docs[patch]: Remove links to missing docs

docs/core_docs/docs/how_to/document_loader_csv.mdx

@@ -1,4 +1,4 @@
-# CSV
+# How to load CSV data
 
 > A [comma-separated values (CSV)](https://en.wikipedia.org/wiki/Comma-separated_values) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.
 

docs/core_docs/docs/how_to/index.mdx

@@ -37,7 +37,6 @@ LangChain Expression Language is a way to create arbitrary custom chains. It is
 - [How to: add values to a chain's state](/docs/how_to/assign)
 - [How to: add message history](/docs/how_to/message_history)
 - [How to: route execution within a chain](/docs/how_to/routing)
-- [How to: inspect runnables](/docs/how_to/inspect)
 - [How to: add fallbacks](/docs/how_to/fallbacks)
 
 ## Components
@@ -69,7 +68,6 @@ Chat Models are newer forms of language models that take messages in and output
 - [How to: get models to return structured output](/docs/how_to/structured_output)
 - [How to: cache model responses](/docs/how_to/chat_model_caching)
 - [How to: get log probabilities](/docs/how_to/logprobs)
-- [How to: create a custom chat model class](/docs/how_to/custom_chat_model)
 - [How to: stream a response back](/docs/how_to/chat_streaming)
 - [How to: track token usage](/docs/how_to/chat_token_usage_tracking)
 
@@ -89,21 +87,14 @@ Output Parsers are responsible for taking the output of an LLM and parsing into
 - [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
 - [How to: parse JSON output](/docs/how_to/output_parser_json)
 - [How to: parse XML output](/docs/how_to/output_parser_xml)
-- [How to: parse YAML output](/docs/how_to/output_parser_yaml)
 - [How to: retry when output parsing errors occur](/docs/how_to/output_parser_retry)
-- [How to: try to fix errors in output parsing](/docs/how_to/output_parser_fixing)
-- [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
 
 ### Document loaders
 
 Document Loaders are responsible for loading documents from a variety of sources.
 
 - [How to: load CSV data](/docs/how_to/document_loader_csv)
 - [How to: load data from a directory](/docs/how_to/document_loader_directory)
-- [How to: load HTML data](/docs/how_to/document_loader_html)
-- [How to: load JSON data](/docs/how_to/document_loader_json)
-- [How to: load Markdown data](/docs/how_to/document_loader_markdown)
-- [How to: load Microsoft Office data](/docs/how_to/document_loader_office_file)
 - [How to: load PDF files](/docs/how_to/document_loader_pdf)
 - [How to: write a custom document loader](/docs/how_to/document_loader_custom)
 
@@ -137,12 +128,8 @@ Retrievers are responsible for taking a query and returning relevant documents.
 - [How to: generate multiple queries to retrieve data for](/docs/how_to/multiple_queries)
 - [How to: use contextual compression to compress the data retrieved](/docs/how_to/contextual_compression)
 - [How to: write a custom retriever class](/docs/how_to/custom_retriever)
-- [How to: add similarity scores to retriever results](/docs/how_to/add_scores_retriever)
-- [How to: combine the results from multiple retrievers](/docs/how_to/ensemble_retriever)
-- [How to: reorder retrieved results to put most relevant documents not in the middle](/docs/how_to/long_context_reorder)
 - [How to: generate multiple embeddings per document](/docs/how_to/multi_vector)
 - [How to: retrieve the whole document for a chunk](/docs/how_to/parent_document_retriever)
-- [How to: generate metadata filters](/docs/how_to/self_query)
 - [How to: create a time-weighted retriever](/docs/how_to/time_weighted_vectorstore)
 - [How to: reduce retrieval latency](/docs/how_to/reduce_retrieval_latency)
 
@@ -162,32 +149,24 @@ LangChain Tools contain a description of the tool (to pass to the language model
 - [How to: define a custom tool](/docs/how_to/custom_tools)
 - [How to: convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
 - [How to: use tools without function calling](/docs/how_to/tools_prompting)
-- [How to: let the LLM choose between multiple tools](/docs/how_to/tools_multiple)
-- [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
-- [How to: do parallel tool use](/docs/how_to/tools_parallel)
-- [How to: handle errors when calling tools](/docs/how_to/tools_error)
-- [How to: call tools using multi-modal data](/docs/how_to/tool_calls_multi_modal)
 
 ### Agents
 
 :::note
 
-For in depth how-to guides for agents, please check out [LangGraph](https://github.com/langchain-ai/langgraph) documentation.
+For in depth how-to guides for agents, please check out [LangGraph](https://langchain-ai.github.io/langgraphjs/) documentation.
 
 :::
 
 - [How to: use legacy LangChain Agents (AgentExecutor)](/docs/how_to/agent_executor)
-- [How to: migrate from legacy LangChain agents to LangGraph](/docs/how_to/migrate_agent)
 
 ### Custom
 
 All of LangChain components can easily be extended to support your own versions.
 
-- [How to: create a custom chat model class](/docs/how_to/custom_chat_model)
 - [How to: create a custom LLM class](/docs/how_to/custom_llm)
 - [How to: write a custom retriever class](/docs/how_to/custom_retriever)
 - [How to: write a custom document loader](/docs/how_to/document_loader_custom)
-- [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
 - [How to: define a custom tool](/docs/how_to/custom_tools)
 
 ## Use cases
@@ -238,7 +217,6 @@ You can use LLMs to do question answering over tabular data.
 - [How to: use prompting to improve results](/docs/how_to/sql_prompting)
 - [How to: do query validation](/docs/how_to/sql_query_checking)
 - [How to: deal with large databases](/docs/how_to/sql_large_db)
-- [How to: deal with CSV files](/docs/how_to/sql_csv)
 
 ### Q&A over graph databases
 

docs/core_docs/docs/tutorials/index.mdx

@@ -25,6 +25,5 @@ New to LangChain or to LLM app development in general? Read this material to qui
 ### Specialized tasks
 
 - [Build an Extraction Chain](/docs/tutorials/extraction)
-- [Generate synthetic data TODO P1](/docs/tutorials/data_generation)
 - [Classify text into labels](/docs/tutorials/classification)
 - [Summarize text](/docs/tutorials/summarization)