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docs: fix more broken links (#27806)

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Fix some broken links
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2
docs/docs/concepts/chat_models.mdx
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@ -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

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docs/docs/concepts/runnables.mdx
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@ -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.

2
docs/docs/concepts/tools.mdx
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@ -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

2
docs/docs/concepts/vectorstores.mdx
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@ -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. |

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docs/docs/how_to/agent_executor.ipynb
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@ -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",

2
docs/docs/how_to/qa_chat_history_how_to.ipynb
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@ -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."
]
},

2
docs/docs/how_to/structured_output.ipynb
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@ -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/)."
]
},
{

2
docs/docs/integrations/chat/naver.ipynb
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@ -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",

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docs/docs/integrations/chat/writer.ipynb
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@ -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",

2
docs/docs/integrations/llms/sambastudio.ipynb
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@ -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",

2
docs/docs/introduction.mdx
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@ -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/).

2
docs/docs/troubleshooting/errors/INVALID_PROMPT_INPUT.mdx
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@ -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.

4
docs/docs/tutorials/agents.ipynb
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@ -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."
]
},
{

2
docs/docs/tutorials/llm_chain.ipynb
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@ -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",

2
docs/docs/tutorials/qa_chat_history.ipynb
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@ -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."
]
},

2
docs/docs/tutorials/sql_qa.ipynb
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@ -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"
]
},
{

2
docs/scripts/kv_store_feat_table.py
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@ -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

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docs/scripts/tool_feat_table.py
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@ -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.