom_edit_url:
 
 # Tools
 
-[Tools](/docs/concepts/#tools) are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models.
+[Tools](/docs/concepts/tools) are utilities designed to be called by a model: their inputs are designed to be generated by models, and their outputs are designed to be passed back to models.
 
 A [toolkit](/docs/concepts#toolkits) is a collection of tools meant to be used together.